Article
Green & Sustainable Science & Technology
Sara Ruiz-Moreno, Jose Ramon D. Frejo, Eduardo F. Camacho
Summary: Using artificial neural networks to approximate the optimal flow rate given by an MPC controller significantly reduces the computational load to 3% of the MPC computation time. The neural network controllers provide practically the same mean power as the MPC controller with less abrupt changes at the output and slight violations of the constraints.
Article
Green & Sustainable Science & Technology
Diogo Ortiz Machado, Adolfo J. Sanchez, Antonio J. Gallego, Gustavo A. de Andrade, Julio E. Normey-Rico, Carlos Bordons, Eduardo F. Camacho
Summary: This paper proposes the first application of a split-range control technique on a concentrating solar collector to improve the production of an absorption plant. Control techniques are simulated and compared in an absorption plant in Spain. The results demonstrate that the proposed controllers significantly reduce the effort of control actuators and improve energy and exergy production.
Article
Green & Sustainable Science & Technology
Adriano Silva Martins Brandao, Paulo Renato da Costa Mendes, Julio Elias Normey-Rico
Summary: This paper presents the development of a simplified analytical optical model for Fresnel solar collectors and analyzes the aiming strategies and defocusing strategy. The results show that the simplest solar aiming method is an effective solution with good optical efficiency. The possibility of implementing partial defocusing for control is also demonstrated.
Article
Energy & Fuels
Ana Sanchez-Amores, Juan Martinez-Piazuelo, Jose M. Maestre, Carlos Ocampo-Martinez, Eduardo F. Camacho, Nicanor Quijano
Summary: This paper proposes a novel coalitional control approach for large-scale parabolic-trough solar collector fields. The approach splits the field into smaller subsystems, each governed by a local controller. Controllers are clustered into coalitions to solve local optimization problems, achieving an approximate solution to the centralized problem in a decentralized manner. A population-dynamics-assisted resource allocation strategy is proposed to decouple the optimization problems of the coalitions, reducing computational burden while ensuring operational constraints and overall performance.
Article
Thermodynamics
Kai Liang, Heng Zhang, Haiping Chen, Dan Gao, Yang Liu
Summary: A new type of Annular Fresnel solar concentrator coupled with a circular Fresnel lens (AFSCFL) was developed in the study to improve solar collecting efficiency. Simulation studies and comparison experiments were conducted, showing that AFSCFL has higher thermal efficiency than AFSC under low solar radiation conditions.
Article
Green & Sustainable Science & Technology
Tahereh Gholaminejad, Ali Khaki-Sedigh
Summary: In this paper, a deep Model Predictive Control (MPC) method based on the Koopman operator is proposed to control the Heat Transfer Fluid (HTF) temperature in concentrated solar power plants. A deep Long Short-Term Memory (LSTM) autoencoder is designed to calculate Koopman eigenfunctions, which are used to convert a non-linear model to a Koopman-based linear model. The results of simulations demonstrate the satisfactory tracking performance of the proposed approach.
Review
Environmental Sciences
Pouya Esfanjani, Sajjad Jahangiri, Ali Heidarian, Mohammad Sadegh Valipour, Saman Rashidi
Summary: By utilizing solar-driven cooling systems, the consumption of fossil fuels can be effectively reduced, the potentials of dish collectors and linear Fresnel reflectors in cooling systems can be enhanced, and carbon dioxide emissions can be decreased while increasing production efficiency. In addition, combining solar energy with biomass energy in hybrid systems can also achieve the goal of energy recovery and utilization.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Energy & Fuels
Marco Fossa, Alessia Boccalatte, Samuele Memme
Summary: Linear Fresnel Collectors (LFC) is a very promising technology for efficient solar energy exploitation at medium to high temperatures, despite their few installations.CloseOperation:setoperation
Article
Green & Sustainable Science & Technology
Yujia Zhang, Guang Li
Summary: This paper introduces an efficient robust tube-based model predictive control (RTMPC) strategy to maximize the energy capture of wave energy converters (WECs) while satisfying safety constraints. The proposed RTMPC method effectively handles plant-model mismatches, ensuring guaranteed constraint satisfaction and improved energy capture efficiency. By integrating disturbance invariant sets into the MPC scheme, the RTMPC controller can mitigate uncertainties from un-modeled WEC dynamics and unmeasured disturbances without increasing computational complexity. Numerical simulations demonstrate the effectiveness of the proposed strategy.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Energy & Fuels
Alexandros Vouros, Emmanouil Mathioulakis, Elias Papanicolaou, Vassilis Belessiotis
Summary: This study investigates the energy efficiency of a small-scale solar concentrating thermal device using Monte-Carlo Ray-Tracing (MCRT) and Computational Fluid Dynamics (CFD) modeling. The device consists of a 1 m rectangular plate with a Fresnel lens collector and a 10 cm plate receiver with drilled cylindrical channels. The results show that the energy efficiency increases with increasing fluid velocity up to 80%, and the use of a selective coating reduces heat losses. Increasing the channel diameter also improves the energy efficiency, while increasing the receiver dimensions has the opposite effect. The addition of a glass cover does not improve the collector's performance due to significant optical losses.
