Periodic Reporting for period 4 - OCONTSOLAR (Optimal Control of Thermal Solar Energy Systems)
Período documentado: 2023-03-01 hasta 2024-08-31
The problem to be is to optimize the amount of solar energy collected by solar thermal plants by developing new control methods using mobile sensors mounted on drones and unmanned ground vehicles (UGV) as an integral part of the control systems allowing the control of systems geographically disperse.
The project aims at developing control techniques to improve the performance of solar energy systems but the technique being developed can also be applied to process such as traffic in highways, agriculture or flood control.
The overall objectives of the project are:
1. Methods to control mobile sensor fleets and integrate them as an essential part of the overall control systems.
2. Spatially distributed solar irradiance estimation methods using a variable fleet of sensors mounted on drones and UGVs.
3. New model predictive control (MPC) algorithms that use mobile solar sensor estimations and predictions to yield safer and more efficient operation of the plants allowing the effective integration of solar energy in systems delivering energy to grids or other systems while satisfying production commitments.
The project aims at developing control techniques to improve the performance of solar energy systems but the technique being developed can also be applied to process such as traffic in highways, agriculture or flood control.
The overall objectives of the project are:
1. Methods to control mobile sensor fleets and integrate them as an essential part of the overall control systems.
2. Spatially distributed solar irradiance estimation methods using a variable fleet of sensors mounted on drones and UGVs.
3. New model predictive control (MPC) algorithms that use mobile solar sensor estimations and predictions to yield safer and more efficient operation of the plants allowing the effective integration of solar energy in systems delivering energy to grids or other systems while satisfying production commitments.
1. Design and procurement of a fleet of drones, UAVs, and solar sensors: The fleet consists of three drones and six UGVs equipped with solar sensors.
2. Development of a VTOL prototype with integrated solar irradiance sensor: A vertical take-off and landing (VTOL) drone prototype has been developed, incorporating a solar irradiance sensor for enhanced data collection.
3. Development of control algorithms for solar irradiance estimation and short-term forecasting: These algorithms are based on data provided by the fleet of mobile sensors and sky-cameras.
4. Development of MPC coalitional control methods for large-scale solar plants: These methods enable efficient control of large solar power plants with hundreds of manipulated variables and use a market based approach.
5. Application of new algorithms for controlling solar power plants: The algorithms utilize sector and loop valves as manipulated variables to maximize the amount of solar energy collected in the solar fields and also reducing the number and severity of the defocus actions. These algorithms have been successfully tested in commercial solar plants.
6. Development of MPC controllers for defocusing solar collectors: These improved controllers, tested in real solar plants, outperform previous ones by reducing oscillations and minimizing actuator wear.
7. Project dissemination: The project outcomes have been published in 65 journal papers, 31 conference papers, 4 book chapters, and shared in 5 keynote and plenary lectures at conferences. Additionally, 5 PhD theses have been produced, along with press releases and public dissemination talks.
2. Development of a VTOL prototype with integrated solar irradiance sensor: A vertical take-off and landing (VTOL) drone prototype has been developed, incorporating a solar irradiance sensor for enhanced data collection.
3. Development of control algorithms for solar irradiance estimation and short-term forecasting: These algorithms are based on data provided by the fleet of mobile sensors and sky-cameras.
4. Development of MPC coalitional control methods for large-scale solar plants: These methods enable efficient control of large solar power plants with hundreds of manipulated variables and use a market based approach.
5. Application of new algorithms for controlling solar power plants: The algorithms utilize sector and loop valves as manipulated variables to maximize the amount of solar energy collected in the solar fields and also reducing the number and severity of the defocus actions. These algorithms have been successfully tested in commercial solar plants.
6. Development of MPC controllers for defocusing solar collectors: These improved controllers, tested in real solar plants, outperform previous ones by reducing oscillations and minimizing actuator wear.
7. Project dissemination: The project outcomes have been published in 65 journal papers, 31 conference papers, 4 book chapters, and shared in 5 keynote and plenary lectures at conferences. Additionally, 5 PhD theses have been produced, along with press releases and public dissemination talks.
New methods for integrating information from sensors mounted on a fleet of vehicles into control systems are being developed.
New algorithms for spatial estimation and short-term forecasting of solar irradiance are in progress. These advancements in estimating and forecasting solar irradiance are expected to enhance the control of solar power plants.
Algorithms for optimizing thermal solar plants by controlling the loop valves, which take into account the short-term forecast of solar irradiance, have been developed. Simulation studies have shown that production can increase by up to 8% when using optimal control algorithms that manipulate the loop valves efficiently.
New coalitional control algorithms have been designed for large solar plants, enabling the application of MPC (Model Predictive Control) to large-scale facilities.
Defocusing control algorithms for thermal solar plants are also being improved. When the received solar power exceeds the plant's generation and storage capacity, the collectors must be defocused. The defocusing algorithms currently used in commercial plants tend to cause high oscillations and deterioration of actuators. The research group has developed an MPC-based algorithm that significantly reduces oscillations and wear on the actuators.
New algorithms for spatial estimation and short-term forecasting of solar irradiance are in progress. These advancements in estimating and forecasting solar irradiance are expected to enhance the control of solar power plants.
Algorithms for optimizing thermal solar plants by controlling the loop valves, which take into account the short-term forecast of solar irradiance, have been developed. Simulation studies have shown that production can increase by up to 8% when using optimal control algorithms that manipulate the loop valves efficiently.
New coalitional control algorithms have been designed for large solar plants, enabling the application of MPC (Model Predictive Control) to large-scale facilities.
Defocusing control algorithms for thermal solar plants are also being improved. When the received solar power exceeds the plant's generation and storage capacity, the collectors must be defocused. The defocusing algorithms currently used in commercial plants tend to cause high oscillations and deterioration of actuators. The research group has developed an MPC-based algorithm that significantly reduces oscillations and wear on the actuators.