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

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.

Automated summary

A robust model predictive control (RMPC)-based bidding strategy is proposed for wind-storage systems to increase revenue in real-time energy and regulation markets. The method optimizes bidding capacities and utilizes storage systems for arbitrage, with validation using PJM market data. The approach involves building an RMPC-based optimization model and applying the bidding strategy in real-time markets.