Abstract
The shared energy storage system has the potential to promote the popularity of the battery energy storage system (BESS). In a shared energy storage system, prosumers could rent capacity and optimize its operation, whereas the operator also seeks to maximize the revenue of the BESS from both rental service and the virtual power plant (VPP) market. To optimize the pricing policy of the BESS, a novel pricing method based on deep reinforcement learning (DRL) is proposed for this energy storage rental service. The interaction between the BESS operator and prosumers is formulated as a bi-level optimization problem, which is further reformulated as a Markov decision process (MDP) and solved through the proximal policy optimization (PPO)-based DRL method. The case study shows that the proposed method could further increase the revenues of the BESS operator.
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