The economic dispatch of concentrating solar power plants with thermal energy storage is strongly affected by uncertainty in irradiance forecasts. This paper proposes a risk-aware stochastic model predictive control (RA-MPC) strategy that combines expected operating cost with the Conditional Value-at-Risk (CVaR) of a composite operational index accounting for economic loss, dispatch deficit, low-storage exposure, and solar curtailment. Using linear auxiliary inequalities and the Rockafellar–Uryasev reformulation, the resulting receding-horizon problem is formulated as a convex quadratic program. Under the affine plant model, removing the CVaR term makes the optimal control sequence scenario-independent; the risk term thus breaks this degeneracy and enables forecast uncertainty to influence dispatch. The controller is evaluated on a 50 MW parabolic-trough plant with 300 MWh of thermal storage and benchmarked against deterministic MPC, scenario-based stochastic MPC, rule-based control, and robust minimax MPC. On six representative days, RA-MPC reduces the CVaR index by 27.4% and the dispatch deficit by 28.3% relative to the non-risk-aware stochastic MPC baseline. Over a 365-day campaign, it increases mean daily revenue by 32.6% (38.01 versus 28.67 kEUR/day), reduces dispatch deficit by 23.9%, and achieves 100% hierarchical solver feasibility. In a continuous 30-day stress test, RA-MPC limits the number of days ending below the 60 MWh terminal-reserve threshold to 3, compared with 11–21 days for the rule-based and minimax baselines. These results demonstrate that explicit tail-risk management can improve both economic performance and operational reliability in renewable generation with storage.

This article proposes using the extended Kalman filter (EKF) for recurrent neural network (RNN) training and fault estimation within a parabolic-trough solar plant. The initial step involves employing an RNN to model the system. Given the challenge of fault discernibility in the collectors, parallel EKFs are employed to reconstruct the parameters of the faults. The parameters are used independently to estimate the system output, and the type of fault is isolated based on the estimation errors using another feedforward neural network. To evaluate the effectiveness of the methodology, simulations are conducted on a loop of the ACUREX plant with irradiances from sunny and cloudy days. The results reveal a fault classification accuracy of approximately 90% and a fault reconstruction error below 3%, with even better accuracies in the cloudy dataset than in the sunny dataset.

Este trabajo presenta una metodología para la estimación del reparto de caudal en plantas termosolares de colectores cilindro-parabólicos combinando técnicas de optimización con redes neuronales recurrentes para reducir su alto coste computacional. Primero, se aplica un algoritmo para estimar la tempertura en el lazo y obtener el reparto de caudal que minimiza los errores de estimación. Después, se entrenan redes neuronales para reproducir el algoritmo. Los caudales obtenidos se utilizan como punto inicial en el proceso de optimización, limitando el espacio de búsqueda y reduciendo significativamente el tiempo de cómputo. El método se evalúa en sectores de distinto tamaño (4, 20 y 50 lazos), comparando tres variantes: optimización, combinación red neuronal+optimización, y red neuronal. Los resultados muestran que el enfoque propuesto mejora la estimación respecto a la suposición clásica de distribución uniforme, y permite una reducción significativa del tiempo de cálculo respecto al uso único del optimizador, especialmente relevante en sectores de gran escala.

This review deals with the control of parabolic trough collector (PTC) solar power plants. After a brief introduction, we present a description of PTC plants. We then provide a short literature review and describe some of our experiences. We also describe new control trends in PTC plants. Recent research has focused on ( a) new control methods using mobile sensors mounted on drones and unmanned ground vehicles as an integral part of the control systems; ( b) spatially distributed solar irradiance estimation methods using a variable fleet of sensors mounted on drones and unmanned ground vehicles; ( c) strategies to achieve thermal balance in large-scale fields; ( d) new model predictive control algorithms using mobile solar sensor estimates and predictions for safer and more efficient plant operation, which allow the effective integration of solar energy and combine coalitional and artificial intelligence techniques; and ( e) fault detection and diagnosis methods to ensure safe operation. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

This letter presents new results on the Gelbrich distance and the corresponding ambiguity sets, to analyze the correlation between two scalar random variables. A closed expression of the minimum disturbance in the Gelbrich metric necessary to achieve a specified correlation between two random variables is proposed. This expression allows us to analytically compute the minimal absolute correlation in a Gelbrich ball. This analysis facilitates the assessment of the robustness of the Pearson coefficient within an ambiguity set. Two numerical examples illustrate the validity of the proposed results.

This paper presents an original algorithm based on the Model Predictive Control strategy for estimating the direct normal irradiance of cloud shaded regions using a mobile robotic sensor system to improve the control of a solar thermal power plant. This new algorithm generates the waypoints of the robot team solving a minimisation problem where the objective function combines several criteria, including the measurements taken by the team. The novel method has been tested by simulation with groups of different numbers of unmanned aerial vehicles using the shape of real cloud shadows projected on the ground extracted from images and it improves the estimation error and the estimation time of previous algorithms.

