Dynamic Reconfiguration of PV‐TEG Hybrid Systems via Improved Whale Optimization Algorithm

The rapid transition of the global energy system has positioned solar photovoltaics as a pivotal source of clean electricity. A persistent challenge, however, is that significant thermal energy released during photovoltaic (PV) operation elevates cell temperature and reduces conversion efficiency. To address this issue, this chapter develops a stacked PV-thermoelectric generator (PV-TEG) hybrid array model that simultaneously harvests PV waste heat and alleviates thermal stress on modules, thereby sustaining reliable PV performance. Beyond temperature effects, partial shading conditions (PSCs) remain a critical cause of power mismatch and multi-peak P-V behavior in hybrid arrays. To mitigate these impacts, we introduce a dynamic reconfiguration strategy driven by an improved whale optimization algorithm (IWOA). The algorithm analyzes irradiance distributions and identifies optimal switching configurations that homogenize string currents, flatten the multi-peak characteristics, and raise overall power yield. Six representative PSC scenarios are simulated on two hybrid arrays of different scales. Comparative results demonstrate that the proposed IWOA-based approach achieves more effective reconfiguration and higher generation efficiency than conventional methods, confirming its value for PV-TEG hybrid system optimization.