Contextual Reinforcement Learning for Offshore Wind Farm Bidding
This addresses bidding optimization for offshore wind farms, but it appears incremental as it presents initial results and future steps without concrete performance numbers.
The authors tackled the problem of energy market bidding for offshore wind farms by proposing a reinforcement learning framework for contextual two-stage stochastic optimization, with initial results showing the DDPG algorithm can learn near-optimal solutions without solving the full stochastic program.
We propose a framework for applying reinforcement learning to contextual two-stage stochastic optimization and apply this framework to the problem of energy market bidding of an off-shore wind farm. Reinforcement learning could potentially be used to learn close to optimal solutions for first stage variables of a two-stage stochastic program under different contexts. Under the proposed framework, these solutions would be learned without having to solve the full two-stage stochastic program. We present initial results of training using the DDPG algorithm and present intended future steps to improve performance.