Article
Automation & Control Systems
Antonio J. Gallego, Manuel Macias, Fernando de Castilla, Adolfo J. Sanchez, Eduardo F. Camacho
Summary: The size of current commercial solar trough plants brings new challenges in the applications of advanced control strategies, while Model Predictive Control algorithms have been proven to perform well in controlling these plants.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Energy & Fuels
Farhat Mahmood, Rajesh Govindan, Amine Bermak, David Yang, Tareq Al-Ansari
Summary: The greenhouse microclimate is complex and controlling temperature requires significant resources due to inefficient design. Model predictive control is a promising strategy, but it does not consider inaccuracies and uncertainties. This study proposes a data-driven robust model predictive control framework for greenhouse temperature control and energy utilization assessment in the presence of uncertainties.
Article
Construction & Building Technology
Zhihao Zhang, Yong Zhou, Xin Xin, Junhao Qian, Yanfeng Liu
Summary: This paper proposes a model predictive control-based (MPC) control strategy for solar heating systems, which uses seq2seq-LSTM to predict major operating parameters and combines the heating system operation model to obtain the control signal at the next moment. The results show that, compared with traditional control strategies, the efficiency of solar collector regulated by MPC is increased and the energy consumption of the system is reduced.
ENERGY AND BUILDINGS
(2023)
Article
Energy & Fuels
Miswar A. Syed, Muhammad Khalid
Summary: The paper proposes a novel neural network model predictive control approach for smoothing photovoltaic power with battery energy storage system, which generates a more accurate predictive model of the plant compared to conventional MPC methods. The neural network model solves issues related to mathematical complexity and optimizes battery state of charge, promoting enhanced battery life.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Engineering, Electrical & Electronic
Zhuoli Zhao, Juntao Guo, Xi Luo, Chun Sing Lai, Ping Yang, Loi Lei Lai, Peng Li, Josep M. Guerrero, Mohammad Shahidehpour
Summary: This paper proposes a distributed robust model predictive control (DRMPC)-based energy management strategy for islanded multi-microgrids to address the issues caused by uncertain renewable energy output in microgrid systems. This strategy combines the advantages of robust optimization and model predictive control, and forms a dynamic energy trading market to enhance the overall economy of the multi-microgrid system.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Computer Science, Interdisciplinary Applications
Angeles Hoyo, Enrique Rodriguez-Miranda, Jose Luis Guzman, Francisco Gabriel Acien, Manuel Berenguel, Jose Carlos Moreno
Summary: Cultivating microalgae in photobioreactors is a complex process affected by various variables. A new software tool has been developed to simulate microalgae production in raceway photobioreactors, allowing for analysis of different scenarios and control strategies to increase productivity. Real data from a semi-industrial raceway is used for simulations.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Energy & Fuels
Jose M. Aguilar-Lopez, Ramon A. Garcia, Adolfo J. Sanchez, Antonio J. Gallego, Eduardo F. Camacho
Summary: This paper presents a mobile sensor system that uses a team of unmanned aerial vehicles (UAVs) to localize and characterize the shadow of mobile clouds for detecting and estimating low direct normal irradiance (DNI) areas caused by clouds shadows. The simulations show that the algorithm used in this system achieves a similar degree of precision in estimating the shape of the cloud shadow, but with a much faster computational time compared to other algorithms described in literature.
Article
Automation & Control Systems
Antonio J. Gallego, Manuel Macias, Fernando de Castilla, Adolfo J. Sanchez, Eduardo F. Camacho
Summary: The size of current commercial solar trough plants brings new challenges in the applications of advanced control strategies, while Model Predictive Control algorithms have been proven to perform well in controlling these plants.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Green & Sustainable Science & Technology
Diogo Ortiz Machado, Adolfo J. Sanchez, Antonio J. Gallego, Gustavo A. de Andrade, Julio E. Normey-Rico, Carlos Bordons, Eduardo F. Camacho
Summary: This paper proposes the first application of a split-range control technique on a concentrating solar collector to improve the production of an absorption plant. Control techniques are simulated and compared in an absorption plant in Spain. The results demonstrate that the proposed controllers significantly reduce the effort of control actuators and improve energy and exergy production.
Article
Green & Sustainable Science & Technology
Sara Ruiz-Moreno, Adolfo J. Sanchez, Antonio J. Gallego, Eduardo F. Camacho
Summary: This study proposes a methodology for detecting and isolating faults in parabolic-trough solar power plants. The methodology consists of three layers, including a neural network for fault detection and classification, analysis of flow rate dynamics, and analysis of thermal losses. The methodology has been applied and achieved high accuracies in fault detection and isolation.
