Application of Robust Model Predictive Control to a Renewable Hydrogen-based Microgrid

In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive control techniques, i. i. e., multi-scenario, tree-based, and chance-constrained model predictive control, which are applied to a nonlinear plant-replacement model that corresponds to a real laboratory-scale plant located in the facilities of the University of Seville. Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems

A Novel Formulation of Economic Model Predictive Control for Periodic Operations

This paper proposes a novel formulation of economic model predictive control (MPC) for linear systems with periodic operations. In this economic MPC design, the optimal periodic trajectory from an economic point of view is unknown, hence it is not possible to follow a standard control strategy in which the MPC uses this trajectory to define a terminal constraint to guarantee closed-loop convergence. The economic cost function is optimized with a periodicity constraint at each time step considering all periodic trajectories in a period including the current state. The recursive feasibility and closed-loop convergence to the optimal periodic trajectory are analyzed using the Karush-Kuhn-Tucker conditions. Finally, two simulations are provided to demonstrate the main results.Agencia Estatal de Investigación DPI2013-48243-C2Agencia Estatal de Investigación DPI2016-76493- C3Ministerio de Ciencia, Innovación y Universidades MDM-2016-065

Multiple-output DC–DC converters: applications and solutions

Multiple-output DC–DC converters are essential in a multitude of applications where different DC output voltages are required. The interest and importance of this type of multiport configuration is also reflected in that many electronics manufacturers currently develop integrated solutions. Traditionally, the different output voltages required are obtained by means of a transformer with several windings, which are in addition to providing electrical isolation. However, the current trend in the development of multiple-output DC–DC converters follows general aspects, such as low losses, high-power density, and high efficiency, as well as the development of new architectures and control strategies. Certainly, simple structures with a reduced number of components and power switches will be one of the new trends, especially to reduce the size. In this sense, the incorporation of devices with a Wide Band Gap (WBG), particularly Gallium Nitride (GaN) and Silicon Carbide (SiC), will establish future trends, advantages, and disadvantages in the development and applications of multiple-output DC–DC converters. In this paper, we present a review of the most important topics related to multiple-output DC–DC converters based on their main topologies and configurations, applications, solutions, and trends. A wide variety of configurations and topologies of multiple-output DC–DC converters are shown (more than 30), isolated and non-isolated, single and multiple switches, and based on soft and hard switching techniques, which are used in many different applications and solutions.info:eu-repo/semantics/publishedVersio

On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Peer ReviewedPostprint (author's final draft

Efficient, High Power Density, Modular Wide Band-gap Based Converters for Medium Voltage Application

Recent advances in semiconductor technology have accelerated developments in medium-voltage direct-current (MVDC) power system transmission and distribution. A DC-DC converter is widely considered to be the most important technology for future DC networks. Wide band-gap (WBG) power devices (i.e. Silicon Carbide (SiC) and Gallium Nitride (GaN) devices) have paved the way for improving the efficiency and power density of power converters by means of higher switching frequencies with lower conduction and switching losses compared to their Silicon (Si) counterparts. However, due to rapid variation of the voltage and current, di/dt and dv/dt, to fully utilize the advantages of the Wide-bandgap semiconductors, more focus is needed to design the printed circuit boards (PCB) in terms of minimizing the parasitic components, which impacts efficiency. The aim of this dissertation is to study the technical challenges associated with the implementation of WBG devices and propose different power converter topologies for MVDC applications. Ship power system with MVDC distribution is attracting widespread interest due to higher reliability and reduced fuel consumption. Also, since the charging time is a barrier for adopting the electric vehicles, increasing the voltage level of the dc bus to achieve the fast charging is considered to be the most important solution to address this concern. Moreover, raising the voltage level reduces the size and cost of cables in the car. Employing MVDC system in the power grid offers secure, flexible and efficient power flow. It is shown that to reach optimal performance in terms of low package inductance and high slew rate of switches, designing a PCB with low common source inductance, power loop inductance, and gate-driver loop are essential. Compared with traditional power converters, the proposed circuits can reduce the voltage stress on switches and diodes, as well as the input current ripple. A lower voltage stress allows the designer to employ the switches and diodes with lower on-resistance RDS(ON) and forward voltage drop, respectively. Consequently, more efficient power conversion system can be achieved. Moreover, the proposed converters offer a high voltage gain that helps the power switches with smaller duty-cycle, which leads to lower current and voltage stress across them. To verify the proposed concept and prove the correctness of the theoretical analysis, the laboratory prototype of the converters using WBG devices were implemented. The proposed converters can provide energy conversion with an efficiency of 97% feeding the nominal load, which is 2% more than the efficiency of the-state-of-the-art converters. Besides the efficiency, shrinking the current ripple leads to 50% size reduction of the input filter inductors

Modeling and Energy Management of a Microgrid Based on Predictive Control Strategies

This work presents the modeling and energy management of a microgrid through models developed based on physical equations for its optimal control. The microgrid’s energy management system was built with one of the most popular control algorithms in microgrid energy management systems: model predictive control. This control strategy aims to satisfy the load demand of an office located in the CIESOL bioclimatic building, which was placed in the University of Almería, using a quadratic cost function. The simulation scenarios took into account real simulation parameters provided by the microgrid of the building. For case studies of one and five days, the optimization was aimed at minimizing the input energy flows of the microgrid and the difference between the energy generated and demanded by the load, subject to a series of physical constraints for both outputs and inputs. The results of this work show how, with the correct tuning of the control strategy, the energy demand of the building is covered through the optimal management of the available energy sources, reducing the energy consumption of the public grid, regarding a wrong tuning of the controller, by 1 kWh per day for the first scenario and 7 kWh for the last

Economic model predictive control based on a periodicity constraint

This paper addresses a novel economic model predictive control (MPC) formulation based on a periodicity constraint to achieve an optimal periodic operation for discrete-time linear systems. The proposed control strategy does not rely on forcing the terminal state by means of a terminal equality constraint and hence it does not require a priori knowledge of a periodic steady trajectory. Instead, at each sampling time step the economic cost function is optimized based on a periodicity constraint over all the periodic trajectories that include the current state. The recursive feasibility and the closed-loop convergence to a periodic steady trajectory are discussed. Moreover, an optimality certificate of this steady trajectory is provided based on the Karush–Kuhn–Tucker (KKT) optimality conditions. Finally, an application to a well-known water distribution network benchmark is presented to demonstrate the proposed economic MPC in which the closed-loop simulation results obtained with a linear model and a virtual–reality simulator are both providedUnión Europea, European Development Research Fund (EDRF) DEOCS (DPI2016-76493) and SCAV (DPI2017-88403-R),Generalitat de Catalunya 2017-SGR-482FPI Grant BES-2014-06831