Powered by Deep Web Technologies
Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


1

Definition: Peak Demand | Open Energy Information  

Open Energy Info (EERE)

Peak Demand Peak Demand Jump to: navigation, search Dictionary.png Peak Demand The highest hourly integrated Net Energy For Load within a Balancing Authority Area occurring within a given period (e.g., day, month, season, or year)., The highest instantaneous demand within the Balancing Authority Area.[1] View on Wikipedia Wikipedia Definition Peak demand is used to refer to a historically high point in the sales record of a particular product. In terms of energy use, peak demand describes a period of strong consumer demand. Related Terms Balancing Authority Area, energy, demand, balancing authority, smart grid References ↑ Glossary of Terms Used in Reliability Standards An inli LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ne Glossary Definition Retrieved from

2

FINAL STAFF FORECAST OF 2008 PEAK DEMAND  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION FINAL STAFF FORECAST OF 2008 PEAK DEMAND STAFFREPORT June 2007 CEC-200 of the information in this paper. #12;Abstract This document describes staff's final forecast of 2008 peak demand demand forecasts for the respective territories of the state's three investor-owned utilities (IOUs

3

Optimization of Demand Response Through Peak Shaving  

E-Print Network (OSTI)

Jul 5, 2013 ... Optimization of Demand Response Through Peak Shaving. G. Zakeri(g.zakeri *** at*** auckland.ac.nz) D. Craigie(David.Craigie ***at*** ...

4

A distributed approach to taming peak demand  

Science Conference Proceedings (OSTI)

A significant portion of all energy capacity is wasted in over-provisioning to meet peak demand. The current state-of-the-art in reducing peak demand requires central authorities to limit device usage directly, and are generally reactive. We apply techniques ...

Michael Sabolish; Ahmed Amer; Thomas M. Kroeger

2012-06-01T23:59:59.000Z

5

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

Dispatch for Macrogrid Peak- Demand Mitigation NicholasDispatch for Macrogrid Peak-Demand Mitigation Nicholasdetermine whether the peak demand on the substation feeder

DeForest, Nicholas

2013-01-01T23:59:59.000Z

6

Automated Demand Response for Critical Peak Pricing  

NLE Websites -- All DOE Office Websites (Extended Search)

Automated Demand Response for Critical Peak Pricing Speaker(s): Naoya Motegi Date: June 9, 2005 - 12:00pm Location: Bldg. 90 California utilities have been exploring the use of...

7

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

NLE Websites -- All DOE Office Websites (Extended Search)

Dispatch for Macrogrid Peak-Demand Mitigation Title Microgrid Dispatch for Macrogrid Peak-Demand Mitigation Publication Type Conference Proceedings Refereed Designation Refereed...

8

Poster: Thermal Energy Storage for Electricity Peak-demand Mitigation...  

NLE Websites -- All DOE Office Websites (Extended Search)

Poster: Thermal Energy Storage for Electricity Peak-demand Mitigation: A Solution in Developing and Developed World Alike Title Poster: Thermal Energy Storage for Electricity...

9

Reducing Peak Demand to Defer Power Plant Construction in Oklahoma  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Reducing Peak Demand to Defer Power Plant Construction in Oklahoma Located in the heart of "Tornado Alley," Oklahoma Gas & Electric Company's (OG&E) electric grid faces significant...

10

Scenario Analysis of Peak Demand Savings for Commercial Buildings with  

NLE Websites -- All DOE Office Websites (Extended Search)

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California Title Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California Publication Type Conference Paper LBNL Report Number LBNL-3636e Year of Publication 2010 Authors Yin, Rongxin, Sila Kiliccote, Mary Ann Piette, and Kristen Parrish Conference Name 2010 ACEEE Summer Study on Energy Efficiency in Buildings Conference Location Pacific Grove, CA Keywords demand response and distributed energy resources center, demand response research center, demand shifting (pre-cooling), DRQAT Abstract This paper reports on the potential impact of demand response (DR) strategies in commercial buildings in California based on the Demand Response Quick Assessment Tool (DRQAT), which uses EnergyPlus simulation prototypes for office and retail buildings. The study describes the potential impact of building size, thermal mass, climate, and DR strategies on demand savings in commercial buildings. Sensitivity analyses are performed to evaluate how these factors influence the demand shift and shed during the peak period. The whole-building peak demand of a commercial building with high thermal mass in a hot climate zone can be reduced by 30% using an optimized demand response strategy. Results are summarized for various simulation scenarios designed to help owners and managers understand the potential savings for demand response deployment. Simulated demand savings under various scenarios were compared to field-measured data in numerous climate zones, allowing calibration of the prototype models. The simulation results are compared to the peak demand data from the Commercial End-Use Survey for commercial buildings in California. On the economic side, a set of electricity rates are used to evaluate the impact of the DR strategies on economic savings for different thermal mass and climate conditions. Our comparison of recent simulation to field test results provides an understanding of the DR potential in commercial buildings.

11

Reducing Peak Demand to Defer Power Plant Construction in Oklahoma  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Reducing Peak Demand to Defer Power Plant Construction in Oklahoma Reducing Peak Demand to Defer Power Plant Construction in Oklahoma Located in the heart of "Tornado Alley," Oklahoma Gas & Electric Company's (OG&E) electric grid faces significant challenges from severe weather, hot summers, and about 2% annual load growth. To better control costs and manage electric reliability under these conditions, OG&E is pursuing demand response strategies made possible by implementation of smart grid technologies, tools, and techniques from 2010-2012. The objective is to engage customers in lowering peak demand using smart technologies in homes and businesses and to achieve greater efficiencies on the distribution system. The immediate goal: To defer two 165 MW power plants currently planned for

12

Density Forecasting for Long-Term Peak Electricity Demand  

E-Print Network (OSTI)

Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long-term context, planners must adopt a probabilistic view of potential peak demand levels. Therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand. Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. A comprehensive forecasting solution is described in this paper. First, semi-parametric additive models are used to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then the demand distributions are forecasted by using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks. The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. The performance of the methodology is evaluated by comparing the forecast results with the actual demand of the summer 2007–2008.

Rob J. Hyndman; Shu Fan

2009-01-01T23:59:59.000Z

13

Potential of solar cooling systems for peak demand reduction  

DOE Green Energy (OSTI)

We investigated the technical feasibility of solar cooling for peak demand reduction using a building energy simulation program (DOE2.1D). The system studied was an absorption cooling system with a thermal coefficient of performance of 0.8 driven by a solar collector system with an efficiency of 50% with no thermal storage. The analysis for three different climates showed that, on the day with peak cooling load, about 17% of the peak load could be met satisfactorily with the solar-assisted cooling system without any thermal storage. A performance availability analysis indicated that the solar cooling system should be designed for lower amounts of available solar resources that coincide with the hours during which peak demand reduction is required. The analysis indicated that in dry climates, direct-normal concentrating collectors work well for solar cooling; however, in humid climates, collectors that absorb diffuse radiation work better.

Pesaran, A.A. [National Renewable Energy Lab., Golden, CO (United States); Neymark, J. [Neymark (Joel), Golden, CO (United States)

1994-11-01T23:59:59.000Z

14

Scalable Scheduling of Building Control Systems for Peak Demand Reduction  

E-Print Network (OSTI)

is model predictive control (MPC) ([6], [7]). In [6] the authors inves- tigated MPC for thermal energyScalable Scheduling of Building Control Systems for Peak Demand Reduction Truong X. Nghiem, Madhur operation of sub- systems such as heating, ventilating, air conditioning and refrigeration (HVAC&R) systems

Pappas, George J.

15

Monitoring System Used to Identify, Track and Allocate Peak Demand Costs  

E-Print Network (OSTI)

In 1994, Thomson Consumer Electronics (RCA) purchased a UtiliTRACK® Monitoring System for a plant in Indianapolis, Indiana primarily to allow utility costs to be billed to individual departments within Thomson as well as to outside organizations leasing space on the site. The most common way to distribute monthly electric costs within a facility when consumption by area or department is available through submetering or other means, is to apply the average cost per KWH from the utility bill to the individual consumption figures. Thomson initially used the data from the UtiliTRACK System in this way. As the plant engineer worked with system data on a daily basis and began to develop a much better understanding of the plant's electrical profile, it was clear that the percentage contribution by department or area to the plant's peak demand was not the same as that assigned based solely upon consumption. With a monthly peak exceeding 8 MW and peak demand charges accounting for more than 60% of the monthly electric bill, he realized that to be accurate and fair, costs must be allocated based both on consumption and peak demand. He asked UtiliTRACK to develop a method for tracking and allocating peak demand costs. The resulting software continuously tracks the total plant demand (the sum of 3 utility meters) and records the contribution of each monitored point at the time the peak occurs. The resulting reports and graphs not only enable the owner to accurately allocate peak demand costs but also provide a means for tracking and managing peaks on a continuous basis.

Holmes, W. A.

1998-04-01T23:59:59.000Z

16

Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building  

E-Print Network (OSTI)

Peak Demand Reduction from Pre-Cooling with Zone TemperatureUniversity of California. Peak Demand Reduction from Pre-shifted in time and the peak demand is reduced. The building

Xu, Peng

2010-01-01T23:59:59.000Z

17

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

E-Print Network (OSTI)

Scenario Analysis of Peak Demand Savings for CommercialScenario Analysis of Peak Demand Savings for CommercialThe whole-building peak demand of a commercial building with

Yin, Rongxin

2010-01-01T23:59:59.000Z

18

Peak demand reduction from pre-cooling with zone temperature reset in an office building  

E-Print Network (OSTI)

an Energy-Efficient Economy. Peak Demand Reduction from Pre-No. DE-AC03-76SF00098. Peak Demand Reduction from Pre-shifted in time and the peak demand is reduced. The building

Xu, Peng; Haves, Philip; Piette, Mary Ann; Braun, James

2004-01-01T23:59:59.000Z

19

Storing hydroelectricity to meet peak-hour demand  

Science Conference Proceedings (OSTI)

This paper reports on pumped storage plants which have become an effective way for some utility companies that derive power from hydroelectric facilities to economically store baseload energy during off-peak hours for use during peak hourly demands. According to the Electric Power Research Institute (EPRI) in Palo Alto, Calif., 36 of these plants provide approximately 20 gigawatts, or about 3 percent of U.S. generating capacity. During peak-demand periods, utilities are often stretched beyond their capacity to provide power and must therefore purchase it from neighboring utilities. Building new baseload power plants, typically nuclear or coal-fired facilities that run 24 hours per day seven days a week, is expensive, about $1500 per kilowatt, according to Robert Schainker, program manager for energy storage at the EPRI. Schainker the that building peaking plants at $400 per kilowatt, which run a few hours a day on gas or oil fuel, is less costly than building baseload plants. Operating them, however, is more expensive because peaking plants are less efficient that baseload plants.

Valenti, M.

1992-04-01T23:59:59.000Z

20

The role of building technologies in reducing and controlling peak electricity demand  

E-Print Network (OSTI)

AND CONTROLLING PEAK ELECTRICITY DEMAND Jonathan Koomey* andData to Improve Electricity Demand Forecasts–Final Report.further research. Electricity demand varies constantly. At

Koomey, Jonathan; Brown, Richard E.

2002-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Scenario Analysis of Peak Demand Savings for Commercial Buildings...  

NLE Websites -- All DOE Office Websites (Extended Search)

Study on Energy Efficiency in Buildings Conference Location Pacific Grove, CA Keywords demand response and distributed energy resources center, demand response research center,...

22

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

N ATIONAL L ABORATORY Microgrid Dispatch for Macrogrid Peak-equal opportunity employer. Microgrid Dispatch for Macrogridutility customers, microgrid solutions – the installation of

DeForest, Nicholas

2013-01-01T23:59:59.000Z

23

Data center demand response: Avoiding the coincident peak via workload shifting and local generation  

Science Conference Proceedings (OSTI)

Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming ... Keywords: Data center, Demand response, Online algorithm, Prediction error, Renewable penetration, Workload management

Zhenhua Liu, Adam Wierman, Yuan Chen, Benjamin Razon, Niangjun Chen

2013-10-01T23:59:59.000Z

24

A Fresh Look at Weather Impact on Peak Electricity Demand and...  

NLE Websites -- All DOE Office Websites (Extended Search)

Building simulation, Energy use, Peak electricity demand, Typical meteorological year, Weather data Abstract Buildings consume more than one third of the world's total primary...

25

Evidence is growing on demand side of an oil peak  

SciTech Connect

After years of continued growth, the number of miles driven by Americans started falling in December 2007. Not only are the number of miles driven falling, but as cars become more fuel efficient, they go further on fewer gallons - further reducing demand for gasoline. This trend is expected to accelerate. Drivers include, along with higher-efficiency cars, mass transit, reversal in urban sprawl, biofuels, and plug-in hybrid vehicles.

NONE

2009-07-15T23:59:59.000Z

26

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series  

E-Print Network (OSTI)

in this report. #12;i ABSTRACT These electricity demand forms and instructions ask load-serving entities and Instructions for Electricity Demand Forecasts. California Energy Commission, Electricity Supply Analysis.................................................................................................................................7 Form 1 Historic and Forecast Electricity Demand

Abdel-Aal, Radwan E.

27

Influence of Air Conditioner Operation on Electricity Use and Peak Demand  

E-Print Network (OSTI)

Electricity demand due to occupant controlled room air conditioners in a large mater-metered apartment building is analyzed. Hourly data on the electric demand of the building and of individual air conditioners are used in analyses of annual and time-of-day peaks. Effects of occupant schedules and behavior are examined. We conclude that room air conditioners cause a sharp annual peak demand because occupants have strongly varying thresholds with respect to toleration of high indoor temperatures. However, time-or-day peaking is smoothed by air conditioning in this building due to significant off-peak operation of air conditioners by some occupants. If occupants were billed directly for electricity, off-peak use would probably diminish making the peaks more pronounced and exacerbating the utility company's load management problems. Future studies of this type in individually metered apartment buildings are recommended.

McGarity, A. E.; Feuermann, D.; Kempton, W.; Norford, L. K.

1987-01-01T23:59:59.000Z

28

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

SciTech Connect

This paper reports on the potential impact of demand response (DR) strategies in commercial buildings in California based on the Demand Response Quick Assessment Tool (DRQAT), which uses EnergyPlus simulation prototypes for office and retail buildings. The study describes the potential impact of building size, thermal mass, climate, and DR strategies on demand savings in commercial buildings. Sensitivity analyses are performed to evaluate how these factors influence the demand shift and shed during the peak period. The whole-building peak demand of a commercial building with high thermal mass in a hot climate zone can be reduced by 30percent using an optimized demand response strategy. Results are summarized for various simulation scenarios designed to help owners and managers understand the potential savings for demand response deployment. Simulated demand savings under various scenarios were compared to field-measured data in numerous climate zones, allowing calibration of the prototype models. The simulation results are compared to the peak demand data from the Commercial End-Use Survey for commercial buildings in California. On the economic side, a set of electricity rates are used to evaluate the impact of the DR strategies on economic savings for different thermal mass and climate conditions. Our comparison of recent simulation to field test results provides an understanding of the DR potential in commercial buildings.

Yin, Rongxin; Kiliccote, Sila; Piette, Mary Ann; Parrish, Kristen

2010-05-14T23:59:59.000Z

29

The Impact of Residential Air Conditioner Charging and Sizing on Peak Electrical Demand  

E-Print Network (OSTI)

Electric utilities have had a number of air conditioner rebate and maintenance programs for many years. The purpose of these programs was to improve the efficiency of the stock of air conditioning equipment and provide better demand-side management. This paper examines the effect of refrigerant charging (proper servicing of the equipment), system sizing, and efficiency on the steady-state, coincident peak utility demand of a residential central air conditioning system. The study is based on the results of laboratory tests of a three-ton, capillary tube expansion, split-system air conditioner, system capacity and efficiency data available from manufacturer's literature, and assumptions about relative sizing of the equipment to cooling load on a residence. A qualitative discussion is provided concerning the possible impacts of transient operation and total energy use on utility program decisions. The analysis indicates that proper sizing of the unit is the largest factor affecting energy demand of the three factors (sizing, charging, and efficiency) studied in this paper. For typical oversizing of units to cooling loads in houses, both overcharging and undercharging showed significant negative impact on peak demand. The impacts of SEER changes in utility peak demand were found to be virtually independent of oversizing. For properly sized units, there was a small peak benefit to higher efficiency air conditioners.

Neal, L.; O'Neal, D. L.

1992-05-01T23:59:59.000Z

30

Electrical Energy Conservation and Peak Demand Reduction Potential for Buildings in Texas: Preliminary Results  

E-Print Network (OSTI)

This paper presents preliminary results of a study of electrical energy conservation and peak demand reduction potential for the building sector in Texas. Starting from 1980 building stocks and energy use characteristics, technical conservation potentials were calculated relative to frozen energy efficiency stock growth over the 1980-2000 period. The application of conservation supply methodology to Texas utilities is outlined, and then the energy use and peak demand savings, and their associated costs, are calculated using a prototypical building technique. Representative results are presented, for residential and commercial building types, as conservation supply curves for several end use categories; complete results of the study are presented in Ref. 1.

Hunn, B. D.; Baughman, M. L.; Silver, S. C.; Rosenfeld, A. H.; Akbari, H.

1985-01-01T23:59:59.000Z

31

Potential For Energy, Peak Demand, and Water Savings in California Tomato Processing Facilities  

E-Print Network (OSTI)

Tomato processing is a major component of California's food industry. Tomato processing is extremely energy intensive, with the processing season coinciding with the local electrical utility peak period. Significant savings are possible in the electrical energy, peak demand, natural gas consumption, and water consumption of facilities. The electrical and natural gas energy usage and efficiency measures will be presented for a sample of California tomato plants. A typical end-use distribution of electrical energy in these plants will be shown. Results from potential electrical efficiency, demand response, and natural gas efficiency measures that have applications in tomato processing facilities will be presented. Additionally, water conservation measures and the associated savings will be presented. It is shown that an estimated electrical energy savings of 12.5%, electrical demand reduction of 17.2%, natural gas savings of 6.0%, and a fresh water usage reduction of 15.6% are achievable on a facility-wide basis.

Trueblood, A. J.; Wu, Y. Y.; Ganji, A. R.

2013-01-01T23:59:59.000Z

32

Impact of Reflective Roofing on Cooling Electrical Use and Peak Demand in a Florida Retail Mall  

E-Print Network (OSTI)

Architects in hot climates have long recognized that reflective roof colors can reduce building cooling load. Experimentation spanning nearly three decades has shown that white roofing surfaces can significantly reduce surface temperatures and cooling loads (Givoni and Hoffmann, 1968; Reagan and Acklam, 1979; Griggs and Shipp, 1988; Anderson, 1989; Anderson et al., 1991 and Bansal et al., 1992). More importantly, measured cooling energy savings of white surfaces have been significant in California's climate (Akbari et al., 1991, 1992, 1997). In Florida, field research by the Florida Solar Energy Center (FSEC) since 1993 has quantified the impact of reflective roof coatings on sub-metered air conditioning (AC) consumption in tests in a dozen occupied homes (Parker et al., 1993; 1994; 1995; 1997). The coatings were applied to the roofs of each home in mid-summer after a month-long period of monitoring during which meteorological conditions, building temperatures and AC energy use were recorded. Using weather periods with similar temperatures and solar insolation, air conditioning energy use was reduced by 10% - 43% in the homes. The average drop in space cooling energy use was about 7.4 kWh/day or 19% of the pre-application air conditioning consumption. Unfortunately, until this project there has been little objective testing of the impact of roof whitening on the AC load of commercial buildings in Florida. Two demonstration sites have been monitored. The first was an elementary school in Cocoa Beach, Florida, which was monitored for a year before and after a white roof coating was applied. A final report on this project was published in the CADDET Newsletter (Parker et al., 1996a, b). The project demonstrated a 10% annual savings in chiller energy with a 30% reduction in peak cooling electrical demand. This paper summarizes the findings from the second demonstration at a commercial strip mall.

Parker, D. S.; Sonne, J. K.; Sherwin, J. R.

2002-01-01T23:59:59.000Z

33

A. G. A. six-month gas demand forecast July-December, 1984  

Science Conference Proceedings (OSTI)

Estimates of the total gas demand for 1984 (including pipeline fuel) range from 18,226 to 19,557 trillion (TBtu). The second half of the year shows a slower recovery rate as economic recovery moderates. The forecast show both actual and projected demand by month, and compares it with 1983 demand and by market sector. 6 tables.

Not Available

1984-01-01T23:59:59.000Z

34

A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of  

NLE Websites -- All DOE Office Websites (Extended Search)

Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year ActualWeather Data Title A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year ActualWeather Data Publication Type Journal Year of Publication 2013 Authors Hong, Tianzhen, Wen-Kuei Chang, and Hung-Wen Lin Keywords Actual meteorological year, Building simulation, Energy use, Peak electricity demand, Typical meteorological year, Weather data Abstract Buildings consume more than one third of the world's total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980 to 2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: 1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; 2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; 3) the weather impact is greater for buildings in colder climates than warmer climates; 4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and 5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

35

The Influence of Air-Conditioning Efficiency in the Peak Load Demand for Kuwait  

E-Print Network (OSTI)

A model co-relating the peak load demand of a utility with the allowable power rating (PR) of air-conditioning (AC) systems has been developed in this paper through a well defined methodology. The model is capable to predict the extent of allowable increase in the capital cost of the AC system for an improvement in PR from its base case as well. Furthermore, effectiveness of better PR of AC system for peak load management has been analyzed for Kuwait as a case study. It is found that up to 5,752 MW in reduction in peak load demand and savings of KD 2,301 million in capital expenditures are possible for the years between 2001 and 2025 if the PR of AC systems are improved to 1.2 kW/RT from its present level of 2.0 kW/RT. Also, it is estimated that extent of increase in capital cost of AC system by 106 % is justified for reducing the expenditure for new power plants. The paper will be useful for the energy planner and policy makers in the countries of Arabian Peninsula with huge demand for air-conditioning.

Ali, A. A.; Maheshwari, G. P.

2007-01-01T23:59:59.000Z

36

Modeling of GE Appliances in GridLAB-D: Peak Demand Reduction  

Science Conference Proceedings (OSTI)

The widespread adoption of demand response enabled appliances and thermostats can result in significant reduction to peak electrical demand and provide potential grid stabilization benefits. GE has developed a line of appliances that will have the capability of offering several levels of demand reduction actions based on information from the utility grid, often in the form of price. However due to a number of factors, including the number of demand response enabled appliances available at any given time, the reduction of diversity factor due to the synchronizing control signal, and the percentage of consumers who may override the utility signal, it can be difficult to predict the aggregate response of a large number of residences. The effects of these behaviors can be modeled and simulated in open-source software, GridLAB-D, including evaluation of appliance controls, improvement to current algorithms, and development of aggregate control methodologies. This report is the first in a series of three reports describing the potential of GE's demand response enabled appliances to provide benefits to the utility grid. The first report will describe the modeling methodology used to represent the GE appliances in the GridLAB-D simulation environment and the estimated potential for peak demand reduction at various deployment levels. The second and third reports will explore the potential of aggregated group actions to positively impact grid stability, including frequency and voltage regulation and spinning reserves, and the impacts on distribution feeder voltage regulation, including mitigation of fluctuations caused by high penetration of photovoltaic distributed generation and the effects on volt-var control schemes.

Fuller, Jason C.; Vyakaranam, Bharat GNVSR; Prakash Kumar, Nirupama; Leistritz, Sean M.; Parker, Graham B.

2012-04-29T23:59:59.000Z

37

Duct Leakage Impacts on Airtightness, Infiltration, and Peak Electrical Demand in Florida Homes  

E-Print Network (OSTI)

Testing for duct leakage was done in 155 homes. Tracer gas tests found that infiltration rates were three times greater when the air handler was operating than when it was off. Infiltration averaged 0.85 air changes per hour (ach) with the air handler (AH) operating continuously and 0.29 ach with the AH off. Return leaks were found to average 10.3% of AH total flow. House airtightness, in 90 of these homes, determined by blower door testing, averaged 12.58 air changes per hour at 50 Pascals (ACHSO). When the duct registers were sealed, ACHSO decreased to 11.04, indicating that 12.2% of the house leaks were in the duct system. Duct leaks have a dramatic impact upon peak electrical demand. Based on theoretical analysis, a fifteen percent return leak from the attic can increase cooling electrical demand by 100%. Duct repairs in a typical. electrically heated Florida home reduce winter peak demand by about 1.6 kW per house at about one-sixth the cost of building new electrical generation capacity.

Cummings, J. B.; Tooley, J. J.; Moyer, N.

1990-01-01T23:59:59.000Z

38

LBNL-6280E A Fresh Look at Weather Impact on Peak Electricity Demand and  

NLE Websites -- All DOE Office Websites (Extended Search)

280E 280E A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30- Year Actual Weather Data Tianzhen Hong 1 , Wen-kuei Chang 2 , Hung-Wen Lin 2 1 Environmental Energy Technologies Division 2 Green Energy and Environment Laboratories, Industrial Technology Research Institute, Taiwan, ROC May 2013 This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, the U.S.-China Clean Energy Research Center for Building Energy Efficiency, of the U.S. Department of Energy under Contract No. DE-AC02-

39

Linking Continuous Energy Management and Open Automated Demand Response  

E-Print Network (OSTI)

minimization Monthly peak demand management Daily time-of-Some tariff designs have peak demand charges that apply tothat may result in a peak demand that occurs in one month to

Piette, Mary Ann

2009-01-01T23:59:59.000Z

40

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

NLE Websites -- All DOE Office Websites (Extended Search)

36E 36E Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California R. Yin, S. Kiliccote, M.A. Piette, K. Parrish Environmental Energy Technologies Division May 2010 Presented at the 2010 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, August 15-20, 2010, and published in the Proceedings DISCLAIMER This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information,

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

Using Compressed Air Efficiency Projects to Reduce Peak Industrial Electric Demands: Lessons Learned  

E-Print Network (OSTI)

"To help customers respond to the wildly fluctuating energy markets in California, Pacific Gas & Electric (PG&E) initiated an emergency electric demand reduction program in October 2000 to cut electric use during peak periods. One component of that wide-ranging program focused on industrial compressed air systems as the target for such electric use reductions. What stands out about the compressed air effort is that customer acceptance of the program was very high (8 out of 10 customer sites implemented at least some of the efficiency projects recommended in the program's air system audits) and overall savings levels were more than 3X the original program goal (550 kW vs. 1730 kW). XENERGY, Inc. designed and carried out the program on behalf of PG&E. Key features of the program included working with compressed air system distributors to identify and qualify good customer leads and post-audit technical assistance to help customer implement recommended projects. This paper reviews the project and outlines some of the lessons learned in completing the project."

Skelton, J.

2003-04-01T23:59:59.000Z

42

Testing of peak demand limiting using thermal mass at a small commercial building  

E-Print Network (OSTI)

IBPSA-USA Conference at MIT, Boston, MA. Demand ResponseDemand- Limiting Setpoint Trajectories in Commercial Buildings Using Short-Term Data Analysis, Proceedings of the 2006 IBPSA-USA

Lee, Kyoung-Ho; Braun, James E; Fredrickson, Steve; Konis, Kyle; Arens, Edward

2007-01-01T23:59:59.000Z

43

OG&E Uses Time-Based Rate Program to Reduce Peak Demand  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

The VPP rates during the five-hour peak period vary daily depending on the cost of electricity. The VPP also includes a critical peak price (CPP) component that is...

44

Thermal energy storage for space cooling. Technology for reducing on-peak electricity demand and cost  

DOE Green Energy (OSTI)

Cool storage technology can be used to significantly reduce energy costs by allowing energy intensive, electrically driven cooling equipment to be predominantly operated during off-peak hours when electricity rates are lower. In addition, some system configurations may result in lower first costs and/or lower operating costs. Cool storage systems of one type or another could potentially be cost-effectively applied in most buildings with a space cooling system. A survey of approximately 25 manufacturers providing cool storage systems or components identified several thousand current installations, but less than 1% of these were at Federal facilities. With the Federal sector representing nearly 4% of commercial building floor space and 5% of commercial building energy use, Federal utilization would appear to be lagging. Although current applications are relatively few, the estimated potential annual savings from using cool storage in the Federal sector is $50 million. There are many different types of cool storage systems representing different combinations of storage media, charging mechanisms, and discharging mechanisms. The basic media options are water, ice, and eutectic salts. Ice systems can be further broken down into ice harvesting, ice-on-coil, ice slurry, and encapsulated ice options. Ice-on-coil systems may be internal melt or external melt and may be charged and discharged with refrigerant or a single-phase coolant (typically a water/glycol mixture). Independent of the technology choice, cool storage systems can be designed to provide full storage or partial storage, with load-leveling and demand-limiting options for partial storage. Finally, storage systems can be operated on a chiller-priority or storage priority basis whenever the cooling load is less than the design conditions. The first section describes the basic types of cool storage technologies and cooling system integration options. The next three sections define the savings potential in the Federal sector, present application advice, and describe the performance experience of specific Federal users. A step-by-step methodology illustrating how to evaluate cool storage options is presented next, followed by a case study of a GSA building using cool storage. Latter sections list manufacturers, selected Federal users, and reference materials. Finally, the appendixes give Federal life-cycle costing procedures and results for a case study.

None

2000-12-01T23:59:59.000Z

45

Green Scheduling of Control Systems for Peak Demand Reduction Truong X. Nghiem, Madhur Behl, Rahul Mangharam and George J. Pappas  

E-Print Network (OSTI)

scheduling scheme for a set of control systems. The proposed model is scalable and effective for the large for commercial buildings and data centers is model predictive control (MPC) ([4], [5], [6], [7]). MPC iGreen Scheduling of Control Systems for Peak Demand Reduction Truong X. Nghiem, Madhur Behl, Rahul

Pappas, George J.

