The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

Wang, Qin; Wu, Hongyu; Florita, Anthony R.; Brancucci Martinez-Anido, Carlo; Hodge, Bri-Mathias

doi:10.1016/j.apenergy.2016.11.016

Title: The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

Journal Article · Fri Nov 11 00:00:00 EST 2016 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2016.11.016· OSTI ID:1336896

Wang, Qin ^[1]; Wu, Hongyu ^[1]; Florita, Anthony R. ^[1]; Brancucci Martinez-Anido, Carlo ^[1]; Hodge, Bri-Mathias ^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States)

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 compared 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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1336896

Alternate ID(s):: OSTI ID: 1414629

Report Number(s):: NREL/JA-5D00-67360

Journal Information:: Applied Energy, Vol. 184, Issue C; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 43 works

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References (29)

Costs and benefits of renewables portfolio standards in the United States Barbose, Galen; Bird, Lori; Heeter, Jenny Renewable and Sustainable Energy Reviews, Vol. 52 https://doi.org/10.1016/j.rser.2015.07.175	journal	December 2015
Cost Analysis and Pricing Policy of Wind Power in China Wang, Qin; Wen, Fushuan; Yang, Aimin Journal of Energy Engineering, Vol. 137, Issue 3 https://doi.org/10.1061/(ASCE)EY.1943-7897.0000020	journal	September 2011
Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction Chen, Niya; Qian, Zheng; Nabney, Ian T. IEEE Transactions on Power Systems, Vol. 29, Issue 2 https://doi.org/10.1109/TPWRS.2013.2282366	journal	March 2014
Short-term wind power forecasting using ridgelet neural network Amjady, Nima; Keynia, Farshid; Zareipour, Hamidreza Electric Power Systems Research, Vol. 81, Issue 12 https://doi.org/10.1016/j.epsr.2011.08.007	journal	December 2011
Wind Energy: Forecasting Challenges for Its Operational Management Pinson, Pierre Statistical Science, Vol. 28, Issue 4 https://doi.org/10.1214/13-STS445	journal	November 2013
An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed Zhao, Jing; Guo, Zhen-Hai; Su, Zhong-Yue Applied Energy, Vol. 162 https://doi.org/10.1016/j.apenergy.2015.10.145	journal	January 2016
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar Applied Energy, Vol. 165 https://doi.org/10.1016/j.apenergy.2015.12.082	journal	March 2016
Performance analysis of four modified approaches for wind speed forecasting Zhang, Wenyu; Wu, Jie; Wang, Jianzhou Applied Energy, Vol. 99 https://doi.org/10.1016/j.apenergy.2012.05.029	journal	November 2012
Short-term wind speed forecasting with Markov-switching model Song, Zhe; Jiang, Yu; Zhang, Zijun Applied Energy, Vol. 130 https://doi.org/10.1016/j.apenergy.2014.05.026	journal	October 2014
Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting Poncela, Marta; Poncela, Pilar; Perán, José Ramón Applied Energy, Vol. 108 https://doi.org/10.1016/j.apenergy.2013.03.041	journal	August 2013
Wind power forecasting based on principle component phase space reconstruction Han, Li; Romero, Carlos E.; Yao, Zheng Renewable Energy, Vol. 81 https://doi.org/10.1016/j.renene.2015.03.037	journal	September 2015
A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining Xu, Qianyao; He, Dawei; Zhang, Ning IEEE Transactions on Sustainable Energy, Vol. 6, Issue 4 https://doi.org/10.1109/TSTE.2015.2429586	journal	October 2015
Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel Zhang, Yao; Wang, Jianxue; Luo, Xu Energy Conversion and Management, Vol. 96 https://doi.org/10.1016/j.enconman.2015.03.012	journal	May 2015
Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine Wan, Can; Xu, Zhao; Pinson, Pierre IEEE Transactions on Power Systems, Vol. 29, Issue 3 https://doi.org/10.1109/TPWRS.2013.2287871	journal	May 2014
