Computational optimization of solar thermal generation with energy storage

Abstract

Integrating renewable energy resources into power systems is essential for achieving sustainability targets. Concentrated solar power can incorporate thermal energy storage, which can provide larger storage capacities than other technologies. In this study, a comprehensive computational framework is developed for the modeling and optimization of a parabolic trough plant with storage. A Particle Swarm Optimization – Mesh Adaptive Direct Search hybrid scheme is applied to optimize hourly plant operations. A 16.5 MWe parabolic trough plant with thermal energy storage, simulated to be located in Daggett, California, is modeled and optimized. The optimal storage duration is four hours with a net present value of $37.40 million. The results show that scenarios including higher peaks in electricity prices yield an optimal storage duration of six hours and net present value of $63.70 million. Importantly, a plant operating under a Purchase Power Agreement without storage results in a 25% decrease in net present value, indicating that it is beneficial to utilize storage and sell at market prices rather than enter into a Power Purchase Agreement at fixed prices. Computational capabilities of the type developed in this study are essential for assessing the impact of future price scenarios on optimal plant design and operations.