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Learning-based optimal power bidding of an overseas photovoltaic-battery storage plant in Singapore electricity market

Author

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  • Xia, Yang
  • Xu, Yan

Abstract

Singapore power grid is planning to import clean power such as photovoltaic (PV) power generation from overseas through subsea cables. To participate in the Singapore electricity market, the power output from PV plants is required to be constant during each bidding period and hence battery storage systems (BSSs) need to be deployed to compensate PV power fluctuations. This paper proposes a learning-based bidding strategy to maximize the expected profit of such a PV-BSS plant. The overall bidding process is modelled as a Markov Decision Process (MDP) and the optimal bidding strategy is acquired through deep reinforcement learning (DRL), considering PV power revenues, penalty payments for power shortage, and battery health degradation cost of the BSS. To more accurately characterize the actual operational behavior of the BSS, a detailed BSS model is built to estimate the state of power (SoP) and state of health (SoH) of the BSS. Numerical case studies are carried out to demonstrate that the proposed method can achieve promising profits compared to classic optimization-based methods and preserve BSS with less SoH degradation.

Suggested Citation

  • Xia, Yang & Xu, Yan, 2026. "Learning-based optimal power bidding of an overseas photovoltaic-battery storage plant in Singapore electricity market," Applied Energy, Elsevier, vol. 407(C).
    • Handle: RePEc:eee:appene:v:407:y:2026:i:c:s0306261925020586
    DOI: 10.1016/j.apenergy.2025.127328
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    References listed on IDEAS

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    1. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
    2. Ochoa, Tomás & Gil, Esteban & Angulo, Alejandro & Valle, Carlos, 2022. "Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets," Applied Energy, Elsevier, vol. 317(C).
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