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Title: Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales

Authors:
; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1397068
Grant/Contract Number:
XGJ-6-62183-01; AC36-08GO28308
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Energy (Oxford)
Additional Journal Information:
Journal Name: Energy (Oxford); Journal Volume: 122; Journal Issue: C; Related Information: CHORUS Timestamp: 2018-02-08 10:36:09; Journal ID: ISSN 0360-5442
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Zhang, Jie, Cui, Mingjian, Hodge, Bri-Mathias, Florita, Anthony, and Freedman, Jeffrey. Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales. United Kingdom: N. p., 2017. Web. doi:10.1016/j.energy.2017.01.104.
Zhang, Jie, Cui, Mingjian, Hodge, Bri-Mathias, Florita, Anthony, & Freedman, Jeffrey. Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales. United Kingdom. doi:10.1016/j.energy.2017.01.104.
Zhang, Jie, Cui, Mingjian, Hodge, Bri-Mathias, Florita, Anthony, and Freedman, Jeffrey. Wed . "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales". United Kingdom. doi:10.1016/j.energy.2017.01.104.
@article{osti_1397068,
title = {Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales},
author = {Zhang, Jie and Cui, Mingjian and Hodge, Bri-Mathias and Florita, Anthony and Freedman, Jeffrey},
abstractNote = {},
doi = {10.1016/j.energy.2017.01.104},
journal = {Energy (Oxford)},
number = C,
volume = 122,
place = {United Kingdom},
year = {Wed Mar 01 00:00:00 EST 2017},
month = {Wed Mar 01 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.energy.2017.01.104

Citation Metrics:
Cited by: 5works
Citation information provided by
Web of Science

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  • The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less
  • The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performedmore » for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.« less
  • The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less