46

The role of building technologies in reducing and controlling peak electricity demand  

E-Print Network (OSTI)

power in real time (costs per kWh at time of system peak canto large increases in marginal costs per kWh, because of the

Koomey, Jonathan; Brown, Richard E.

2002-01-01T23:59:59.000Z

47

SmartCap: Flattening Peak Electricity Demand in Smart Homes Sean Barker, Aditya Mishra, David Irwin, Prashant Shenoy, and Jeannie Albrecht  

E-Print Network (OSTI)

SmartCap: Flattening Peak Electricity Demand in Smart Homes Sean Barker, Aditya Mishra, David Irwin--Flattening household electricity demand reduces generation costs, since costs are disproportionately affected by peak demands. While the vast majority of household electrical loads are interactive and have little scheduling

Shenoy, Prashant

48

Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building  

E-Print Network (OSTI)

Use of Building Thermal Mass to Offset Cooling Loads. ASHRAEThe Role of Thermal Mass on the Cooling Load of Buildings.to reduce peak cooling loads with thermal mass control.

Xu, Peng

2010-01-01T23:59:59.000Z

49

Peak demand reduction from pre-cooling with zone temperature reset in an office building  

E-Print Network (OSTI)

Use of Building Thermal Mass to Offset Cooling Loads. ASHRAEThe Role of Thermal Mass on the Cooling Load of Buildings.to reduce peak cooling loads with thermal mass control.

Xu, Peng; Haves, Philip; Piette, Mary Ann; Braun, James

2004-01-01T23:59:59.000Z

50

Property:OpenEI/UtilityRate/FlatDemandMonth5 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth5 FlatDemandMonth5 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth5" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 1 +

51

Property:OpenEI/UtilityRate/FixedDemandChargeMonth1 | Open Energy  

Open Energy Info (EERE)

Fixed Demand Charge Month 1 Fixed Demand Charge Month 1 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth1" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 8.28 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 3.35 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

52

Property:OpenEI/UtilityRate/FlatDemandMonth3 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth3 FlatDemandMonth3 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth3" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

53

Property:OpenEI/UtilityRate/FlatDemandMonth2 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth2 FlatDemandMonth2 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth2" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

54

Property:OpenEI/UtilityRate/FlatDemandMonth4 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth4 FlatDemandMonth4 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth4" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

55

Property:OpenEI/UtilityRate/FlatDemandMonth7 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth7 FlatDemandMonth7 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth7" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 1 +

56

Property:OpenEI/UtilityRate/FlatDemandMonth1 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth1 FlatDemandMonth1 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth1" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 2 +

57

Property:OpenEI/UtilityRate/FlatDemandMonth6 | Open Energy Information  

Open Energy Info (EERE)

FlatDemandMonth6 FlatDemandMonth6 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth6" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 + 002661b8-2e71-48b8-b657-44ff7372f757 + 1 + 0026b4d3-dd02-4423-95e1-56430d887b28 + 1 +

58

Swing options: a mechanism for pricing IT peak demand, http: //www.hpl.hp.com/research/idl/papers/swings  

E-Print Network (OSTI)

Since usage patterns of information technology within organizations can be bursty, the peak demand for IT resources can at times exceed the installed capacity within the enterprise. If providers of such peak capacity emerge, as was the case for electricity and natural gas, the problem arises as to how to efficiently provide and price such peak demand. We present a swing option mechanism that allows for the efficient pricing of IT resources ranging from CPU usage to storage and bandwidth. This mechanism allows users to buy the right but not the obligation to future peak use. A statistical simulation tool allows the users to price these swings according to their own utilization patterns and to recover some of their costs if the options are not exercised. The provider in turn exploits its ability to statistically multiplex its resources to price peak usage. The use of these swing options serves as an incentive to the users to accurately forecasts of their own needs, thus leading to more efficient utilization of the provider’s resources.

Scott H. Clearwater; Bernardo A. Huberman

2005-01-01T23:59:59.000Z

59

Industrial-Load-Shaping: The Practice of and Prospects for Utility/Industry Cooperation to Manage Peak Electricity Demand  

E-Print Network (OSTI)

Load-management programs designed to reduce demand for electricity during peak periods are becoming increasingly important to electric utilities. For a growing number of utilities, however, such peak-reduction programs don't go far enough in the face of new problems and challenges, and hence are proving ineffective or counterproductive. For example, many of a utility's largest customers--especially industrial customers who may be "locked into" seemingly inflexible process activities--have limited ability to respond to load-management programs that employ price signals as a central peak-reduction tool. Moreover, utilities in general are finding that vigorous efforts to reduce electric load can result in underutilization of base-load generating facilities. In these and other instances, "load-shaping," which emphasizes a shift of electric load or demand from peak to off-peak periods and provides for greater customer flexibility, may be a more effective strategy. This paper explains the need for and presents the components of a load-shaping program, and describes Pacific Gas and Electric Company's (PGandE) recent experience in designing and pursuing an industrial-load-shaping program. The paper also outlines important obstacles and opportunities likely to confront other utilities and industrial customers interested in working together to develop such programs.

Bules, D. J.; Rubin, D. E.; Maniates, M. F.

1986-06-01T23:59:59.000Z

60

Chapter 10, Peak Demand and Time-Differentiated Energy Savings Cross-Cutting Protocols: The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

0: Peak Demand and 0: Peak Demand and Time-Differentiated Energy Savings Cross-Cutting Protocols Frank Stern, Navigant Consulting Subcontract Report NREL/SR-7A30-53827 April 2013 The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures 10 - 1 Chapter 10 - Table of Contents 1 Introduction .............................................................................................................................2 2 Purpose of Peak Demand and Time-differentiated Energy Savings .......................................3 3 Key Concepts ..........................................................................................................................5 4 Methods of Determining Peak Demand and Time-Differentiated Energy Impacts ...............7

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

The development of a charge protocol to take advantage of off- and on-peak demand economics at facilities  

DOE Green Energy (OSTI)

This document reports the work performed under Task 1.2.1.1: 'The development of a charge protocol to take advantage of off- and on-peak demand economics at facilities'. The work involved in this task included understanding the experimental results of the other tasks of SOW-5799 in order to take advantage of the economics of electricity pricing differences between on- and off-peak hours and the demonstrated charging and facility energy demand profiles. To undertake this task and to demonstrate the feasibility of plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) bi-directional electricity exchange potential, BEA has subcontracted Electric Transportation Applications (now known as ECOtality North America and hereafter ECOtality NA) to use the data from the demand and energy study to focus on reducing the electrical power demand of the charging facility. The use of delayed charging as well as vehicle-to-grid (V2G) and vehicle-to-building (V2B) operations were to be considered.

Jeffrey Wishart

2012-02-01T23:59:59.000Z

62

Factors Influencing Water Heating Energy Use and Peak Demand in a Large Scale Residential Monitoring Study  

E-Print Network (OSTI)

A load research project by the Florida Power Corporation (FPC) is monitoring 200 residences in Central Florida, collecting detailed end-use load data. The monitoring is being performed to better estimate the impact of FPC's load control program, as well as obtain improved appliance energy consumption indexes and load profiles. A portion of the monitoring measures water heater energy use and demand in each home on a 15-minute basis.

Bouchelle, M. P.; Parker, D. S.; Anello, M. T.

2000-01-01T23:59:59.000Z

63

Property:OpenEI/UtilityRate/FixedDemandChargeMonth11 | Open Energy  

Open Energy Info (EERE)

Name: Fixed Demand Charge Month 11 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth11" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 8.28 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 3.35 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

64

Property:OpenEI/UtilityRate/FixedDemandChargeMonth12 | Open Energy  

Open Energy Info (EERE)

Name: Fixed Demand Charge Month 12 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth12" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 8.28 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 3.35 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

65

Property:OpenEI/UtilityRate/FixedDemandChargeMonth10 | Open Energy  

Open Energy Info (EERE)

Name: Fixed Demand Charge Month 10 Pages using the property "OpenEI/UtilityRate/FixedDemandChargeMonth10" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 7 + 00101108-073b-4503-9cd4-01769611c26f + 1.71 + 0030a241-5084-4404-9fe4-ed558aad8b59 + 10.59 + 0049111b-fba2-46ba-827d-7ce95609a1d9 + 9.51 + 0055db46-f535-4dc9-a192-920d1bdf382b + 3.2 + 0070a37f-0d41-4331-8115-df40c62e00f3 + 13.24 + 007f7b1f-0cba-450c-9023-df962aa387a4 + 5.28 + 008960d4-14ad-4822-b293-140640cf0bcf + 4.924 + 00cdded9-47a1-49b6-a217-10941ffbefc6 + 1.468 + 00e0b930-90c6-43c2-971a-91dade33f76a + 2.71 + 010f37ad-90a9-4aa8-bbdf-c55e72ee1495 + 4.74 + 017a32a0-140a-4e0b-a10c-f6f67905829c + 4.5 + 019941c8-cc3b-452c-b12e-201301099603 + 11.95 +

66

Property:OpenEI/UtilityRate/FlatDemandMonth8 | Open Energy Information  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:OpenEI/UtilityRate/FlatDemandMonth8 Jump to: navigation, search This is a property of type Number. The allowed values for this property are: 1 2 3 4 5 6 7 8 9 Pages using the property "OpenEI/UtilityRate/FlatDemandMonth8" Showing 25 pages using this property. (previous 25) (next 25) 0 0000827d-84d0-453d-b659-b86869323897 + 1 + 000e60f7-120d-48ab-a1f9-9c195329c628 + 1 + 00101108-073b-4503-9cd4-01769611c26f + 1 + 001361ca-50d2-49bc-b331-08755a2c7c7d + 1 + 0016f771-cda9-4312-afc2-63f10c8d8bf5 + 1 + 00178d3d-17cb-46ed-8a58-24c816ddce96 + 1 + 001d1952-955c-411b-8ce4-3d146852a75e + 1 + 001ea8be-7a59-4bcb-a923-e8f1015946ee + 1 + 001eaca9-6ce7-4c0f-8578-44fc29654e97 + 1 + 0022e9a5-942c-4e97-94d7-600f5d315ce8 + 1 +

67

Peak Demand Reduction with Dual-Source Heat Pumps Using Municipal Water  

E-Print Network (OSTI)

The objective of this project was to examine a dual-source (air and/or water-coupled) heat pump concept which would reduce or eliminate the need for supplemental electrical resistance heating (strip heaters). The project examined two system options: switching on demand between completely air-source and completely water-coupled or using a concurrent partial water-coupled and partial air-coupled mode operation. The water supply for the water-coupled mode of operation would be the municipal water system. An estimate of the economic worth of this system concept was made by examining the incremental cost to install such a system against the expected savings associated with these systems.

Morehouse, J. H.; Khan, J. A.; Connor, L. N.; Pal, D.

1992-05-01T23:59:59.000Z

68

Talking Points from ACEEE Report U072: Estimating Peak Demand Impacts of Energy Efficiency Programs: A National Review of Practices and Experience  

E-Print Network (OSTI)

1. Demand-side management is a proven way to affect customer energy use a. Over 2 decades of experience with programs b. Two broad program categories: i. Energy efficiency programs primarily seek to reduce customer energy use (kilowatthours--kWh) on a permanent basis through the installation of energy-efficient technologies. ii. Load management generally focuses on either curtailing or shifting demand (kilowatts--kW) away from high cost, peak demand periods. Demand-response programs are really a type of load management---more “market-based” c. Over 2 decades of program evaluation experience, too. d. Are new drivers for peak demand reduction: reliability; volatile markets and high costs of new generation, transmission and distribution; reducing negative environmental impacts. 2. Peak load management and energy efficiency a. Are clearly overlaps, but peak demand impacts of energy efficiency programs have generally not been program priorities---which, in turn, has affected evaluation approaches and

unknown authors

2007-01-01T23:59:59.000Z

69

Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks  

Science Conference Proceedings (OSTI)

Neural networks have been widely used for short-term, and to a lesser degree medium and long-term, demand forecasting. In the majority of cases for the latter two applications, multivariate modeling was adopted, where the demand time series is related ... Keywords: Abductive networks, Energy demand, Medium-term load forecasting, Neural networks, Time series forecasting, Univariate time series analysis

R. E. Abdel-Aal

2008-05-01T23:59:59.000Z

70

Monthly load data report, fiscal year 1984  

SciTech Connect

Monthly tables are given for TVA megawatt demands and related information by customer class, at point of measurement (generation). Peak day profile graphs are also included. (DLC)

1984-01-01T23:59:59.000Z

71

Building America Top Innovations Hall of Fame Profile Â… High-Performance with Solar Electric Reduced Peak Demand: Premier Homes Rancho Cordoba, CA  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

95 homes in Premier Gardens are 95 homes in Premier Gardens are equipped with photovoltaic panels that take advantage of solar energy to offset peak power loads during the hottest part of the day. As the housing market continues to evolve toward zero net-energy ready homes, Building America research has provided essential guidance for integrating renewable energy systems with high-performance homes and showing how they align with utility peak-demand reduction interests. Solar photovoltaic technology is an attractive option for utilities because they can reduce reliance on fossil-fuel energy. More significantly, it reduces peak demand because systems produce the most power on sunny summer afternoons coincident with the highest demand for air conditioning. Photovoltaic systems have been a part of several research projects conducted by

72

Program Design Analysis using BEopt Building Energy Optimization Software: Defining a Technology Pathway Leading to New Homes with Zero Peak Cooling Demand; Preprint  

SciTech Connect

An optimization method based on the evaluation of a broad range of different combinations of specific energy efficiency and renewable-energy options is used to determine the least-cost pathway to the development of new homes with zero peak cooling demand. The optimization approach conducts a sequential search of a large number of possible option combinations and uses the most cost-effective alternatives to generate a least-cost curve to achieve home-performance levels ranging from a Title 24-compliant home to a home that uses zero net source energy on an annual basis. By evaluating peak cooling load reductions on the least-cost curve, it is then possible to determine the most cost-effective combination of energy efficiency and renewable-energy options that both maximize annual energy savings and minimize peak-cooling demand.

Anderson, R.; Christensen, C.; Horowitz, S.

2006-08-01T23:59:59.000Z

73

Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Peak load diagram Demand Response Demand Response (DR) is a set of time-dependent activities that reduce or shift electricity use to improve electric grid reliability, manage...

74

Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Peak load diagram Demand Response Demand response (DR) is a set of time-dependent activities that reduce or shift electricity use to improve electric grid reliability, manage...

75

Western Area Power Administration Starting Forecast Month: Sierra...  

NLE Websites -- All DOE Office Websites (Extended Search)

Starting Forecast Month: Sierra Nevada Region Through Values at Load Center (Tracy Substation) Reg & Res CVP Maximum Capability CVP Energy Generation Peak Project Use Demand...

76

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

14 Peak Demand Baselinewinter morning electric peak demand in commercial buildings.California to reduce peak demand during summer afternoons,

Kiliccote, Sila

2010-01-01T23:59:59.000Z

77

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: March 2012 Regional Wholesale Markets: March 2012 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at selected pricing locations and daily peak demand for selected electricity systems in the Nation. The range of daily prices and demand data is shown for the report month and for the year ending with the report month. Prices and demand are shown for six Regional Transmission Operator (RTO) markets: ISO New England (ISO-NE), New York ISO (NYISO), PJM Interconnection (PJM), Midwest ISO (MISO), Electric Reliability Council of Texas (ERCOT), and two locations in the California ISO (CAISO). Also shown are wholesale prices at trading hubs in Louisiana (into Entergy), Southwest

78

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: November 2011 Regional Wholesale Markets: November 2011 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at selected pricing locations and daily peak demand for selected electricity systems in the U.S. The range of daily prices and demand data is shown for the report month and for the year ending with the report month. Prices and demand are shown for six Regional Transmission Operator (RTO) markets: ISO New England (ISO-NE), New York ISO (NYISO), PJM Interconnection (PJM), Midwest ISO (MISO), Electric Reliability Council of Texas (ERCOT), and two locations in the California ISO (CAISO). Also shown are wholesale prices at trading hubs in Louisiana (into Entergy), Southwest

79

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: December 2011 Regional Wholesale Markets: December 2011 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at selected pricing locations and daily peak demand for selected electricity systems in the nation. The range of daily prices and demand data is shown for the report month and for the year ending with the report month. Prices and demand are shown for six Regional Transmission Operator (RTO) markets: ISO New England (ISO-NE), New York ISO (NYISO), PJM Interconnection (PJM), Midwest ISO (MISO), Electric Reliability Council of Texas (ERCOT), and two locations in the California ISO (CAISO). Also shown are wholesale prices at trading hubs in Louisiana (into Entergy), Southwest

80

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: January 2012 Regional Wholesale Markets: January 2012 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at selected pricing locations and daily peak demand for selected electricity systems in the nation. The range of daily prices and demand data is shown for the report month and for the year ending with the report month. Prices and demand are shown for six Regional Transmission Operator (RTO) markets: ISO New England (ISO-NE), New York ISO (NYISO), PJM Interconnection (PJM), Midwest ISO (MISO), Electric Reliability Council of Texas (ERCOT), and two locations in the California ISO (CAISO). Also shown are wholesale prices at trading hubs in Louisiana (into Entergy), Southwest

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: October 2011 Regional Wholesale Markets: October 2011 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at selected pricing locations and daily peak demand for selected electricity systems in the U.S. The range of daily prices and demand data is shown for the report month and for the year ending with the report month. Prices and demand are shown for six Regional Transmission Operator (RTO) markets: ISO New England (ISO-NE), New York ISO (NYISO), PJM Interconnection (PJM), Midwest ISO (MISO), Electric Reliability Council of Texas (ERCOT), and two locations in the California ISO (CAISO). Also shown are wholesale prices at trading hubs in Louisiana (into Entergy), Southwest

82

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: February 2012 Regional Wholesale Markets: February 2012 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at selected pricing locations and daily peak demand for selected electricity systems in the Nation. The range of daily prices and demand data is shown for the report month and for the year ending with the report month. Prices and demand are shown for six Regional Transmission Operator (RTO) markets: ISO New England (ISO-NE), New York ISO (NYISO), PJM Interconnection (PJM), Midwest ISO (MISO), Electric Reliability Council of Texas (ERCOT), and two locations in the California ISO (CAISO). Also shown are wholesale prices at trading hubs in Louisiana (into Entergy), Southwest

83

The European Electricity Grid System and Winter Peak Load Stress: For how long can the european grid system survive the ever increasing demand during cold winter days?  

E-Print Network (OSTI)

The rich countries of Western Europe and its citizens benefited during at least the last 30 years from an extraordinary stable electricity grid. This stability was achieved by the european grid system and a large flexible and reliable spare power plant capacity. This system allowed a continuous demand growth during the past 10-20 years of up to a few % per year. However, partially due to this overcapacity, no new large power plants have been completed during the past 10-15 years. The obvious consequence is that the reliable spare capacity has been reduced and that a further yearly demand growth of 1-2% for electric energy can only be achieved if new power plants will be constructed soon. Data from various European countries, provided by the UCTE, indicate that the system stress during peak load times and especially during particular cold winter days is much larger than generally assumed. In fact, the latest UCTE data on reliable power capacity indicate that already during the Winter 2007/8 only a few very col...

Dittmar, Michael

2008-01-01T23:59:59.000Z

84

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: September 2011 Regional Wholesale Markets: September 2011 The United States. has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at selected pricing locations and daily peak demand for selected electricity systems in the U.S. The range of daily price and demand data is shown for the month of September 2011 and for the year ending on September 30, 2011. Prices and demand are shown for six Regional Transmission Operator (RTO) markets: ISO New England (ISO-NE), New York ISO (NYISO), PJM Interconnection (PJM), Midwest ISO (MISO), Electric Reliability Council of Texas (ERCOT), and California ISO (CAISO). Also shown are wholesale prices at trading hubs in Louisiana (into Entergy), Southwest (Palo Verde) and

85

Automated Demand Response Strategies and Commissioning Commercial Building Controls  

E-Print Network (OSTI)

Braun (Purdue). 2004. Peak demand reduction from pre-coolingthe average and maximum peak demand savings. The electricityuse charges, demand ratchets, peak demand charges, and other

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Linkugel, Eric

2006-01-01T23:59:59.000Z

86

Strategies for Demand Response in Commercial Buildings  

E-Print Network (OSTI)

the average and maximum peak demand savings. The electricity1: Average and Maximum Peak Electric Demand Savings during

Watson, David S.; Kiliccote, Sila; Motegi, Naoya; Piette, Mary Ann

2006-01-01T23:59:59.000Z

87

Addressing Energy Demand through Demand Response: International Experiences and Practices  

E-Print Network (OSTI)

time. 4 Reducing this peak demand through DR programs meansthat a 5% reduction in peak demand would have resulted insame 5% reduction in the peak demand of the US as a whole.

Shen, Bo

2013-01-01T23:59:59.000Z

88

Demand Responsive Lighting: A Scoping Study  

E-Print Network (OSTI)

with total Statewide peak demand and on peak days isto examine the electric peak demand related to lighting inDaily) - TOU Savings - Peak Demand Charges - Grid Peak -Low

Rubinstein, Francis; Kiliccote, Sila

2007-01-01T23:59:59.000Z

89

Demand Response Valuation Frameworks Paper  

E-Print Network (OSTI)

xxxv Option Value of Electricity Demand Response, Osmanelasticity in aggregate electricity demand. With these newii) reduction in electricity demand during peak periods (

Heffner, Grayson

2010-01-01T23:59:59.000Z

90

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

29 5.6. Peak and hourly demand43 6.6. Peak and seasonal demandthe average percent of peak demand) significantly impact the

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

91

Effects of the drought on California electricity supply and demand  

E-Print Network (OSTI)

for Electricity and Power Peak Demand . . • . . ELECTRICITYby Major Utility Service Area Projected Peak Demand for1977 Historical Peak Demand by Utility Service Area Weather-

Benenson, P.

2010-01-01T23:59:59.000Z

92

Climate, extreme heat, and electricity demand in California  

E-Print Network (OSTI)

projected extreme heat and peak demand for electricity areadequately kept up with peak demand, and electricity supplytrend in aggregate peak demand in California is expected to

Miller, N.L.

2008-01-01T23:59:59.000Z

93

Energy Demands and Efficiency Strategies in Data Center Buildings  

E-Print Network (OSTI)

Total Annual Energy Usage Peak Electric Demand Power UsageSetpoint (°C) Peak Electric Demand Power Usage Effective-Total Annual Energy Usage Peak Electric Demand Scenario

Shehabi, Arman

2010-01-01T23:59:59.000Z

94

Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results  

E-Print Network (OSTI)

together  during  this  peak  demand period to use power 21 Peak Demand Baseline study.  Their average peak demand reduction was 14% of the 

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila

2007-01-01T23:59:59.000Z

95

Coordination of Energy Efficiency and Demand Response  

E-Print Network (OSTI)

percent of 2008 summer peak demand (FERC, 2008). Moreover,138,000 MW (14 percent of peak demand) by 2019 (FERC, 2009).non-coincident summer peak demand by 157 GW” by 2030, or 14–

Goldman, Charles

2010-01-01T23:59:59.000Z

96

Retail Demand Response in Southwest Power Pool  

E-Print Network (OSTI)

pricing tariffs have a peak demand reduction potential ofneed to reduce summer peak demand that is used to set demandcustomers and a system peak demand of over 43,000 MW. SPP’s

Bharvirkar, Ranjit

2009-01-01T23:59:59.000Z

97

Peak Power at Peak Efficiency  

Peak Power At Peak Efficiency. 21. st. Industry Growth Forum. October 2008. PJ Piper (857) 350?3100. ... At <$10/bbl oil, QM Power’s electric ...

98

Effects of the drought on California electricity supply and demand  

E-Print Network (OSTI)

DEMAND . . . .Demand for Electricity and Power PeakDemand . . • . . ELECTRICITY REQUIREMENTS FOR AGRICULTUREResults . . Coriclusions ELECTRICITY SUPPLY Hydroelectric

Benenson, P.

2010-01-01T23:59:59.000Z

99

Electricity Monthly Update  

Annual Energy Outlook 2012 (EIA)

CorrectionUpdate November 28, 2012 Map of Electric System Selected for Daily Peak Demand was replaced with the correct map showing Selected Wholesale Electricity and Natural Gas...

100

STAFF FORECAST OF 2007 PEAK STAFFREPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION STAFF FORECAST OF 2007 PEAK DEMAND STAFFREPORT June 2006 CEC-400.................................................................................. 9 Sources of Forecast Error....................................................................... .................11 Tables Table 1: Revised versus September 2005 Peak Demand Forecast ......................... 2

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Demand Response in U.S. Electricity Markets: Empirical Evidence  

E-Print Network (OSTI)

concerns during system peak demand conditions, and failurerelative to national peak demand, was about 5.0% in 2006 [2]to a region’s summer peak demand (see Fig. 2). Demand

Cappers, Peter

2009-01-01T23:59:59.000Z

102

Optimization of Demand Response Through Peak Shaving  

E-Print Network (OSTI)

Jun 19, 2013 ... periods, for which he incurs an energy charge (per megawatt hour ... before the prevalence of electricity markets, in the context of public utility pricing and ra- ... §

103

Open Automated Demand Response for Small Commerical Buildings  

E-Print Network (OSTI)

of the small commercial peak demand.  The majority of the less than 200 kW of peak demand, make up 20?25% of  peak the small commercial  peak demand.  A ten percent reduction 

Dudley, June Han

2009-01-01T23:59:59.000Z

104

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

E-Print Network (OSTI)

power generators during peak demand periods. 13 Onsite powerit can be used during peak-demand periods. 15 Implementingtreatment loads from peak demand hours to off-peak hours is

Thompson, Lisa

2008-01-01T23:59:59.000Z

105

Estimating Demand Response Market Potential Among Large Commercial and Industrial Customers: A Scoping Study  

E-Print Network (OSTI)

residential customers with peak demand greater than 350 kWs) Eligible Customers (peak demand) Optional hourly pricingis relatively small; the peak demand of its large, non-

Goldman, Charles; Hopper, Nicole; Bharvirkar, Ranjit; Neenan, Bernie; Cappers, Peter

2007-01-01T23:59:59.000Z

106

Demand Responsive and Energy Efficient Control Technologies and Strategies in Commercial Buildings  

E-Print Network (OSTI)

Contribution to Peak Demand?..5 3.potential to reduce peak demand in commercial buildingsbuildings’ contribution to peak demand and the use of energy

Piette, Mary Ann; Kiliccote, Sila

2006-01-01T23:59:59.000Z

107

What China Can Learn from International Experiences in Developing a Demand Response Program  

E-Print Network (OSTI)

new approach to meet its peak demand, this paper highlightsaimed at both reducing the peak demand and improving theshortages during the peak demand season. A Barclays report

Shen, Bo

2013-01-01T23:59:59.000Z

108

Advanced Controls and Communications for Demand Response and Energy Efficiency in Commercial Buildings  

E-Print Network (OSTI)

for a large portion of summer peak demand. Research resultspotential to reduce peak demand in commercial buildingsbuilding’s contribution to peak demand and the use of energy

Kiliccote, Sila; Piette, Mary Ann; Hansen, David

2006-01-01T23:59:59.000Z

109

2008-2010 Research Summary: Analysis of Demand Response Opportunities in California Industry  

E-Print Network (OSTI)

534 megawatts (MW) of peak demand reduction and 1 gigawatt (power generators during peak demand periods. Onsite powerit can be used during peak-demand periods. Implementing load

Goli, Sasank

2013-01-01T23:59:59.000Z

110

Electrical Demand Management  

E-Print Network (OSTI)

The Demand Management Plan set forth in this paper has proven to be a viable action to reduce a 3 million per year electric bill at the Columbus Works location of Western Electric. Measures are outlined which have reduced the peak demand 5% below the previous year's level and yielded $150,000 annual savings. These measures include rescheduling of selected operations and demand limiting techniques such as fuel switching to alternate power sources during periods of high peak demand. For example, by rescheduling the startup of five heat treat annealing ovens to second shift, 950 kW of load was shifted off peak. Also, retired, non-productive steam turbine chillers and a diesel air compressor have been effectively operated to displaced 1330 kW during peak periods each day. Installed metering devices have enabled the recognition of critical demand periods. The paper concludes with a brief look at future plans and long range objectives of the Demand Management Plan.

Fetters, J. L.; Teets, S. J.

1983-01-01T23:59:59.000Z

111

Definition: On-Peak | Open Energy Information  

Open Energy Info (EERE)

Definition Definition Edit with form History Facebook icon Twitter icon » Definition: On-Peak Jump to: navigation, search Dictionary.png On-Peak Those hours or other periods defined by NAESB business practices, contract, agreements, or guides as periods of higher electrical demand.[1] View on Wikipedia Wikipedia Definition Peak demand is used to refer to a historically high point in the sales record of a particular product. In terms of energy use, peak demand describes a period of strong consumer demand. Also Known As peak load Related Terms demand, peak demand References ↑ Glossary of Terms Used in Reliability Standards Temp Like Like You like this.Sign Up to see what your friends like. late:ISGANAttributionsmart grid,smart grid, Retrieved from "http://en.openei.org/w/index.php?title=Definition:On-Peak&oldid=502536"

112

Dynamic Controls for Energy Efficiency and Demand Response: Framework Concepts and a New Construction Study Case in New York  

E-Print Network (OSTI)

and J.E. Braun. 2004. “Peak Demand Reduction from Pre-contributor to summer peak demand, with large increases inin driving summer peak demands suggest that commercial

Kiliccote, Sila; Piette, Mary Ann; Watson, David S.; Hughes, Glenn

2006-01-01T23:59:59.000Z

113

Assessing Vehicle Electricity Demand Impacts on California Electricity Supply  

E-Print Network (OSTI)

serves to partially fill off-peak demand troughs. If passivehigher before or after the peak demand hour when hydro powerare highest during off-peak demand hours, and are low at

McCarthy, Ryan W.

