A Rule-Based Optimization Technique for Minimizing Conversion losses in Pumped Storage Plant Systems through Floating Solar PV Integration

This paper presents a mathematical rule-based optimization framework for minimizing conversion losses and enhancing overall system efficiency of a 1000 MW pumped storage power plant (PSP) through the integration of floating solar PV (FSPV) systems. The study employs a dynamic efficiency model technique incorporating solar generation variability developed using mixed integer nonlinear programming with sequential quadratic programming (MINLP-SQP) methodology. Notably, the proposed method shows a significant reduction in conversion losses with FSPV deployment and achieved a substantial 5.06% of absolute reduction (20.75% to 15.69%), and an increase in system efficiency by 15.64% through effective optimization of generation and pumping cycles. Multi-scenario validation was performed showing mean efficiency improvement of 8.17% over baseline operation. The results validate the role of renewable sources of energy such as FSPV in improving the performance of PSP systems, particularly through variable seasonal conditions that often presents challenges in energy generation and efficiency. The optimization technique was refined with optimal constraint satisfaction rate across operational scenarios enhancing the operational efficiency of the integrated system and provides significant resources in executing practical strategies. This work provides industrial-scale validation of PSP-FSPV optimization, providing advancements in sustainable energy systems, establishing quantified performance benchmarks and delivering effective milestones in meeting the ever-increasing storage energy demands.