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This paper presents a unified approach,which is based on a PIDapproximation of the filtered Smith predictor, for tuning PID controllersfor stable, integrative, and unstable dead-time processes. The proposedcontrol tuning method is simple to analyze and use. Case studies areincluded to illustrate the advantages of the proposed tuning rules.Comparisons with other existing methods are also presented to showthat the proposed unified method provides promising results. Furthermore,tuning rules to obtain a reasonable trade-off between robustness andperformance are derived. [ABSTRACT FROM AUTHOR]

• The discussion of possible uncertainties that arise in Energy Hub models for microgrids. • Robustness conditions are derived for a bank of LPV FE observers. • The study of Robust Stability and Robust Performance conditions via mu-analysis. • Realistic simulations are presented. As displayed in recent literature, Fault Estimation schemes can be designed for renewable microgrids considering the use of multiple Linear Parameter Varying (LPV) observers, derived from Linear Matrix Inequalities for the mixed H 2 / H ∞ norm minimization. In this work, the study of such observers is extended: now, such method is discussed in terms of Robustness, using the Small Gain Theorem and μ -analysis. The possibility of uncertainties that may arise on Energy Hub models is discussed, in terms of unrealistic assumptions in the modelling/identification phase. Via frequencial analysis, this study also investigates the effect of noise and load disturbances upon fault estimation in the uncertain (model/plant-mismatch) situations. High-fidelity simulations are also presented to assess the robustness qualities of such LPV observer method, whilst the noticeable performance deterioration is quantified. [ABSTRACT FROM AUTHOR]

The aim of this study is to create a digital twin of a commercial absorption chiller for control and optimization purposes. The chiller is a complex system that is affected by solar intermittency and non-linearities. The authors use Adaptive Neuro-fuzzy Inference System (ANFIS) to model the chiller's behavior during transients and part-load events. The chiller is divided into four sub-models, each modeled by ANFIS, and trained and validated using data from 15 days of operation. The ANFIS models are precise, accurate, and fast, with a worst-case Mean Absolute Percentage Error (MAPE) of 3.30% and reduced error dispersion ( σ E = 0. 88) and Standard Error (SE=0.01). The models outperformed literature models in terms of MAPE, with MAPEs of 1.12%, 2.21%, and 3.24% for the High Temperature Generator (HTG), absorber + condenser, and evaporator outlet temperatures, respectively. The computational execution time of the model is also a valuable asset, with an average simulation step taking less than 0.20 ms and a total simulation time of 8.9 s for three days of operation. The resulting digital twin is suitable for Model Predictive Control applications and fast what-if analysis and optimization due to its gray-box representation and computational speed. • Four adaptive neuro-fuzzy inference systems describe a commercial absorption chiller. • The learning considers 15 days of continuous measurement and sampling time of 20 s. • The dynamic model has generalized adaptive learning despite sun intermittency. • The model is accurate and precise — worst error of 0.09 ± 3.6 °C (95%). • The model is fast — takes 8.9 s to simulate three days of operation. [ABSTRACT FROM AUTHOR]