2009-01-01T23:59:59.000Z

114

California Baseline Energy Demands to 2050 for Advanced Energy Pathways  

E-Print Network (OSTI)

by Sector Residential Peak Demand (MW) Commercial IndustrialTable 16. Non-coincident peak demand by sector. growth Avg.IEPR Projected non-coincident peak demand (MW) 3.1.2. Hourly

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

115

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network (OSTI)

the entire forecast period, primarily because both weather-adjusted peak and electricity consumption were forecast. Keywords Electricity demand, electricity consumption, demand forecast, weather normalization, annual peak demand, natural gas demand, self-generation, conservation, California Solar Initiative. #12

116

Western Area Power Administration Starting Forecast Month: Sierra...  

NLE Websites -- All DOE Office Websites (Extended Search)

Reg & Res Maximum CVP Capacity CVP Energy Generation Peak Project Use Demand Project Use (PU) Load Energy First Pref. (FP) Peak Demand First Pref. (FP) Load Energy Estimated...

117

Home Network Technologies and Automating Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in...

118

Demand Response | Department of Energy  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Demand Response Demand Response Demand Response Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. Demand response programs are being used by electric system planners and operators as resource options for balancing supply and demand. Such programs can lower the cost of electricity in wholesale markets, and in turn, lead to lower retail rates. Methods of engaging customers in demand response efforts include offering time-based rates such as time-of-use pricing, critical peak pricing, variable peak pricing, real time pricing, and critical peak rebates. It also includes direct load control programs which provide the

119

California Baseline Energy Demands to 2050 for Advanced Energy Pathways  

E-Print Network (OSTI)

Figure 16 Annual peak electricity demand by sector. Tableincludes an hourly electricity demand (i.e. power) profileof aggregating sectoral electricity demands into a statewide

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

120

Advanced Control Technologies and Strategies Linking Demand Response and Energy Efficiency  

E-Print Network (OSTI)

and M.A. Piette, J. Braun “Peak Demand Reduction from Pre-to reduce Electrical Peak Demands in Commercial Buildings”Management (Daily) - TOU - Peak Demand Charges - Grid Peak -

Kiliccote, Sila; Piette, Mary Ann

2005-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Regional Wholesale Markets: August 2011 The U.S. has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale prices at...

122

Automated Critical Peak Pricing Field Tests: Program Description and Results  

E-Print Network (OSTI)

E-2: Baseline Peak and Maximum Demand Savings at Each Auto-45 Table 4-8: Maximum Demand saving by Site and Non-and the non-coincident maximum demand savings. If all twelve

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Xu, Peng

2006-01-01T23:59:59.000Z

123

California Baseline Energy Demands to 2050 for Advanced Energy Pathways  

E-Print Network (OSTI)

Table 22. Agricultural natural gas demand by planning area.23. “Other” sector natural gas demand by planning area.Projections Monthly natural gas demands are depicted in

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

124

Definition: Variable Peak Pricing | Open Energy Information  

Open Energy Info (EERE)

Variable Peak Pricing Variable Peak Pricing Jump to: navigation, search Dictionary.png Variable Peak Pricing Variable Peak Pricing (VPP) is a hybrid of time-of-use and real-time pricing where the different periods for pricing are defined in advance (e.g., on-peak=6 hours for summer weekday afternoon; off-peak= all other hours in the summer months), but the price established for the on-peak period varies by utility and market conditions.[1] Related Terms real-time pricing References ↑ https://www.smartgrid.gov/category/technology/variable_peak_pricing [[C LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ategory: Smart Grid Definitionssmart grid,off-peak,on-peak,smart grid, |Template:BASEPAGENAME]]smart grid,off-peak,on-peak,smart grid, Retrieved from "http://en.openei.org/w/index.php?title=Definition:Variable_Peak_Pricing&oldid=50262

125

Peak shaving through resource buffering  

E-Print Network (OSTI)

Abstract. We introduce and solve a new problem inspired by energy pricing schemes in which a client is billed for peak usage. At each timeslot the system meets an energy demand through a combination of a new request, an unreliable amount of free source energy (e.g. solar or wind power), and previously received energy. The added piece of infrastructure is the battery, which can store surplus energy for future use. More generally, the demands could represent required amounts of energy, water, or any other tenable resource which can be obtained in advance and held until needed. In a feasible solution, each demand must be supplied on time, through a combination of newly requested energy, energy withdrawn from the battery, and free source. The goal is to minimize the maximum request. In the online version of this problem, the algorithm must determine each request without knowledge of future demands or free source availability, with the goal of maximizing the amount by which the peak is reduced. We give efficient optimal algorithms for the offline problem, with and without a bounded battery. We also show how to find the optimal offline battery size, given the requirement that the final battery level equals the initial battery level. Finally, we give efficient Hn-competitive algorithms assuming the peak effective demand is revealed in advance, and provide matching lower bounds. 1

Amotz Bar-noy; Matthew P. Johnson; Ou Liu

2007-01-01T23:59:59.000Z

126

Automated Demand Response Tests  

Science Conference Proceedings (OSTI)

This report includes assessments and test results of four end-use technologies, representing products in the residential, commercial, and industrial sectors, each configured to automatically receive real-time pricing information and critical peak pricing (CPP) demand response (DR) event notifications. Four different vendors were asked to follow the interface requirements set forth in the Open Automated Demand Response (OpenADR) standard that was introduced to the public in 2008 and currently used in two ...

2008-12-22T23:59:59.000Z

127

Automated Demand Response Tests  

Science Conference Proceedings (OSTI)

This report, which is an update to EPRI Report 1016082, includes assessments and test results of four end-use vendor technologies. These technologies represent products in the residential, commercial, and industrial sectors, each configured to automatically receive real-time pricing information and critical peak pricing (CPP) demand response (DR) event notifications. Four different vendors were asked to follow the interface requirements set forth in the Open Automated Demand Response (OpenADR) Communicat...

2009-03-30T23:59:59.000Z

128

Addressing Energy Demand through Demand Response: International...  

NLE Websites -- All DOE Office Websites (Extended Search)

Addressing Energy Demand through Demand Response: International Experiences and Practices Title Addressing Energy Demand through Demand Response: International Experiences and...

129

Addressing Energy Demand through Demand Response: International...  

NLE Websites -- All DOE Office Websites (Extended Search)

Energy Demand through Demand Response: International Experiences and Practices Title Addressing Energy Demand through Demand Response: International Experiences and Practices...

130

Monthly Reports  

NLE Websites -- All DOE Office Websites (Extended Search)

Environmental Management Monthly Reports - FY 2012 The Department of Energy Nevada Field Office Environmental Management Program creates monthly reports for the NSSAB. These...

131

Monthly Reports  

NLE Websites -- All DOE Office Websites (Extended Search)

Environmental Management Monthly Reports - FY 2013 The Department of Energy Nevada Field Office Environmental Management Program creates monthly reports for the NSSAB. These...

132

Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings  

E-Print Network (OSTI)

buildings can reduce peak demand from 5 to 15% with anof events. We benchmark the peak demand of this sample ofyears. This is done with peak demand intensities and load

Kiliccote, Sila

2010-01-01T23:59:59.000Z

133

Estimating Large-Customer Demand Response Market Potential: Integrating Price and Customer Behavior  

E-Print Network (OSTI)

reductions in their class peak demand in response to pricesresidential customers with peak demand greater than 350 kWs) Eligible Customers (peak demand) > 1,500 kW > 2000 kW >

Goldman, Charles; Hopper, Nicole; Bharvirkar, Ranjit; Neenan, Bernie; Cappers, Peter

2007-01-01T23:59:59.000Z

134

Demand Shifting With Thermal Mass in Large Commercial Buildings: Field Tests, Simulation and Audits  

E-Print Network (OSTI)

Braun (Purdue). 2004. Peak demand reduction from pre-coolingmass for load shifting and peak demand reduction has beenpre-cooling strategies on peak demand. In addition, a set of

Xu, Peng; Haves, Philip; Piette, Mary Ann; Zagreus, Leah

2005-01-01T23:59:59.000Z

135

Demand Shifting with Thermal Mass in Large Commercial Buildings in a California Hot Climate Zone  

E-Print Network (OSTI)

J. E. Braun. 2004. “Peak demand reduction from pre-coolingReducing electrical peak demand has a huge economic andmass for load shifting and peak demand reduction has been

Xu, Peng

2010-01-01T23:59:59.000Z

136

A Methodology for Estimating Large-Customer Demand Response Market Potential  

E-Print Network (OSTI)

reductions in their class peak demand in response to pricesresidential customers with peak demand greater than 350 kWs) Eligible Customers (peak demand) > 1,500 kW > 2000 kW

Goldman, Charles; Hopper, Nicole; Bharvirkar, Ranjit; Neenan, Bernie; Cappers, Peter

2008-01-01T23:59:59.000Z

137

demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

138

Off peak ice storage generation  

DOE Green Energy (OSTI)

Due to the high costs associated with peak demand charges imposed by most electrical companies today, various means of shifting the peak HVAC load have been identified by the industry. This paper discusses the results of a study based upon a building site located in the high desert of the southwestern United States that evaluated ice storage as a mechanism of operating cost reductions. The discussion addresses both the seasonal and the annual cost and energy impacts of an ice storage system when used in place of an air-to-air heat pump system.

Davis, R.E.; Cerbo, F.J.

1985-01-01T23:59:59.000Z

139

Projecting Monthly Natural Gas Sales for Space Heating Using a Monthly Updated Model and Degree-days from Monthly Outlooks  

Science Conference Proceedings (OSTI)

The problem of projecting monthly residential natural gas sales and evaluating interannual changes in demand is investigated using a linear regression model adjusted monthly. with lagged monthly heating degree-days as the independent variable. ...

Richard L. Lehman; Henry E. Warren

1994-01-01T23:59:59.000Z

140

Scaling distributed energy storage for grid peak reduction  

Science Conference Proceedings (OSTI)

Reducing peak demand is an important part of ongoing smart grid research efforts. To reduce peak demand, utilities are introducing variable rate electricity prices. Recent efforts have shown how variable rate pricing can incentivize consumers to use ... Keywords: battery, electricity, energy, grid, peak shaving

Aditya Mishra, David Irwin, Prashant Shenoy, Ting Zhu

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Electric grid planners: demand response and energy efficiency to ...  

U.S. Energy Information Administration (EIA)

Source: Form EIA-411, Coordinated Bulk Power Demand and Supply Report Note: All data are reported for time of summer peak, rather than overall demand.

142

Western Area Power Administration Starting Forecast Month: Sierra...  

NLE Websites -- All DOE Office Websites (Extended Search)

Pref. (FP) Peak Demand First Pref. (FP) Load Energy Estimated Ancillary Services Capacity PU Forward Purchase Off- Peak Energy PU & FP Capacity Purchase Reqmts. Additional PU & FP...

143

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

for the most natural gas usage (33% and 51% of total demanddependence in natural gas usage, and consequently, Januarygas demand exhibits a strong winter peak in residential usage

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

144

Northwest Open Automated Demand Response Technology Demonstration...  

NLE Websites -- All DOE Office Websites (Extended Search)

morning and summer afternoon peak electricity demand in commercial buildings the Seattle area. LBNL performed this demonstration for the Bonneville Power Administration (BPA)...

145

Energy Demands and Efficiency Strategies in Data Center Buildings  

E-Print Network (OSTI)

DX Cooling Total Annual Energy Usage Peak Electric DemandDX Cooling Total Annual Energy Usage Scenario Supply/ ReturnDX Cooling Total Annual Energy Usage Peak Electric Demand

Shehabi, Arman

2010-01-01T23:59:59.000Z

146

Opportunities for Open Automated Demand Response in Wastewater Treatment Facilities in California - Phase II Report. San Luis Rey Wastewater Treatment Plant Case Study  

E-Print Network (OSTI)

demand of 1.3 MW, with peak demand reaching 2 MW. Figure 1summer period. SDG&E’s peak demand period is between 11 AMlast 10 with the highest peak demand (Coughlin 2008). Unlike

Thompson, Lisa

2010-01-01T23:59:59.000Z

147

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Methodology Methodology Methodology Demonstrated Peak Working Gas Capacity Estimates: Estimates are based on aggregation of the noncoincident peak levels of working gas inventories at individual storage fields as reported monthly over a 60-month period ending in April 2010 on Form EIA-191M, "Monthly Natural Gas Underground Storage Report." The months of measurement for the peak storage volumes by facilities may differ; i.e., the months do not necessarily coincide. As such, the noncoincident peak for any region is at least as big as any monthly volume in the historical record. Data from Form EIA-191M, "Monthly Natural Gas Underground Storage Report," are collected from storage operators on a field-level basis. Operators can report field-level data either on a per reservoir basis or on an aggregated reservoir basis. It is possible that if all operators reported on a per reservoir basis that the demonstrated peak working gas capacity would be larger. Additionally, these data reflect inventory levels as of the last day of the report month, and a facility may have reached a higher inventory on a different day of the report month, which would not be recorded on Form EIA-191M.

148

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network (OSTI)

summer peak demand, with hydro power and wind integration,of its hydro system, continued load growth, wind power

Kiliccote, Sila

2010-01-01T23:59:59.000Z

149

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST  

E-Print Network (OSTI)

Policy Report, over the entire forecast period, primarily because both weather-adjusted peak and commercial sectors. Keywords Electricity demand, electricity consumption, demand forecast, weather normalization, annual peak demand, natural gas demand, self-generation, California Solar Initiative. #12;ii #12

150

Optimal Sizing of Energy Storage and Photovoltaic Power Systems for Demand Charge Mitigation (Poster)  

DOE Green Energy (OSTI)

Commercial facility utility bills are often a strong function of demand charges -- a fee proportional to peak power demand rather than total energy consumed. In some instances, demand charges can constitute more than 50% of a commercial customer's monthly electricity cost. While installation of behind-the-meter solar power generation decreases energy costs, its variability makes it likely to leave the peak load -- and thereby demand charges -- unaffected. This then makes demand charges an even larger fraction of remaining electricity costs. Adding controllable behind-the-meter energy storage can more predictably affect building peak demand, thus reducing electricity costs. Due to the high cost of energy storage technology, the size and operation of an energy storage system providing demand charge management (DCM) service must be optimized to yield a positive return on investment (ROI). The peak demand reduction achievable with an energy storage system depends heavily on a facility's load profile, so the optimal configuration will be specific to both the customer and the amount of installed solar power capacity. We explore the sensitivity of DCM value to the power and energy levels of installed solar power and energy storage systems. An optimal peak load reduction control algorithm for energy storage systems will be introduced and applied to historic solar power data and meter load data from multiple facilities for a broad range of energy storage system configurations. For each scenario, the peak load reduction and electricity cost savings will be computed. From this, we will identify a favorable energy storage system configuration that maximizes ROI.

Neubauer, J.; Simpson, M.

2013-10-01T23:59:59.000Z

151

Oil Peak or Panic?  

SciTech Connect

In this balanced consideration of the peak-oil controversy, Gorelick comes down on the side of the optimists.

Greene, David L [ORNL

2010-01-01T23:59:59.000Z

152

Method and system for regulating peak residential power demand  

SciTech Connect

A temperature monitoring system that monitors temperature outside the residence and a supply system responsive to the monitoring system that controls the supply of electrical power to major home appliances such as air conditioning devices, food preparation devices, clothes drying devices, and water heating devices is described. The major home appliances are arranged in pairs and connected to a main power distribution system in these pair arrangements through a load dispatcher including continuity sensitive switches. The appliances are continuously connected to the electrical power distribution system when the outdoor temperature is below a predetermined value. However, when the outdoor temperature exceeds the predetermined value, the continuity switches then control the supply of power to the appliances by supplying power to one of the appliances to the exclusion of the other in each pair arrangement. Whenever electrical power is not being supplied to one of the appliances in the pair arrangement requiring power, the other of the appliances is supplied with electrical power. In accordance with another aspect of the invention, the outdoor temperature is monitored and controls the operation of an air conditioning unit. When the outdoor temperature exceeds a predetermined value, the air conditioner is cycled between on and off conditions on a timed, periodic basis without regard to the temperature inside the residence at least until the temperature outside the residence drops below the predetermined value. The air conditioner may be cycled between on and off conditions on the periodic basis until the outdoor temperature drops a predetermined amount below the predetermined value, for example, drops at least 5/sup 0/ or 6/sup 0/ below the predetermined value. 12 Claims, 5 Drawing Figures.

Dixon, W.A.

1975-12-09T23:59:59.000Z

153

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

the installation of distributed generation and other energyof 2 MW. The presence of distributed generation and storage

DeForest, Nicholas

2013-01-01T23:59:59.000Z

154

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

TOU tariff, which levies high fees for both energy and powerE-20 Industrial Tariff (PG&E 2012) The Distributed Energya TOU electricity tariff. These energy improvements—along

DeForest, Nicholas

2013-01-01T23:59:59.000Z

155

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

bills incurred under a TOU electricity tariff. These energyTable 1 Time of Use Electricity Tariff at SRJ Period Summerby imposing the tariff rates to the electricity purchases of

DeForest, Nicholas

2013-01-01T23:59:59.000Z

156

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

electricity. The benefits of microgrids are however a two-customer-operated microgrids can potentially reduce theIf the utility expects microgrids like SRJ to engage in this

DeForest, Nicholas

2013-01-01T23:59:59.000Z

157

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

Energy Reliability, Distributed Energy Program of the U.S.myriad of on-site distributed energy resources (DER) locatedTariff (PG&E 2012) The Distributed Energy Resources Customer

DeForest, Nicholas

2013-01-01T23:59:59.000Z

158

TRENDS IN ELECTRICITY CONSUMPTION, PEAK DEMAND, AND GENERATING CAPACITY IN  

E-Print Network (OSTI)

relative to increases in its consumption at a higher rate than all but two states (in part because California is the lowest user of electricity per capita and per dollar of gross state product in the west). Annual WSCC consumption increased 64% from 1977 to 1998, but California's consumption grew by only 44

California at Berkeley. University of

159

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

of 2009 SRJ Electricity Bills DER-CAM Optimization Managingthe composition of 2009 electricity bills at SRJ. Note thatTable 2 August Electricity Bill by Storage Schedule Charge

DeForest, Nicholas

2013-01-01T23:59:59.000Z

160

Microgrid Dispatch for Macrogrid Peak-Demand Mitigation  

E-Print Network (OSTI)

The installed battery has an energy capacity of 4 MWh and acapacity appears to be decreasing with increasing battery

DeForest, Nicholas

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Electricity demand changes in predictable patterns - Today in ...  

U.S. Energy Information Administration (EIA)

... winter months tend to be higher than demand levels during the fall and spring "shoulder" seasons when system demand for space conditioning (heating or cooling) ...

162

Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets  

E-Print Network (OSTI)

as Large Comm. Interval metering system with monthly dataDynamic Pricing, Advanced Metering and Demand Response inE Dynamic Pricing, Advanced Metering, and Demand Response in

Borenstein, Severin; Jaske, Michael; Rosenfeld, Arthur

2002-01-01T23:59:59.000Z

163

The Influence of Residential Solar Water Heating on Electric Utility Demand  

E-Print Network (OSTI)

Similar sets of residences in Austin, Texas with electric water heaters and solar water heaters with electric back-up were monitored during 1982 to determine their instantaneous electric demands, the purpose being to determine the influence of residential solar water heating on electric utility demand. The electric demand of solar water hears was found to be approximately 0.39 kW lass than conventional electric water heaters during the late late afternoon, early evening period in the summer months when the Austin utility experiences its peak demand. The annual load factor would be only very slightly reduced if there were a major penetration of solar water heaters in the all electric housing sector. Thus solar water heating represents beneficial load management for utilities experiencing summer peaks.

Vliet, G. C.; Askey, J. L.

1984-01-01T23:59:59.000Z

164

Agency datasets monthly list | Data.gov  

NLE Websites -- All DOE Office Websites (Extended Search)

Supply and Demand Estimates (WASDE) report is prepared monthly and includes forecasts for U.S. and world wheat, rice, and coarse grains (corn, barley, sorghum, and oats),...

165

Both Distillate Supply and Demand Reached Extraordinary Levels This Winter  

Gasoline and Diesel Fuel Update (EIA)

6 6 Notes: This chart shows some critical differences in distillate supply and demand during this winter heating season, in comparison to the past two winters. Typically, distillate demand peaks during the winter months, but "new supply" (refinery production and net imports) cannot increase as much, so the remaining supply needed is drawn from inventories. This pattern is evident in each of the past two winter heating seasons. This winter, however, the pattern was very different, for several reasons: With inventories entering the season at extremely low levels, a "typical" winter stockdraw would have been nearly impossible, particularly in the Northeast, the region most dependent on heating oil. Demand reached near-record levels in December, as colder-than-normal

166

Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

page intentionally left blank page intentionally left blank 69 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Transportation Demand Module The NEMS Transportation Demand Module estimates transportation energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), buses, freight and passenger aircraft, freight and passenger rail, freight shipping, and miscellaneous

167

Ups and downs of demand limiting  

SciTech Connect

Electric power load management by limiting power demand can be used for energy conservation. Methods for affecting demand limiting, reducing peak usage in buildings, particularly usage for heating and ventilating systems, and power pricing to encourage demand limiting are discussed. (LCL)

Pannkoke, T.

1976-12-01T23:59:59.000Z

168

Price Responsive Demand in New York Wholesale Electricity Market using OpenADR  

E-Print Network (OSTI)

months when buildings' electricity demand is also high dueoptimize buildings' electricity demand according to hourlymonths when buildings' electricity demand is also high due

Kim, Joyce Jihyun

2013-01-01T23:59:59.000Z

169

Peak Load Shifting by Thermal Energy Storage  

Science Conference Proceedings (OSTI)

This technical update from the Electric Power Research Institute (EPRI) reviews the technology of storing energy in hot water and explores the potential for implementing this form of thermal energy storagethrough means of smart electric water heatersas a way to shift peak load on the electric grid. The report presents conceptual background, discusses strategies for peak load shifting and demand response, documents a series of laboratory tests conducted on a representative model of smart water heater, and...

2011-12-14T23:59:59.000Z

170

Peaks Over Threshold Plot  

Science Conference Proceedings (OSTI)

... CAPTURE POT.OUT PEAKS OVER THRESHOLD PLOT Y17 R END OF CAPTURE . SKIP 0 READ DPST2F.DAT ITER NPOINTS THRESH R2 XR . ...

2010-12-06T23:59:59.000Z

171

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

172

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

173

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

174

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

175

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

176

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

177

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

178

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

179

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Methodology and Documentation Methodology and Documentation General The Electricity Monthly Update is prepared by the Electric Power Operations Team, Office of Electricity, Renewables and Uranium Statistics, U.S. Energy Information Administration (EIA), U.S. Department of Energy. Data published in the Electricity Monthly Update are compiled from the following sources: U.S. Energy Information Administration, Form EIA-826,"Monthly Electric Utility Sales and Revenues with State Distributions Report," U.S. Energy Information Administration, Form EIA-923, "Power Plant Operations Report," fuel spot prices from Bloomberg Energy, electric power prices from SNL Energy, electric system demand data from Ventyx Energy Velocity Suite, and weather data and imagery from the National Oceanic and Atmospheric

180

Potential Peak Load Reductions From Residential Energy Efficient Upgrades  

E-Print Network (OSTI)

The demand for electricity is continuing to grow at a substantial rate. Utilities are interested in managing this growth's peak demand for a number of reasons including: costly construction of new generation capacity can be deferred; the reliability of the distribution network can be improved; and added environmental pollution can be minimized. Energy efficiency improvements, especially through residential programs, are increasingly being used to mitigate this rise in peak demand. This paper examines the potential peak load reductions from residential energy efficiency upgrades in hot and humid climates. First, a baseline scenario is established. Then, the demand and consumption impacts of individual upgrade measures are assessed. Several of these upgrades are then combined into a package to assess the synergistic demand and energy impacts. A sensitivity analysis is then performed to assess the impacts of housing characteristics on estimated demand and energy savings. Finally, the demand, energy, and environmental impacts are estimated at the community level.

Meisegeier, D.; Howes, M.; King, D.; Hall, J.

2002-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Distributed Battery Control for Peak Power Shaving in Datacenters  

E-Print Network (OSTI)

Distributed Battery Control for Peak Power Shaving in Datacenters Baris Aksanli and Tajana Rosing to shave peak power demands. Our novel distributed battery control design has no performance impact, reduces the peak power needs, and accurately estimates and maximizes the battery lifetime. We demonstrate

Simunic, Tajana

182

Overview of Demand Response  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

08 PJM 08 PJM www.pjm.com ©2003 PJM Overview of Demand Response PJM ©2008 PJM www.pjm.com ©2003 PJM Growth, Statistics, and Current Footprint AEP, Dayton, ComEd, & DUQ Dominion Generating Units 1,200 + Generation Capacity 165,000 MW Peak Load 144,644 MW Transmission Miles 56,070 Area (Square Miles) 164,250 Members 500 + Population Served 51 Million Area Served 13 States and DC Generating Units 1,200 + Generation Capacity 165,000 MW Peak Load 144,644 MW Transmission Miles 56,070 Area (Square Miles) 164,250 Members 500 + Population Served 51 Million Area Served 13 States and DC Current PJM RTO Statistics Current PJM RTO Statistics PJM Mid-Atlantic Integrations completed as of May 1 st , 2005 ©2008 PJM

183

Electric Power Monthly - Monthly Data Tables Monthly electricity...  

Open Energy Info (EERE)

Electric Power Monthly - Monthly Data Tables Monthly electricity generation figures (and the fuel consumed to produce it). Source information available at

184

Definition: Demand Side Management | Open Energy Information  

Open Energy Info (EERE)

Side Management Side Management Jump to: navigation, search Dictionary.png Demand Side Management The term for all activities or programs undertaken by Load-Serving Entity or its customers to influence the amount or timing of electricity they use.[1] View on Wikipedia Wikipedia Definition Energy demand management, also known as demand side management (DSM), is the modification of consumer demand for energy through various methods such as financial incentives and education. Usually, the goal of demand side management is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times such as nighttime and weekends. Peak demand management does not necessarily decrease total energy consumption, but could be expected to reduce the need

185

Demand Response  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Assessment for Eastern Interconnection Youngsun Baek, Stanton W. Hadley, Rocio Martinez, Gbadebo Oladosu, Alexander M. Smith, Fran Li, Paul Leiby and Russell Lee Prepared for FY12 DOE-CERTS Transmission Reliability R&D Internal Program Review September 20, 2012 2 Managed by UT-Battelle for the U.S. Department of Energy DOE National Laboratory Studies Funded to Support FOA 63 * DOE set aside $20 million from transmission funding for national laboratory studies. * DOE identified four areas of interest: 1. Transmission Reliability 2. Demand Side Issues 3. Water and Energy 4. Other Topics * Argonne, NREL, and ORNL support for EIPC/SSC/EISPC and the EISPC Energy Zone is funded through Area 4. * Area 2 covers LBNL and NREL work in WECC and

186

building demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

187

Coproduction of peaking fuels in IGCC power plants: a process-screening study. Final report  

SciTech Connect

This study evaluated and compared various options for processing a portion of the medium BTU gas (MBG) produced in a coal gasification combined cycle (GCC) power plant to produce a fuel which might be suitable for peaking or intermediate load use. Two alternate objectives were investigated in separate phases of the study. The first phase examined options for processing and storing a fuel which could be withdrawn and used in absorbing daily load swings in power generation demand. The second phase investigated options for meeting the seasonal peaks in gas demand of a joint gas/electric utility by converting a portion of the MBG to substitute natural gas (SNG) during the months of peak gas demand. For each phase, process designs and cost estimates were completed for several cases, based on both Texaco and BGC-Lurgi Slagging Gasification Technology. For the purposes of this screening study, it was assumed that the peaking fuel production facilities are incremental to the base GCC plant. The costs to produce and store the peaking fuel, excluding the cost of the MBG feed, were calculated by the revenue requirement method. Various sensitivities were evaluated on case assumptions, including a sensitivity to MBG feed value. For daily peaking use, the co-production of methanol and electricity by the ''once-through'' scheme (as studied in EPRI Report AP-2212) proved the most attractive option. Other options which produced gaseous fuels (hydrogen or SNG) for on-site storage were at least 30% more costly. Storage of SNG in an existing natural gas pipeline system was at least 10% higher, excluding pipeline charges. For seasonal SNG production there was little difference between the options studied, within the accuracy of the estimates. 13 refs., 72 tabs.

Shenoy, T.A.; Solomon, J.; O'Brien, V.J.

1986-07-01T23:59:59.000Z

188

Rapid increases in electricity demand challenge both ...  

U.S. Energy Information Administration (EIA)

... on April 1 was the steepest so far this year in SPP. The rate of increase in electricity demand peaked at 12.4% between 6 a.m. and 7 a.m. ...

