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Title: Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping

Abstract

Electric utility residential demand response programs typically reduce load a few times a year during periods of peak energy use. In the future, utilities and consumers may monetarily and environmentally benefit from continuously shaping load by alternatively encouraging or discouraging the use of electricity. One way to shape load and introduce elasticity is to broadcast forecasts of dynamic electricity prices that orchestrate electricity supply and demand in order to maximize the efficiency of conventional generation and the use of renewable resources including wind and solar energy. A binary control algorithm that influences the on and off states of end uses was developed and applied to empirical time series data to estimate price-based instantaneous opportunities for shedding and adding electric load. To overcome the limitations of traditional stochastic methods in quantifying diverse, non-Gaussian, non-stationary distributions of observed appliance behaviour, recent developments in wavelet-based analysis were applied to capture and simulate time-frequency domain behaviour. The performance of autoregressive and spectral reconstruction methods was compared, with phase reconstruction providing the best simulation ensembles. Results show spatiotemporal differences in the amount of load that can be shed and added, which suggest further investigation is warranted in estimating the benefits anticipated from the wide-scale deploymentmore » of continuous automatic residential load shaping. Empirical data and documented software code are included to assist in reproducing and extending this work.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Univ. of Colorado, Boulder, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1558141
Alternate Identifier(s):
OSTI ID: 1559428
Report Number(s):
NREL/JA-5D00-74293
Journal ID: ISSN 1996-1073; ENERGA
Grant/Contract Number:  
AC36-08GO28308; GMLC.10246.09.01.14
Resource Type:
Published Article
Journal Name:
Energies (Basel)
Additional Journal Information:
Journal Name: Energies (Basel); Journal Volume: 12; Journal Issue: 17; Journal ID: ISSN 1996-1073
Publisher:
MDPI AG
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; electric load modeling; demand response; price-response; spectral representation

Citation Formats

Cruickshank, Robert, Henze, Gregor, Balaji, Rajagopalan, Hodge, Bri-Mathias, and Florita, Anthony. Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping. United States: N. p., 2019. Web. doi:10.3390/en12173204.
Cruickshank, Robert, Henze, Gregor, Balaji, Rajagopalan, Hodge, Bri-Mathias, & Florita, Anthony. Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping. United States. doi:10.3390/en12173204.
Cruickshank, Robert, Henze, Gregor, Balaji, Rajagopalan, Hodge, Bri-Mathias, and Florita, Anthony. Wed . "Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping". United States. doi:10.3390/en12173204.
@article{osti_1558141,
title = {Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping},
author = {Cruickshank, Robert and Henze, Gregor and Balaji, Rajagopalan and Hodge, Bri-Mathias and Florita, Anthony},
abstractNote = {Electric utility residential demand response programs typically reduce load a few times a year during periods of peak energy use. In the future, utilities and consumers may monetarily and environmentally benefit from continuously shaping load by alternatively encouraging or discouraging the use of electricity. One way to shape load and introduce elasticity is to broadcast forecasts of dynamic electricity prices that orchestrate electricity supply and demand in order to maximize the efficiency of conventional generation and the use of renewable resources including wind and solar energy. A binary control algorithm that influences the on and off states of end uses was developed and applied to empirical time series data to estimate price-based instantaneous opportunities for shedding and adding electric load. To overcome the limitations of traditional stochastic methods in quantifying diverse, non-Gaussian, non-stationary distributions of observed appliance behaviour, recent developments in wavelet-based analysis were applied to capture and simulate time-frequency domain behaviour. The performance of autoregressive and spectral reconstruction methods was compared, with phase reconstruction providing the best simulation ensembles. Results show spatiotemporal differences in the amount of load that can be shed and added, which suggest further investigation is warranted in estimating the benefits anticipated from the wide-scale deployment of continuous automatic residential load shaping. Empirical data and documented software code are included to assist in reproducing and extending this work.},
doi = {10.3390/en12173204},
journal = {Energies (Basel)},
number = 17,
volume = 12,
place = {United States},
year = {2019},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.3390/en12173204

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