  This paper introduces a deep reinforcement learning (RL) framework for
optimizing the operations of power plants pairing renewable energy with
storage. The objective is to maximize revenue from energy markets while
minimizing storage degradation costs and renewable curtailment. The framework
handles complexities such as time coupling by storage devices, uncertainty in
renewable generation and energy prices, and non-linear storage models. The
study treats the problem as a hierarchical Markov Decision Process (MDP) and
uses component-level simulators for storage. It utilizes RL to incorporate
complex storage models, overcoming restrictions of optimization-based methods
that require convex and differentiable component models. A significant aspect
of this approach is ensuring policy actions respect system constraints,
achieved via a novel method of projecting potentially infeasible actions onto a
safe state-action set. The paper demonstrates the efficacy of this approach
through extensive experiments using data from US and Indian electricity
markets, comparing the learned RL policies with a baseline control policy and a
retrospective optimal control policy. It validates the adaptability of the
learning framework with various storage models and shows the effectiveness of
RL in a complex energy optimization setting, in the context of multi-market
bidding, probabilistic forecasts, and accurate storage component models.