This paper proposes the first application of a split-range control technique on a concentrating solar collector to improve an absorption plant production. Solar absorption plants have solar power availability in phase with cooling demand under design conditions. Thus, it is a powerful cooling technology in the context of renewable energy and energy efficiency. These plants need control systems to cope with solar irradiance intermittency, reject irradiation disturbances, manage fossil fuels backup systems and dump closed-loop thermal-hydraulic oscillations. In this work, control techniques are proposed and simulated in an absorption plant in Spain. The plant consists of a concentrating Fresnel solar collector connected to an absorption chiller. The objectives are to operate with 100% renewable solar energy and avoid safety defocus events while reducing temperature oscillations and control actuators effort. Firstly, the current available plant controllers are defined, then two modifications are proposed. The first modification is a split-range controller capable of manipulating both flow and defocus of the Fresnel collector, the second modification is a PI controller to substitute the original chiller on-off controller. The results compare, through validated models, the different control systems and indicate that using both proposed controllers reduces 94% of the sum of actuators effort and 43% of the integral of absolute set-point tracking error compared to the plant's factory pre-set controllers. The suggested controllers increase 66% of energy production and 63% of exergy production. Besides, the split-range technique can be extended to any concentrating solar collector control. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes a cascade series control structure and design for two series processes represented by first-order plus dead-time FOPDT models. The proposed controller uses two series predictors, one for each process, and can deal with stable, unstable, or integrative processes. The design follows similar principles of the simplified filtered Smith Predictor (SFSP) for a single-loop dead-time system. Initially, the primary controller, composed of a set-point static gain and two feedback controllers, is tuned to achieve the desired set-point tracking. Then, the predictor filters are tuned to ensure stability, robustness, and disturbance attenuation. Different from standard cascaded SFSP, one of the feedback controllers, instead of only a static gain, includes a finite time integral. The main advantage of this approach is that disturbances generated in the primary or secondary process can be handled independently by the predictor filters, simplifying the tuning procedure and enhancing the overall control performance. Additionally, for the nominal case, the proposed cascaded controller allows obtaining an ideal set-point tracking and disturbance rejection. After the dead-time effect, the proposed cascade controller achieves the set point exponentially and rejects exponentially step-like disturbances where the user defines the time constants of the exponentials. Simulation results demonstrate the advantages of the proposed controller compared to other recently published approaches, mainly in the inner loop disturbance rejection, which is precisely what is expected from a series cascade controller. • A new time-delay compensator applied to cascade series delayed process is proposed. • The proposed controller can be applied to cascaded open-loop stable, unstable, and integrative processes with time delay. • One can independently tune the disturbance attenuation of the primary and secondary processes without affecting the entire system. • The tuning rules are as simple as in a traditional filtered Smith predictor. • The proposed controller allows the achievement of the ideal setpoint tracking and step-like disturbance rejection. [ABSTRACT FROM AUTHOR]

This paper considers off-grid microgrids (MGs) from the Philippine archipelago and analyses their energy generation in differents aspects. Seven different energy clusters are used, representing realistic configurations and renewable energy shares. A Robust Model Predictive Control (MPC) framework is used for the energy management and coordination task of these island MGs. The MPC is based on a min./max. optimization procedure, which takes into account the whole uncertainty set. The reliability of the MG operations are analysed with respect to the different clusters; this evaluation is conducted using μ -analysis, performed with respect to the baseline model and the uncertainty set. The demand-side compliance of the MG is also investigated, with respect to stochastic behaviours of the demands and of the renewable sources (wind and solar). Numerical simulation results are presented in order to demonstrate that reliable power outlets are produced despite variation in renewables and of the demands. This paper offers a thorough analysis of simple energy system coordinated via MPC, showing how this method can indeed be used for renewable MG management, offering robustness and ensuring reliability. • A generalized model is presented for Off-Grid Microgrids with multiple carriers. • The Philippine Off-grid islands are considered, with diesel, solar, and wind energy. • A Robust Model Predictive Control scheme is proposed to manage these Microgrids. • Demand compliance and operation reliability are assessed through robustness analysis. • Numerical simulation illustrate reliability under multiple scenarios. [ABSTRACT FROM AUTHOR]

• A framework to model faulty microgrids is proposed. • A Fault Estimation layer, based on LPV observers, is developed. • A Model Predictive Controller is designed as Fault-Tolerant Energy Management Systems. • Realistic numerical simulation results are presented. This paper presents a solution for the Fault-Tolerant Energy Management problem of renewable energy microgrids. This solution is a Energy Management System (EMS) derived from a Model Predictive Controller (MPC) synthesized upon a Linear Parameter Varying (LPV) prediction model. This model describes the energy-generation process in both healthy and faulty operation conditions. The MPC is tuned to adequately coordinate the operation of the microgrid, aiming to optimally use its energetic resources, enlarge the renewable generation share and guarantee maximal efficiency and profit, despite the presence of faults (or even failures) in its subsystems. The quantification of the level of faults in the energy system is provided by an extended-state LPV fault estimation observer that works in parallel to the MPC. The proposed EMS , that acts at an hourly rate, finds time-varying control policies, that are passed as energy-generation set-points for the lower-layer subsystems, with respect to operational constraints, internal demands and taking into account the future (estimation) behaviour of the renewables. To validate the proposed fault-tolerant control scheme, a realistic, high-fidelity case study from the Brazilian sugarcane industry is considered. The achieved simulation results assess the effectiveness and qualities of the proposed energy management strategy; an overall good behaviour is exhibited in both faulty and healthy energy-generation conditions. [ABSTRACT FROM AUTHOR]

