Sizing of Battery Considering Renewable Energy Bidding Strategy with Reinforcement Learning

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.