Design and dispatch optimization of solar power plants with storage

Abstract

Concentrating solar power, when coupled with thermal energy storage, presents a promising path towards utility-scale dispatchable renewable energy. The performance of these plants is a consequence of both the relative sizing of systems and dispatch decisions, which together possess numerous degrees of freedom. In this dissertation, we develop and solve nonlinear, and non-convex optimization models to assist decision makers in the economically-efficient design and dispatch of concentrating and hybrid solar power plants with storage. We first extend a concentrating solar power dispatch optimization model for real-time operations; the resulting revenue-maximizing non-convex mixed-integer, quadradically-constrained program determines a dispatch schedule with sub-hourly time fidelity and considers temperature-dependent power cycle efficiency. We present exact and inexact techniques to improve problem tractability and demonstrate the model's suitability for decision support in a real-time setting. To address design decisions, we then develop an approach to analyze the economic performance of hybrid and single-technology solar power plants, which incorporates optimal dispatch and considers the expected weather and market conditions. We apply formal design-of-experiment sampling and black-box optimization techniques to demonstrate the value of optimal plant sizing, and compare the economic performance of the following designs: (i) photovoltaic-with-battery and, (ii) concentrating solar power with thermal energy storage, in a revenue-maximizing scenario. To investigate the sensitivity of our approach, we consider various weather and market conditions, renewable energy incentives, and plant operating restrictions. Together, our contributions in this dissertation progress a dispatch optimization model, associated solution techniques, and a design optimization approach for concentrating and hybrid solar power plants with storage. We demonstrate an approach for the use of a nonlinear and non-convex optimization for real-time decision support that yields solutions within 3 percent of global optimality in five minutes, show that lifetime plant benefit-to-cost ratio can be improved 6 to 19 percent through optimal sizing, and explore the sensitivity of optimal sizing with respect to several input parameters.