Operation Optimization of PV-CSP Hybrid Power Plants Combined with Power-to-Heat Systems

Concentrated Solar Power (CSP) and Photovoltaics (PV) are the leading technologies for converting solar energy into electricity. While Solar PV has achieved grid parity and has become cost-competitive with conventional grid electricity in many countries, the affordability of large-scale electrical storage solutions remains a significant obstacle. In contrast, CSP systems, which utilize thermal energy, present a viable option for thermal energy storage. However, the cost of electricity generated through CSP is currently higher than that of PV-generated electricity. The integration of both technologies into a single hybrid system offers promising potential as a cost-effective strategy for ensuring flexible and reliable solar power generation and storage. Although various PV-CSP hybrid configurations are being developed across multiple projects, the degree of hybridization has so far been limited to joint power grid contributions from the two independent systems. Integrating a power to heat system in PV-CSP hybrid power plants with thermal energy storage (TES) has gained attention in recent years. The operation optimization of such hybrid systems with electric heaters as the P2H system was investigated in this dissertation by developing a so-called Solar Plant Optimization Tool (SPOT). SPOT performs quasi-dynamic simulations and optimizations to analyze system performance, focusing on annual yield, levelized cost of energy (LCOE), and energy distribution patterns while enabling the optimization of system configurations under time-of-delivery (TOD) tariffs and advanced operating strategies. The evaluation of SPOT was closely guided by a detailed dynamic model implemented in Dymola, which simulated the transient thermodynamic behavior of the system, particularly during daytime charging periods. While the dynamic model provided high physical accuracy, it came with significant computational demands. SPOT was therefore developed as a faster, more flexible alternative. Initial comparisons between both models revealed discrepancies in the thermal behavior modeling, particularly in the parabolic trough collector (PTC) output. To enhance SPOT's accuracy, empirical coefficients for thermal losses and additional measures reflecting the higher thermal inertia of the PTC system were incorporated. These refinements significantly minimized inconsistencies, reducing relative differences in stored energy to less than 1 %. Consequently, SPOT was verified as a reliable and computationally efficient alternative to complex dynamic models, enabling comprehensive system optimization. Innovative operational strategies were introduced to optimize energy distribution. The baseline Network Priority Strategy (NPS) and Storage Priority Strategy (SPS) were extended with the Combination Strategy (CbS) and Smooth Strategy (SmS), which utilize weather forecasts to adapt energy flow based on day type classifications. The SmS strategy demonstrated particular efficacy, balancing daytime and nighttime energy production while minimizing energy losses. Additionally, the integration of a Middle Tank (MT) improved system flexibility, allowing independent flow rates for the system's main components, thereby enhancing overall energy efficiency and adaptability. SPOT's optimization mode highlighted the economic and technical advantages of hybrid PV-CSP systems against both a co-located hybrid configuration and a PV and battery energy storage system (PV + BESS). Under scenarios where nighttime electricity holds greater value than daytime electricity, hybrid systems demonstrated reductions in the levelized cost of energy (LCOE) of over 20 % compared to both alternatives. Sensitivity analyses further examined the impacts of system degradation, geographic location, and forecast accuracy. SmS consistently demonstrated resilience to degradation and forecast uncertainties, while location-specific analyses underscored the critical role of direct normal irradiance (DNI) and global horizontal irradiance (GHI) in tailoring system designs for optimal performance. This research underscores the scalability and practicality of fully hybrid PV-CSP systems as a sustainable energy solution. By integrating advanced operational strategies, hardware enhancements, and robust optimization tools, this dissertation offers a comprehensive framework for improving dispatchability, cost efficiency, and adaptability in renewable energy systems, addressing the growing demand for reliable and flexible energy solutions in diverse environments.