The high thermal energy consumption is one of the main drawbacks hampering the commercial implementation of Membrane Distillation (MD) technology. The development of adequate operating strategies can help to reduce these energy requirements. Accordingly, this paper focuses on the optimal management of the array of MD modules composing a commercial-scale MD plant, trying to reduce their thermal energy consumption while ensuring a given water need. For this aim, the array of MD modules is modelled as a Mixed Integer Programming (MIP) system to consider that some modules can be turned on/off depending on the operation specifications. An algorithm based on the Generalized Bender Decomposition (GBD) is then developed for the efficient solution of the problem. This algorithm is incorporated in a Model Predictive Control (MPC) strategy allowing to manage the plant in real time. The effectiveness of the proposed strategy is verified using a practical example. The obtained results are compared with a manual and a previous strategy presented in literature, showing that for a sunny day, around the 65 and 55% of the thermal energy consumed by these methodologies can be saved, which means important thermal energy savings that can be relevant for the industrial implementation of MD technology. • An optimal management of an industrial-scale membrane distillation plant is addressed. • A controller based on the Generalized Benders Decomposition method is proposed. • The control method is aimed at reducing the thermal energy consumption. • A case study based on two real plants located in Almer__a is used as testbed. • The results demonstrate the bene_ts achievable through the developed approach. [ABSTRACT FROM AUTHOR]

Hydrogen production systems supplied by photovoltaic solar energy have nonlinear dynamics and discontinuities which must be taken into account when a control system is applied. The main purpose of the control system is to maintain the electrolyzer current at the desired operating point and, at the same time, to optimize the grid energy consumption despite the solar energy variability. Classic controllers, like PID ones, are not able to obtain good performance over the whole operation range of these kinds of plants because of the aforementioned characteristics. To overcome these limitations, an optimal control strategy and a linear hybrid model predictive controller (HMPC) are applied to a hydrogen production system in this work. Regarding the optimal control design, a systematic framework is presented in order to obtain the optimal (in the sense of minimal grid energy consumption) trajectory of the states by converting the control problem into a boundary value problem by means of the Pontryagin's Maximum Principle. Interestingly, the resulting control law is explicit and piecewise continuous. Regarding the linear HMPC strategy, a mixed logical dynamical description of the linearized equations of the system is considered in order to obtain the control law by solving an optimization problem in the form of a mixed integer quadratic programming. For this control strategy three cost functions associating the grid energy consumption and the electrolyzer efficiency are presented. The proposed controllers are tested through numerical simulations for both the nominal and uncertain cases and different performance indexes are considered. Finally, a discussion of the main advantages and disadvantages of each controller in real-life applications is presented. • Two controllers are designed for the control problem of a hydrogen production system. • An explicit optimal control law is derived. • The HMPC algorithm approximates the optimal behavior. • Simulation tests with real data are performed for the nominal and uncertain cases. • The advantages and drawbacks of each of the proposed controllers are discussed. [ABSTRACT FROM AUTHOR]

This paper formulates a Model Predictive Control (MPC) policy to mitigate the COVID-19 contagion in Brazil, designed as optimal On-Off social isolation strategy. The proposed optimization algorithm is able to determine the time and duration of social distancing policies in the country. The achieved results are based on data from the period between March and May of 2020, regarding the cumulative number of infections and deaths due to the SARS-CoV-2 virus. This dataset is assumably largely sub-notified due to the absence of mass testing in Brazil. Thus, the MPC is based on a SIR model which is identified using an uncertainty-weighted Least-Squares criterion. Furthermore, this model includes an additional dynamic variable that mimics the response of the population to the social distancing policies determined by the government, which affect the COVID-19 transmission rate. The proposed control method is set within a mixed-logical formalism, since the decision variable is forcefully binary (existence or the absence of social distance policy). A dwell-time constraint is included to avoid too frequent shifts between these two inputs. The achieved simulation results illustrate how such optimal control method would operate in practice, pointing out that no social distancing should be relaxed before mid August 2020. If relaxations are necessary, they should not be performed before this date and should be in small periods, no longer than 25 days. This paradigm would proceed roughly until January/2021. The results also indicate a possible second peak of infections, which has a forecast to the beginning of October. This peak can be reduced if the periods of days with relaxed social isolation measures are shortened. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of all possible faults on a sugarcane-based microgrid. • Fault representation methodology, able to describe any kind of faults for any energy microgrid. • Proposed approach to estimate faults, based on a bank of LPV observers. • Proposed Diagnosis algorithm to categorize estimated faults. Abstract This works addresses the problem of Fault Detection and Diagnosis and provides a solution based on the use of multiple Linear Parameter Varying (LPV) extended-state observers coupled with simple search algorithm. This methodology is applied to a Grid-Connected Hybrid Power Plant, with different renewable sources, such as photovoltaic panels, wind power generation and the use of biomass. This plant might present different possible faults, that can lead it not to comply to its operational constraints, such as communication problems, valve malfunctions, vapor leakages and others. All these possible faults are carefully categorized based on empirical information from real plants in Brazil. The Fault Detection and Diagnosis (FDD) system designed aims to estimate and categorize these faults and, to do so, the proposed LPV observers are derived from LMI computation of the mixed H 2 / H ∞ norm minimization, in such a way to reduce the effect of noise and external disturbances upon the fault estimation. Through high-fidelity simulations, the benefits of the presented method is discussed. [ABSTRACT FROM AUTHOR]

