This study develops a hybrid photovoltaic-thermoelectric generator (PV-TEG) system to reduce dependence on fossil fuels and promote sustainable energy generation. However

the inherent randomness of real-world operational environments introduces challenges such as partial shading conditions and uneven temperature distribution within PV and TEG modules. These factors can significantly degrade system performance and reduce energy conversion efficiency. To tackle these challenges

this paper proposes an advanced optimal power extraction strategy and develops a chaotic RIME (c-RIME) optimizer to achieve dynamic maximum power point tracking (MPPT) across varying operational scenarios. Compared with existing methods

this approach enhances the effectiveness and robustness of MPPT

particularly under complex working conditions. Furthermore

the study incorporates a comprehensive assessment framework that integrates both technical performance and sustainability considerations. A broader range of realistic operational scenarios are analyzed

with case studies utilizing onsite data from Hong Kong and Ningxia for technical and environmental evaluations. Simulation results reveal that the c-RIME-based MPPT technique can effectively enhance system energy output with smaller power fluctuations than existing methods. For instance

under startup testing conditions

the c-RIME optimizer achieves energy output increase by up to 126.67% compared to the arithmetic optimization algorithm.