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An Energy Management System (EMS), based on Model Predictive Control (MPC) ideas, is proposed here to balance the consumption of power by a set of electrolysis units in an offshore platform. In order to produce renewable hydrogen, the power is locally generated by wind turbines and wave energy converters and fully used by the electrolyzers. The energy generated at the platform by wind and wave is balanced by regulating the operating point of each electrolysis unit and its connections or disconnections, using an MPC based on a Mixed-Integer-Quadratic-Programming algorithm. This Predictive Control algorithm makes it possible to take into account predictions of available power and power consumption, to improve the balance and reduce the number of connections and disconnections of the devices. Two case studies are carried out on different installations composed of wave and wind energies feeding a set of alkaline electrolyzers. Validation using measured data at the target location of the platforms shows the adequate operation of the proposed EMS. [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]

Thiswork proposes the use of solar radiation as energy sourceto support energy demands in the optimal control process for ethanolproduction. The control system proposed for the reactor has two layers:the local one composed by PID (Proportional-Integrative-Derivative)controllers for the pH, level, and temperature loops of the reactorand the master controller which uses an advanced MIMO (Multiple-Inputand Multiple-Output) MPC (Model Predictive Control) strategy. Thecontrol of the solar cooling system is performed by means of a switchingcontrol scheme. Since there is no availability of a practical systemwhich integrates the experimental solar cooling plant and a fermentationunit, the Hardware in the Loop technique has been performed on theplant of Centro de Investigación de Energía Solar (CIESOL),located at the University of Almería (Spain), interconnectedwith a simulator that represents the ethanol fermentation in Brazilianfactories. Three scenarios are analyzed in detail, in which the incrementsin ethanol production are shown. [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]

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]

• 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]