This paper presents the design of a Model Predictive Control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system.To estimate the unknown inputs/outputs arising from the uncontrolled pumps, a Digital Twin (DT) in the framework of a Matlab-SIC 2 is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy.The canal is characterized by two operating modes due to high and low tides.As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multi-objective control problems, one for the high tide and another for the low tide.Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states.The simulation results show that the different objectives are met satisfactorily.

This paper presents a clustering-based model predictive controller for optimizing the heat transfer fluid (HTF) flow rates circulating through every loop in solar parabolic trough plants. In particular, we present a hierarchical approach consisting of two layers: a bottom layer, composed of a set of model predictive control (MPC) agents; and a top layer, which dynamically partitions the set of loops into clusters. Likewise, the top layer allocates a certain share of the total available HTF to each cluster, which is then distributed among the loops by the bottom layer in response to the varying conditions of the solar field, e.g., to deal with passing clouds. The dynamic clustering of the system reduces the number of variables to be coordinated in comparison with centralized MPC, thereby speeding up the computations. Moreover, the loops efficiencies and the heat losses coefficients, which influence the loops control model, are also estimated at the bottom layer. Numerical results on a 10-loop and an 80-loop plant are provided.

Optimal energy planning is a key topic in thermal solar trough plants. Obtaining a profitable energy schedule is difficult due to the stochastic nature of solar irradiance and electricity prices. This article focuses on optimal energy planning for thermal solar trough plants, particularly by developing a model predictive control algorithm based on multiple scenarios to deal with uncertainties. The results obtained using the proposed scheme have been tested and compared to other well-known approaches to energy scheduling through a realistic and reliable comparison to evaluate their performances and establish their advantages and weaknesses. Simulations were carried out for a 50 MW parabolic trough concentrating solar plant with a thermal energy storage system, considering different types of days classified according to their solar irradiance, meteorological forecast, and electrical market. Simulation results show that the proposed method outperforms other scheduling methods in dealing with uncertainties by selling energy to the grid at the right times, generating the highest income of about 7.58%.

This article presents a distributed model predictive controller with time-varying partitioning based on the augmented Lagrangian alternating direction inexact Newton method (ALADIN). In particular, we address the problem of controlling the temperature of a heat transfer fluid (HTF) in a set of loops of solar parabolic collectors by adjusting its flow rate. The control problem involves a nonlinear prediction model, decoupled inequality constraints, and coupled affine constraints on the system inputs. The application of ALADIN to address such a problem is combined with a dynamic clustering-based partitioning approach that aims at reducing, with minimum performance losses, the number of variables to be coordinated. Numerical results on a 10-loop plant are presented.

This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, which can form coalition with other controllers to attain its local goals while contributing to the overall objective. The formation of coalitions is based on a market-based mechanism in which the heat transfer fluid is traded. To relieve the computational burden online, we propose a learning-based approach that approximates optimization problems so that the controller can be applied in real time. Finally, simulations in a 100-loop solar collector field are used to assess the coalitional strategy based on neural networks in comparison with the coalitional model predictive control. The results show that the coalitional strategy based on neural networks provides a reduction in computing time of up to 99.74% and a minimal reduction in performance compared to the coalitional model predictive controller used as the baseline.

This paper presents a Model Predictive Control (MPC) based autopilot for a fixed-wing Unmanned Aircraft Vehicle (UAV) for meteorological data sampling tasks, named Aerosonde. Aerosonde missions are featured by predetermined operating conditions, allowing the design of ad-hoc controllers for each control task by using the future knowledge of the reference signals driving the aircraft during operations. To develop the controller, the nonlinear dynamics of the vehicle has been described by a Linear Parameter-Varying (LPV) model identified from the plant data by using a subspace identification technique. The LPV model is used to design a MPC to drive the UAV. Two different Linear Parameter-Varying MPC (MPCLPV) algorithms have been proposed by introducing the previewing technique in the controller due to the a priori knowledge of full reference signals. In the design of the inner Attitude Controller (AC), a future LPV scheduling parameters estimation policy has been introduced (PF −MPCLPV) for improving the control results of the standard Previewing MPCLPV (P-MPCLPV). Furthermore, an anticipative switching approach (PS −MPCS) has been considered for the altitude External Controller (EC) to improve the control performances of the standard previewing switching MPC (P-MPCS). Both PF −MPCLPV and PS −MPCS algorithms have been compared to the P-MPCLPV and P-MPCS baseline algorithms, showing the effectiveness of proposed methods.

A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).