Robust MPC-based bidding strategy for wind storage systems in real-time energy and regulation markets

•A RMPC based Bidding Strategy optimization model is proposed.•The storage system is employed to make arbitrages.•The RMPC-based nonlinear model is transformed to a MILP model.•The efficiency of the proposed method is validated with PJM market data. This paper presents a robust model predictive cont...

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Vydáno v:International journal of electrical power & energy systems Ročník 124; s. 106361
Hlavní autoři: Xie, Yunyun, Guo, Weiqing, Wu, Qiuwei, Wang, Ke
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.01.2021
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ISSN:0142-0615, 1879-3517
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Abstract •A RMPC based Bidding Strategy optimization model is proposed.•The storage system is employed to make arbitrages.•The RMPC-based nonlinear model is transformed to a MILP model.•The efficiency of the proposed method is validated with PJM market data. This paper presents a robust model predictive control (RMPC)-based bidding strategy for wind-storage systems to increase their revenue in real-time energy and regulation markets. The bidding capacities of the wind-storage system in the energy and regulation market are optimized to maximize revenue. Additionally, storage systems are employed to make arbitrage by absorbing low-cost energy in the energy market and selling it in the energy and regulation market. The uncertainties of wind power outputs and electricity prices are described as predefined uncertainty sets. A mixed-integer nonlinear programming model based on RMPC is built to generate the optimal strategy in the next several prediction horizons, and the bidding strategy in the first prediction horizon is applied to the real-time market. The nonlinear optimization model is transformed into a mixed-integer programming (MILP) model that can be solved efficiently by CPLEX. The effectiveness of the proposed method is validated with PJM market data.
AbstractList •A RMPC based Bidding Strategy optimization model is proposed.•The storage system is employed to make arbitrages.•The RMPC-based nonlinear model is transformed to a MILP model.•The efficiency of the proposed method is validated with PJM market data. This paper presents a robust model predictive control (RMPC)-based bidding strategy for wind-storage systems to increase their revenue in real-time energy and regulation markets. The bidding capacities of the wind-storage system in the energy and regulation market are optimized to maximize revenue. Additionally, storage systems are employed to make arbitrage by absorbing low-cost energy in the energy market and selling it in the energy and regulation market. The uncertainties of wind power outputs and electricity prices are described as predefined uncertainty sets. A mixed-integer nonlinear programming model based on RMPC is built to generate the optimal strategy in the next several prediction horizons, and the bidding strategy in the first prediction horizon is applied to the real-time market. The nonlinear optimization model is transformed into a mixed-integer programming (MILP) model that can be solved efficiently by CPLEX. The effectiveness of the proposed method is validated with PJM market data.
ArticleNumber 106361
Author Guo, Weiqing
Xie, Yunyun
Wang, Ke
Wu, Qiuwei
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  surname: Wang
  fullname: Wang, Ke
  organization: Electric Power Research Institute of State Grid, Nanjing 211003, Jiangsu Province, China
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Keywords Real-time energy market
Regulation market
Robust model predictive control
Bidding strategy
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Snippet •A RMPC based Bidding Strategy optimization model is proposed.•The storage system is employed to make arbitrages.•The RMPC-based nonlinear model is transformed...
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StartPage 106361
SubjectTerms Bidding strategy
Real-time energy market
Regulation market
Robust model predictive control
Title Robust MPC-based bidding strategy for wind storage systems in real-time energy and regulation markets
URI https://dx.doi.org/10.1016/j.ijepes.2020.106361
Volume 124
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