A Learning‐Based Joint Bidding Strategy for Photovoltaic‐Energy Storage System Power Plant in Singapore Electricity Market

With the rapid development of clean energy, photovoltaic (PV) power plants have gained increasing attention. However, the inherent intermittency of PV generation requires the integration of energy storage system (ESS) to smooth power output and enhance grid stability. Coordinating multiple PV–ESS plants is essential to maintain system reliability, balance stochastic renewable outputs with real‐time load demands, and leverage time‐varying electricity prices for economic benefits. In this paper, a learning‐based joint bidding framework is proposed to maximise the aggregated profit of PV–ESS plants. First, a multi‐PV–ESS model is built to emulate the coordinated operation of PV and ESS units in the power grid, aiming to maximise PV power revenues while considering penalty payments for power shortages, real‐time load demands and dynamic power prices. Then, the joint bidding operations of PV–ESS plants are formulated as a Markov decision process, and a deep reinforcement learning algorithm is developed to learn optimal bidding strategies that adapt to load dynamics and price fluctuations. Extensive case studies on distribution systems of different scales, including the IEEE 33‐bus and 69‐bus systems, are conducted to demonstrate the effectiveness of the proposed method.