Accurate modeling and simulation (M&S) of spacecraft solar array power under degradation is essential for mission planning, remaining useful life assessment, and lifetime extension. A relevant example is ESA’s Cluster spacecraft fleet, launched in 2000 and operated at the European Space Operation Centre (ESOC), whose solar arrays have suffered severe degradation due to space radiation that has caused challenges to routine operations and mission planning. However, currently available physics-based and machine learning models have been proven ineffective in modeling the drastic reduction in power generation over the long operational life of the spacecraft.In response to these limitations, this work introduces a framework to model solar array degradation and predict power generation. It embeds a novel simplified physics-based model and a meta-heuristic optimization algorithm which exploits domain-specific knowledge and monitoring data for robust model parameter calibration and accurate power generation predictions. The results show the effectiveness of the proposed approach in avoiding overfitting and providing an accurate estimate of Cluster solar array power evolution.