Robust MPC-based Bidding Strategy for Wind Storage Systems in Real-time Energy and Regulation Markets

Yunyun Xie*, Weiqing Guo, Qiuwei Wu, Ke Wang

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.
Original languageEnglish
Article number106361
JournalInternational Journal of Electrical Power & Energy Systems
Volume124
Number of pages17
ISSN0142-0615
DOIs
Publication statusPublished - 2020

Keywords

  • Real-time energy market
  • Regulation market
  • Bidding strategy
  • Robust model predictive control

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