189

Storm Peak Lab Cloud Property Validation  

NLE Websites -- All DOE Office Websites (Extended Search)

Storm Peak Lab Cloud Storm Peak Lab Cloud Property Validation Experiment (STORMVEX) Operated by the Atmospheric Radiation Measurement (ARM) Climate Research Facility for the U.S. Department of Energy, the second ARM Mobile Facility (AMF2) begins its inaugural deployment November 2010 in Steamboat Springs, Colorado, for the Storm Peak Lab Cloud Property Validation Experiment, or STORMVEX. For six months, the comprehensive suite of AMF2 instruments will obtain measurements of cloud and aerosol properties at various sites below the heavily instrumented Storm Peak Lab, located on Mount Werner at an elevation of 3220 meters. The correlative data sets that will be created from AMF2 and Storm Peak Lab will equate to between 200 and 300 in situ aircraft flight hours in liquid, mixed phase, and precipitating

190

High Temperatures & Electricity Demand  

E-Print Network (OSTI)

High Temperatures & Electricity Demand An Assessment of Supply Adequacy in California Trends.......................................................................................................1 HIGH TEMPERATURES AND ELECTRICITY DEMAND.....................................................................................................................7 SECTION I: HIGH TEMPERATURES AND ELECTRICITY DEMAND ..........................9 BACKGROUND

191

Measuring the capacity impacts of demand response  

Science Conference Proceedings (OSTI)

Critical peak pricing and peak time rebate programs offer benefits by increasing system reliability, and therefore, reducing capacity needs of the electric power system. These benefits, however, decrease substantially as the size of the programs grows relative to the system size. More flexible schemes for deployment of demand response can help address the decreasing returns to scale in capacity value, but more flexible demand response has decreasing returns to scale as well. (author)

Earle, Robert; Kahn, Edward P.; Macan, Edo

2009-07-15T23:59:59.000Z

192

Tri-State Demand Response Framework  

Science Conference Proceedings (OSTI)

This report provides the results of a demand response framework development project of Tri-State Generation and Transmission, a wholesale provider to a number of rural electric associations in the Rocky Mountain west. Tri-State has developed an assortment of planned demand response and energy shaping products and services designed to both shave peak and shift consumption to off-peak hours. The applications, networks, and devices that will be needed to support these needs will involve many ...

2013-03-28T23:59:59.000Z

193

Demand Response Research in Spain  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Research in Spain Demand Response Research in Spain Speaker(s): Iñigo Cobelo Date: August 22, 2007 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Mary Ann Piette The Spanish power system is becoming increasingly difficult to operate. The peak load grows every year, and the permission to build new transmission and distribution infrastructures is difficult to obtain. In this scenario Demand Response can play an important role, and become a resource that could help network operators. The present deployment of demand response measures is small, but this situation however may change in the short term. The two main Spanish utilities and the transmission network operator are designing research projects in this field. All customer segments are targeted, and the research will lead to pilot installations and tests.

194

Propane Demand is Highly Seasonal, But Fresh Supply is Not  

Gasoline and Diesel Fuel Update (EIA)

4 Notes: Propane, like heating oil, has a highly seasonal demand pattern. Demand increases about 50% from its low point to its peak. Production and net imports, on the other hand,...

195

PEAK READING VOLTMETER  

DOE Patents (OSTI)

An improvement in peak reading voltmeters is described, which provides for storing an electrical charge representative of the magnitude of a transient voltage pulse and thereafter measuring the stored charge, drawing oniy negligible energy from the storage element. The incoming voltage is rectified and stored in a condenser. The voltage of the capacitor is applied across a piezoelectric crystal between two parallel plates. Amy change in the voltage of the capacitor is reflected in a change in the dielectric constant of the crystal and the capacitance between a second pair of plates affixed to the crystal is altered. The latter capacitor forms part of the frequency determlning circuit of an oscillator and means is provided for indicating the frequency deviation which is a measure of the peak voltage applied to the voltmeter.

Dyer, A.L.

1958-07-29T23:59:59.000Z

196

INTEGRATION OF PV IN DEMAND RESPONSE  

E-Print Network (OSTI)

of the baseline defining a customer's load profile, and (2) PVs cannot be turned on at will for scheduled tests customers to curtail demand when needed to reduce risk of grid failure during times of peak loading load. The value of this credit may reach or exceed $100/kW/year [1] Demand response is typically

Perez, Richard R.

197

Residential Energy Demand Reduction Analysis and Monitoring Platform...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Dramatic Peak Residential Demand Reduction in the Desert Southwest Yahia Baghzouz Center for Energy Research University of Nevada, Las Vegas Golden, CO Overview * Project...

198

How to Get More Response from Demand Response  

Science Conference Proceedings (OSTI)

Despite all the rhetoric, demand response's contribution to meet peak load will remain elusive in the absence of enabling technology and standardized business protocols. (author)

Neumann, Scott; Sioshansi, Fereidoon; Vojdani, Ali; Yee, Gaymond

2006-10-15T23:59:59.000Z

199

1995 Demand-Side Managment - Energy Information Administration  

U.S. Energy Information Administration (EIA)

and more detailed data on energy savings, peak load reductions and costs attributable to DSM. Target Audience ... Profile: U.S. Electric Utility Demand-Side

200

FERC sees huge potential for demand response  

Science Conference Proceedings (OSTI)

The FERC study concludes that U.S. peak demand can be reduced by as much as 188 GW -- roughly 20 percent -- under the most aggressive scenario. More moderate -- and realistic -- scenarios produce smaller but still significant reductions in peak demand. The FERC report is quick to point out that these are estimates of the potential, not projections of what could actually be achieved. The main varieties of demand response programs include interruptible tariffs, direct load control (DLC), and a number of pricing schemes.

NONE

2010-04-15T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

Advanced Control Technologies and Strategies Linking Demand Response and Energy Efficiency  

E-Print Network (OSTI)

Electrical Peak Demands in Commercial Buildings” Center for Analysis and Dissemination of Demonstrated Energy Technologies (CADDET), IEA/OECD Analyses

Kiliccote, Sila; Piette, Mary Ann

2005-01-01T23:59:59.000Z

202

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Highlights: August 2011 Highlights: August 2011 Extreme heat in Texas, New Mexico, Colorado and Arizona drove significant increases in the retail sales of electricity in the Southwest. Wind generation increased in much of the United States, except the middle of the country where total generation declined. Bituminous coal stocks dropped 14% from August 2010. Key indicators Same Month 2010 Year to date Total Net Generation -1% 11% Residential Retail Price -6% 11% Cooling Degree-Days -3% 2% Natural Gas Price, Henry Hub -6% -9% Bituminous Coal Stocks -14% -14% Subbituminous Coal Stocks -10% -17% Heat wave drives record demand and wholesale prices in Texas A prolonged August heat wave in Texas stressed available generating capacity and produced very high wholesale prices in the Electric

203

The Year of Peak Production  

U.S. Energy Information Administration (EIA)

When world conventional oil production will peak is, of course, the bottom-line question. It has already peaked in the United States, in 1970.

204

Automated Demand Response: The Missing Link in the Electricity Value Chain  

E-Print Network (OSTI)

promise in reducing the electricity demand of the industrialchanges the time of electricity demand to off-peak hours.Load shedding curtails electricity demand during a DR event.

McKane, Aimee

2010-01-01T23:59:59.000Z

205

Advanced Demand Responsive Lighting  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Demand Responsive Lighting Host: Francis Rubinstein Demand Response Research Center Technical Advisory Group Meeting August 31, 2007 10:30 AM - Noon Meeting Agenda * Introductions (10 minutes) * Main Presentation (~ 1 hour) * Questions, comments from panel (15 minutes) Project History * Lighting Scoping Study (completed January 2007) - Identified potential for energy and demand savings using demand responsive lighting systems - Importance of dimming - New wireless controls technologies * Advanced Demand Responsive Lighting (commenced March 2007) Objectives * Provide up-to-date information on the reliability, predictability of dimmable lighting as a demand resource under realistic operating load conditions * Identify potential negative impacts of DR lighting on lighting quality Potential of Demand Responsive Lighting Control

206

Demand Response Spinning Reserve  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Spinning Reserve Title Demand Response Spinning Reserve Publication Type Report Year of Publication 2007 Authors Eto, Joseph H., Janine Nelson-Hoffman, Carlos...

207

Transportation Demand This  

Annual Energy Outlook 2012 (EIA)

69 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2012 Transportation Demand Module The NEMS Transportation Demand Module estimates...

208

Addressing Energy Demand  

NLE Websites -- All DOE Office Websites (Extended Search)

Addressing Energy Demand through Demand Response: International Experiences and Practices Bo Shen, Girish Ghatikar, Chun Chun Ni, and Junqiao Dudley Environmental Energy...

209

Propane Sector Demand Shares  

U.S. Energy Information Administration (EIA)

... agricultural demand does not impact regional propane markets except when unusually high and late demand for propane for crop drying combines with early cold ...

210

Patterns of crude demand: Future patterns of demand for crude oil as a func-  

E-Print Network (OSTI)

from the perspective of `peak oil', that is from the pers- pective of the supply of crude, and price#12;2 #12;Patterns of crude demand: Future patterns of demand for crude oil as a func- tion is given on the problems within the value chain, with an explanation of the reasons why the price of oil

Langendoen, Koen

211

Residential Energy Demand Reduction Analysis and Monitoring Platform - REDRAMP  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Dramatic Peak Residential Dramatic Peak Residential Demand Reduction in the Desert Southwest Yahia Baghzouz Center for Energy Research University of Nevada, Las Vegas Golden, CO Overview * Project description * Subdivision energy efficiency features * Home energy monitoring * Demand side management * Feeder loading * Battery Energy Storage System * Future Work Team Members Project Objective and Methodology * The main objective is to reduce peak power demand of a housing subdivision by 65% (compared to housing development that is built to conventional code). * This objective will be achieved by - Energy efficient home construction with roof- integrated PV system - Demand Side Management - Battery Energy Storage System Project schematic Diagram Project Physical Location: Las Vegas, NV Red Rock Hotel/Casino

212

Demand Shifting With Thermal Mass in Large Commercial Buildings: Case  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Shifting With Thermal Mass in Large Commercial Buildings: Case Demand Shifting With Thermal Mass in Large Commercial Buildings: Case Studies and Tools Speaker(s): Peng Xu Date: March 9, 2007 - 12:00pm Location: 90-3122 The idea of pre-cooling and demand limiting is to pre-cool buildings at night or in the morning during off-peak hours, storing cooling energy in the building thermal mass and thereby reducing cooling loads during the peak periods. Savings are achieved by reducing on-peak energy and demand charges. The potential for utilizing building thermal mass for load shifting and peak demand reduction has been demonstrated in a number of simulation, laboratory, and field studies. Case studies in a number of office buildings in California has found that a simple demand limiting strategy reduced the chiller power by 20-100% (0.5-2.3W/ft2) during six

213

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Definitions Definitions Definitions Since 2006, EIA has reported two measures of aggregate capacity, one based on demonstrated peak working gas storage, the other on working gas design capacity. Demonstrated Peak Working Gas Capacity: This measure sums the highest storage inventory level of working gas observed in each facility over the 5-year range from May 2005 to April 2010, as reported by the operator on the Form EIA-191M, "Monthly Underground Gas Storage Report." This data-driven estimate reflects actual operator experience. However, the timing for peaks for different fields need not coincide. Also, actual available maximum capacity for any storage facility may exceed its reported maximum storage level over the last 5 years, and is virtually certain to do so in the case of newly commissioned or expanded facilities. Therefore, this measure provides a conservative indicator of capacity that may understate the amount that can actually be stored.

214

Electricity Demand and Energy Consumption Management System  

E-Print Network (OSTI)

This project describes the electricity demand and energy consumption management system and its application to the Smelter Plant of Southern Peru. It is composted of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks, with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows management the peak demand before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules allow the proper planning because it allows knowing the behavior of the hourly demand and the consumption patterns of the plant, in...

Sarmiento, Juan Ojeda

2008-01-01T23:59:59.000Z

215

MonthlyReport1stQrtr  

NLE Websites -- All DOE Office Websites (Extended Search)

35 35 0 0 0 35 Number of charging events² 800 0 0 0 800 Electricity consumed (AC MWh) 5.25 0.00 0.00 0.00 5.25 Percent of time with a vehicle connected to charging unit 36% 0% 0% 0% 36% Percent of time with a vehicle drawing power from charging unit 7% 0% 0% 0% 7% Max electricity demand across all days Min electricity demand across all days Electricity demand on single calendar day with highest peak Max percentage of charging units connected across all days Min percentage of charging units connected across all days Percentage of charging units connected on single calendar day with peak electricity demand Region: All Report period: January 2011 through March 2011 Number of EV Project vehicles in region: 35 5/19/2011 5:37:48 PM INL/LTD-11-22097 1 of 2 © 2011 ECOtality

216

MonthlyReport2ndQrtr  

NLE Websites -- All DOE Office Websites (Extended Search)

955 955 0 11 0 966 Number of charging events² 35,134 0 56 0 35,190 Electricity consumed (AC MWh) 248.96 0.00 0.25 0.00 249.22 Percent of time with a vehicle connected to charging unit 30% 0% 5% 0% 30% Percent of time with a vehicle drawing power from charging unit 6% 0% 1% 0% 6% Max electricity demand across all days Min electricity demand across all days Electricity demand on single calendar day with highest peak Max percentage of charging units connected across all days Min percentage of charging units connected across all days Percentage of charging units connected on single calendar day with peak electricity demand Region: ALL Report period: April 2011 through June 2011 Number of EV Project vehicles in region: 956 8/10/2011 1:13:31 PM INL/LTD-11-22097 © 2011 ECOtality

217

Variable-response model of electricity demand by time of day: Results of a Wisconsin pricing experiment: Final report  

Science Conference Proceedings (OSTI)

Observationally alike households may differ in demand parameters and thus in economic quantities that are functions of those parameters. We have proposed a methodology for dealing with this variation. Estimation of both translog and CES versions of the model with data from the Wisconsin Electricity Pricing Experiment revealed considerable variation among households in time-of-day electricity consumption demand parameters for both summer and winter seasons and for several different definitions of the peak period. Observed household characteristics explained only a small share of total household differences, but permanent household differences dominated month-to-month variation in either expenditure shares or log consumption ratios in most cases. Permanent differences among households are important relative to total variation, including transitory month-to-month variation. We calculated various economic variables from the demand parameters, including the partial elasticity of substitution, compensated and uncompensated elasticities, and a measure of electricity expenditure under peak load pricing required to maintain the utility level under flat rate pricing relative to the flat rate expenditure. Because these are nonlinear functions of the household demand parameters, the mean parameter value over households with different demand parameters may be substantially different from the value of the function at mean values, under the representative household paradigm. For time-of-day electricity demand, variation among households is significant but small relative to mean parameter values. Therefore, controlling for the effect of household variation makes little difference in these mean calculations, but it does imply substantial variation among households in the welfare implications (and elasticities of response) of the introduction of time-of-day pricing. 25 refs., 12 tabs.

Lillard, L.

1987-06-01T23:59:59.000Z

218

The Annual Peak in the SST Anomaly Spectrum  

Science Conference Proceedings (OSTI)

The manner in which monthly mean sea surface temperature anomalies (SSTAs) show enhanced variance at the annual period in the extratropics (an annual peak in the variance spectrum) is illustrated by observations and model simulations. A mechanism,...

Jens Möller; Dietmar Dommenget; Vladimir A. Semenov

2008-06-01T23:59:59.000Z

219

Demand Response and Open Automated Demand Response Opportunities...  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response and Open Automated Demand Response Opportunities for Data Centers Title Demand Response and Open Automated Demand Response Opportunities for Data Centers...

220

Addressing Energy Demand through Demand Response: International Experiences and Practices  

E-Print Network (OSTI)

of integrating demand response and energy efficiencyand D. Kathan (2009), Demand Response in U.S. ElectricityFRAMEWORKS THAT PROMOTE DEMAND RESPONSE 3.1. Demand Response

Shen, Bo

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Coordination of Energy Efficiency and Demand Response  

Science Conference Proceedings (OSTI)

This paper reviews the relationship between energy efficiency and demand response and discusses approaches and barriers to coordinating energy efficiency and demand response. The paper is intended to support the 10 implementation goals of the National Action Plan for Energy Efficiency's Vision to achieve all cost-effective energy efficiency by 2025. Improving energy efficiency in our homes, businesses, schools, governments, and industries - which consume more than 70 percent of the nation's natural gas and electricity - is one of the most constructive, cost-effective ways to address the challenges of high energy prices, energy security and independence, air pollution, and global climate change. While energy efficiency is an increasingly prominent component of efforts to supply affordable, reliable, secure, and clean electric power, demand response is becoming a valuable tool in utility and regional resource plans. The Federal Energy Regulatory Commission (FERC) estimated the contribution from existing U.S. demand response resources at about 41,000 megawatts (MW), about 5.8 percent of 2008 summer peak demand (FERC, 2008). Moreover, FERC recently estimated nationwide achievable demand response potential at 138,000 MW (14 percent of peak demand) by 2019 (FERC, 2009).2 A recent Electric Power Research Institute study estimates that 'the combination of demand response and energy efficiency programs has the potential to reduce non-coincident summer peak demand by 157 GW' by 2030, or 14-20 percent below projected levels (EPRI, 2009a). This paper supports the Action Plan's effort to coordinate energy efficiency and demand response programs to maximize value to customers. For information on the full suite of policy and programmatic options for removing barriers to energy efficiency, see the Vision for 2025 and the various other Action Plan papers and guides available at www.epa.gov/eeactionplan.

Goldman, Charles; Reid, Michael; Levy, Roger; Silverstein, Alison

2010-01-29T23:59:59.000Z

222

Demand Trading: Building Liquidity  

Science Conference Proceedings (OSTI)

Demand trading holds substantial promise as a mechanism for efficiently integrating demand-response resources into regional power markets. However, regulatory uncertainty, the lack of proper price signals, limited progress toward standardization, problems in supply-side markets, and other factors have produced illiquidity in demand-trading markets and stalled the expansion of demand-response resources. This report shows how key obstacles to demand trading can be overcome, including how to remove the unce...

2002-11-27T23:59:59.000Z

223

Retail Demand Response in Southwest Power Pool  

SciTech Connect

In 2007, the Southwest Power Pool (SPP) formed the Customer Response Task Force (CRTF) to identify barriers to deploying demand response (DR) resources in wholesale markets and develop policies to overcome these barriers. One of the initiatives of this Task Force was to develop more detailed information on existing retail DR programs and dynamic pricing tariffs, program rules, and utility operating practices. This report describes the results of a comprehensive survey conducted by LBNL in support of the Customer Response Task Force and discusses policy implications for integrating legacy retail DR programs and dynamic pricing tariffs into wholesale markets in the SPP region. LBNL conducted a detailed survey of existing DR programs and dynamic pricing tariffs administered by SPP's member utilities. Survey respondents were asked to provide information on advance notice requirements to customers, operational triggers used to call events (e.g. system emergencies, market conditions, local emergencies), use of these DR resources to meet planning reserves requirements, DR resource availability (e.g. seasonal, annual), participant incentive structures, and monitoring and verification (M&V) protocols. Nearly all of the 30 load-serving entities in SPP responded to the survey. Of this group, fourteen SPP member utilities administer 36 DR programs, five dynamic pricing tariffs, and six voluntary customer response initiatives. These existing DR programs and dynamic pricing tariffs have a peak demand reduction potential of 1,552 MW. Other major findings of this study are: o About 81percent of available DR is from interruptible rate tariffs offered to large commercial and industrial customers, while direct load control (DLC) programs account for ~;;14percent. o Arkansas accounts for ~;;50percent of the DR resources in the SPP footprint; these DR resources are primarily managed by cooperatives. o Publicly-owned cooperatives accounted for 54percent of the existing DR resources among SPP members. For these entities, investment in DR is often driven by the need to reduce summer peak demand that is used to set demand charges for each distribution cooperative. o About 65-70percent of the interruptible/curtailable tariffs and DLC programs are routinely triggered based on market conditions, not just for system emergencies. Approximately, 53percent of the DR resources are available with less than two hours advance notice and 447 MW can be dispatched with less than thirty minutes notice. o Most legacy DR programs offered a reservation payment ($/kW) for participation; incentive payment levels ranged from $0.40 to $8.30/kW-month for interruptible rate tariffs and $0.30 to $4.60/kW-month for DLC programs. A few interruptible programs offered incentive payments which were explicitly linkedto actual load reductions during events; payments ranged from 2 to 40 cents/kWh for load curtailed.

Bharvirkar, Ranjit; Heffner, Grayson; Goldman, Charles

2009-01-30T23:59:59.000Z

224

An Innovative Approach Towards National Peak Load Management  

E-Print Network (OSTI)

An innovative approach was developed and implemented in eight governmental buildings to reduce their load during the peak demand hours in summer of 2007. The innovative approach implemented in these buildings included pre-closing treatment (PCT) between 13:00 and 14:00 h and time-of-day control (TDC) after 14:00 h for air conditioning (A/C) and lighting systems. PCT realized an overall reduction of 3.43 MW, a saving of 11.7% of the buildings peak power demand; while TDC realized a total savings of 8.67 MW at 15:00 h, a saving of 30.7% of the buildings peak power demand at that hour. The temperature build up inside the buildings due to PCT and TDC was within the acceptable range, which validated the technical viability of these measures. The implementation of the innovative approach in the eight governmental buildings with a total measured peak demand of 29.3 MW achieved a reduction of 8.89 MW. This power is now available to other users leading to financial savings of $13.5 million for the nation towards the cost of constructing new power plants and distribution network equipment. More importantly, this reduction in peak power demand of well over 30% involved zero or limited expenditure. A nationwide implementation of this innovative approach in all the governmental and institutional buildings is likely to reduce the national peak power demand by 154 MW which amounts to a capital savings of $232 million towards the cost of new power generation equipment and distribution network.

Al-Mulla, A.; Maheshwari, G. P.; Al-Nakib, D.; ElSherbini, A.; Alghimlas, F.; Al-Taqi, H.; Al-Hadban, Y.

2008-10-01T23:59:59.000Z

225

Demand Impacted by Weather  

U.S. Energy Information Administration (EIA)

When you look at demand, it’s also interesting to note the weather. The weather has a big impact on the demand of heating fuels, if it’s cold, consumers will use ...

226

Mass Market Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Mass Market Demand Response Mass Market Demand Response Speaker(s): Karen Herter Date: July 24, 2002 - 12:00pm Location: Bldg. 90 Demand response programs are often quickly and poorly crafted in reaction to an energy crisis and disappear once the crisis subsides, ensuring that the electricity system will be unprepared when the next crisis hits. In this paper, we propose to eliminate the event-driven nature of demand response programs by considering demand responsiveness a component of the utility obligation to serve. As such, demand response can be required as a condition of service, and the offering of demand response rates becomes a requirement of utilities as an element of customer service. Using this foundation, we explore the costs and benefits of a smart thermostat-based demand response system capable of two types of programs: (1) a mandatory,

227

The Integration of Energy Efficiency, Renewable Energy, Demand Response and Climate Change: Challenges and Opportunities for Evaluators and Planners  

E-Print Network (OSTI)

demand at night, then baseload plants and emissions willare typically used for baseload and peak capacity plants,

Vine, Edward

2007-01-01T23:59:59.000Z

228

Petroleum Marketing Monthly  

U.S. Energy Information Administration (EIA)

ii U.S. Energy Information Administration/Petroleum Marketing Monthly August 2011 Preface The Petroleum Marketing Monthly (PMM) provides information and statistical ...

229

February Natural Gas Monthly  

Annual Energy Outlook 2012 (EIA)

Gas Annual. Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

230

November Natural Gas Monthly  

Annual Energy Outlook 2012 (EIA)

Gas Annual. Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

231

January Natural Gas Monthly  

Annual Energy Outlook 2012 (EIA)

Gas Annual. Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

232

March Natural Gas Monthly  

Gasoline and Diesel Fuel Update (EIA)

Gas Annual. Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

233

May Natural Gas Monthly  

Annual Energy Outlook 2012 (EIA)

Gas Annual. Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

234

Demand Trading Toolkit  

Science Conference Proceedings (OSTI)

Download report 1006017 for FREE. The global movement toward competitive markets is paving the way for a variety of market mechanisms that promise to increase market efficiency and expand customer choice options. Demand trading offers customers, energy service providers, and other participants in power markets the opportunity to buy and sell demand-response resources, just as they now buy and sell blocks of power. EPRI's Demand Trading Toolkit (DTT) describes the principles and practice of demand trading...

2001-12-10T23:59:59.000Z

235

Peak Oil, Peak Energy Mother Nature Bats Last  

E-Print Network (OSTI)

Peak Oil, Peak Energy Mother Nature Bats Last Martin Sereno 1 Feb 2011 (orig. talk: Nov 2004) #12;Oil is the Lifeblood of Industrial Civilization · 80 million barrels/day, 1000 barrels/sec, 1 cubicPods to the roads themselves) · we're not "addicted to oil" -- that's like saying a person has an "addiction

Sereno, Martin

236

Peak Oil, Peak Energy Mother Nature Bats Last  

E-Print Network (OSTI)

/Predicted (2006) Discovery, Production FSU (former Soviet Union) history Soviet Union collapse 80's oil pricePeak Oil, Peak Energy Mother Nature Bats Last Martin Sereno 1 Feb 2011 (orig. talk: Nov 2004) #12;Oil is the Lifeblood of Industrial Civilization · 80 million barrels/day, 1000 barrels/sec, 1 cubic

Sereno, Martin

237

Texas Nuclear Profile - Comanche Peak  

U.S. Energy Information Administration (EIA) Indexed Site

Comanche Peak" "Unit","Summer capacity (mw)","Net generation (thousand mwh)","Summer capacity factor (percent)","Type","Commercial operation date","License expiration date"...

238

Peak oil: diverging discursive pipelines.  

E-Print Network (OSTI)

??Peak oil is the claimed moment in time when global oil production reaches its maximum rate and henceforth forever declines. It is highly controversial as… (more)

Doctor, Jeff

2012-01-01T23:59:59.000Z

239

Coordination of Energy Efficiency and Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Coordination of Energy Efficiency and Demand Response Coordination of Energy Efficiency and Demand Response Title Coordination of Energy Efficiency and Demand Response Publication Type Report Refereed Designation Unknown Year of Publication 2010 Authors Goldman, Charles A., Michael Reid, Roger Levy, and Alison Silverstein Pagination 74 Date Published 01/2010 Publisher LBNL City Berkeley Keywords electricity markets and policy group, energy analysis and environmental impacts department Abstract This paper reviews the relationship between energy efficiency and demand response and discusses approaches and barriers to coordinating energy efficiency and demand response. The paper is intended to support the 10 implementation goals of the National Action Plan for Energy Efficiency's Vision to achieve all cost-effective energy efficiency by 2025.1 Improving energy efficiency in our homes, businesses, schools, governments, and industries-which consume more than 70 percent of the nation's natural gas and electricity-is one of the most constructive, cost-effective ways to address the challenges of high energy prices, energy security and independence, air pollution, and global climate change. While energy efficiency is an increasingly prominent component of efforts to supply affordable, reliable, secure, and clean electric power, demand response is becoming a valuable tool in utility and regional resource plans. The Federal Energy Regulatory Commission (FERC) estimated the contribution from existing U.S. demand response resources at about 41,000 megawatts (MW), about 5.8 percent of 2008 summer peak demand (FERC, 2008). Moreover, FERC recently estimated nationwide achievable demand response potential at 138,000 MW (14 percent of peak demand) by 2019 (FERC, 2009).2 A recent Electric Power Research Institute study estimates that "the combination of demand response and energy efficiency programs has the potential to reduce non-coincident summer peak demand by 157 GW" by 2030, or 14-20 percent below projected levels (EPRI, 2009a). This paper supports the Action Plan's effort to coordinate energy efficiency and demand response programs to maximize value to customers. For information on the full suite of policy and programmatic options for removing barriers to energy efficiency, see the Vision for 2025 and the various other Action Plan papers and guides available at www.epa.gov/eeactionplan.

240

Peaks in Raindrop Size Distributions  

Science Conference Proceedings (OSTI)

The multipeak behavior of raindrop size distributions has been studied. Peaks have been found for distinct drop diameters: 0.7, 1.0, 1.9, and possibly 3.2 mm. The probability is about 65% that at least one of these peaks exists in an observed ...

M. Steiner; A. Waldvogel

1987-10-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Peaking World Oil Production: Impacts, Mitigation and Risk Management  

E-Print Network (OSTI)

The peaking of world oil production presents the U.S. and the world with an unprecedented risk management problem. As peaking is approached, liquid fuel prices and price volatility will increase dramatically, and, without timely mitigation, the economic, social, and political costs will be unprecedented. Viable mitigation options exist on both the supply and demand sides, but to have substantial impact, they must be initiated more than a decade in advance of peaking. In 2003, the world consumed nearly 80 million barrels per day (MM bpd) of oil. U.S. consumption was almost 20 MM bpd,

Robert L. Hirsch; Roger H. Bezdek; Robert M. Wendling

2005-01-01T23:59:59.000Z

242

Cooling commercial buildings with off-peak power  

Science Conference Proceedings (OSTI)

Large commercial buildings use more electricity for cooling than for heating, and can account for 40% of summer peak demand. A cool storage technique in which compressors chill or freeze water during off-peak periods and the water is circulated during peak hours is in use in 100 commercial buildings. Reports indicate that these systems are economical, although little information is available, but engineers are hesitant to incorporate them because of possible damage from leaks or rust and other uncertainties. The Electric Power Research Institute is evaluating the performance of several systems to answer some of the operating and maintenance questions raised by engineers. 3 references, 3 figures. (DCK)

Lihach, N.; Rabl, V.

1983-10-01T23:59:59.000Z

243

Demand Response Enabling Technologies and Approaches for Industrial Facilities  

E-Print Network (OSTI)

There are numerous programs sponsored by Independent System Operators (ISOs) and utility or state efficiency programs that have an objective of reducing peak demand. Most of these programs have targeted the residential and commercial sector, however, there are also huge opportunities for demand response in the industrial sector. This paper describes some of the demand response initiatives that are currently active in New York State, explaining applicability of industrial facilities. Next, we discuss demand response-enabling technologies, which can help an industrial plant effectively address demand response needs. Finally, the paper is concluded with a discussion of case study projects that illustrate application of some of these demand response enabling technologies for process operations. These case studies, illustrating some key projects from the NYSERDA Peak Load Reduction program, will describe the technologies and approaches deployed to achieve the demand reduction at the site, the quantitative impact of the project, and a discussion of the overall successes at each site.

Epstein, G.; D'Antonio, M.; Schmidt, C.; Seryak, J.; Smith, C.

2005-01-01T23:59:59.000Z

244

Demand Response and Open Automated Demand Response Opportunities...  