Highlights • The study proposes an advanced control structure in 3 layers aiming the optimal efficiency energy management for Grid-Connected Hybrid Power Plant. • The control structure is based on a Stochastic Model Predictive Control and an optimal finite-state machine to take into account disturbances variations. • Disturbance estimation techniques based on of NAR Neural Networks are applied to have prediction of renewable sources. • The studied control method is compared, through simulation, to a deterministic MPC approach to illustrate an improved performance. Abstract This study presents a complete advanced control structure aimed at the optimal and most efficient energy management for a Grid-Connected Hybrid Power plant. This control scheme is composed of process supervision and process control layers, and it is a possible technology to enable improvements in the energy consumption of industrial systems subject to constraints and process demands. The proposed structure consists of the combination of a Model-Based Predictive Controller, formulated within the Chance Constraints framework to deal with stochastic disturbances (renewable sources, as solar irradiance), an optimal finite-state machine decision system and the use of disturbance estimation techniques for the prediction of renewable sources. The predictive controller uses feedforward compensation of estimated future disturbances, obtained by the use of Nonlinear Auto-Regressive Neural Networks with time delays. The proposed controller aims to perform the management of which energy system to use and to decide where to store energy between multiple storage options. This has to be done while always maximizing the use of renewable energy and optimizing energy generation due to contract rules (maintain maximal economic profit). The proposed method is applied to a case study of energy generation in a sugar cane power plant, with non-dispatchable renewable sources (such as photovoltaic and wind power generation), as well as dispatchable sources (as biomass and biogas). This hybrid power system is subject to operational constraints, as to produce steam in different pressures, sustain internal demands and, imperiously, produce and maintain an amount of electric power throughout each month, defined by strict contract rules with a local Distribution Network Operator (DNO). This paper aims to justify the use of this novel approach to optimal energy generation in hybrid microgrids through simulation, illustrating the performance improvement for different cases. [ABSTRACT FROM AUTHOR]

Highlights • A temperature delay is observed in solar fields based on flat-plate collectors. • The effect of the apparent delay can be demonstrated with first-order models. • Dynamic model validation is improved through the inclusion of the apparent delay in a bilinear model. Abstract This study presents an analysis of the effect of the transport delay which occurs in solar flat-plate collector fields and how to include its behavior in dynamic models suitable for control purposes. This investigation has been carried out using simplified models based on dynamic energy balances and models based on step response methods and experimental tests. The solar flat-plate collector field encompasses parallel absorber pipes intertwined with pipelines which act as several first-order plus dead time systems in parallel. The effect is an apparent delay observed at the outlet temperature that must be included in the dynamic model in order to reduce the error between the real measurement and the model prediction. The main contribution of this paper is the procedure to evaluate the apparent delay to obtain adequate dynamic models aimed to be used for control purposes. [ABSTRACT FROM AUTHOR]

This paper outlines the development of models of a solar field designed to provide thermal energy to a Multi-Effect Desalination (MED) plant. The dynamic model can be used both for simulation and control purposes. Some of these models have been developed based on static and dynamic energy and mass balances, and some others are based on step response methods (experimental tests). The solar field comprises a flat-plate collector field, an air cooler, a heat exchanger and the corresponding pipelines and interconnections. The main purpose of the solar field is to feed the MED unit with hot water within a specific temperature range using two thermal storage tanks as input buffers to the MED system. The main achievement of this paper is that the developed model provides an adequate tradeoff between complexity and performance. [ABSTRACT FROM AUTHOR]