Article
Automation & Control Systems
Antonio J. Gallego, Adolfo J. Sanchez, J. M. Escano, Eduardo F. Camacho
Summary: This paper proposes a non-linear model predictive algorithm that uses a hydraulic model of the solar field to compute the aperture of the input valves, aiming to solve the challenges of thermal balance and energy losses in commercial solar trough plants when applying advanced control strategies.
EUROPEAN JOURNAL OF CONTROL
(2022)
Article
Thermodynamics
Igor M. L. Pataro, Juan D. Gil, Jose L. Guzman, Manuel Berenguel, Joao M. Lemos
Summary: This work proposes a hierarchical framework for controlling a solar thermal facility to provide operating conditions for an absorption chiller machine. A case study of the CIESOL thermal plant is conducted with verified subsystems and valves in a simulation environment. Three different models are used for absorption chiller modeling, and a hybrid nonlinear predictive controller is formulated for hierarchical control. A lower layer with PI controllers is designed to handle valve nonlinear dynamics and disturbance rejection. Results show that the hierarchical structure extends the operating time of the solar-powered absorption chiller by approximately 115 minutes compared to conventional operation, with reduced fossil fuel usage.
Article
Mathematics
Pablo Otalora, Jose Luis Guzman, Manuel Berenguel, Francisco Gabriel Acien
Summary: The industrial production of microalgae is a sustainable and interesting process, especially in terms of its applications in wastewater treatment. Neural network models have been developed to optimize the process and characterize the pH dynamics in different raceway reactors. These models are able to predict pH profiles using available measurable process data and demonstrate the potential of artificial neural networks in modeling continuous dynamic systems in the industry.
Article
Automation & Control Systems
Igor M. L. Pataro, Juan D. Gil, Jose L. Guzman, Manuel Berenguel, Joao M. Lemos
Summary: This article presents a study on the pH control of raceway photobioreactors (PBRs) using a learning-based model predictive control (LBMPC) approach. The LBMPC demonstrates satisfactory results and outperforms the conventional nominal MPC strategy, achieving up to four times superior performance in terms of the average error index. The results highlight the importance of employing robust adaptive control strategies for highly nonlinear and multi-disturbed systems like the variant biological-chemical microalgae process in PBRs.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Automation & Control Systems
Igor M. L. Pataro, Rita Cunha, Juan D. Gil, Jose L. Guzman, Manuel Berenguel, Joao M. Lemos
Summary: This study introduces an adaptive optimal model-free controller for solar collector fields (SCFs) that overcomes the challenges of using high-complex models. The proposed controller is based on the Reinforcement Q-Learning algorithm and achieves optimal performance using only plant measurements. It outperforms model-based controllers by handling nonlinearities, time-varying model parameters, and computational costs associated with nonlinear models. Simulations using actual data from a thermal plant demonstrate the effectiveness of the model-free controller, as the Q-Learning algorithm converges to the optimal gains of the Linear Quadratic Tracking (LQT) controller.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Igor M. L. Pataro, Juan D. Gil, Marcus V. Americano da Costa, Lidia Roca, Jose L. Guzman, Manuel Berenguel
Summary: Improving temperature reference tracking is crucial for enhancing the performance of solar thermal plants. This study proposes two control strategies, lead-lag and nonlinear reference feedforwards, to achieve low rise time and no overshoot in temperature reference tracking. Simulation experiments and real-world testing in a solar plant validate the effectiveness of these strategies under different operating conditions.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Igor M. L. Pataro, Juan D. Gil, Marcus V. Americano da Costa, Lidia Roca, Jose L. Guzman, Manuel Berenguel
Summary: This study proposes a stochastic model predictive control (MPC) based on a chance-constraint formulation for controlling a real solar thermal plant. The controller, named CC practical nonlinear MPC (CC-PNMPC), is implemented in the AQUASOL-II facility to validate and demonstrate the advantages of the proposed control approach. The results show that the stochastic strategy can account for disturbance uncertainties and improve the control system's performance.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Francisco Garcia-Manas, Francisco Rodriguez, Manuel Berenguel, Jose Maria Maestre
Summary: This paper presents a stochastic model predictive control (SMPC) strategy to maximize the economic profit of a greenhouse crop production. The SMPC strategy considers the uncertainty of market price by using its historical evolution per year as multiple price scenarios in the cost function. The results show that MS-MPC can improve economic profits compared to the use of an average price scenario for the MPC calculations.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Archaeology
Maria Juana Lopez Medina, Maria de la Paz Roman Diaz, Manuela Garcia Pardo, Manuel Berenguel
Summary: The exploitation of natural resources in the Cabo de Gata-Nijar Natural Park has influenced population centers since ancient times. Human activities, especially since the 1950s, have disrupted the delicate coastal erosion-sedimentation balance, resulting in changes in the coastline and impacting archaeological sites. Our research aims to reconstruct the original environment of these sites, taking into account these transformations.
ARQUEOLOGIA IBEROAMERICANA
(2022)