NLE Websites -- All DOE Office Websites (Extended Search)

Response and Open Automated Demand Response Opportunities for Data Centers Title Demand Response and Open Automated Demand Response Opportunities for Data Centers Publication Type...

245

Electric Power Monthly  

Gasoline and Diesel Fuel Update (EIA)

Electric Power Monthly > Electric Power Monthly Back Issues Electric Power Monthly > Electric Power Monthly Back Issues Electric Power Monthly Back Issues Monthly Excel files zipped 2010 January February March April May June July August September October November December 2009 January February March April May June July August September October November December 2008 January February March March Supplement April May June July August September October November December 2007 January February March April May June July August September October November December 2006 January February March April May June July August September October November December 2005 January February March April May June July August September October November December

246

A new approach for modeling the peak utility impacts from a proposed CUAC standard  

E-Print Network (OSTI)

an October-peaking load profile, rather than a more credibleof the space cooling load profiles for the months ofcommercial space cooling load profile for ECAR. This figure

LaCommare, Kristina Hamachi; Gumerman, Etan; Marnay, Chris; Chan, Peter; Coughlin, Katie

2004-01-01T23:59:59.000Z

247

Application of Thermal Storage, Peak Shaving and Cogeneration for Hospitals  

E-Print Network (OSTI)

Energy costs of hospitals can be managed by employing various strategies to control peak electrical demand (KW) while at the same time providing additional security of operation in the event that an equipment failure or a disruption of power from the electric utility occurs. Some electric utilities offer their customers demand (KW) reduction rate incentives. Many hospitals have additional emergency back-up needs for electrical energy. Demand is relatively constant in many hospitals due to high internal loads. These factors coupled with the present competitive alternate fuel market and present opportunities for hospitals to significantly reduce operating costs and provide additional stand-by or back-up electric sources. This paper employs a hospital case study to define and illustrate three energy planning strategies applicable to hospitals. These strategies are peak shaving, thermal storage, cogeneration and/or paralleling with the electric utility.

McClure, J. D.; Estes, J. M.; Estes, M. C.

1987-01-01T23:59:59.000Z

248

Black Peak and Enchantments - CECM  

E-Print Network (OSTI)

Black Peak, North Cascades. A nice two day outing. We hiked on the Maple Pass trail, from Hwy. 20, to Heather Pass, and then on a path to Lewis lake, where ...

249

Demand Dispatch-Intelligent  

NLE Websites -- All DOE Office Websites (Extended Search)

and energy efficiency throughout the value chain resulting in the most economical price for electricity. Having adequate quantities and capacities of demand resources is a...

250

Automated Demand Response Strategies and Commissioning CommercialBuilding Controls  

SciTech Connect

California electric utilities have been exploring the use of dynamic critical peak pricing (CPP) and other demand response programs to help reduce peaks in customer electric loads. CPP is a new electricity tariff design to promote demand response. This paper begins with a brief review of terminology regarding energy management and demand response, followed by a discussion of DR control strategies and a preliminary overview of a forthcoming guide on DR strategies. The final section discusses experience to date with these strategies, followed by a discussion of the peak electric demand savings from the 2005 Automated CPP program. An important concept identified in the automated DR field tests is that automated DR will be most successful if the building commissioning industry improves the operational effectiveness of building controls. Critical peak pricing and even real time pricing are important trends in electricity pricing that will require new functional tests for building commissioning.

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Linkugel, Eric

2006-05-01T23:59:59.000Z

251

Summary of the 2006 Automated Demand Response Pilot  

E-Print Network (OSTI)

This paper discusses the specific concept for, design of, and results from a pilot program to automate demand response with critical peak pricing. California utilities have been exploring the use of critical peak pricing (CPP) to help reduce peak day summer time electric loads. CPP is a form of price-responsive demand response. This Automated Critical Peak Pricing (Auto-CPP) project from 2006 draws upon three years of previous research and demonstrations from the years of 2003, 2004, and 2005. The purpose of automated demand response (DR) is to improve the responsiveness and participation of electricity customers in DR programs and lower overall costs to achieve DR. Auto-CPP is a form of automated demand response (Auto-DR).

Piette, M.; Kiliccote, S.

2007-01-01T23:59:59.000Z

252

Open Automated Demand Response Dynamic Pricing Technologies and Demonstration  

E-Print Network (OSTI)

oasis. Last accessed: Con Edison. 2010. “Demand Response/is Southern California Edison’s real-time pricing tariff. 2.and Southern California Edison’s Critical Peak Pricing

Ghatikar, Girish

2010-01-01T23:59:59.000Z

253

Automated Demand Response and Commissioning  

E-Print Network (OSTI)

Fully-Automated Demand Response Test in Large Facilities14in DR systems. Demand Response using HVAC in Commercialof Fully Automated Demand Response in Large Facilities”

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-01-01T23:59:59.000Z

254

Demand Response Spinning Reserve Demonstration  

E-Print Network (OSTI)

F) Enhanced ACP Date RAA ACP Demand Response – SpinningReserve Demonstration Demand Response – Spinning Reservesupply spinning reserve. Demand Response – Spinning Reserve

2007-01-01T23:59:59.000Z

255

U.S. Propane Demand  

U.S. Energy Information Administration (EIA)

Demand is higher in 1999 due to higher petrochemical demand and a strong economy. We are also seeing strong demand in the first quarter of 2000; however, ...

256

Monthly Energy Review The Monthly Energy Review  

Gasoline and Diesel Fuel Update (EIA)

use use by Members of Congress, Federal and State agencies, energy analysts, and the general public. EIA welcomes suggestions from readers regarding data series in the MER and in other EIA publications. Related Publication: Readers of the MER may also be interested in EIA's Annual Energy Review, where many of the same data series are provided annually beginning with 1949. Contact our National Energy Information Center at 202-586-8800 for more information. Timing of Release: MER data are normally released in the afternoon of the third-from-the-last workday of each month and are usually available electronically late that day. Internet Addresses: E-Mail: infoctr@eia.doe.gov World Wide Web Site: http://www.eia.doe.gov Gopher Site: gopher://gopher.eia.doe.gov FTP Site: ftp://ftp.eia.doe.gov The Monthly Energy Review (ISSN 0095-7356) is published monthly by the Energy Information

257

Fermilab | Women's History Month  

NLE Websites -- All DOE Office Websites (Extended Search)

Women's History Month at Fermilab Fermilab recognized Women's History Month through a series of lab-wide events during March 2010. During the past five decades, women from all...

258

Software demonstration: Demand Response Quick Assessment Tool  

NLE Websites -- All DOE Office Websites (Extended Search)

Software demonstration: Demand Response Quick Assessment Tool Software demonstration: Demand Response Quick Assessment Tool Speaker(s): Peng Xu Date: February 4, 2008 - 12:00pm Location: 90-3122 The potential for utilizing building thermal mass for load shifting and peak demand reduction has been demonstrated in a number of simulation, laboratory, and field studies. The Demand Response Quick Assessment Tools developed at LBNL will be demonstrated. The tool is built on EnergyPlus simulation and is able to evaluate and compare different DR strategies, such as global temperature reset, chiller cycling, supply air temperature reset, etc. A separate EnergyPlus plotting tool will also be demonstrated during this seminar. Users can use the tool to test EnergyPlus models, conduct parametric analysis, or compare multiple EnergyPlus simulation

259

National Action Plan on Demand Response  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

David Kathan, Ph.D David Kathan, Ph.D Federal Energy Regulatory Commission U.S. DOE Electricity Advisory Committee October 29, 2010 Demand Response as Power System Resources The author's views do not necessarily represent the views of the Federal Energy Regulatory Commission 2 Demand Response * FERC (Order 719) defines demand response as: - A reduction in the consumption of electric energy by customers from their expected consumption in response to an increase in the price of electric energy or to in incentive payments designed to induce lower consumption of electric energy. * The National Action Plan on Demand Response released by FERC staff broadens this definition to include - Consumer actions that can change any part of the load profile of a utility or region, not just the period of peak usage

260

Fast Automated Demand Response to Enable the Integration of Renewable Resources  

E-Print Network (OSTI)

peak demand, and natural gas demand forecasts for eachnatural gas and other fossil fuels are the predominant heating fuels for California’s commercial buildings, heating electricity demandDemand. The California End Use Survey 2004 (CEUS 2004) provides statewide hourly electricity and natural gas

Watson, David S.

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Peaking of world oil production: Impacts, mitigation, & risk management  

SciTech Connect

The peaking of world oil production presents the U.S. and the world with an unprecedented risk management problem. As peaking is approached, liquid fuel prices and price volatility will increase dramatically, and, without timely mitigation, the economic, social, and political costs will be unprecedented. Viable mitigation options exist on both the supply and demand sides, but to have substantial impact, they must be initiated more than a decade in advance of peaking.... The purpose of this analysis was to identify the critical issues surrounding the occurrence and mitigation of world oil production peaking. We simplified many of the complexities in an effort to provide a transparent analysis. Nevertheless, our study is neither simple nor brief. We recognize that when oil prices escalate dramatically, there will be demand and economic impacts that will alter our simplified assumptions. Consideration of those feedbacks will be a daunting task but one that should be undertaken. Our aim in this study is to-- • Summarize the difficulties of oil production forecasting; • Identify the fundamentals that show why world oil production peaking is such a unique challenge; • Show why mitigation will take a decade or more of intense effort; • Examine the potential economic effects of oil peaking; • Describe what might be accomplished under three example mitigation scenarios. • Stimulate serious discussion of the problem, suggest more definitive studies, and engender interest in timely action to mitigate its impacts.

Hirsch, R.L. (SAIC); Bezdek, Roger (MISI); Wendling, Robert (MISI)

2005-02-01T23:59:59.000Z

262

CONSULTANT REPORT DEMAND FORECAST EXPERT  

E-Print Network (OSTI)

CONSULTANT REPORT DEMAND FORECAST EXPERT PANEL INITIAL forecast, end-use demand modeling, econometric modeling, hybrid demand modeling, energyMahon, Carl Linvill 2012. Demand Forecast Expert Panel Initial Assessment. California Energy

263

Automated Demand Response and Commissioning  

E-Print Network (OSTI)

internal conditions. Maximum Demand Saving Intensity [W/ft2]automated electric demand sheds. The maximum electric shed

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-01-01T23:59:59.000Z

264

Commercial & Industrial Demand Response Within Hawaiian Electric Company Service Territory  

Science Conference Proceedings (OSTI)

By reducing power usage during peak demand periods, demand response (DR) programs can help utilities manage power loads and complement energy efficiency activities while providing ratepayers an opportunity to substantially reduce their electric bills. This project assessed the costs and benefits of potential DR programs for Hawaiian Electric Company's (HECO's) commercial and industrial (CI) customers.

2007-06-04T23:59:59.000Z

265

Open Automated Demand Response for Small Commerical Buildings  

Science Conference Proceedings (OSTI)

This report characterizes small commercial buildings by market segments, systems and end-uses; develops a framework for identifying demand response (DR) enabling technologies and communication means; and reports on the design and development of a low-cost OpenADR enabling technology that delivers demand reductions as a percentage of the total predicted building peak electric demand. The results show that small offices, restaurants and retail buildings are the major contributors making up over one third of the small commercial peak demand. The majority of the small commercial buildings in California are located in southern inland areas and the central valley. Single-zone packaged units with manual and programmable thermostat controls make up the majority of heating ventilation and air conditioning (HVAC) systems for small commercial buildings with less than 200 kW peak electric demand. Fluorescent tubes with magnetic ballast and manual controls dominate this customer group's lighting systems. There are various ways, each with its pros and cons for a particular application, to communicate with these systems and three methods to enable automated DR in small commercial buildings using the Open Automated Demand Response (or OpenADR) communications infrastructure. Development of DR strategies must consider building characteristics, such as weather sensitivity and load variability, as well as system design (i.e. under-sizing, under-lighting, over-sizing, etc). Finally, field tests show that requesting demand reductions as a percentage of the total building predicted peak electric demand is feasible using the OpenADR infrastructure.

Dudley, June Han; Piette, Mary Ann; Koch, Ed; Hennage, Dan

2009-05-01T23:59:59.000Z

266

Is Real-Time Pricing Green?: The Environmental Impacts of Electricity Demand Variance  

E-Print Network (OSTI)

production costs of hydroelectricity are typically low) bute?ects likely driven by hydroelectricity availability. Thus,demand for peak-shaving hydroelectricity. Finally, the four

Holland, Stephen P.; Mansur, Erin T.

2004-01-01T23:59:59.000Z

267

U.S. Energy Demand, Offshore Oil Production and  

E-Print Network (OSTI)

;Summary of Conclusions. . . The global rate of production of oil is peaking now, coal will peak in 2U.S. Energy Demand, Offshore Oil Production and BP's Macondo Well Spill Tad Patzek, Petroleum that run the U.S. Complexity, models, risks Gulf of Mexico's oil and gas production Conclusions ­ p.3/4 #12

Patzek, Tadeusz W.

268

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA

269

Peak Power Reduction Strategies for the Lighting Systems in Government Buildings  

E-Print Network (OSTI)

Lighting systems are the second major contributor to the peak power demand and energy consumption in buildings after A/C systems. They account for nearly 20% of the peak power demand and 15% of the annual energy consumption. Thus energy efficient lighting systems and their smart operation can be very effective in reducing the national peak power and energy consumption, particularly for a country like Kuwait where power demand grew from 6750 MW in 2001 to 9075 MW in 2007 (MEW, 2002- 2008). This paper presents an approach developed to reduce the peak power demand in the lighting. The approach included optimum use of daylight, time of day control and delamping. The implementation of this approach for eight government buildings with occupancy of between 7:30 and 2:30 and peak power demand of 29.3 MW achieved a reduction of 2 MW in the peak power demand (around 7%). More importantly this 7% in peak load reduction and 10,628 MWh reduction in the annual energy consumption was achieved without any added cost. Also, the paper includes recommendations for retrofitting cost effective energy efficient lighting systems and implementation of more effective control.

Al-Nakib, D.; Al-Mulla, A. A.; Maheshwari, G. P.

2010-01-01T23:59:59.000Z

270

Peaks, Plans and (Persnickety) Prices  

Reports and Publications (EIA)

This presentation provides information about EIA's estimates of working gas peak storage capacity, and the development of the natural gas storage industry. Natural gas shale and the need for high deliverability storage are identified as key drivers in natural gas storage capacity development. The presentation also provides estimates of planned storage facilities through 2012.

Information Center

2010-10-28T23:59:59.000Z

271

Native American Heritage Month  

Energy.gov (U.S. Department of Energy (DOE))

This month, we celebrate the rich heritage and myriad contributions of American Indians and Alaska Natives, and we rededicate ourselves to supporting tribal sovereignty, tribal self-determination,...

272

Natural Gas Monthly  

U.S. Energy Information Administration (EIA)

sector organizations associated with the natural gas industry. Volume and price data are presented each month for ... Tables 1 and 2 ...

273

Electric Power Monthly  

U.S. Energy Information Administration (EIA)

Electric Power Monthly with Data for October 2012. December 2012 . Independent Statistics & Analysis . www.eia.gov . U.S. Department of Energy . ...

274

Electric Power Monthly  

U.S. Energy Information Administration (EIA)

Electric Power Monthly with Data for August 2012. October 2012 . Independent Statistics & Analysis . www.eia.gov . U.S. Department of Energy . ...

275

Natural Gas Monthly Update  

Annual Energy Outlook 2012 (EIA)

2013 | Next Release: February 28, 2013 | full report  | Re-Release Date: February 22, 2013 Previous Issues Month: December 2012 November 2012 October 2012 September 2012 August...

276

Petroleum Supply Monthly  

U.S. Energy Information Administration (EIA)

Energy Information Administration/Petroleum Supply Monthly, October 2011 49 Table 37. Imports of Crude Oil and Petroleum Products by PAD District, ...

277

Monthly Energy Review  

U.S. Energy Information Administration (EIA)

U.S. Energy Information Administration September 2013 Monthly Energy Review. Note: Information about data precision and revisions. Release Date: September 25, 2013

278

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are further subdivided into the energy- intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Demand Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Demand Module calculates energy consumption for the four Census Regions (see Figure 5) and disaggregates the energy consumption

279

Demand Response Database & Demo  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Database & Demo Speaker(s): Mike Graveley William M. Smith Date: June 7, 2005 - 12:00pm Location: Bldg. 90 Seminar HostPoint of Contact: Mary Ann Piette Infotility...

280

Tankless Demand Water Heaters  

Energy.gov (U.S. Department of Energy (DOE))

Demand (tankless or instantaneous) water heaters have heating devices that are activated by the flow of water, so they provide hot water only as needed and without the use of a storage tank. They...

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

Owen Comstock

2012-12-19T23:59:59.000Z

282

Industrial Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Module. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

Kelly Perl

2013-05-14T23:59:59.000Z

283

Industrial Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Module. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

Kelly Perl

2013-09-30T23:59:59.000Z

284

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

Owen Comstock

2013-11-05T23:59:59.000Z

285

Transportation Demand This  

U.S. Energy Information Administration (EIA) Indexed Site

Transportation Demand Transportation Demand This page inTenTionally lefT blank 75 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Transportation Demand Module The NEMS Transportation Demand Module estimates transportation energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific and associated technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), buses, freight and passenger aircraft, freight

286

18-Month Outlook Executive Summary  

E-Print Network (OSTI)

This report presents an assessment of the security and adequacy of the Ontario Electricity System for the 18-month period from April 2002 through September 2003. This assessment is based on forecasts of electricity demand and available supply combined with current information on the configuration and capability of the transmission system. Outage plans of generators and transmitters are based on information available as of February 2002. During the Outlook period, the IMO forecasts show that Ontario’s available generation exceeds projected demands. Over this period, approximately 3,000 MW of additional generation resources are expected to either return to service or be placed in service for the first time – thereby enhancing the reliability of the Ontario electricity system. During the first half of the Outlook there are periods when Ontario’s available reserves are forecast to be between 2,000 and 2,500 MW. These reserves are below the IMO’s required planning reserve levels, but do not account for additional resources from outside Ontario that are expected to be available. Reserves are planning buffers identified to address circumstances that cannot be accurately predicted such as weather variations and unscheduled maintenance. The IMO anticipates that the Ontario market will be effective in attracting additional resources to provide adequate reliability. However, there

unknown authors

2002-01-01T23:59:59.000Z

287

METHOD OF PEAK CURRENT MEASUREMENT  

DOE Patents (OSTI)

The measurement and recording of peak electrical currents are described, and a method for utilizing the magnetic field of the current to erase a portion of an alternating constant frequency and amplitude signal from a magnetic mediums such as a magnetic tapes is presented. A portion of the flux from the current carrying conductor is concentrated into a magnetic path of defined area on the tape. After the current has been recorded, the tape is played back. The amplitude of the signal from the portion of the tape immediately adjacent the defined flux area and the amplitude of the signal from the portion of the tape within the area are compared with the amplitude of the signal from an unerased portion of the tape to determine the percentage of signal erasure, and thereby obtain the peak value of currents flowing in the conductor.

Baker, G.E.

1959-01-20T23:59:59.000Z

288

Evaluation of concurrent peak responses  

SciTech Connect

This report deals with the problem of combining two or more concurrent responses which are induced by dynamic loads acting on nuclear power plant structures. Specifically, the acceptability of using the square root of the sum of the squares (SRSS) value of peak values as the combined response is investigated. Emphasis is placed on the establishment of a simplified criterion that is convenient and relatively easy to use by design engineers.

Wang, P.C.; Curreri, J.; Reich, M.

1983-01-01T23:59:59.000Z

289

Demand and Price Volatility: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

shift in the short-run price elasticity of gasoline demand.A meta-analysis of the price elasticity of gasoline demand.2007. Consumer demand un- der price uncertainty: Empirical

Scott, K. Rebecca

2011-01-01T23:59:59.000Z

290

California Independent System Operator demand response & proxy demand resources  

Science Conference Proceedings (OSTI)

Demand response programs are designed to allow end use customers to contribute to energy load reduction individually or through a demand response provider. One form of demand response can occur when an end use customer reduces their electrical usage ...

John Goodin

2012-01-01T23:59:59.000Z

291

Centralized and Decentralized Control for Demand Response  

Science Conference Proceedings (OSTI)

Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generation resources are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their relative advantages and disadvantages in terms of delay time, predictability, complexity, and reliability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the predictability and simplicity of centralized control to achieve the best performance of the smart grid.

Lu, Shuai; Samaan, Nader A.; Diao, Ruisheng; Elizondo, Marcelo A.; Jin, Chunlian; Mayhorn, Ebony T.; Zhang, Yu; Kirkham, Harold

2011-04-29T23:59:59.000Z

292

Monthly Biodiesel Production Report  

U.S. Energy Information Administration (EIA) Indexed Site

Monthly Biodiesel Production Monthly Biodiesel Production Report November 2013 With Data for September 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Monthly Biodiesel Production Report This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or

293

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 39 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial.

294

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS 27 data.

295

Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" (UEC) by appliance (in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type

296

Geographic Area Month  

Gasoline and Diesel Fuel Update (EIA)

Fuels by PAD District and State (Cents per Gallon Excluding Taxes) - Continued Geographic Area Month No. 1 Distillate No. 2 Distillate a No. 4 Fuel b Sales to End Users Sales for...

297

December Natural Gas Monthly  

Annual Energy Outlook 2012 (EIA)

DOEEIA-0130(9712) Distribution CategoryUC-950 Natural Gas Monthly December 1997 Energy Information Administration Office of Oil and Gas U.S. Department of Energy Washington, DC...

298

Petroleum Supply Monthly Archives  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Supply Monthly Petroleum Supply Monthly Petroleum Supply Monthly Archives With Data for December 2011 | Release Date: February 29, 2012 Changes to Table 26. "Production of Crude Oil by PAD District and State": Current State-level data are now included in Table 26, in addition to current U.S. and PAD District sums. State offshore production for Louisiana, Texas, Alaska, and California, which are included in the State totals, are no longer reported separately in a "State Offshore Production" category. Previously, State-level values lagged 2 months behind the U.S. and PAD District values. Beginning with this publication, they will be on the same cycle. Also included in this publication are two additional pages for Table 26 that provide October and November data. With the release of

299

Historical Monthly Energy Review  

Gasoline and Diesel Fuel Update (EIA)

73-92) 73-92) Distribution Category UC-950 Historical Monthly Energy Review 1973-1992 Energy Information Administration Office of Energy Markets and End Use U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the Department of Energy. The information contained herein should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. Historical Monthly Energy Review The Historical Monthly Energy Review (HMER) presents monthly and annual data from 1973 through 1992 on production, consumption, stocks, imports, exports, and prices of the principal energy commodities in the United States. Also included are data on international

300

National Women's History Month  

Energy.gov (U.S. Department of Energy (DOE))

During Women's History Month, we recall that the pioneering legacy of our grandmothers and great-grandmothers is revealed not only in our museums and history books, but also in the fierce...

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Black History Month  

Energy.gov (U.S. Department of Energy (DOE))

During National African American History Month, we pay tribute to the contributions of past generations and reaffirm our commitment to keeping the American dream alive for the next generation.  In...

302

Petroleum Supply Monthly  

U.S. Energy Information Administration (EIA)

Energy Information Administration/Petroleum Supply Monthly, October 2011 11 Table 4. U.S. Year-to-Date Daily Average Supply and Disposition of Crude Oil and Petroleum ...

303

MonthlyReportAll  

NLE Websites -- All DOE Office Websites (Extended Search)

MonthlyReportAllFleet Summary Report - Hymotion Prius (Kvaser 1 2102010 4:19:25 PM Vehicle Technologies Program 30 Notes: 1 - 9. Please see http:avt.inel.govphevreportnotes...

304

Demand Response In California  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Energy Efficiency & Energy Efficiency & Demand Response Programs Dian M. Grueneich, Commissioner Dian M. Grueneich, Commissioner California Public Utilities Commission California Public Utilities Commission FUPWG 2006 Fall Meeting November 2, 2006 Commissioner Dian M. Grueneich November 2, 2006 1 Highest Priority Resource Energy Efficiency is California's highest priority resource to: Meet energy needs in a low cost manner Aggressively reduce GHG emissions November 2, 2006 2 Commissioner Dian M. Grueneich November 2, 2006 3 http://www.cpuc.ca.gov/PUBLISHED/REPORT/51604.htm Commissioner Dian M. Grueneich November 2, 2006 4 Energy Action Plan II Loading order continued "Pursue all cost-effective energy efficiency, first." Strong demand response and advanced metering

305

Automated Demand Response Today  

Science Conference Proceedings (OSTI)

Demand response (DR) has progressed over recent years beyond manual and semi-automated DR to include growing implementation and experience with fully automated demand response (AutoDR). AutoDR has been shown to be of great value over manual and semi-automated DR because it reduces the need for human interactions and decisions, and it increases the speed and reliability of the response. AutoDR, in turn, has evolved into the specification known as OpenADR v1.0 (California Energy Commission, PIER Program, C...

2012-03-29T23:59:59.000Z

306

Travel Demand Modeling  

SciTech Connect

This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming, and agent-based microsimulation.

Southworth, Frank [ORNL; Garrow, Dr. Laurie [Georgia Institute of Technology

2011-01-01T23:59:59.000Z

307

United States lubricant demand  

Science Conference Proceedings (OSTI)

This paper examines United States Lubricant Demand for Automotive and Industrial Lubricants by year from 1978 to 1992 and 1997. Projected total United States Lubricant Demand for 1988 is 2,725 million (or MM) gallons. Automotive oils are expected to account for 1,469MM gallons or (53.9%), greases 59MM gallons (or 2.2%), and Industrial oils will account for the remaining 1,197MM gallons (or 43.9%) in 1988. This proportional relationship between Automotive and Industrial is projected to remain relatively constant until 1992 and out to 1997. Projections for individual years between 1978 to 1992 and 1997 are summarized.

Solomon, L.K.; Pruitt, P.R.

1988-01-01T23:59:59.000Z

308

Demand Response Valuation Frameworks Paper  

E-Print Network (OSTI)

No. ER06-615-000 CAISO Demand Response Resource User Guide -8 2.1. Demand Response Provides a Range of Benefits to8 2.2. Demand Response Benefits can be Quantified in Several

Heffner, Grayson

2010-01-01T23:59:59.000Z

309

SnowPeak Energy | Open Energy Information  

Open Energy Info (EERE)

it. SnowPeak Energy is a company located in Reno, Nevada . References "SnowPeak Energy" Retrieved from "http:en.openei.orgwindex.php?titleSnowPeakEnergy&oldid35121...

310

On Demand Guarantees in Iran.  

E-Print Network (OSTI)

??On Demand Guarantees in Iran This thesis examines on demand guarantees in Iran concentrating on bid bonds and performance guarantees. The main guarantee types and… (more)

Ahvenainen, Laura

2009-01-01T23:59:59.000Z

311

Monthly Energy Review, August 1997  

Annual Energy Outlook 2012 (EIA)

Monthly Energy Review The Monthly Energy Review (MER) presents an overview of the Energy Information Administration's recent monthly energy statistics. The statistics cover the...

312

Monthly Energy Review, October 1997  

Annual Energy Outlook 2012 (EIA)

Monthly Energy Review The Monthly Energy Review (MER) presents an overview of the Energy Information Administration's recent monthly energy statistics. The statistics cover the...

313

Windpower Monthly | Open Energy Information  

Open Energy Info (EERE)

Windpower Monthly Jump to: navigation, search Name Windpower Monthly Place Knebel, Denmark Zip DK-8420 Knebe Sector Wind energy Product Windpower Monthly is a energy news magazine....

314

Transportation Demand Management Plan  

E-Print Network (OSTI)

Transportation Demand Management Plan FALL 2009 #12;T r a n s p o r t a t i o n D e m a n d M a n the transportation impacts the expanded enrollment will have. Purpose and Goal The primary goal of the TDM plan is to ensure that adequate measures are undertaken and maintained to minimize the transportation impacts

315

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2012-11-15T23:59:59.000Z

316

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2013-10-10T23:59:59.000Z

317

Peak Electricity Impacts of Residential Water Use  

NLE Websites -- All DOE Office Websites (Extended Search)

Peak Electricity Impacts of Residential Water Use Title Peak Electricity Impacts of Residential Water Use Publication Type Report LBNL Report Number LBNL-5736E Year of Publication...

318

Price Server System for Automated Critical Peak Pricing  

NLE Websites -- All DOE Office Websites (Extended Search)

Price Server System for Automated Critical Peak Pricing Price Server System for Automated Critical Peak Pricing Speaker(s): David S. Watson Date: June 3, 2005 - 12:00pm Location: 90-3148 Overview of current California Energy Commission (CEC)/Demand Response Research Center (DRRC) Auto-CPP project: This summer, some select commercial CPP customers of PG&E will have the option of joining the Automated Critical Peak Pricing pilot. The pilot will have the same tariffs as standard CPP programs, but will include an added feature: automated shedding of electric loads. Through use of the Price Server System, day-ahead CPP event signals initiated by PG&E will ultimately cause electric loads to be automatically curtailed on commercial customer sites. These optional predetermined shed strategies will occur without

319

Petroleum supply monthly, February 1988. [Contains glossary  

Science Conference Proceedings (OSTI)

Total US demand for petroleum products during February 1988 averaged about 17.6 million barrels per day, 0.9 million barrels per day above the average of a year earlier. This marks the third consecutive month in which total product supplied has exceeded 17.0 million barrels per day. For the most part, the disposition of the major products continued to follow seasonal patterns. Total products stocks dropped by 26.0 million barrels to 683.1 million barrels. Refinery utilization fell from January's 82.8 percent rate to 81.1 percent. Crude oil imports from Saudi Arabia rose to 1.2 million barrels per day, 0.4 million barrels per day above the average for January. Unusually mild weather, especially in the Mid-Atlantic states and New England, kept deliveries of both distillate and residual fuel oil virtually unchanged from January's high seasonal levels, although both were still well above the levels for these products the same time last year. Distillate demand averaged 3.5 million barrels per day in February, five percent above the February 1987 average. Residual fuel oil demand was 1.6 million barrels per day this month, nine percent greater than a year ago. Part of this increase in demand from the previous year reflects the improved competitive position of residual fuel oil in some utility and industrial markets, mostly due to increases in natural gas prices starting in the fourth quarter of 1987. 12 figs.

