研究目的
To develop a control algorithm for reducing heat losses caused by clouds in large solar fields using a Mixed Logical Dynamical (MLD) representation and a Practical Nonlinear Model Predictive Controller (PNMPC).
研究成果
The proposed Mixed Logical Dynamical Nonlinear Model Predictive Controller effectively reduces heat losses in large solar fields by optimally deactivating subfields with negative delta temperatures. Simulation results show energy gains of 0.52% compared to a basic method without deactivation and 0.36% compared to a conditional method using if-then-else rules. The controller can be implemented with existing hardware and is suitable for complex hybrid systems modeled with MLD frameworks.
研究不足
The study is based on simulations and may not account for all real-world complexities; the constraints involve approximations that could lead to non-convexity issues, and the algorithm's performance depends on tuning parameters and the accuracy of the model.
1：Experimental Design and Method Selection:
The methodology involves using a Mixed Logical Dynamical (MLD) system to model the solar field and applying a Practical Nonlinear Model Predictive Controller (PNMPC) to compute optimal control actions, transforming the problem into a mixed integer quadratic programming (MIQP) problem. A simplified lumped parameters model is used for prediction and simulation.
2：Sample Selection and Data Sources:
Simulation of a large-scale solar field with four subfields in parallel configuration, using experimental data for disturbances such as ambient temperature, input temperature, and solar irradiation profiles modified to simulate cloud presence.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned in the paper; simulation-based study without physical equipment details.
4：Experimental Procedures and Operational Workflow:
The simulation was performed with a sample time of 15 seconds, using the nonlinear model equations. The controller parameters include prediction horizon NP=20, control horizon NC=5, and hysteresis limits ΔT U=5, ΔT L=-5, D=12. The algorithm involves predicting output temperatures, calculating control actions, and applying constraints to deactivate subfields when necessary.
5：The algorithm involves predicting output temperatures, calculating control actions, and applying constraints to deactivate subfields when necessary.
Data Analysis Methods:
5. Data Analysis Methods: The results are analyzed by comparing the mean acquired heat from simulations of three control methods (Optimal, Conditional, Basic) to evaluate energy gain.
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