Abstract
Solar Thermal Power Plants (STPPs) use solar energy for large-scale electricity production but face significant operational challenges. These include variations in solar radiation, cloud cover, electricity demand fluctuations, and the need for frequent shutdowns if energy storage is inadequate. Deciding an optimal STPP operating conditions is challenging due to these factors. While revenue maximization has been used as an objective in existing literature, current models are often static and fail to capture the dynamic nature of STPPs. In contrast, this work proposes a dynamic model-based revenue optimization approach that accounts for plant dynamics and operational constraints, such as solar radiation variability and changing electricity demand. The objective function is designed to maximize revenue while considering power generation and fluctuating electricity prices. A simulation model of 1 MWe hybrid solar thermal power plant in Gurgaon, India, featuring two solar fields—Parabolic Trough Collector (PTC) and Linear Fresnel Reflector (LFR), is chosen as a case study. The optimization focuses on the oil mass flow rate through the PTC, which influences steam production. The mass flow rate of oil side is optimized at discrete time intervals, forming the decision variables in a nonlinear programming problem (NLP). The results demonstrate the effectiveness of this approach in significantly enhancing the STPP’s revenue.