This work develops digital entities of a commercial Fresnel Solar Collector (FSC) installed in an absorption cooling plant. The objective is to create and validate models that describe the FSC dynamics across its whole operation range during the day and the night. Thus, the temperatures range between operation temperature of 180 ° C and almost ambient temperature due to overnight heat losses. In the same sense, the flow range between zero to 13 m 3 / h. The idea is that the digital twin will aid start-up and shut-down optimization and control design reliability. The paper employs two modeling approaches, then evaluates their twinning/adaptation time and performance validation. One model uses phenomenological modeling through Partial Differential Equations (PDE) and parameters identification, and another uses a data-driven technique with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The available measurement data sets comprise 25 days of operation with a sampling time of 20 s which, after outlier removal, filtering and treatment, resulted in 108416 samples. The validation considers six separate operating days. Results show that both models can twinning/adapt considering measured data. The models present pretty good results and are suitable for control and optimization. Besides, this is the first paper considering the FSC mirror defocus action on dynamic modeling and validation. • This work validates neuro-fuzzy (NF) and differential (PDE) models with massive data. • The models generally represent the process day and night. • The models are fairly accurate and precise—worst-case MAPE of 2.49%. • The NF model has ten times faster simulation time than the PDE model. • The validated dynamic models are the first accounting with mirror's focus action. [ABSTRACT FROM AUTHOR]

This paper presents a framework to deal with distributed optimization problems composed by binary and continuous variables. Instead of using a mixed integer quadratic programming (MIQP), the approach proposed here transforms the MIQP into a set of quadratic programming's (QP) that are easier to solve. In this way an instance of the controller related to each feasible combination of binary variables is created. The distributed controller performs an iterative process where the set of agents must agree on the value of continuous interconnection variables, while each agent must decide the values of local binary variables. During the iteration procedure the instances are rated according to a performance index and the instances with best performance are selected until the best one is obtained. The proposed methodology is applied to economic optimization of networked microgrids. [ABSTRACT FROM AUTHOR]

In this paper we present a new closed-loop automatic tuning methodology for decentralised proportional–integral–derivative (PID) controllers applied to two-inputs–two-outputs (TITO) non-singular processes. The main feature of the technique is the estimation of the process parameters by evaluating two closed-loop set-point step responses with two roughly tuned PID controllers already in place. Then, once a process model has been obtained, the PID controllers can be retuned by using any rule available in the literature and suitable for the application. Simulation examples, comparative results with other works and a real application on a neonatal incubator prototype are given to illustrate the methodology and to show its effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for economic optimization of a laboratory microgrid. The microgrid incorporates a hybrid storage system composed of a battery bank and a hydrogen storage and it has a connection with the external electrical network and a charging station for electric vehicles. To study the impact of use of renewable energy power systems, the microgrid has a programmable power supply that can emulate the dynamic behavior of a wind turbine and/or a photovoltaic field. The system modeling was carried out using the Energy Hubs methodology. A hierarchical control structure is proposed based on Model Predictive Control and acting in different time scales, where the first level is responsible for maintaining the microgrid stability and the second level has the task of performing the management of electricity purchase and sale to the power grid, maximize the use of renewable energy sources, manage the use of energy storages and perform the charge of the parked vehicles. Practical experiments were performed with different weather conditions of solar irradiation and wind. The results show a reliable operation of the proposed control system. [ABSTRACT FROM AUTHOR]

This work presents the development of an advanced control strategy using Model Predictive Control (MPC) for controlling the gas compression system of an offshore platform. It includes details about the complete phenomenological model of the system and of the software infrastructure developed to support the system implementation in real conditions. The proposed control structure has two main goals: (i) avoid unwanted regions of operation; and (ii) increase stability margins and availability. These goals are achieved by using a zone-control MPC and by adequately interacting with the regulatory control level. Although the proposed structure is general, this work exemplifies its application in a particular compression unit of a real offshore platform. Simulation results are presented in two different scenarios, one to test how the controller rejects a gas-load varying disturbance and another to analyze how the controller copes with an abnormal situation, losing real-time data of process variables or manipulated variables during operation. The good performance obtained in these two cases confirm the benefits provided by the proposed MPC strategy to the operation of the gas compression unit. • Control of an offshore gas compression system with model predictive control (MPC). • MPC works with zone-control and local controllers to avoid undesirable conditions. • Phenomenological model of the gas compression system. • Model based on real world data provided by PETROBRAS, the Brazilian state oil company. • Software description of the algorithm, which is ready for real-world use. [ABSTRACT FROM AUTHOR]

