Optimal hour-ahead commitment and storage decisions of wind power producers

Renewable energy generators often rely on their battery deployments to meet their commitments in electricity markets. We consider the joint energy commitment and storage problem for a wind farm paired with a battery. The power producer decides, in each hour of a finite planning horizon, how much energy to commit to dispatching or purchasing for the next hour, how much wind energy to generate, and how much energy to charge or discharge. The power producer pays a penalty cost if they do not fully meet their commitment. Using a Markov decision process model under uncertainties in electricity price (assumed to be positive) and wind speed, we first prove the optimality of a state-dependent threshold policy for the power producer’s problem. This policy partitions the state space into several disjoint domains, each associated with a different action type, making it optimal to bring storage and commitment levels to different threshold pairs in each domain. We then employ our structural results to develop a heuristic solution procedure in a more general setting where the electricity price can also be negative. Numerical results show the high efficiency and scalability of this procedure. It provides solutions with an average deviation of only 0.3% from optimality and achieves a speedup of two to three orders of magnitude compared to the standard dynamic programming algorithm, reducing computation times from several hours to just a few minutes.