Optimization of Concentrated Solar Power Subsystems with a focus on green certificate acquisition

This study presents an advanced optimization framework for designing large-scale 100% clean energy photothermal power station subsystems. By integrating green certificate acquisition with global optimization, the framework employs hybrid deep learning techniques (RA-LSGAN-AE-DE-Kmeans) to generate and reduce diverse, realistic scenarios. A multi-objective optimization algorithm (MONRBO), based on Pareto optimization, is developed to balance economic efficiency and renewable energy utilization. Incorporating green certificate acquisition ensures market viability and regulatory compliance, simplifying the selection of Pareto-optimal solutions. The framework supports informed decision-making for solar thermal power stations, facilitating practical deployment and adaptability to engineering applications while aligning with China's clean energy goals. It has been validated in five key areas: scenario generation and reduction, model construction, global optimization, green certificate-driven decision-making, and regulatory alignment. Testing produced eight representative scenarios for wind, solar irradiance, and load data, optimizing approximately 800 decision variables and 3,000 constraints. The framework achieved an optimal configuration with a solar multiple of 1.09, a heat storage capacity of 11.82, and 14 power stations. This work lays a foundation for CSP planning in large-scale 100% clean energy bases and anticipates broader applications. Future research may focus on optimizing various CSP systems, enhancing their roles in power markets, and increasing storage and operational flexibility.