Towards optimal resource management in solar thermal applications: CSP and desalination

This research encompasses two complementary studies on two intrinsically linked resources: water and energy. The first part focuses on the efficient management of water resources for power generation, while the second explores the efficient use of solar energy for clean water production. Concentrated Solar Power (CSP) is poised to be a crucial contributor to the energy transition away from fossil fuels. The first phase of this transition is well underway, driven by the massive deployment of low-cost and non-dispatchable renewable technologies such as wind and solar photovoltaics. However, the second and more challenging phase, which involves achieving large-scale dispatchable renewable generation, is still ahead. Concentrated Solar Power (CSP) stands out as a renewable and scalable dispatchable technology with the potential to outcompete combined-cycle and coal-fired power plants. One of the key challenges in CSP systems lies in cooling the power block, which is typically associated with high water consumption. The first part of this research is therefore dedicated to the efficient management of water resources in CSP plants. An optimal water management strategy is proposed for CSP systems that integrate novel combined dry and wet cooling configurations. To this end, the annual performance of different cooling alternatives is evaluated for a commercial 50 MWe CSP plant, Andasol-II, located in southern Spain. Specifically, three cooling systems, all modelled and validated, are compared: the plant's existing Wet Cooling Tower (WCT), and two variants of a novel Combined Cooling (CC) system with dry cooler capacities of 75% and 100% of the nominal thermal load of the WCT system. For each alternative, plant operation is optimized under the same water-scarcity scenario using a multi-stage optimization framework. This framework minimizes the cooling cost, encompassing both electricity and water expenses, while ensuring that the cooling demand is satisfied. The key challenge lies in effectively managing the limited water resource. Results show that the integration of the CC system can reduce specific cooling costs by up to 80% and annual water consumption by about 48%, with 38% savings during the driest months. These benefits arise primarily from reduced reliance on costly alternative water sources. The CC alternatives also provide more stable and cost-effective operation throughout the year compared to the WCT, which is highly sensitive to water availability. Thermal Desalination, particularly Multi-Effect Distillation (MED), can play an important role in mitigating water scarcity. These systems use thermal and electrical energy to separate seawater or contaminated feedwater into fresh water and concentrated brine. Although it may not become the dominant desalination technology, it is well suited for specific applications. Its competitiveness can be improved by leveraging opportunities such as brine mining or industrial wastewater treatment, which enhance both economic feasibility and environmental benefits. To expand their applicability, MED systems must improve in two directions: (i) by enhancing efficiency through wider operating ranges, or (ii) by adapting to low-temperature applications in which their heat demands can be partially or fully met using low-exergy sources such as waste heat or solar thermal energy. However, the true cost of thermal energy, and consequently the performance of such systems, is often difficult to quantify, particularly because there is no unified set of criteria for evaluating their performance. To address this challenge, this research proposes a standardized methodology for evaluating the performance of MED processes, which can also be extended to other thermal separation technologies. The method covers key aspects such as instrumentation requirements, process control, and the suitability of performance metrics. It also includes uncertainty quantification and an algorithm for automatic steady-state detection. The proposed approach enhances the reliability and robustness of experimental evaluations under variable conditions. Experimental results confirm that the methodology is both reliable and consistent, enabling fair comparisons of MED systems across different operating scenarios. The experimental campaign includes evaluations at high Top Brine Temperatures (TBT). Results analyzed using several performance metrics and scale formation risks demonstrate that the MED process can operate at high TBT without significant scaling and can achieve higher concentrations. However, no substantial improvements in thermal performance or reconcentration capacity are observed unless specific design modifications are introduced. Finally, a novel operational strategy is proposed to enable the seamless, autonomous, and optimal integration of a solar-driven MED system. The method explicitly determines when to start and stop each subsystem while considering a two-day prediction horizon. This allows the optimization to account not only for immediate performance but also for the effect of present decisions on future production. The approach is based on an experimentally validated system model that includes the electrical consumption of each component, combined with the most comprehensive data-driven MED model currently available in the literature. The control architecture follows a hierarchical three-layer structure, in which the upper layer solves a Mixed-Integer Nonlinear Programming (MINLP) economic optimization problem aimed at maximizing water production while minimizing operating costs. Results from a week-long system simulation are compared with two alternative strategies, a baseline rule-based operation and a fixed-schedule optimized operation. The proposed method significantly increases system performance by 32% relative to the heuristic baseline and by 21% relative to the fixed-schedule strategy. These gains arise from the proposed method ability to fully exploit the temporal flexibility provided by the thermal storage while accounting for operational costs.