Sizing of Battery Considering Renewable Energy Bidding Strategy with Reinforcement Learning
Taiyo Mantani, Hikaru Hoshino, Tomonari Kanazawa, Eiko Furutani

TL;DR
This paper introduces a new reinforcement learning-based algorithm for optimally sizing Battery Energy Storage Systems in conjunction with renewable energy bidding strategies, improving efficiency and uncertainty management.
Contribution
It presents a novel integrated RL framework that co-optimizes BESS sizing and bidding strategies, unlike traditional two-stage approaches.
Findings
Effective management of renewable and market uncertainties
Enhanced computational efficiency through parallel processing
Improved BESS sizing accuracy in renewable energy markets
Abstract
This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two-stage methods, our algorithm enables the cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q-Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long-term data.
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Taxonomy
TopicsMicrogrid Control and Optimization · Smart Grid Energy Management · Advanced Battery Technologies Research