Not Available

1988-04-25T23:59:59.000Z

320

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electric Power Sector Coal Stocks: November 2011 Electric Power Sector Coal Stocks: November 2011 Stocks As discussed in this month's feature story, electric power sector coal stocks continued to replenish after the summer burn in November, though stockpile levels remain below 2010 and 2009 levels. All coal stockpile levels declined from November 2010, with bituminous coal stockpile levels 9 percent lower than the same month of 2010. Days of Burn Days of burn Coal capacity The average number of days of burn held at electric power plants is a forward looking estimate of coal supply given a power plantâ€(tm)s current stockpile and past consumption patterns. The average number of days of burn held on hand at electric power plants dropped slightly from last month and remained below levels seen in November of 2010 or 2009. While

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Monthly Energy Statistics  

Gasoline and Diesel Fuel Update (EIA)

July July 2003 E n e r g y P l u g : R e s i d e n t i a l E n e r g y C o n s u m p t i o n Cover Image: Optical glass fibers, though many times thinner than a human hair, carry vastly greater quantities of data than metallic wires, occupy less space, and are more secure. First introduced in the 1970s, high-purity optical fibers are capable of transmitting data over long distances and have replaced wires in many telecommunications, computing, and electronics applications. Timing of release: MER data are normally released in the afternoon of the third-to-last workday of each month and are usually available electronically the following day. Released for Printing: July 28, 2003 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) presents an overview of the

322

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electric Power Sector Coal Stocks: March 2012 Electric Power Sector Coal Stocks: March 2012 Stocks The seasonal winter drawdown of coal stocks was totally negated during the winter months this year due to low natural gas prices and unseasonably warm temperatures throughout the continental United States. In fact, March 2012 was the seventh straight month that coal stockpiles at power plants increased from the previous month. The largest driver of increasing stockpiles has been declining consumption of coal due to unseasonably warm weather and declining natural gas prices. Because much of the coal supplied to electric generators is purchased through long-term contracts, increasing coal stockpiles have proven difficult for electric power plant operators to handle. Some operators have inventories so high that they are refusing

323

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electric Power Sector Coal Stocks: October 2013 Electric Power Sector Coal Stocks: October 2013 Stocks In October 2013, total coal stocks increased 0.8 percent from the previous month. This follows the normal seasonal pattern for this time of year as the country begins to build up coal stocks to be consumed during the winter months. Compared to last October, coal stocks decreased 17.7 percent. This occurred because coal stocks in October 2012 were at an extremely high level. Days of Burn Days of burn Coal capacity The average number of days of burn held at electric power plants is a forward looking estimate of coal supply given a power plant's current stockpile and past consumption patterns. The total bituminous supply decreased from 85 days the previous month to 78 days in October 2013, while the total subbituminous supply decreased from 63 days in September 2013 to

324

monthly | OpenEI  

Open Energy Info (EERE)

714 714 Varnish cache server Browse Upload data GDR 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2142280714 Varnish cache server monthly Dataset Summary Description The Florida Geological Survey is where data related to oil, gas, and geothermal resources for the state of Florida are made public. This dataset contains monthly oil and gas production data for January and February of 2011. The dataset is in .xls format, and displays well status, well production, and cumulative data. Source Florida Geological Survey Date Released February 28th, 2011 (3 years ago) Date Updated Unknown Keywords data Florida gas monthly oil production Data application/vnd.ms-excel icon February 2011 oil and gas production data (xls, 33.8 KiB)

325

Monthly Energy Review  

Gasoline and Diesel Fuel Update (EIA)

May May 26, 1998 Electronic Access Monthly Energy Review (MER) data are also avail- able through these electronic means: * ASCII text, Lotus (wk1), and Excel (xls) versions of the MER tables are available through EIA's Internet homepage at: http://www.eia.doe.gov/emeu/mer/con- tents.html * A portable document format (pdf) file of the com- plete MER including text, tables, and graphs can be downloaded via the homepage at: http://www.eia.doe.gov/bookshelf/multi.html * MER data series in ASCII comma delimited file format (previously available on diskettes) can be downloaded via EIA's ftp site at ftp://ftp.eia.doe.gov/pub/energy.over- view/monthly.energy/current.mer * For information about the Energy Info Disc, call 1-800-STAT-USA. This CD-ROM con- tains over 200 reports, databases, and models. DOE/EIA-0035(98/05) Distribution Category UC-950 Monthly Energy Review

326

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

Science Conference Proceedings (OSTI)

Wastewater treatment is an energy intensive process which, together with water treatment, comprises about three percent of U.S. annual energy use. Yet, since wastewater treatment facilities are often peripheral to major electricity-using industries, they are frequently an overlooked area for automated demand response opportunities. Demand response is a set of actions taken to reduce electric loads when contingencies, such as emergencies or congestion, occur that threaten supply-demand balance, and/or market conditions occur that raise electric supply costs. Demand response programs are designed to improve the reliability of the electric grid and to lower the use of electricity during peak times to reduce the total system costs. Open automated demand response is a set of continuous, open communication signals and systems provided over the Internet to allow facilities to automate their demand response activities without the need for manual actions. Automated demand response strategies can be implemented as an enhanced use of upgraded equipment and facility control strategies installed as energy efficiency measures. Conversely, installation of controls to support automated demand response may result in improved energy efficiency through real-time access to operational data. This paper argues that the implementation of energy efficiency opportunities in wastewater treatment facilities creates a base for achieving successful demand reductions. This paper characterizes energy use and the state of demand response readiness in wastewater treatment facilities and outlines automated demand response opportunities.

Thompson, Lisa; Song, Katherine; Lekov, Alex; McKane, Aimee

2008-11-19T23:59:59.000Z

327

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400 .......................................................................................................................................1-1 ENERGY DEMAND FORECASTING AT THE CALIFORNIA ENERGY COMMISSION: AN OVERVIEW

328

Demand Forecast INTRODUCTION AND SUMMARY  

E-Print Network (OSTI)

Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required of any forecast of electricity demand and developing ways to reduce the risk of planning errors that could arise from this and other uncertainties in the planning process. Electricity demand is forecast

329

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 51 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are further subdivided into the energy- intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Module calculates

330

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

4 4 The commercial module forecasts consumption by fuel 15 at the Census division level using prices from the NEMS energy supply modules, and macroeconomic variables from the NEMS Macroeconomic Activity Module (MAM), as well as external data sources (technology characterizations, for example). Energy demands are forecast for ten end-use services 16 for eleven building categories 17 in each of the nine Census divisions (see Figure 5). The model begins by developing forecasts of floorspace for the 99 building category and Census division combinations. Next, the ten end-use service demands required for the projected floorspace are developed. The electricity generation and water and space heating supplied by distributed generation and combined heat and power technologies are projected. Technologies are then

331

On Demand Paging Using  

E-Print Network (OSTI)

The power consumption of the network interface plays a major role in determining the total operating lifetime of wireless handheld devices. On demand paging has been proposed earlier to reduce power consumption in cellular networks. In this scheme, a low power secondary radio is used to wake up the higher power radio, allowing the latter to sleep or remain off for longer periods of time. In this paper we present use of Bluetooth radios to serve as a paging channel for the 802.11 wireless LAN. We have implemented an on-demand paging scheme on a WLAN consisting of iPAQ PDAs equipped with Bluetooth radios and Cisco Aironet wireless networking cards. Our results show power saving ranging from 19% to 46% over the present 802.11b standard operating modes with negligible impact on performance.

Bluetooth Radios On; Yuvraj Agarwal; Rajesh K. Gupta

2003-01-01T23:59:59.000Z

332

November 2010 monthly report  

Science Conference Proceedings (OSTI)

These viewgraphs are to be provided to NNSA to update the status of the B61 Life Extension Project work and activities. The viewgraphs cover such issues as budget, schedule, scope, and the like. They are part of the monthly reporting process.

Neff, Warren E [Los Alamos National Laboratory

2010-12-07T23:59:59.000Z

333

Monthly energy review  

Science Conference Proceedings (OSTI)

This document presents an overview of the Energy Information Administration`s (EIA) recent monthly energy statistics. The statistics cover the major activities of U.S. production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors.

NONE

1997-12-01T23:59:59.000Z

334

Monthly Energy Review  

Science Conference Proceedings (OSTI)

This publication presents an overview of the Energy information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. Two brief ``energy plugs`` (reviews of EIA publications) are included, as well.

NONE

1996-05-28T23:59:59.000Z

335

Natural gas monthly  

Science Conference Proceedings (OSTI)

Monthly highlights of activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry are presented. Feature articles for this issue are: Natural Gas Overview for Winter 1983-1984 by Karen A. Kelley; and an Analysis of Natural Gas Sales by John H. Herbert. (PSB)

Not Available

1983-11-01T23:59:59.000Z

336

Net Demand3 Production  

E-Print Network (OSTI)

Contract Number: DE-FE0004002 (Subcontract: S013-JTH-PPM4002 MOD 00) Summary The US DOE has identified a number of materials that are both used by clean energy technologies and are at risk of supply disruptions in the short term. Several of these materials, especially the rare earth elements (REEs) yttrium, cerium, and lanthanum were identified by DOE as critical (USDOE 2010) and are crucial to the function and performance of solid oxide fuel cells (SOFCs) 1. In addition, US DOE has issued a second Request For Information regarding uses of and markets for these critical materials (RFI;(USDOE 2011)). This report examines how critical materials demand for SOFC applications could impact markets for these materials and vice versa, addressing categories 1,2,5, and 6 in the RFI. Category 1 – REE Content of SOFC Yttria (yttrium oxide) is the only critical material (as defined for the timeframe of interest for SOFC) used in SOFC 2. Yttrium is used as a dopant in the SOFC’s core ceramic cells.. In addition, continuing developments in SOFC technology will likely further reduce REE demand for SOFC, providing credible scope for at least an additional 50 % reduction in REE use if desirable. Category 2 – Supply Chain and Market Demand SOFC developers expect to purchase

J. Thijssen Llc

2011-01-01T23:59:59.000Z

337

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

E-Print Network (OSTI)

and Pre-cooling of Commercial Buildings with Thermal Mass inthe high thermal storage during the pre-cooling period. Forwith low thermal mass is limited, the pre-cooling period can

Yin, Rongxin

2010-01-01T23:59:59.000Z

338

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

E-Print Network (OSTI)

in a California Hot Climate Zone. ” Lawrence Berkeleythermal mass in a hot climate zone can be reduced by 30%data in numerous climate zones, allowing calibration of the

Yin, Rongxin

2010-01-01T23:59:59.000Z

339

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

E-Print Network (OSTI)

2007). “Impact of Electricity Rate Structure on Energy Costside, a set of electricity rates are used to evaluate theto understand the impact of electricity rate structures on

Yin, Rongxin

2010-01-01T23:59:59.000Z

340

Scenario Analysis of Peak Demand Savings for Commercial Buildings with Thermal Mass in California  

E-Print Network (OSTI)

Utilization. ” Journal of Solar Energy Engineering, 127, 37-Mass. ” Journal of Solar Energy Engineering, 125, 292-301.

Yin, Rongxin

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

The Role of the North Atlantic Oscillation in Shaping Regional-Scale Peak Seasonal Precipitation across the Indian Subcontinent  

Science Conference Proceedings (OSTI)

The present study focuses on the impact of the North Atlantic Oscillation (NAO) in shaping the regional-level precipitation during the peak months of the two main rainy seasons over the Indian subcontinent. Monthly precipitation data from 1871 to ...

Shouraseni Sen Roy

2011-01-01T23:59:59.000Z

342

Late January Cold Impacted Both Supply & Demand  

Gasoline and Diesel Fuel Update (EIA)

A brief cold spell occurred in the second half of January on top of A brief cold spell occurred in the second half of January on top of the low stocks. Cold weather increases demand, but it also can interfere with supply, as happened this past January. During the week ending January 22, temperatures in the New England and the Mid-Atlantic areas shifted from being15 percent and 17 percent warmer than normal, respectively, to 24 percent and 22 percent colder than normal. The weather change increased weekly heating requirements by about 40 percent. Temperature declines during the winter affect heating oil demand in a number of ways: Space heating demand increases; Electricity peaking demand increases and power generators must turn to distillate to meet the new peak needs; Fuel switching from natural gas to distillate occurs among large

343

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

Natural Gas Demands..xi Annual natural gas demand for each alternativeused in natural gas demand projections. 34

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

344

Peak load management: Potential options  

SciTech Connect

This report reviews options that may be alternatives to transmission construction (ATT) applicable both generally and at specific locations in the service area of the Bonneville Power Administration (BPA). Some of these options have potential as specific alternatives to the Shelton-Fairmount 230-kV Reinforcement Project, which is the focus of this study. A listing of 31 peak load management (PLM) options is included. Estimated costs and normalized hourly load shapes, corresponding to the respective base load and controlled load cases, are considered for 15 of the above options. A summary page is presented for each of these options, grouped with respect to its applicability in the residential, commercial, industrial, and agricultural sectors. The report contains comments on PLM measures for which load shape management characteristics are not yet available. These comments address the potential relevance of the options and the possible difficulty that may be encountered in characterizing their value should be of interest in this investigation. The report also identifies options that could improve the efficiency of the three customer utility distribution systems supplied by the Shelton-Fairmount Reinforcement Project. Potential cogeneration options in the Olympic Peninsula are also discussed. These discussions focus on the options that appear to be most promising on the Olympic Peninsula. Finally, a short list of options is recommended for investigation in the next phase of this study. 9 refs., 24 tabs.

Englin, J.E.; De Steese, J.G.; Schultz, R.W.; Kellogg, M.A.

1989-10-01T23:59:59.000Z

345

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

346

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

347

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

348

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

349

Petroleum marketing monthly  

SciTech Connect

Petroleum Marketing Monthly (PPM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o. b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data in the Petroleum Marketing Monthly.

1996-07-01T23:59:59.000Z

350

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

351

Project of the Month  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

project-of-the-month Office of Environmental project-of-the-month Office of Environmental Management 1000 Independence Ave., SW Washington, DC 20585 202-586-7709 en One-of-a-Kind Facility Now in Safe Shutdown http://energy.gov/em/articles/one-kind-facility-now-safe-shutdown One-of-a-Kind Facility Now in Safe Shutdown

352

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

353

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

354

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Highlights: March 2012 Highlights: March 2012 Average natural gas prices at the Henry Hub declined for the eighth straight month leading to a nearly 40% increase in consumption for electricity during March 2012. The warmest March on record for much of the central U.S. drove a 5% decrease in residential retail sales when compared to March 2011. U.S. coal supplies as measured by days of burn were above 80 days for the third straight month in March as declining coal consumption drove coal stockpile increases. Key Indicators Mar 2012 % Change from Mar 2011 Total Net Generation (Thousand MWh) 309,709 -2.9% Residential Retail Price (cents/kWh) 11.76 1.5% Retail Sales (Thousand MWh) 282,453 -2.6% Heating Degree-Days 377 -36.4% Natural Gas Price, Henry Hub ($/MMBtu) 2.22 -45.7% Coal Stocks

355

End of Month Working  

Gasoline and Diesel Fuel Update (EIA)

The level of gas in storage at the end of the last heating season (March The level of gas in storage at the end of the last heating season (March 31, 2000) was 1,150 billion cubic feet (Bcf), just above the 1995-1999 average of 1,139 Bcf. Underground working gas storage levels are currently about 8-9 percent below year-ago levels. In large part, this is because injection rates since April 1 have been below average. Storage injections picked up recently due to warm weather in the last half of October. The month of November is generally the last month available in the year for injections into storage. A cold November would curtail net injections into storage. If net injections continue at average levels this winter, we project that storage levels will be low all winter, reaching a level of 818 Bcf at the end of March, the lowest level since 1996

356

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

March 2012 | Release Date: May 29, 2012 | Next March 2012 | Release Date: May 29, 2012 | Next Release Date: June 26, 2012 | Re-Release Date: November 28, 2012 (correction) Previous Issues Issue: November 2013 October 2013 September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 Previous issues Format: html xls Go Highlights: March 2012 Average natural gas prices at the Henry Hub declined for the eighth straight month leading to a nearly 40% increase in consumption for electricity during March 2012. The warmest March on record for much of the central U.S. drove a 5% decrease in residential retail sales when compared to March 2011. U.S. coal supplies as measured by days of burn were above 80 days for the third straight month in March as declining coal consumption drove

357

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

358

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electric Power Sector Coal Stocks: February 2012 Electric Power Sector Coal Stocks: February 2012 Stocks The unseasonably warm temperatures that the continental United States experienced throughout the winter, coupled with low natural gas prices, caused coal stocks at power plants to increase throughout the winter of 2011 - 2012. During this period, coal stocks usually see a seasonal decline due to the added need for electricity generation from coal plants for spacing heating load. However, it was the sixth straight month that coal stocks increased from the previous month, with this trend likely to continue as the country enters into spring. Days of Burn Days of burn Coal capacity The average number of days of burn held at electric power plants is a forward looking estimate of coal supply given a power plant's current

359

Petroleum marketing monthly  

SciTech Connect

The Petroleum Marketing Monthly (PMM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data in the Petroleum Marketing Monthly.

NONE

1996-02-01T23:59:59.000Z

360

Petroleum marketing monthly  

Science Conference Proceedings (OSTI)

The Petroleum Marketing Monthly (PMM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data in the Petroleum Marketing Monthly.

NONE

1995-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Monthly Update Explained Electricity Monthly Update Explained Highlights The Highlights page features in the center a short article about a major event or an informative topic. The left column contains bulleted highlights at the top and key indicators in a table and graphics - data you might be interested in at a glance. The right column is used for navigation. End-Use: Retail Rates/Prices and Consumption The second section presents statistics on end-use: retail rates/prices and consumption of electricity. End-use data is the first "data page" based on the assumption that information about retail electricity service is of greatest interest to a general audience. The term rates/prices is used because charges for retail service are based primarily on set rates approved by State regulators. However, a number of

362

Monthly Energy Review  

Gasoline and Diesel Fuel Update (EIA)

December December 23, 1997 Electronic Access Monthly Energy Review (MER) data are also available through these electronic means: * ASCII text, Lotus (wk1), and Excel (xls) versions of the MER tables are available through EIA's Internet homepage at: http://www.eia.doe.gov/emeu/mer/contents.html * A portable document format (pdf) file of the complete MER including text, tables, and graphs can be downloaded via the homepage at: http://www.eia.doe.gov/bookshelf/multi.html * MER data series in ASCII comma delimited file format (previously available on diskettes) can be downloaded via EIA's ftp site at ftp://ftp.eia.doe.gov/pub/energy.overview/monthly .energy/current.mer * For information about the Energy Info Disc, call 1-800-STAT-USA. This CD-ROM contains over 200 reports, databases, and models. Timing of Release: MER data are normally released in the afternoon of the third-from-the-last

363

Electric power monthly  

SciTech Connect

The Electric Power Monthly is prepared by the Survey Management Division; Office of Coal, Nuclear, Electric and Alternate Fuels, Energy Information Administration (EIA), Department of Energy. This publication provides monthly statistics at the national, Census division, and State levels for net generation, fuel consumption, fuel stocks, quantity and quality of fuel, cost of fuel, electricity sales, revenue, and average revenue per kilowatthour of electricity sold. Data on net generation, fuel consumption, fuel stocks, quantity and cost of fuel are also displayed for the North American Electric Reliability Council (NERC) regions. Additionally, statistics by company and plant are published in the EPM on capability of new plants, new generation, fuel consumption, fuel stocks, quantity and quality of fuel, and cost of fuel.

1992-05-01T23:59:59.000Z

364

Electric power monthly  

SciTech Connect

The Energy Information Administration (EIA) prepares the Electric Power Monthly (EPM) for a wide audience including Congress, Federal and State agencies, the electric utility industry, and the general public. This publication provides monthly statistics for net generation, fossil fuel consumption and stocks, quantity and quality of fossil fuels, cost of fossil fuels, electricity sales, revenue, and average revenue per kilowatthour of electricity sold. Data on net generation, fuel consumption, fuel stocks, quantity and cost of fossil fuels are also displayed for the North American Electric Reliability Council (NERC) regions. The EIA publishes statistics in the EPM on net generation by energy source, consumption, stocks, quantity, quality, and cost of fossil fuels; and capability of new generating units by company and plant. The purpose of this publication is to provide energy decisionmakers with accurate and timely information that may be used in forming various perspectives on electric issues that lie ahead.

1995-08-01T23:59:59.000Z

365

California Energy Demand Scenario Projections to 2050  

E-Print Network (OSTI)

Minimum demand and Maximum demand incorporate assumptionslevels, or very minor Maximum demand household size, growthvehicles in Increasing Maximum demand 23 mpg truck share

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

366

Industrial Lift Truck Battery Charger Demand Response Impact Study  

Science Conference Proceedings (OSTI)

Demand response and load shifting are two common energy management strategies used by lift truck fleet operators to mitigate on-peak energy consumption, reduce electricity costs, and react to electric system emergency curtailment requests. When customers elect to participate in demand response programs, they are contacted and asked to reduce load during power shortage situations. Alternatively, customers may implement longer-term economic load shifting strategies by reducing power to their lift truck bat...

2008-04-03T23:59:59.000Z

367

Petroleum Marketing Monthly Archives  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Marketing Monthly Archives Petroleum Marketing Monthly Archives Choose the year of the Petroleum Marketing Monthly you wish to view. + EXPAND ALL 2014-2015 2014 2015 Data ending January 10/2013 2012-2013 2012 2013 Data ending January 10/2011 January 10/2012 February 11/2011 February 11/2012 March 12/2011 March 12/2012 April 1/2012 April 1/2013 May 2/2012 May 2/2013 June 3/2012 June 3/2013 July 4/2012 July 4/2013 August 5/2012 August 5/2013 September 6/2012 September 6/2013 October 7/2012 October 7/2013 November 8/2012 November 8/2013 December 9/2012 December 9/2013 2010 - 2011 2010 2011 Data ending Data ending January 10/2009 January 10/2010 February 11/2009 February 11/2010 March 12/2009 March 12/2010 April 1/2010 April 1/2011 May 2/2010 May 2/2011

368

Management of Power Demand through Operations of Building Systems  

E-Print Network (OSTI)

In hot summers, the demand for electrical power is dominated by the requirements of the air-conditioning and lighting systems. Such systems account for more than 80% of the peak electrical demand in Kuwait. A study was conducted to explore the potential for managing the peak electrical demand through improved operation strategies for building systems. Two buildings with partial occupancy patterns and typical peak loads of 1 and 2.2 MW were investigated. Changes to the operation of building systems included utilizing the thermal mass to reduce cooling production and distribution during the last hour of occupancy, time-of-day control of chillers and auxiliaries, and de-lamping. The implemented operational changes led to significant reductions in building loads during the hours of national peak demand. The achieved savings reached 31% during the critical hour, and up to 47% afterwards. Daily energy savings of 13% represented an added benefit. Additional operational changes could lead to further savings in peak power when implemented.

ElSherbini, A. I.; Maheshwari, G.; Al-Naqib, D.; Al-Mulla, A.

2009-11-01T23:59:59.000Z

369

Dividends with Demand Response  

SciTech Connect

To assist facility managers in assessing whether and to what extent they should participate in demand response programs offered by ISOs, we introduce a systematic process by which a curtailment supply curve can be developed that integrates costs and other program provisions and features. This curtailment supply curve functions as bid curve, which allows the facility manager to incrementally offer load to the market under terms and conditions acceptable to the customer. We applied this load curtailment assessment process to a stylized example of an office building, using programs offered by NYISO to provide detail and realism.

Kintner-Meyer, Michael CW; Goldman, Charles; Sezgen, O.; Pratt, D.

2003-10-31T23:59:59.000Z

370

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Highlights: February 2012 Highlights: February 2012 Warm temperatures across much of the U.S. led to lower retail sales of electricity during February 2012. Natural gas-fired generation increased in every region of the United States when compared to February 2011. Wholesale electricity prices remained in the low end of the annual range for most wholesale markets due to low demand and depressed natural gas prices Key Indicators Feb 2012 % Change from Feb. 2011 Total Net Generation (Thousand MWh) 310,298 -1.0% Residential Retail Price (cents/kWh) 11.55 3.9% Retail Sales (Thousand MWh) 285,684 -3.5% Heating Degree-Days 654 -12.0% Natural Gas Price, Henry Hub ($/MMBtu) 2.60 -38.1% Coal Stocks (Thousand Tons) 186,958 -13.6% Coal Consumption (Thousand Tons) 62,802 -14.6% Natural Gas Consumption

371

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

February 2012 | Release Date: Apr. 30, February 2012 | Release Date: Apr. 30, 2012 | Next Release Date: May 25, 2012 | Re-Release Date: November 28, 2012 (correction) Previous Issues Issue: November 2013 October 2013 September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 Previous issues Format: html xls Go Highlights: February 2012 Warm temperatures across much of the U.S. led to lower retail sales of electricity during February 2012. Natural gas-fired generation increased in every region of the United States when compared to February 2011. Wholesale electricity prices remained in the low end of the annual range for most wholesale markets due to low demand and depressed natural gas prices Key Indicators Feb 2012 % Change from Feb. 2011

372

title Automated Price and Demand Response Demonstration for Large Customers  

NLE Websites -- All DOE Office Websites (Extended Search)

Automated Price and Demand Response Demonstration for Large Customers Automated Price and Demand Response Demonstration for Large Customers in New York City using OpenADR booktitle International Conference for Enhanced Building Operations ICEBO year month address Montreal Quebec abstract p class p1 Open Automated Demand Response OpenADR an XML based information exchange model is used to facilitate continuous price responsive operation and demand response participation for large commercial buildings in New York who are subject to the default day ahead hourly pricing We summarize the existing demand response programs in New York and discuss OpenADR communication prioritization of demand response signals and control methods Building energy simulation models are developed and field tests are conducted to evaluate continuous energy management

373

Chinese demand drives global deforestation Chinese demand drives global deforestation  

E-Print Network (OSTI)

Chinese demand drives global deforestation Chinese demand drives global deforestation By Tansa Musa zones and do not respect size limits in their quest for maximum financial returns. "I lack words economy. China's demand for hardwood drives illegal logging says "Both illegal and authorized

374

Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand  

E-Print Network (OSTI)

: Properties of the AIDS Generalized Maximum Entropy Estimator 24 #12;Estimating a Demand SystemEstimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand Amos Golan* Jeffrey with nonnegativity constraints is presented. This approach, called generalized maximum entropy (GME), is more

Perloff, Jeffrey M.

375

CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Demand Forecast report is the product of the efforts of many current and former California Energy Commission staff. Staff contributors to the current forecast are: Project Management and Technical Direction

376

Petroleum Supply Monthly  

U.S. Energy Information Administration (EIA) Indexed Site

With Data for September 2013 With Data for September 2013 November 2013 Energy Information Administration/Petroleum Supply Monthly, ii September 2013 EIA DATA ARE AVAILABLE IN ELECTRONIC FORM All current EIA publications are available on the EIA web site. Users can view and download selected pages or entire reports, search for information, download EIA data and analysis applications, and find out about new EIA information products and services: World Wide Web: http://www.eia.doe.gov FTP: ftp://ftp.eia.doe.gov Customers who do not have access to the Internet may call the National Energy Information Center (NEIC) to request a single print-

377

Petroleum Supply Monthly  

Gasoline and Diesel Fuel Update (EIA)

December 2011 December 2011 February 2012 Energy Information Administration/Petroleum Supply Monthly, ii December 2011 EIA DATA ARE AVAILABLE IN ELECTRONIC FORM All current EIA publications are available on the EIA web site. Users can view and download selected pages or entire reports, search for information, download EIA data and analysis applications, and find out about new EIA information products and services: World Wide Web: http://www.eia.doe.gov FTP: ftp://ftp.eia.doe.gov Customers who do not have access to the Internet may call the National Energy Information Center (NEIC) to request a single print-

378

Petroleum marketing monthly  

SciTech Connect

The Petroleum Marketing Monthly (PMM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data.

NONE

1995-11-01T23:59:59.000Z

379

Peak Oil Food Network | Open Energy Information  

Open Energy Info (EERE)

Network Network Jump to: navigation, search Name Peak Oil Food Network Place Crested Butte, Colorado Zip 81224 Website http://www.PeakOilFoodNetwork. References Peak Oil Food Network[1] LinkedIn Connections This article is a stub. You can help OpenEI by expanding it. The Peak Oil Food Network is a networking organization located in Crested Butte, Colorado, and is open to the general public that seeks to promote the creation of solutions to the challenge of food production impacted by the peak phase of global oil production. Private citizens are encouraged to join and contribute by adding comments, writing blog posts or adding to discussions about food and oil related topics. Peak Oil Food Network can be followed on Twitter at: http://www.Twitter.com/PeakOilFoodNtwk Peak Oil Food Network on Twitter

380

Peak Underground Working Natural Gas Storage Capacity  

U.S. Energy Information Administration (EIA)

Peak Working Natural Gas Capacity. Data and Analysis from the Energy Information Administration (U.S. Dept. of Energy)

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Determination of Hydrogen Peak Temperatures and Trapping ...  

Science Conference Proceedings (OSTI)

Presentation Title, Determination of Hydrogen Peak Temperatures and Trapping Energies of Various Lattice Defects In Iron Using Thermal Desorption ...