Demand Dispatch and Probabilistic Wind Power Forecasting in Unit Commitment and Economic Dispatch: A Case Study of Illinois Botterud, Audun; Zhou, Zhi; Wang, Jianhui IEEE Transactions on Sustainable Energy, Vol. 4, Issue 1 https://doi.org/10.1109/TSTE.2012.2215631	journal	January 2013
A review on the recent history of wind power ramp forecasting Gallego-Castillo, Cristobal; Cuerva-Tejero, Alvaro; Lopez-Garcia, Oscar Renewable and Sustainable Energy Reviews, Vol. 52 https://doi.org/10.1016/j.rser.2015.07.154	journal	December 2015
Wind Power Forecasting in U.S. Electricity Markets Botterud, Audun; Wang, Jianhui; Miranda, Vladimiro The Electricity Journal, Vol. 23, Issue 3 https://doi.org/10.1016/j.tej.2010.03.006	journal	April 2010
Wind power forecasting uncertainty and unit commitment Wang, J.; Botterud, A.; Bessa, R. Applied Energy, Vol. 88, Issue 11 https://doi.org/10.1016/j.apenergy.2011.04.011	journal	November 2011
The value of day-ahead solar power forecasting improvement Brancucci Martinez-Anido, Carlo; Botor, Benjamin; Florita, Anthony R. Solar Energy, Vol. 129 https://doi.org/10.1016/j.solener.2016.01.049	journal	May 2016
Quantifying the value of improved wind energy forecasts in a pool-based electricity market Mc Garrigle, E. V.; Leahy, P. G. Renewable Energy, Vol. 80 https://doi.org/10.1016/j.renene.2015.02.023	journal	August 2015
Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu IEEE Transactions on Sustainable Energy, Vol. 7, Issue 4 https://doi.org/10.1109/TSTE.2016.2560628	journal	October 2016
The Wind Integration National Dataset (WIND) Toolkit Draxl, Caroline; Clifton, Andrew; Hodge, Bri-Mathias Applied Energy, Vol. 151, p. 355-366 https://doi.org/10.1016/j.apenergy.2015.03.121	journal	August 2015
A suite of metrics for assessing the performance of solar power forecasting Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias Solar Energy, Vol. 111 https://doi.org/10.1016/j.solener.2014.10.016	journal	January 2015
Risk-Based Locational Marginal Pricing and Congestion Management Wang, Qin; Zhang, Guangyuan; McCalley, James D. IEEE Transactions on Power Systems, Vol. 29, Issue 5 https://doi.org/10.1109/TPWRS.2014.2305303	journal	September 2014
A Novel Market Simulation Methodology on Hydro Storage Gu, Yang; Bakke, Jordan; Zhou, Zheng IEEE Transactions on Smart Grid, Vol. 5, Issue 2 https://doi.org/10.1109/TSG.2013.2289915	journal	March 2014
Review of energy system flexibility measures to enable high levels of variable renewable electricity Lund, Peter D.; Lindgren, Juuso; Mikkola, Jani Renewable and Sustainable Energy Reviews, Vol. 45 https://doi.org/10.1016/j.rser.2015.01.057	journal	May 2015
Integrating large scale wind power into the electricity grid in the Northeast of Brazil de Jong, Pieter; Kiperstok, Asher; Sánchez, Antonio Santos Energy, Vol. 100 https://doi.org/10.1016/j.energy.2015.12.026	journal	April 2016
Analyzing operational flexibility of electric power systems Ulbig, Andreas; Andersson, Göran International Journal of Electrical Power & Energy Systems, Vol. 72 https://doi.org/10.1016/j.ijepes.2015.02.028	journal	November 2015
Grid flexibility and storage required to achieve very high penetration of variable renewable electricity Denholm, Paul; Hand, Maureen Energy Policy, Vol. 39, Issue 3 https://doi.org/10.1016/j.enpol.2011.01.019	journal	March 2011

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A universal power-law model for wind speed uncertainty Wan, Jie; Liu, Jinfu; Ren, Guorui Cluster Computing, Vol. 22, Issue S4 https://doi.org/10.1007/s10586-017-1350-1	journal	November 2017
Optimal energy storage sizing and offering strategy for the presence of wind power plant with energy storage in the electricity market Aghajani, Afshin; Kazemzadeh, Rasool; Ebrahimi, Afshin International Transactions on Electrical Energy Systems, Vol. 28, Issue 11 https://doi.org/10.1002/etep.2621	journal	May 2018
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