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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
Témata:
Bidding strategy
Real-time energy market
Regulation market
Robust model predictive control
Real-time energy market
Regulation market
Robust model predictive control
Bidding strategy
ISSN:0142-0615, 1879-3517
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•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.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2020.106361