382

ELECTRICITY DEMAND FORECAST COMPARISON REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005 ..............................................................................3 Residential Forecast Comparison ..............................................................................................5 Nonresidential Forecast Comparisons

383

Monthly Energy Review - December 2004  

Annual Energy Outlook 2012 (EIA)

for Printing: December 22, 2004 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

384

Monthly Energy Review - March 2003  

Annual Energy Outlook 2012 (EIA)

Released for Printing: April 2, 2003 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) presents an overview of the Energy Information...

385

Monthly Energy review - July 2009  

Annual Energy Outlook 2012 (EIA)

9 July 2009 DOEEIA-0035(200907) Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Administration's (EIA) primary report of recent and historical...

386

Monthly Energy Review - December 2007  

Annual Energy Outlook 2012 (EIA)

2) December 2007 Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Administration's (EIA) primary report of recent and historical energy statistics....

387

Monthly Energy Review - February 2005  

Annual Energy Outlook 2012 (EIA)

for Printing: February 23, 2005 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

388

Monthly Energy Review - December 2005  

Annual Energy Outlook 2012 (EIA)

for Printing: December 22, 2005 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

389

Monthly Energy Review - May 2005  

Annual Energy Outlook 2012 (EIA)

Released for Printing: May 25, 2005 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

390

Monthly Energy Review - August 2005  

Annual Energy Outlook 2012 (EIA)

Released for Printing: August 29, 2005 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

391

Monthly Energy Review - March 2005  

Annual Energy Outlook 2012 (EIA)

Released for Printing: March 31, 2005 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

392

Monthly Energy Review - June 2004  

Annual Energy Outlook 2012 (EIA)

Released for Printing: June 25, 2004 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

393

Monthly Energy Review - April 2004  

Annual Energy Outlook 2012 (EIA)

Released for Printing: April 28, 2004 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

394

Monthly Energy Review - February 2007  

Annual Energy Outlook 2012 (EIA)

7 DOEEIA-0035(200702) Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Administration's (EIA) primary report of recent and historical energy...

395

Monthly Energy Review - June 2005  

Annual Energy Outlook 2012 (EIA)

Released for Printing: June 27, 2005 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

396

Monthly Energy Review - August 2006  

Annual Energy Outlook 2012 (EIA)

Released for Printing: August 28, 2006 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

397

Monthly Energy Review - June 2006  

Annual Energy Outlook 2012 (EIA)

Released for Printing: June 27, 2006 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

398

Monthly Energy Review - May 2003  

Annual Energy Outlook 2012 (EIA)

Released for Printing: June 10, 2003 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) presents an overview of the Energy Information...

399

Monthly Energy Review - February 2009  

Annual Energy Outlook 2012 (EIA)

2) Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Administration's (EIA) primary report of recent and historical energy statistics. Included are...

400

Monthly Energy Review - April 2006  

Annual Energy Outlook 2012 (EIA)

Released for Printing: May 25, 2006 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) is the Energy Information Admin- istration's...

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Monthly Energy Review - December 2003  

Annual Energy Outlook 2012 (EIA)

for Printing: December 23, 2003 Printed with soy ink on recycled paper. Monthly Energy Review The Monthly Energy Review (MER) presents an overview of the Energy Information...

402

Natural Gas Monthly, December 1996  

Annual Energy Outlook 2012 (EIA)

1996 79 Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

403

Natural Gas Monthly October 1996  

Annual Energy Outlook 2012 (EIA)

1996 77 Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

404

Natural Gas Monthly, June 1996  

Gasoline and Diesel Fuel Update (EIA)

Gas Annual. Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

405

Natural Gas Monthly, September 1996  

Annual Energy Outlook 2012 (EIA)

1996 77 Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

406

Natural Gas Monthly, July 1996  

Gasoline and Diesel Fuel Update (EIA)

July 1996 77 Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

407

Natural Gas Monthly, May 1996  

Annual Energy Outlook 2012 (EIA)

Gas Annual. Preliminary Monthly Data Preliminary monthly data in the "balancing item" cat- egory are calculated by subtracting dry gas production, withdrawals from storage,...

408

Modeling, Analysis, and Control of Demand Response Resources  

NLE Websites -- All DOE Office Websites (Extended Search)

Modeling, Analysis, and Control of Demand Response Resources Modeling, Analysis, and Control of Demand Response Resources Speaker(s): Johanna Mathieu Date: April 27, 2012 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Sila Kiliccote While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can play an active role in power systems via Demand Response (DR). Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present a regression-based baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are

409

Firing Excess Refinery Butane in Peaking Gas Turbines  

E-Print Network (OSTI)

New environmentally-driven regulations for motor gasoline volatility will significantly alter refinery light ends supply/demand balancing. This, in turn, will impact refinery economics. This paper presumes that one outcome will be excess refinery normal butane production, which will reduce refinery normal butane value and price. Explored is an opportunity for a new use for excess refinery normal butane- as a fuel for utility peaking gas turbines which currently fire kerosene and #2 oil. Our paper identifies the fundamental driving forces which are changing refinery butane economics, examines how these forces influence refinery production, and evaluates the potential for using normal butanes as peaking utility gas turbine fuel, especially on the US East Coast.

Pavone, A.; Schreiber, H.; Zwillenberg, M.

1989-09-01T23:59:59.000Z

410

Peak load control energy saving and cycling system  

SciTech Connect

A control system for limiting peak load demand and/or saving electrical energy by cycling the individual loads within an electrical distribution system is described. Electrical power usage in a distribution system is continuously monitored and compared to a pre-set limit. Loads can be added and cycled according to a limit set by the operator. Loads can also be dropped in response to a signal proportional to the electrical power usage in a distribution system within limits defined by the operator.

Burch, J.

1976-10-19T23:59:59.000Z

411

Monthly Energy Review - June 2008  

Gasoline and Diesel Fuel Update (EIA)

Monthly Publications: Other monthly EIA reports are Petroleum Supply Monthly Publications: Other monthly EIA reports are Petroleum Supply Monthly, Petroleum Marketing Monthly, Natural Gas Monthly, Electric Power Monthly, and Inter- national Petroleum Monthly. For more information, contact the National Energy Information Center at 202-586-8800 or InfoCtr@eia.doe.gov. Electronic Access The MER is available on EIA's Web site in a variety of formats at: http://www.eia.doe.gov/mer. Complete MER, and individual MER sections: Portable Document Format (PDF) files. Individual table and graph pages: PDF files. Data files for individual tables: Excel (XLS) files and ASCII comma-delimited (CSV) files. Note: PDF files display selected annual and monthly data. Excel and CSV files display all avail- able annual and monthly data, often at a greater level of precision than the PDF files.

412

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electric Power Sector Coal Stocks: September 2011 Electric Power Sector Coal Stocks: September 2011 Stocks Electric power sector coal stocks continued to replenish after the summer burn in October, though stockpile levels remain well below 2010 levels. All coal stockpile levels declined from October 2010, with bituminous coal stockpile levels 12 percent lower than the same month of 2010. Days of Burn Days of burn Coal capacity The average number of days of burn held at electric power plants is a forward looking estimate of coal supply given a power plant's current stockpile and past consumption patterns. The average number of days of burn held on hand at electric power plants was generally flat in October 2011 compared to September of this year. The summer of 2011 saw significant declines in total U.S. stockpile levels, which were replenished in the

413

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electricity Reports Electricity Reports Electricity Monthly Update With Data for October 2013 | Release Date: Dec. 20, 2013 | Next Release Date: Jan. 22, 2014 Previous Issues Issue: November 2013 October 2013 September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 Previous issues Format: html xls Go Highlights: October 2013 Thirty-one states saw the average cost of electricity increase by more than two percent, with fourteen states experiencing increases of at least five percent compared to a year ago. Texas (ERCOT) and the Midwest (MISO) experienced above average wholesale electricity prices for October due to unseasonable temperatures. The New York City (Transco Zone 6 NY) natural gas price was

414

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

January 2012 | Release Date: Mar. 27, January 2012 | Release Date: Mar. 27, 2012 | Next Release Date: Apr. 27, 2012 | Re-Release Date: November 28, 2012 (correction) Previous Issues Issue: November 2013 October 2013 September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 Previous issues Format: html xls Go Highlights: January 2012 Warm temperatures across much of the U.S. led to lower retail sales of electricity during January 2012. Coal-fired generation decreased in every region of the United States when compared to January 2011. Coal stocks recovered due to decreased consumption this January compared to the same month of 2011. Key Indicators Jan 2012 % Change from Jan. 2011 Total Net Generation (Thousand MWh) 340,743 -6.4%

415

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Highlights: January 2012 Highlights: January 2012 Warm temperatures across much of the U.S. led to lower retail sales of electricity during January 2012. Coal-fired generation decreased in every region of the United States when compared to January 2011. Coal stocks recovered due to decreased consumption this January compared to the same month of 2011. Key Indicators Jan 2012 % Change from Jan. 2011 Total Net Generation (Thousand MWh) 340,743 -6.4% Residential Retail Price (cents/kWh) 11.43 4.4% Retail Sales (Thousand MWh) 310,859 -6.5% Heating Degree-Days 751 -21.4% Natural Gas Price, Henry Hub ($/MMBtu) 2.75 -40.3% Coal Stocks (Thousand Tons) 181,621 10.2% Coal Consumption (Thousand Tons) 70,595 -21.7% Natural Gas Consumption (Mcf) 676,045 19.9% Nuclear Outages (MW) 9,567 2.1%

416

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Electric Power Sector Coal Stocks: December 2011 Electric Power Sector Coal Stocks: December 2011 Stocks Temperate weather throughout the fall has allowed electric power sector coal stocks to replenish from the summer burn. All coal stockpile levels were essentially flat when compared to December 2010 and were a mostly up year-to-date. Days of Burn Days of burn Coal capacity The average number of days of burn held at electric power plants is a forward looking estimate of coal supply given a power plantâ€(tm)s current stockpile and past consumption patterns. The average number of days of burn held on hand at electric power plants was essentially flat compared to last month and remained below levels seen in December of 2010 or 2009. While stockpile levels have recovered from summer lows, the increasing

417

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

August 2011 | Release Date: October 25, August 2011 | Release Date: October 25, 2011 | Next Release Date: November 21, 2011 Previous Issues Issue: November 2013 October 2013 September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 Previous issues Format: html xls Go Highlights: August 2011 Extreme heat in Texas, New Mexico, Colorado and Arizona drove significant increases in the retail sales of electricity in the Southwest. Wind generation increased in much of the United States, except the middle of the country where total generation declined. Bituminous coal stocks dropped 14% from August 2010. Key indicators Same Month 2010 Year to date Total Net Generation -1% 11% Residential Retail Price -6% 11% Cooling Degree-Days -3% 2%

418

Electricity Monthly Update  

Gasoline and Diesel Fuel Update (EIA)

Coal Stocks: August 2011 Coal Stocks: August 2011 Stocks Coal stocks continued the usual summer decline as utilities burned into their summer stockpile in August. Sigificant declines from August 2010 were seen in total coal stockpiles, driven by a 14 percent drop in bituminous coal stockpiles as well as a 10 percent drop in subbituminous coal stockpiles. Lignite stockpiles declined by 6 percent over the same time period. Days of burn The average number of days of burn held at electric power plants is a forward looking estimate of coal supply given a power plant's current stockpile and past consumption patterns. The average number of days of burn held on hand at electric power plants increased slightly in August 2011 compared to previous months. This was largely driven by increases in

419

March Natural Gas Monthly  

Gasoline and Diesel Fuel Update (EIA)

'PGTI[+PHQTOCVKQP#FOKPKUVTCVKQP0CVWTCN)CU/QPVJN[/CTEJ 'PGTI[+PHQTOCVKQP#FOKPKUVTCVKQP0CVWTCN)CU/QPVJN[/CTEJ EIA Corrects Errors in Its Drilling Activity Estimates Series William Trapmann and Phil Shambaugh Introduction The Energy Information Administration (EIA) has published monthly and annual estimates of oil and gas drilling activity since 1978. These data are key information for many industry analysts, serving as a leading indicator of trends in the industry and a barometer of general industry status. They are assessed directly for trends, as well as in combination with other measures to assess the productivity and profitability of upstream industry operations. They are a major reference point for policymakers at both the Federal and State level. Users in the private sector include financial

420

Does RTP Deliver Demand Response?: Case Studies of Niagara Mohawk RTP and  

E-Print Network (OSTI)

/ educational 40% 46% Average monthly maximum demand 3.0 MW 3.4 MW Option 2 9% 18% The survey response rateDoes RTP Deliver Demand Response?: Case Studies of Niagara Mohawk RTP and ~43 Voluntary Utility RTP Programs Charles Goldman Lawrence Berkeley National Laboratory Mid-Atlantic Demand Response Initiative

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Home Network Technologies and Automating Demand Response  

Science Conference Proceedings (OSTI)

Over the past several years, interest in large-scale control of peak energy demand and total consumption has increased. While motivated by a number of factors, this interest has primarily been spurred on the demand side by the increasing cost of energy and, on the supply side by the limited ability of utilities to build sufficient electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in electricity use through the use of price incentives. DR systems are also be design to shift or curtail energy demand at critical times when the generation, transmission, and distribution systems (i.e. the 'grid') are threatened with instabilities. To be effectively deployed on a large-scale, these proposed DR systems need to be automated. Automation will require robust and efficient data communications infrastructures across geographically dispersed markets. The present availability of widespread Internet connectivity and inexpensive, reliable computing hardware combined with the growing confidence in the capabilities of distributed, application-level communications protocols suggests that now is the time for designing and deploying practical systems. Centralized computer systems that are capable of providing continuous signals to automate customers reduction of power demand, are known as Demand Response Automation Servers (DRAS). The deployment of prototype DRAS systems has already begun - with most initial deployments targeting large commercial and industrial (C & I) customers. An examination of the current overall energy consumption by economic sector shows that the C & I market is responsible for roughly half of all energy consumption in the US. On a per customer basis, large C & I customers clearly have the most to offer - and to gain - by participating in DR programs to reduce peak demand. And, by concentrating on a small number of relatively sophisticated energy consumers, it has been possible to improve the DR 'state of the art' with a manageable commitment of technical resources on both the utility and consumer side. Although numerous C & I DR applications of a DRAS infrastructure are still in either prototype or early production phases, these early attempts at automating DR have been notably successful for both utilities and C & I customers. Several factors have strongly contributed to this success and will be discussed below. These successes have motivated utilities and regulators to look closely at how DR programs can be expanded to encompass the remaining (roughly) half of the state's energy load - the light commercial and, in numerical terms, the more important residential customer market. This survey examines technical issues facing the implementation of automated DR in the residential environment. In particular, we will look at the potential role of home automation networks in implementing wide-scale DR systems that communicate directly to individual residences.

McParland, Charles

2009-12-01T23:59:59.000Z

422

Oxygenate Supply/Demand Balances  

Gasoline and Diesel Fuel Update (EIA)

Oxygenate Supply/Demand Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model By Tancred C.M. Lidderdale This article first appeared in the Short-Term Energy Outlook Annual Supplement 1995, Energy Information Administration, DOE/EIA-0202(95) (Washington, DC, July 1995), pp. 33-42, 83-85. The regression results and historical data for production, inventories, and imports have been updated in this presentation. Contents * Introduction o Table 1. Oxygenate production capacity and demand * Oxygenate demand o Table 2. Estimated RFG demand share - mandated RFG areas, January 1998 * Fuel ethanol supply and demand balance o Table 3. Fuel ethanol annual statistics * MTBE supply and demand balance o Table 4. EIA MTBE annual statistics * Refinery balances

423

Impact of Smart Grid Technologies on Peak Load to 2050 | Open Energy  

Open Energy Info (EERE)

Impact of Smart Grid Technologies on Peak Load to 2050 Impact of Smart Grid Technologies on Peak Load to 2050 Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Impact of Smart Grid Technologies on Peak Load to 2050 Focus Area: Crosscutting Topics: Deployment Data Website: www.iea.org/papers/2011/smart_grid_peak_load.pdf Equivalent URI: cleanenergysolutions.org/content/impact-smart-grid-technologies-peak-l Language: English Policies: "Deployment Programs,Regulations" is not in the list of possible values (Deployment Programs, Financial Incentives, Regulations) for this property. DeploymentPrograms: Demonstration & Implementation Regulations: Cost Recovery/Allocation This working paper analyses the evolution of peak load demand to 2050 in four key regions: Organisation for Economic Co-operation and Development

424

Natural gas monthly  

Science Conference Proceedings (OSTI)

This report presents current data on the consumption, disposition, production, prices, storage, import and export of natural gas in the United States. Also included are operating and financial data for major interstate natural gas pipeline companies plus data on fillings, ceiling prices, and transportation under the Natural Gas Policy Act of 1978. A feature article, entitled Main Line Natural Gas Sales to Industrial Users, 1981, is included. Highlights of this month's publication are: Marketed production of natural gas during 1982 continued its downward trend compared to 1981, with November production of 1511 Bcf compared to 1583 Bcf for November 1981; total natural gas consumption also declined when compared to 1981; as of November 1982, working gas in underground storage was running ahead of a similar period in 1981 by 109 Bcf (3.4 percent); the average wellhead price of natural gas continued to rise in 1982; and applications for determination of maximum lawful prices under the Natural Gas Policy Act (NGPA) showed a decrease from October to November, principally for Section 103 classification wells (new onshore production wells).

Not Available

1983-01-01T23:59:59.000Z

425

Demand Response Programs, 6. edition  

Science Conference Proceedings (OSTI)

The report provides a look at the past, present, and future state of the market for demand/load response based upon market price signals. It is intended to provide significant value to individuals and companies who are considering participating in demand response programs, energy providers and ISOs interested in offering demand response programs, and consultants and analysts looking for detailed information on demand response technology, applications, and participants. The report offers a look at the current Demand Response environment in the energy industry by: defining what demand response programs are; detailing the evolution of program types over the last 30 years; discussing the key drivers of current initiatives; identifying barriers and keys to success for the programs; discussing the argument against subsidization of demand response; describing the different types of programs that exist including:direct load control, interruptible load, curtailable load, time-of-use, real time pricing, and demand bidding/buyback; providing examples of the different types of programs; examining the enablers of demand response programs; and, providing a look at major demand response programs.

NONE

2007-10-15T23:59:59.000Z

426

Addressing Energy Demand through Demand Response: International Experiences and Practices  

E-Print Network (OSTI)

2007 EMCS EPACT ERCOT FCM FERC FRCC demand side managementEnergy Regulatory Commission (FERC). EPAct began the processin wholesale markets, which FERC Order 888 furthered by

Shen, Bo

2013-01-01T23:59:59.000Z

427

Mt Peak Utility | Open Energy Information  

Open Energy Info (EERE)

Peak Utility Peak Utility Jump to: navigation, search Name Mt Peak Utility Facility Mt Peak Utility Sector Wind energy Facility Type Small Scale Wind Facility Status In Service Owner Mnt Peak Utility Energy Purchaser Mnt Peak Utility Location Midlothian TX Coordinates 32.42144978°, -97.02427357° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":32.42144978,"lon":-97.02427357,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

428

World Crude Production Not Keeping Pace with Demand  

Gasoline and Diesel Fuel Update (EIA)

5 5 Notes: The crude market is the major factor behind todayÂ’s low stocks. This graph shows the balance between world production and demand for petroleum. Normally, production exceeds demand in the summer, building stocks, and is less than demand in the winter months, drawing the stocks back down (dark blue areas). However, production exceeded demand through most of 1997 and 1998, building world stocks to very high levels and driving prices down. But the situation reversed in 1999. Recently, there has been more petroleum demand than supply, requiring the use of stocks to meet petroleum needs. Following the extremely low crude oil prices at the beginning of 1999, OPEC agreed to remove about 6% of world production from the market in order to work off excess inventories and bring prices back up.

429

Load Reduction, Demand Response and Energy Efficient Technologies and Strategies  

SciTech Connect

The Department of Energy’s (DOE’s) Pacific Northwest National Laboratory (PNNL) was tasked by the DOE Office of Electricity (OE) to recommend load reduction and grid integration strategies, and identify additional demand response (energy efficiency/conservation opportunities) and strategies at the Forest City Housing (FCH) redevelopment at Pearl Harbor and the Marine Corps Base Hawaii (MCBH) at Kaneohe Bay. The goal was to provide FCH staff a path forward to manage their electricity load and thus reduce costs at these FCH family housing developments. The initial focus of the work was at the MCBH given the MCBH has a demand-ratchet tariff, relatively high demand (~18 MW) and a commensurate high blended electricity rate (26 cents/kWh). The peak demand for MCBH occurs in July-August. And, on average, family housing at MCBH contributes ~36% to the MCBH total energy consumption. Thus, a significant load reduction in family housing can have a considerable impact on the overall site load. Based on a site visit to the MCBH and meetings with MCBH installation, FCH, and Hawaiian Electric Company (HECO) staff, recommended actions (including a "smart grid" recommendation) that can be undertaken by FCH to manage and reduce peak-demand in family housing are made. Recommendations are also made to reduce overall energy consumption, and thus reduce demand in FCH family housing.

Boyd, Paul A.; Parker, Graham B.; Hatley, Darrel D.

2008-11-19T23:59:59.000Z

430

Installation and Commissioning Automated Demand Response Systems  

Science Conference Proceedings (OSTI)

Demand Response (DR) can be defined as actions taken to reduce electric loads when contingencies, such as emergencies and congestion, occur that threaten supply-demand balance, or market conditions raise supply costs. California utilities have offered price and reliability DR based programs to customers to help reduce electric peak demand. The lack of knowledge about the DR programs and how to develop and implement DR control strategies is a barrier to participation in DR programs, as is the lack of automation of DR systems. Most DR activities are manual and require people to first receive notifications, and then act on the information to execute DR strategies. Levels of automation in DR can be defined as follows. Manual Demand Response involves a labor-intensive approach such as manually turning off or changing comfort set points at each equipment switch or controller. Semi-Automated Demand Response involves a pre-programmed demand response strategy initiated by a person via centralized control system. Fully-Automated Demand Response does not involve human intervention, but is initiated at a home, building, or facility through receipt of an external communications signal. The receipt of the external signal initiates pre-programmed demand response strategies. We refer to this as Auto-DR (Piette et. al. 2005). Auto-DR for commercial and industrial facilities can be defined as fully automated DR initiated by a signal from a utility or other appropriate entity and that provides fully-automated connectivity to customer end-use control strategies. One important concept in Auto-DR is that a homeowner or facility manager should be able to 'opt out' or 'override' a DR event if the event comes at time when the reduction in end-use services is not desirable. Therefore, Auto-DR is not handing over total control of the equipment or the facility to the utility but simply allowing the utility to pass on grid related information which then triggers facility defined and programmed strategies if convenient to the facility. From 2003 through 2006 Lawrence Berkeley National Laboratory (LBNL) and the Demand Response Research Center (DRRC) developed and tested a series of demand response automation communications technologies known as Automated Demand Response (Auto-DR). In 2007, LBNL worked with three investor-owned utilities to commercialize and implement Auto-DR programs in their territories. This paper summarizes the history of technology development for Auto-DR, and describes the DR technologies and control strategies utilized at many of the facilities. It outlines early experience in commercializing Auto-DR systems within PG&E DR programs, including the steps to configure the automation technology. The paper also describes the DR sheds derived using three different baseline methodologies. Emphasis is given to the lessons learned from installation and commissioning of Auto-DR systems, with a detailed description of the technical coordination roles and responsibilities, and costs.

Global Energy Partners; Pacific Gas and Electric Company; Kiliccote, Sila; Kiliccote, Sila; Piette, Mary Ann; Wikler, Greg; Prijyanonda, Joe; Chiu, Albert

2008-04-21T23:59:59.000Z

431

electricity demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description The New Zealand Ministry of Economic Development publishes energy data including many datasets related to electricity. Included here are three electricity consumption and demand datasets, specifically: annual observed electricity consumption by sector (1974 to 2009); observed percentage of consumers by sector (2002 - 2009); and regional electricity demand, as a percentage of total demand (2009). Source New Zealand Ministry of Economic Development Date Released Unknown Date Updated July 03rd, 2009 (5 years ago) Keywords Electricity Consumption electricity demand energy use by sector New Zealand Data application/vnd.ms-excel icon Electricity Consumption by Sector (1974 - 2009) (xls, 46.1 KiB) application/vnd.ms-excel icon Percentage of Consumers by Sector (2002 - 2009) (xls, 43.5 KiB)

432

Annual World Oil Demand Growth  

Gasoline and Diesel Fuel Update (EIA)

6 6 Notes: Following relatively small increases of 1.3 million barrels per day in 1999 and 0.9 million barrels per day in 2000, EIA is estimating world demand may grow by 1.6 million barrels per day in 2001. Of this increase, about 3/5 comes from non-OECD countries, while U.S. oil demand growth represents more than half of the growth projected in OECD countries. Demand in Asia grew steadily during most of the 1990s, with 1991-1997 average growth per year at just above 0.8 million barrels per day. However, in 1998, demand dropped by 0.3 million barrels per day as a result of the Asian economic crisis that year. Since 1998, annual growth in oil demand has rebounded, but has not yet reached the average growth seen during 1991-1997. In the Former Soviet Union, oil demand plummeted during most of the

433

Demand Response Opportunities and Enabling Technologies for Data Centers:  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Opportunities and Enabling Technologies for Data Centers: Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies Title Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies Publication Type Report LBNL Report Number LBNL-5763E Year of Publication 2012 Authors Ghatikar, Girish, Venkata Ganti, Nance Matson, and Mary Ann Piette Publisher PG&E/SDG&E/CEC/LBNL Keywords communication and standards, control systems, data centers, demand response, enabling technologies, end-use technologies, load migration, market sectors, technologies Abstract The energy use in data centers is increasing and, in particular, impacting the data center energy cost and electric grid reliability during peak and high price periods. As per the 2007 U.S. Environmental Protection Agency (EPA), in the Pacific Gas and Electric Company territory, data centers are estimated to consume 500 megawatts of annual peak electricity. The 2011 data confirm the increase in data center energy use, although it is slightly lower than the EPA forecast. Previous studies have suggested that data centers have significant potential to integrate with supply-side programs to reduce peak loads. In collaboration with California data centers, utilities, and technology vendors, this study conducted field tests to improve the understanding of the demand response opportunities in data centers. The study evaluated an initial set of control and load migration strategies and economic feasibility for four data centers. The findings show that with minimal or no impact to data center operations a demand savings of 25% at the data center level or 10% to 12% at the whole building level can be achieved with strategies for cooling and IT equipment, and load migration. These findings should accelerate the grid-responsiveness of data centers through technology development, integration with the demand response programs, and provide operational cost savings.

434

Automated Demand Response and Commissioning  

NLE Websites -- All DOE Office Websites (Extended Search)

and Commissioning Title Automated Demand Response and Commissioning Publication Type Conference Paper LBNL Report Number LBNL-57384 Year of Publication 2005 Authors Piette, Mary...

435

Demand Response Valuation Frameworks Paper  

E-Print Network (OSTI)

lvi Southern California Edison filed its SmartConnectinfrastructure (e.g. , Edison Electric Institute, DemandSouthern California Edison Standard Practice Manual

Heffner, Grayson

2010-01-01T23:59:59.000Z

436

Demand Uncertainty and Price Dispersion.  

E-Print Network (OSTI)

??Demand uncertainty has been recognized as one factor that may cause price dispersion in perfectly competitive markets with costly and perishable capacity. With the persistence… (more)

Li, Suxi

2007-01-01T23:59:59.000Z

437

1995 Demand-Side Managment  

U.S. Energy Information Administration (EIA)

U.S. Electric Utility Demand-Side Management 1995 January 1997 Energy Information Administration Office of Coal, Nuclear, Electric and Alternate Fuels

438

Coordination of Energy Efficiency and Demand Response  

E-Print Network (OSTI)

energy efficiency and demand response programs and tariffs.energy efficiency and demand response program and tariffenergy efficiency and demand response programs and tariffs.

Goldman, Charles

2010-01-01T23:59:59.000Z

439

Wireless Demand Response Controls for HVAC Systems  

E-Print Network (OSTI)

Strategies Linking Demand Response and Energy Efficiency,”Fully Automated Demand Response Tests in Large Facilities,technical support from the Demand Response Research Center (

Federspiel, Clifford

2010-01-01T23:59:59.000Z

440

Demand Response Quick Assessment Tool (DRQAT)  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Quick Assessment Tool (DRQAT) The opportunities for demand reduction and cost saving with building demand responsive control vary tremendously with building type...

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

Demand Responsive Lighting: A Scoping Study  

E-Print Network (OSTI)

2 2.0 Demand ResponseFully Automated Demand Response Tests in Large Facilities,was coordinated by the Demand Response Research Center and

Rubinstein, Francis; Kiliccote, Sila

2007-01-01T23:59:59.000Z

442

Coupling Renewable Energy Supply with Deferrable Demand  

E-Print Network (OSTI)

8.4 Demand Response Integration . . . . . . . . . . .for each day type for the demand response study - moderatefor each day type for the demand response study - moderate

Papavasiliou, Anthony

2011-01-01T23:59:59.000Z

443

Installation and Commissioning Automated Demand Response Systems  

E-Print Network (OSTI)

their partnership in demand response automation research andand Techniques for Demand Response. LBNL Report 59975. Mayof Fully Automated Demand Response in Large Facilities.

Kiliccote, Sila; Global Energy Partners; Pacific Gas and Electric Company

2008-01-01T23:59:59.000Z

444

Coordination of Energy Efficiency and Demand Response  

E-Print Network (OSTI)

and D. Kathan (2009). Demand Response in U.S. ElectricityEnergy Financial Group. Demand Response Research Center [2008). Assessment of Demand Response and Advanced Metering.

