mlsanityData to 14 Oct 2026

Sizing of Battery Considering Renewable Energy Bidding Strategy with Reinforcement Learning

arXiv 2602.19428 ↗First posted 23 Feb 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 18 references that are in the map. First posted on arXiv 23 Feb 2026.

Builds on

4 of 18 references.

References · 4

  1. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 465
  2. 2018Distributed Prioritized Experience Replaycited by 269
  3. 2018Jointly Learning to Construct and Control Agents using Deep Reinforcement Learningcited by 55
  4. 2020Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learningcited by 32

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  2. 2024Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
  3. 2024Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
  4. 2025The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage
  5. 2026Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty ↗
  6. 2026Reinforcement Learning-based Home Energy Management with Heterogeneous Batteries and Stochastic EV Behaviour
  7. 2024Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
  8. 2025Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices