关键词:

"> 动态补偿；混合PV-TEG系统；软演员评论家；部分遮蔽；强化学习；重构；失配损耗抑制；余热回收

Abstract: [Objective] Although the photovoltaic-thermoelectric generation (PV-TEG) hybrid system can improve energy utilization efficiency by recovering photovoltaic waste heat, it still faces problems such as asynchronous electro-thermal parameter optimization, rigid battery compensation mechanism, and traditional algorithms being prone to local optimality or unstable strategies under partial shading conditions (PSC), which seriously restrict the system efficiency. [Method] A dynamic compensation strategy based on the improved soft actor-critic (ISAC) algorithm is proposed. By optimizing PV array topology logic, TEG power regulation law and battery adaptive compensation mechanism, the synchronous optimization of electro-thermal parameters and dynamic environment adaptation are realized. The MATLAB/Simulink simulation platform is adopted to conduct performance verification on arrays of different scales such as 6×4 and 6×6, and the optimization effects are compared with those of traditional heuristic algorithms and conventional reinforcement learning algorithms. [Result] Case verification under 10 PSC scenarios indicates that, compared with traditional algorithms, the proposed strategy reduces the mismatch loss of 6×4 and 6×6 arrays by 12.3%~18.7% and 13.6%~19.2% respectively, with an overall power increase of 4.2%~7.3%. [Conclusion] The algorithm and its corresponding strategy effectively solve the core pain points of poor coordination and weak dynamic adaptability of existing PV-TEG systems, provide reliable technical support for the efficient utilization of solar energy under complex shading environments, and are of great significance for improving the operation efficiency and stability of renewable energy systems.

Key words:

dynamic compensation, hybrid PV-TEG systems, ISAC, PSC, reinforcement learning, reconstruction, mismatch loss suppression, waste heat recovery