Periodic Reporting for period 1 - OCoSEP (Optimal Control of Solar Energy Plants)
Reporting period: 2023-10-01 to 2025-03-31
Concentrating solar power (CSP) systems use optical devices and sun-tracking systems to focus a large area of sunlight onto a smaller receiving area. This concentrated solar energy is then harnessed as a heat source for a conventional power plant. Commercial solar trough plants consist of hundreds of parallel loops of collectors connected in series, typically with four collectors per loop. Each parabolic trough collector is generally 100 to 150 meters in length, with an aperture width of about 5 to 6 meters. Managing the heat transfer fluid (HTF) flow in these loops is crucial for maximizing the solar energy collected. However, the resulting multivariable nonlinear Model Predictive Control (MPC) problem has historically been too complex to handle with existing MPC controllers.
The main objective of this project was to demonstrate that the coalitional market-based MPC developed in OCOTSOLAR can be implemented on the existing Distributed Control System (DCS) of a commercial 50 MW solar trough plant. This implementation can significantly increase the amount of solar energy collected and reduce maintenance costs. Enhancing the profitability of thermal solar plants will attract more investment, thereby boosting solar energy production. Furthermore, these results could be extended to other systems, such as photovoltaic (PV) solar plants, which have even greater market potential in the future.
The main objective of this project was to demonstrate that the coalitional market-based MPC developed in OCOTSOLAR can be implemented on the existing Distributed Control System (DCS) of a commercial 50 MW solar trough plant. This implementation can significantly increase the amount of solar energy collected and reduce maintenance costs. Enhancing the profitability of thermal solar plants will attract more investment, thereby boosting solar energy production. Furthermore, these results could be extended to other systems, such as photovoltaic (PV) solar plants, which have even greater market potential in the future.
A model of the solar field for the chosen plant was developed by tuning previously created models of trough plants by the research team. Most of the data required for tuning was provided by the involved company; however, some specific tests were necessary to fine-tune valve behavior.
The market price strategy control algorithms, developed in the advanced grant, had to be tuned and simplified for implementation on the solar plant's Distributed Control System (DCS) with the available information. The total heat transfer fluid (HTF) flow in the solar field was regulated by an MPC controller, which controlled the overall solar field temperature. The controller distributed the HTF flow across sectors and loops to maximize the collected energy while minimizing defocusing actions, thereby reducing maintenance costs. Artificial neural networks (ANNs) were used to approximate the controller. The resulting approximated controller was tested through extensive simulations using the dynamic model of the plant's solar field.
The controller was then programmed into the solar plant’s DCS, and tests were performed as planned. Figure 1 presents the results of an experiment conducted in the West 2 sector of a 50 MW plant (HelioEnergy 1). As shown, after the controller was activated at 12:15, the hottest loop "bought" oil allowance from the cooler loops by opening their loop valves, while the least efficient loops "sold" their HTF allowance to the most efficient loops by closing their loop valves. This process allowed the most efficient loops to receive more oil than the less efficient ones, thus maximizing the amount of collected energy. This gain is also evident from the increase in the solar radiation collection factor of the solar field, which rose by approximately 2%.
The market price strategy control algorithms, developed in the advanced grant, had to be tuned and simplified for implementation on the solar plant's Distributed Control System (DCS) with the available information. The total heat transfer fluid (HTF) flow in the solar field was regulated by an MPC controller, which controlled the overall solar field temperature. The controller distributed the HTF flow across sectors and loops to maximize the collected energy while minimizing defocusing actions, thereby reducing maintenance costs. Artificial neural networks (ANNs) were used to approximate the controller. The resulting approximated controller was tested through extensive simulations using the dynamic model of the plant's solar field.
The controller was then programmed into the solar plant’s DCS, and tests were performed as planned. Figure 1 presents the results of an experiment conducted in the West 2 sector of a 50 MW plant (HelioEnergy 1). As shown, after the controller was activated at 12:15, the hottest loop "bought" oil allowance from the cooler loops by opening their loop valves, while the least efficient loops "sold" their HTF allowance to the most efficient loops by closing their loop valves. This process allowed the most efficient loops to receive more oil than the less efficient ones, thus maximizing the amount of collected energy. This gain is also evident from the increase in the solar radiation collection factor of the solar field, which rose by approximately 2%.
This is the first time that a market based control strategy has been implemented in a DCS of a commercial solar plant. The results have demonstrated that approximately 2% gains in solar field energy collection can be achieved with the controller. The controller has been successfully tested in one plant, and negotiations are underway to implement it in additional plants owned by the involved company. However, further research is required to apply these results to other companies, particularly in terms of market studies and targeted publicity aimed at stakeholders.
The remaining six months of the project will focus on analyzing the controller's performance over an extended period and under varying environmental conditions, such as cloudy days and power constraints imposed by regulatory authorities overseeing the power grid.
The remaining six months of the project will focus on analyzing the controller's performance over an extended period and under varying environmental conditions, such as cloudy days and power constraints imposed by regulatory authorities overseeing the power grid.