A Stochastic Nonlinear Predictive Controller for Solar Collector Fields Under Solar Irradiance Forecast Uncertainties

Predictive control strategies with implicit feedforward action are known for enhancing solar collector field system performance. Nevertheless, the nature of the systems' disturbances, such as solar irradiance, is characterized mainly as being stochastic, which compromises the disturbance reject...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2024-01, Vol.32 (1), p.99-111
Main Authors: Pataro, Igor M. L., Gil, Juan D., Americano da Costa, Marcus V., Roca, Lidia, Guzman, Jose L., Berenguel, Manuel
Format: Article
Language:English
Subjects:
Chance constraints (CCs)
Controllers
Costs
Feedforward control
Irradiance
irradiance forecast
model predictive control (MPC)
Nonlinear control
Parameter identification
Predictive control
Predictive models
Solar energy
Solar heating
Solar thermal electric power plants
stochastic control
Stochastic models
Stochastic processes
Temperature control
Uncertainty
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Summary:Predictive control strategies with implicit feedforward action are known for enhancing solar collector field system performance. Nevertheless, the nature of the systems' disturbances, such as solar irradiance, is characterized mainly as being stochastic, which compromises the disturbance rejection performance due to the model prediction uncertainties. Therefore, this work proposes a stochastic model predictive control (MPC) based on a chance-constraint (CC) formulation for controlling a real solar thermal plant. The controller is presented as a CC practical nonlinear MPC (CC-PNMPC), and it is implemented in the AQUASOL-II facility located at Plataforma Solar de Almería, Almería, Spain. This work first investigates the solar collector field plant model based on a parameter identification framework and the irradiance model predictions, using three different models for forecasting. After studying the benefits of the CC-PNMPC in distinct simulated scenarios, which presented about 7% less error out of the output limits than the deterministic strategy, the stochastic controller is implemented in the actual AQUASOL-II facility to validate and demonstrate the advantages of the proposed control approach. The results show that the stochastic strategy can straightforwardly account for disturbance uncertainties in the control optimization layer without additional computational cost or mathematical efforts. Furthermore, for the irradiance prediction uncertainties case, simulations demonstrate that the CC-PNMPC systematically reduces the temperature threshold extrapolation compared to the deterministic strategy.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2023.3298230