• A Moving Horizon Fault Estimation method is proposed for renewable microgrids. • A Fault Tolerant Model Predictive Control is proposed as an Energy Management System. • A fault reconfiguration mechanism coordinates both QPs. • Realistic numerical simulation scenarios are presented, showing the effectiveness of the method. This work presents an optimization-based control method for the fault-tolerant energy management task of an industrial energy microgrid, based on a sugarcane power plant. The studied microgrid has several renewable energy sources, such as photovoltaic panels, wind turbines and biomass power generation, being subject to different operational constraints and load demands. The proposed management policy guarantees that these demands are met at every sampling instant, despite eventual faults. This law is derived from the solution of an optimization problem that combines the formalism of a Moving Horizon Estimation (MHE) scheme (to estimate faults) and a Model Predictive Control (MPC) loop (for fault-tolerant control goals); it chooses which energy source to use, seeking maximal profit and increased sustainability. The predictive controller part of the scheme is based on a linear time-varying model of the process, which is scheduled with respect to the fault estimation brought up by the MHE. Via numerical simulations, it is demonstrated that the proposed method, when compared to other MPC strategies, exhibits enhanced performances. [ABSTRACT FROM AUTHOR]

• Three cooperative sugarcane-based microgrids are considered. • They should work together to produce energy. • A two-layer RTO plus MGCCs strategy is proposed. • The RTO coordinates biomass transport and long-term energy generation goals. • The MGCCs are MPC that guarantee reference tracking. • Realistic numerical simulation scenarios are presented. This paper presents a solution for the energy magement and resource sharing problem of cooperative renewable-energy-based microgrids. Such solution is based on a two-level hierarchical optimization procedure to coordinate the operation of networked microgrids and optimally share their combined energetic resources, aiming to enlarge the renewable generation share while guaranteeing maximal efficiency and profit. The supervision layer is a Real-Time Optimization scheme, designed to determine energy-generation set-points for the distributed plants as well as resource sharing rules, taking into account their models, operational constraints and future predicted behaviour of the renewable sources. The lower layer is a Model Predictive Control loop, designed to follow energy generation set-points passed top-down by the upper layer and abide by demands, while respecting operational constraints. This work is focused on cooperative microgrids based on Brazilian sugarcane industries. Such plants are usually located in various sites, while being owned by the same company that has a fixed energy contract with the Distribution Network Operator. The biomass sources may be transported from one plant to another, if deemed convenient. This case study is taken into account and high-fidelity numerical simulations are presented to assess the effectiveness and qualities of the proposed energy management strategy, which yields overall good results. [ABSTRACT FROM AUTHOR]

Thermal energy storage tanks are habitually combined with solar thermal fields to improve the dispatchability of these facilities. From the dynamical point of view, the start-up phase is relevant since if the storage device is unloaded in terms of energy or widely stratified, the transitory regime can take long time until reaching the operating point. In this paper, an optimal real-time procedure based on a hierarchical controller for improving the start-up phase is proposed. The hierarchical controller is composed of two layers based on a Model Predictive Control (MPC) technique and Proportional Integer Derivative (PID) controllers. Real experimental tests were performed in a pilot facility located at Plataforma Solar de Almería (Almería, Spain). In addition, a comparison in simulation with the typical manual procedure and with two techniques proposed previously in the literature for the same plant is provided. The results demonstrate the benefits obtained by using the proposed method; since it reduces the start-up phase in 34 [min] in comparison with the manual operation, and in 26 and 6 [min] with respect to the two previous techniques. • This paper proposes an optimal start-up policy for solar thermal fields. • The procedure is based on a hierarchical controller that uses a real time optimizer. • Experimental tests were conducted in a real facility with direct storage. • A simulated comparative analysis with other start-up techniques is also provided. • The start-up stage time can be reduced up to 10.5% with the proposed methodology. [ABSTRACT FROM AUTHOR]