Goldman, Charles

2010-01-01T23:59:59.000Z

445

Strategies for Demand Response in Commercial Buildings  

E-Print Network (OSTI)

Fully Automated Demand Response Tests in Large Facilities”of Fully Automated Demand Response in Large Facilities”,was coordinated by the Demand Response Research Center and

Watson, David S.; Kiliccote, Sila; Motegi, Naoya; Piette, Mary Ann

2006-01-01T23:59:59.000Z

446

Retail Demand Response in Southwest Power Pool  

E-Print Network (OSTI)

23 ii Retail Demand Response in SPP List of Figures and10 Figure 3. Demand Response Resources by11 Figure 4. Existing Demand Response Resources by Type of

Bharvirkar, Ranjit

2009-01-01T23:59:59.000Z

447

Option Value of Electricity Demand Response  

E-Print Network (OSTI)

Table 1. “Economic” demand response and real time pricing (Implications of Demand Response Programs in CompetitiveAdvanced Metering, and Demand Response in Electricity

Sezgen, Osman; Goldman, Charles; Krishnarao, P.

2005-01-01T23:59:59.000Z

448

Demand Responsive Lighting: A Scoping Study  

E-Print Network (OSTI)

8 Figure 7: Maximum Demands Savings Intensity due toaddressed in this report. Maximum Demand Savings Intensity (Echelon Figure 7: Maximum Demands Savings Intensity due to

Rubinstein, Francis; Kiliccote, Sila

2007-01-01T23:59:59.000Z

449

Assessing the Control Systems Capacity for Demand Response in California  

NLE Websites -- All DOE Office Websites (Extended Search)

the Control Systems Capacity for Demand Response in California the Control Systems Capacity for Demand Response in California Industries Title Assessing the Control Systems Capacity for Demand Response in California Industries Publication Type Report LBNL Report Number LBNL-5319E Year of Publication 2012 Authors Ghatikar, Girish, Aimee T. McKane, Sasank Goli, Peter L. Therkelsen, and Daniel Olsen Date Published 01/2012 Publisher CEC/LBNL Keywords automated dr, controls and automation, demand response, dynamic pricing, industrial controls, market sectors, openadr Abstract California's electricity markets are moving toward dynamic pricing models, such as real-time pricing, within the next few years, which could have a significant impact on an industrial facility's cost of energy use during the times of peak use. Adequate controls and automated systems that provide industrial facility managers real-time energy use and cost information are necessary for successful implementation of a comprehensive electricity strategy; however, little is known about the current control capacity of California industries. To address this gap, Lawrence Berkeley National Laboratory, in close collaboration with California industrial trade associations, conducted a survey to determine the current state of controls technologies in California industries. This study identifies sectors that have the technical capability to implement Demand Response (DR) and Automated Demand Response (Auto-DR). In an effort to assist policy makers and industry in meeting the challenges of real-time pricing, facility operational and organizational factors were taken into consideration to generate recommendations on which sectors Demand Response efforts should be focused. Analysis of the survey responses showed that while the vast majority of industrial facilities have semi- or fully automated control systems, participation in Demand Response programs is still low due to perceived barriers. The results also showed that the facilities that use continuous processes are good Demand Response candidates. When comparing facilities participating in Demand Response to those not participating, several similarities and differences emerged. Demand Response-participating facilities and non-participating facilities had similar timings of peak energy use, production processes, and participation in energy audits. Though the survey sample was smaller than anticipated, the results seemed to support our preliminary assumptions. Demonstrations of Auto-Demand Response in industrial facilities with good control capabilities are needed to dispel perceived barriers to participation and to investigate industrial subsectors suggested of having inherent Demand Response potential.

450

Monthly Energy Review - January 2007  

Gasoline and Diesel Fuel Update (EIA)

publications: Other monthly EIA reports are Petroleum Supply Monthly, Petroleum publications: Other monthly EIA reports are Petroleum Supply Monthly, Petroleum Marketing Monthly, Natural Gas Monthly, Electric Power Monthly, and International Petroleum Monthly. Readers of the MER may also be interested in EIA's Annual Energy Review, where many of the same data series are provided annually beginning with 1949. For more information, contact the National Energy Information Center at 202-586-8800 or InfoCtr@eia.doe.gov. Electronic Access The MER is available on EIA's Web site in a variety of formats at: http://www.eia.doe.gov/mer. Complete MER, and individual MER sections: Portable Document Format (PDF) files. Individual table and graph pages: PDF files. Data files for individual tables: Excel (XLS) files and ASCII comma-delimited (CSV) files.

451

Demand Responsive and Energy Efficient Control Technologies andStrategies in Commercial Buildings  

SciTech Connect

Commercial buildings account for a large portion of summer peak electric demand. Research results show that there is significant potential to reduce peak demand in commercial buildings through advanced control technologies and strategies. However, a better understanding of commercial buildings contribution to peak demand and the use of energy management and control systems is required to develop this demand response resource to its full potential. The main objectives of the study were: (1) To evaluate the size of contributions of peak demand commercial buildings in the U.S.; (2) To understand how commercial building control systems support energy efficiency and DR; and (3) To disseminate the results to the building owners, facility managers and building controls industry. In order to estimate the commercial buildings contribution to peak demand, two sources of data are used: (1) Commercial Building Energy Consumption Survey (CBECS) and (2) National Energy Modeling System (NEMS). These two sources indicate that commercial buildings noncoincidental peak demand is about 330GW. The project then focused on technologies and strategies that deliver energy efficiency and also target 5-10% of this peak. Based on a building operations perspective, a demand-side management framework with three main features: (1) daily energy efficiency, (2) daily peak load management and (3) dynamic, event-driven DR are outlined. A general description of DR, its benefits, and nationwide DR potential in commercial buildings are presented. Case studies involving these technologies and strategies are described. The findings of this project are shared with building owners, building controls industry, researchers and government entities through a webcast and their input is requested. Their input is presented in the appendix section of this report.

Piette, Mary Ann; Kiliccote, Sila

2006-09-01T23:59:59.000Z

452

Monthly energy review, August 1997  

SciTech Connect

The Monthly Energy Review for the month of August 1997, presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of U.S. production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors.

NONE

1997-08-01T23:59:59.000Z

453

The Boson peak in supercooled water  

E-Print Network (OSTI)

We perform extensive molecular dynamics simulations of the TIP4P/2005 model of water to investigate the origin of the Boson peak reported in experiments on supercooled water in nanoconfined pores, and in hydration water around proteins. We find that the onset of the Boson peak in supercooled bulk water coincides with the crossover to a predominantly low-density-like liquid below the Widom line $T_W$. The frequency and onset temperature of the Boson peak in our simulations of bulk water agree well with the results from experiments on nanoconfined water. Our results suggest that the Boson peak in water is not an exclusive effect of confinement. We further find that, similar to other glass-forming liquids, the vibrational modes corresponding to the Boson peak are spatially extended and are related to transverse phonons found in the parent crystal, here ice Ih.

Pradeep Kumar; K. Thor Wikfeldt; Daniel Schlesinger; Lars G. M. Pettersson; H. E. Stanley

2013-05-19T23:59:59.000Z

454

Permanent Peak Load Shift Product Deployment for Smart Grid Integration and Operation  

Science Conference Proceedings (OSTI)

This project tested and evaluated an innovative energy storage technology that provides permanent peak load shifting using electro-thermal energy storage in combination with commercial unitary rooftop air conditioning systems. Four Ice Bear 30 units were deployed at a Staples facility to store an estimated 32 kWh each of energy in 10 off-peak hours and reduce an estimated 5 kW of site energy demand for an on-peak six-hour period. The Ice Bear units are monitored and controlled with a smart grid ...

2012-11-14T23:59:59.000Z

455

Harnessing the power of demand  

Science Conference Proceedings (OSTI)

Demand response can provide a series of economic services to the market and also provide ''insurance value'' under low-likelihood, but high-impact circumstances in which grid reliablity is enhanced. Here is how ISOs and RTOs are fostering demand response within wholesale electricity markets. (author)

Sheffrin, Anjali; Yoshimura, Henry; LaPlante, David; Neenan, Bernard

2008-03-15T23:59:59.000Z

456

China, India demand cushions prices  

SciTech Connect

Despite the hopes of coal consumers, coal prices did not plummet in 2006 as demand stayed firm. China and India's growing economies, coupled with solid supply-demand fundamentals in North America and Europe, and highly volatile prices for alternatives are likely to keep physical coal prices from wide swings in the coming year.

Boyle, M.

2006-11-15T23:59:59.000Z

457

Automated Critical Peak Pricing Field Tests: Program Description and Results  

E-Print Network (OSTI)

Development for Demand Response Calculation - Findings andStrategies Linking Demand Response and Energy Efficiency.and Communications for Demand Response and Energy Efficiency

Piette, Mary Ann; Watson, David; Motegi, Naoya; Kiliccote, Sila; Xu, Peng

2006-01-01T23:59:59.000Z

458

Demand Response for Ancillary Services  

Science Conference Proceedings (OSTI)

Many demand response resources are technically capable of providing ancillary services. In some cases, they can provide superior response to generators, as the curtailment of load is typically much faster than ramping thermal and hydropower plants. Analysis and quantification of demand response resources providing ancillary services is necessary to understand the resources economic value and impact on the power system. Methodologies used to study grid integration of variable generation can be adapted to the study of demand response. In the present work, we describe and illustrate a methodology to construct detailed temporal and spatial representations of the demand response resource and to examine how to incorporate those resources into power system models. In addition, the paper outlines ways to evaluate barriers to implementation. We demonstrate how the combination of these three analyses can be used to translate the technical potential for demand response providing ancillary services into a realizable potential.

Alkadi, Nasr E [ORNL; Starke, Michael R [ORNL

2013-01-01T23:59:59.000Z

459

Energy Information Administration/Natural Gas Monthly October 2000  

U.S. Energy Information Administration (EIA) Indexed Site

Natural Gas Monthly October 2000 Natural Gas Monthly October 2000 vii Status of Natural Gas Pipeline System Capacity Entering the 2000-2001 Heating Season During the summer and fall of 2000 natural gas prices reached record highs for a nonheating season period. The dramatic rise in prices resulted from an upsurge in natural gas demand, mainly from electric generation needs during a warmer-than-usual spring and summer. The increased demand has occurred while domestic production levels have continued to decrease over the past several years. 1 Low natural gas prices during 1998 and 1999 dampened exploration and development efforts and caused some lower producing wells to be shut in or abandoned. Natural gas pipeline capacity, on the other hand, has grown with end-use demand, and as sources of new supply have developed, new pipelines have been

460

Northwest Open Automated Demand Response Technology Demonstration Project  

SciTech Connect

Lawrence Berkeley National Laboratory (LBNL) and the Demand Response Research Center (DRRC) performed a technology demonstration and evaluation for Bonneville Power Administration (BPA) in Seattle City Light's (SCL) service territory. This report summarizes the process and results of deploying open automated demand response (OpenADR) in Seattle area with winter morning peaking commercial buildings. The field tests were designed to evaluate the feasibility of deploying fully automated demand response (DR) in four to six sites in the winter and the savings from various building systems. The project started in November of 2008 and lasted 6 months. The methodology for the study included site recruitment, control strategy development, automation system deployment and enhancements, and evaluation of sites participation in DR test events. LBNL subcontracted McKinstry and Akuacom for this project. McKinstry assisted with recruitment, site survey collection, strategy development and overall participant and control vendor management. Akuacom established a new server and enhanced its operations to allow for scheduling winter morning day-of and day-ahead events. Each site signed a Memorandum of Agreement with SCL. SCL offered each site $3,000 for agreeing to participate in the study and an additional $1,000 for each event they participated. Each facility and their control vendor worked with LBNL and McKinstry to select and implement control strategies for DR and developed their automation based on the existing Internet connectivity and building control system. Once the DR strategies were programmed, McKinstry commissioned them before actual test events. McKinstry worked with LBNL to identify control points that can be archived at each facility. For each site LBNL collected meter data and trend logs from the energy management and control system. The communication system allowed the sites to receive day-ahead as well as day-of DR test event signals. Measurement of DR was conducted using three different baseline models for estimation peak load reductions. One was three-in-ten baseline, which is based on the site electricity consumption from 7 am to 10 am for the three days with the highest consumption of the previous ten business days. The second model, the LBNL outside air temperature (OAT) regression baseline model, is based on OAT data and site electricity consumption from the previous ten days, adjusted using weather regressions from the fifteen-minute electric load data during each DR test event for each site. A third baseline that simply averages the available load data was used for sites less with less than 10 days of historical meter data. The evaluation also included surveying sites regarding any problems or issues that arose during the DR test events. Question covered occupant comfort, control issues and other potential problems.

Kiliccote, Sila; Dudley, Junqiao Han; Piette, Mary Ann

2009-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Demand Response Opportunities in Industrial Refrigerated Warehouses...  

NLE Websites -- All DOE Office Websites (Extended Search)

Demand Response Opportunities in Industrial Refrigerated Warehouses in California Title Demand Response Opportunities in Industrial Refrigerated Warehouses in California...

462

Elasticities of Electricity Demand in Urban Indian Households  

E-Print Network (OSTI)

Energy demand, and in particular electricity demand in India has been growing at a very rapid rate over the last decade. Given, current trends in population growth, industrialisation, urbanisation, modernisation and income growth, electricity consumption is expected to increase substantially in the coming decades as well. Tariff reforms could play a potentially important role as a demand side management tool in India. However, the effects of any price revisions on consumption will depend on the price elasticity of demand for electricity. In the past, electricity demand studies for India published in international journals have been based on aggregate macro data at the country or sub-national / state level. In this paper, price and income elasticities of electricity demand in the residential sector of all urban areas of India are estimated for the first time using disaggregate level survey data for over thirty thousand households. Three electricity demand functions have been estimated using monthly data for the following seasons: winter, monsoon and summer. The results show electricity demand is income and price inelastic in all three seasons, and that household, demographic and geographical variables are important in determining electricity demand, something that is not possible to determine using aggregate macro models alone. Key Words Residential electricity demand, price elasticity, income elasticity Short Title Electricity demand in Indian households Acknowledgements: The authors would like to gratefully acknowledge the National Sample Survey Organisation, Department of Statistics of the Government of India, for making available to us the unit level, household survey data. We would also like to thank Prof. Daniel Spreng for his support of our research. 2 1.

Shonali Pachauri

2002-01-01T23:59:59.000Z

463

Demand Shifting With Thermal Mass in Large Commercial Buildings:Field Tests, Simulation and Audits  

SciTech Connect

The principle of pre-cooling and demand limiting is to pre-cool buildings at night or in the morning during off-peak hours, storing cooling in the building thermal mass and thereby reducing cooling loads and reducing or shedding related electrical demand during the peak periods. Cost savings are achieved by reducing on-peak energy and demand charges. The potential for utilizing building thermal mass for load shifting and peak demand reduction has been demonstrated in a number of simulation, laboratory, and field studies (Braun 1990, Ruud et al. 1990, Conniff 1991, Andresen and Brandemuehl 1992, Mahajan et al. 1993, Morris et al. 1994, Keeney and Braun 1997, Becker and Paciuk 2002, Xu et al. 2003). This technology appears to have significant potential for demand reduction if applied within an overall demand response program. The primary goal associated with this research is to develop information and tools necessary to assess the viability of and, where appropriate, implement demand response programs involving building thermal mass in buildings throughout California. The project involves evaluating the technology readiness, overall demand reduction potential, and customer acceptance for different classes of buildings. This information can be used along with estimates of the impact of the strategies on energy use to design appropriate incentives for customers.

Xu, Peng; Haves, Philip; Piette, Mary Ann; Zagreus, Leah

2005-09-01T23:59:59.000Z

464

Demand Dispatch Based on Smart Charging of Plug-in Electric Vehicles  

Science Conference Proceedings (OSTI)

Uncontrolled charging of Plug-in Electric Vehicles (PEVs) has a negative impact on the peak load and brings potential challenges to electric utility. In this paper, we apply a statistical load model of PEVs charging demand to simulate the driving habits ... Keywords: Plug-in Electric Vehicles, Demand dispatch, Smart charging, Driving habits, Load model

Ting Wu, Gang Wu, Zhejing Bao, Wenjun Yan, Yiyan Zhang

2012-07-01T23:59:59.000Z

465

Electric Power Monthly - Monthly Data Tables | OpenEI  

Open Energy Info (EERE)

Power Monthly - Monthly Data Tables Power Monthly - Monthly Data Tables Dataset Summary Description Monthly electricity generation figures (and the fuel consumed to produce it). Source information available at EIA. Source EIA Date Released July 20th, 2010 (4 years ago) Date Updated July 20th, 2010 (4 years ago) Keywords consumption EIA Electricity Electricity Consumption Electricity Generation Data application/vnd.ms-excel icon generation_state_mon.xls (xls, 32.5 MiB) application/vnd.ms-excel icon consumption_state_mon.xls (xls, 14.7 MiB) Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Monthly Time Period License License Other or unspecified, see optional comment below Comment Work of the U.S. Federal Government Rate this dataset Usefulness of the metadata

466

A computational intelligence scheme for the prediction of the daily peak load  

Science Conference Proceedings (OSTI)

Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important ... Keywords: Computational intelligence, Daily peak load, Mid-term load forecasting, Self-organizing map, Support vector machine

Jawad Nagi; Keem Siah Yap; Farrukh Nagi; Sieh Kiong Tiong; Syed Khaleel Ahmed

2011-12-01T23:59:59.000Z

467

An efficient load model for analyzing demand side management impacts  

SciTech Connect

The main objective of implementing Demand Side Management (DSM) in power systems is to change the utility's load shape--i.e. changes in the time pattern and magnitude of utility's load. Changing the load shape as a result of demand side activities could change the peak load, base load and/or energy demand. Those three variables have to be explicitly modeled into the load curve for properly representing the effects of demand side management. The impact of DSM will be manifested as higher or lower reliability levels. This paper presents an efficient technique to model the system load such that the impact of demand side management on the power system can be easily and accurately evaluated. The proposed technique to model the load duration curve will facilitate the representation of DSM impacts on loss-of-load probability, energy not served and energy consumption. This will provide an analytical method to study the impact of DSM on capacity requirements. So far iterative methods have been applied to study these impacts. The proposed analytical method results in a faster solution with higher accuracy. It takes only 18 seconds on an 80486 PC to solve each case study involving different peak and base loads, and energy use.

Rahman, S.; Rinaldy (Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States))

1993-08-01T23:59:59.000Z

468

Demand and Price Volatility: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

Scott, K. Rebecca

2011-01-01T23:59:59.000Z

469

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

470

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

Model of the Global Crude Oil Market and the U.S. RetailNoureddine. 2002. World crude oil and natural gas: a demandanalysis of the demand for oil in the Middle East. Energy

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

471

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network (OSTI)

global gasoline and diesel price and income elasticities.shift in the short-run price elasticity of gasoline demand.Habits and Uncertain Relative Prices: Simulating Petrol Con-

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

472

Tracking Progress Last updated 5/8/2013 Statewide Energy Demand 1  

E-Print Network (OSTI)

Electricity consumption grew at a rate of about 1.5 percent from 1990 to 2000, 1 percent from 2000 to 2008. Net peak is total electricity demand at peak on the customer side, plus utility transmission,884 2022* 74,049 70,946 66,916 Note: Historical values are shaded. *Weather-normalized: a weather

473

US electric utility demand-side management, 1994  

SciTech Connect

The report presents comprehensive information on electric power industry demand-side management (DSM) activities in US at the national, regional, and utility levels. Objective is provide industry decision makers, government policy makers, analysts, and the general public with historical data that may be used in understanding DSM as it relates to the US electric power industry. The first chapter, ``Profile: US Electric Utility Demand-Side Management,`` presents a general discussion of DSM, its history, current issues, and a review of key statistics for the year. Subsequent chapters present discussions and more detailed data on energy savings, peak load reductions, and costs attributable to DSM.

NONE

1995-12-26T23:59:59.000Z

474

Peak Underground Working Natural Gas Storage Capacity  

Gasoline and Diesel Fuel Update (EIA)

Note: 1) 'Demonstrated Peak Working Gas Capacity' is the sum of the highest storage inventory level of working gas observed in each facility over the prior 5-year period as...

475

Measured Peak Equipment Loads in Laboratories  

SciTech Connect

This technical bulletin documents measured peak equipment load data from 39 laboratory spaces in nine buildings across five institutions. The purpose of these measurements was to obtain data on the actual peak loads in laboratories, which can be used to rightsize the design of HVAC systems in new laboratories. While any given laboratory may have unique loads and other design considerations, these results may be used as a 'sanity check' for design assumptions.

Mathew, Paul A.

2007-09-12T23:59:59.000Z

476

Monthly Energy Review - December 2009  

Gasoline and Diesel Fuel Update (EIA)

Formats Formats (PDF) files; however, all annual data are shown in the Excel and comma-separated values (CSV) files. Also, only two to three years of monthly data are displayed in the PDF files; however, for many series, monthly data beginning with January 1973 are available in the Excel and CSV files. Comprehensive Changes: Each month, most MER tables and figures carry a new month of data, which is usually preliminary (and sometimes estimated or even forecast) and likely to be revised in the succeeding month. Annual Data From 1949: The emphasis of the MER is on recent monthly and annual data trends. Analysts may wish to use the data in this report in conjunction with EIA's Annual Energy Review (AER) that offers annual data beginning in 1949 for many of the data series found in the

477

Prompt-Month Energy Futures  

Gasoline and Diesel Fuel Update (EIA)

Prompt-Month Energy Futures Prompt-Month Energy Futures Prices and trading activity shown are for prompt-month (see definition below) futures contracts for the energy commodities listed in the table below. Note that trading for prompt-month futures contracts ends on different dates at the end of the month for the various commodities; therefore, some commodity prices may reference delivery for the next month sooner than other commodity prices. Product Description Listed With Crude Oil ($/barrel) West Texas Intermediate (WTI) light sweet crude oil delivered to Cushing, Oklahoma More details | Contract specifications New York Mercantile Exchange (Nymex) Gasoline-RBOB ($/gallon) Reformulated gasoline blendstock for oxygenate blending (RBOB) gasoline delivered to New York Harbor More details | Contract specifications Nymex

478

Effects of the drought on California electricity supply and demand  

E-Print Network (OSTI)

Acknowledgments SUMMARY Electricity Demand ElectricityAdverse Impacts ELECTRICITY DEMAND . . . .Demand forElectricity Sales Electricity Demand by Major Utility

Benenson, P.

2010-01-01T23:59:59.000Z

479

Monthly energy review, January 1998  

SciTech Connect

This report presents an overview of recent monthly energy statistics. Major activities covered include production, consumption, trade, stocks, and prices for fossil fuels, electricity, and nuclear energy.

NONE

1998-01-01T23:59:59.000Z

480

Monthly energy review. May 1998  

SciTech Connect

This report presents recent energy monthly statistics on the production, consumption, trade, stocks, and prices of petroleum, natural gas, coal, electricity, and nuclear energy.

NONE

1998-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly peak demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Electric Power Monthly January 2012  

U.S. Energy Information Administration (EIA)

Electric Power Monthly January 2012 With Data for November 2011 ... Electric Utility Power Generation Station (PGS) 2 CA 57696 1 3.8 OBG GT

482

Natural gas monthly, July 1997  

Science Conference Proceedings (OSTI)

The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the NGM features articles designed to assist readers in using and interpreting natural gas information. The feature article this month is entitled ``Intricate puzzle of oil and gas reserves growth.`` A special report is included on revisions to monthly natural gas data. 6 figs., 24 tabs.

NONE

1997-07-01T23:59:59.000Z

483

Natural gas monthly, May 1994  

Science Conference Proceedings (OSTI)

The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the NGM features articles designed to assist readers in using and interpreting natural gas information. The featured articles for this month are: Opportunities with fuel cells, and revisions to monthly natural gas data.

Not Available

1994-05-25T23:59:59.000Z

484

Natural gas monthly, August 1995  

SciTech Connect

The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the NGM features articles designed to assist readers in using and interpreting natural gas information. This month`s feature article is on US Natural Gas Imports and Exports 1994.

NONE

1995-08-24T23:59:59.000Z

485

Monthly Energy Review - November 1999  

Annual Energy Outlook 2012 (EIA)

November 23, 1999 Electronic Access The Monthly Energy Review is available on the Energy Information Administration's website in a variety of formats: * ASCII text, Lotus (wk1),...

486

Monthly Energy Review - March 2000  

Annual Energy Outlook 2012 (EIA)

March 28, 2000 Electronic Access The Monthly Energy Review is available on the Energy Information Administration's website in a variety of formats: * ASCII text, Lotus (wk1), and...

487

Electric Power Monthly March 2011  

U.S. Energy Information Administration (EIA)

order to provide an integrated view of the electric power ... EIA, Department of Energy prepares the EPM. This publication provides monthly statistics at the State

488

EIA - AEO2010 - Electricity Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Demand Electricity Demand Annual Energy Outlook 2010 with Projections to 2035 Electricity Demand Figure 69. U.S. electricity demand growth 1950-2035 Click to enlarge » Figure source and data excel logo Figure 60. Average annual U.S. retail electricity prices in three cases, 1970-2035 Click to enlarge » Figure source and data excel logo Figure 61. Electricity generation by fuel in three cases, 2008 and 2035 Click to enlarge » Figure source and data excel logo Figure 62. Electricity generation capacity additions by fuel type, 2008-2035 Click to enlarge » Figure source and data excel logo Figure 63. Levelized electricity costs for new power plants, 2020 and 2035 Click to enlarge » Figure source and data excel logo Figure 64. Electricity generating capacity at U.S. nuclear power plants in three cases, 2008, 2020, and 2035

489

Winter Demand Impacted by Weather  

Gasoline and Diesel Fuel Update (EIA)

8 Notes: Heating oil demand is strongly influenced by weather. The "normal" numbers are the expected values for winter 2000-2001 used in EIA's Short-Term Energy Outlook. The chart...

490

Demand for money in China .  

E-Print Network (OSTI)

??This research investigates the long-run equilibrium relationship between money demand and its determinants in China over the period 1952-2004 for three definitions of money –… (more)

Zhang, Qing

2006-01-01T23:59:59.000Z

491

STEO December 2012 - coal demand  

U.S. Energy Information Administration (EIA) Indexed Site

coal demand seen below 1 billion tons in 2012 for fourth year in a row Coal consumption by U.S. power plants to generate electricity is expected to fall below 1 billion tons in...

492

Distillate Demand Strong Last Winter  

Gasoline and Diesel Fuel Update (EIA)

4 Notes: Well, distillate fuel demand wasn't the reason that stocks increased in January 2001 and kept prices from going higher. As you will hear shortly, natural gas prices spiked...

493

Thermal Mass and Demand Response  

NLE Websites -- All DOE Office Websites (Extended Search)

Thermal Mass and Demand Response Speaker(s): Gregor Henze Phil C. Bomrad Date: November 2, 2011 - 12:00pm Location: 90-4133 Seminar HostPoint of Contact: Janie Page The topic of...

494

Automated Demand Response and Commissioning  

E-Print Network (OSTI)

Conference on Building Commissioning: May 4-6, 2005 Motegi,National Conference on Building Commissioning: May 4-6, 2005Demand Response and Commissioning Mary Ann Piette, David S.

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-01-01T23:59:59.000Z

495

Leslie Mancebo (7234) Transportation Demand &  

E-Print Network (OSTI)

Leslie Mancebo (7234) Transportation Demand & Marketing Coordinator 1 FTE, 1 HC Administrative Vice Chancellor Transportation and Parking Services Clifford A. Contreras (0245) Director 30.10 FTE Alternative Transportation & Marketing Reconciliation Lourdes Lupercio (4723) Michelle McArdle (7512) Parking

Hammock, Bruce D.

496

Demand Response Spinning Reserve Demonstration  

Science Conference Proceedings (OSTI)

The Demand Response Spinning Reserve project is a pioneeringdemonstration of how existing utility load-management assets can providean important electricity system reliability resource known as spinningreserve. Using aggregated demand-side resources to provide spinningreserve will give grid operators at the California Independent SystemOperator (CAISO) and Southern California Edison (SCE) a powerful, newtool to improve system reliability, prevent rolling blackouts, and lowersystem operating costs.

Eto, Joseph H.; Nelson-Hoffman, Janine; Torres, Carlos; Hirth,Scott; Yinger, Bob; Kueck, John; Kirby, Brendan; Bernier, Clark; Wright,Roger; Barat, A.; Watson, David S.

2007-05-01T23:59:59.000Z

497

Application of Building Precooling to Reduce Peak Cooling Requirements  

E-Print Network (OSTI)

A building cooling control strategy was developed and tested for a 1.4 million square foot (130,000 square meter) office building located in Hoffman Estates, IL. The goal of the control strategy was to utilize building thermal mass to limit the peak cooling load for continued building operation in the event of the loss of one of the four central chiller units. The algorithm was first developed and evaluated through simulation and then evaluated through tests on two identical buildings. The east building utilized the existing building control strategy while the west building used the precooling strategy developed for this project. Consistent with simulation predictions, the precooling control strategy successfully limited the peak load to 75 % of the cooling capacity for the west building, while the east building operated at 100 % of capacity. Precooling of the building mass provided an economical alternative to the purchase of an additional chiller unit. The estimated cost of installing an additional chiller was approximately $500,000. Computer models developed for this project also showed that precooling based upon cooling cost minimization could result in savings of approximately $25,000 per month during the peak cooling season. The building model was validated with experimental results and could be used in the development of a cost minimization strategy.

Kevin R. Keeney; James E. Braun, Ph.D.

1997-01-01T23:59:59.000Z

498

Monthly energy review, November 1996  

SciTech Connect

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 75 tabs.

1996-11-01T23:59:59.000Z

499

Monthly energy review, May 1999  

Science Conference Proceedings (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 61 tabs.

NONE

1999-05-01T23:59:59.000Z

500

Monthly energy review, November 1997  

Science Conference Proceedings (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 91 tabs.

NONE

1997-11-01T23:59:59.000Z