Optimization Challenges in Bidding for Battery Energy Storage Systems and Participating across Multiple Markets

Optimal market bidding for battery energy storage systems (BESS) is a challenging problem, mainly due to the unknown market outcome and resulting market clearing prices. To manage the uncertainty, stochastic optimization [1] can be used along various machine learning-based decision support tools. In this paper, we focus on stochastic optimization due to its ability to consider electricity price uncertainty and potential to achieve optimal solutions with correct input parameters. The mixed-integer linear programming (MILP)-based approach is flexible and can adapt to any environment without a need for historical data, training or an initial learning phase. Open questions are how to efficiently handle uncertain forecast data and use the heterogenous market landscape to ensure reliable…