With the government’s cancellation of subsidies for newly registered centralized photovoltaic (PV) power stations and the exacerbation of solar curtailment in China, PV and energy storage (PV–ES) investors are urgently in need of transitioning to market‐oriented operational models to enhance their revenues. Distribution system markets and distributed transactions offer PV–ES investors channels for trading and opportunities for value enhancement. However, given the diverse personalized preferences and privacy protection requirements of prosumers within the local market, PV–ES investors lack a transactional decision‐making methodology across multiple distribution network local energy markets (LEMs), making it challenging to discern the profit signals from different distribution network LEMs. This paper proposes a metamodel‐based optimization algorithm for PV–ES investors to participate in multi‐LEM transactions. A representative industrial park’s local energy trading market is modeled, integrating electricity and carbon credit trading. A bilevel optimization model is then established to determine PV–ES investors’ trading strategies across multiple LEMs within the park. To protect privacy, the LEM model is solved using the alternating direction method of multipliers (ADMMs). To address high computational demands, a hybrid solution algorithm combining differential evolution (DE) and dynamic partial least squares Kriging metamodel (HA‐DEDKM) is proposed. The results show that the proposed strategy effectively enhances the profitability of PV–ES investors. The employed solution method avoids frequent invocation of lower‐level market transaction models, significantly reducing computational load while preserving privacy and improving solution efficiency.
Tang et al. (2026) studied this question.