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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.14043 |
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| _version_ | 1866909041105567744 |
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| author | Hoshino, Hikaru Mantani, Taiyo Furutani, Eiko |
| author_facet | Hoshino, Hikaru Mantani, Taiyo Furutani, Eiko |
| contents | The rapid growth of variable renewable energy has increased the need for flexible and efficiently coordinated energy resources. In this context, hybrid resources that combine renewable generation and battery storage within a single market-participating entity have attracted growing attention. Such hybrid resources can have multiple revenue streams, while allocating limited power and energy capacity across multiple electricity markets including energy and ancillary services. This multi-market coordination increases operational complexity and complicates profitability assessment, making optimal system sizing a challenging design problem. In addition, uncertainty in renewable generation and market prices makes it difficult for conventional optimization approaches to determine system designs that remain effective under stochastic operating conditions. To address these challenges, this paper proposes a deep reinforcement learning-based co-optimization framework for hybrid solar-battery resources. The framework embeds system design variables directly into the policy learning process, enabling joint optimization of hybrid system sizing and coordinated multi-market bidding strategies within a unified stochastic formulation. Case studies using historical renewable generation and market data demonstrate the effectiveness of the proposed framework in identifying economically rational hybrid system design considering multi-market operation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14043 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty Hoshino, Hikaru Mantani, Taiyo Furutani, Eiko Systems and Control The rapid growth of variable renewable energy has increased the need for flexible and efficiently coordinated energy resources. In this context, hybrid resources that combine renewable generation and battery storage within a single market-participating entity have attracted growing attention. Such hybrid resources can have multiple revenue streams, while allocating limited power and energy capacity across multiple electricity markets including energy and ancillary services. This multi-market coordination increases operational complexity and complicates profitability assessment, making optimal system sizing a challenging design problem. In addition, uncertainty in renewable generation and market prices makes it difficult for conventional optimization approaches to determine system designs that remain effective under stochastic operating conditions. To address these challenges, this paper proposes a deep reinforcement learning-based co-optimization framework for hybrid solar-battery resources. The framework embeds system design variables directly into the policy learning process, enabling joint optimization of hybrid system sizing and coordinated multi-market bidding strategies within a unified stochastic formulation. Case studies using historical renewable generation and market data demonstrate the effectiveness of the proposed framework in identifying economically rational hybrid system design considering multi-market operation. |
| title | Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.14043 |