Luis Valverde

Design, Planning and Management of a Hydrogen-Based Microgrid

Efficient energy generation and consumption is a key factor to achieve ambitious goals related to air pollution and climate change. Modern electricity networks can include different kind of sources, such as renewable energy sources (RES). Then, hybrid systems are obtained by combining several sources and storage types in the new concept called microgrid (MG). In order to draw the best performance from these hybrid systems, a proper design and operation is essential. The purpose of this paper is to present a detailed report to properly undertake the building and management of a hydrogen MG in a simple and reliable way to continue struggling for more comprehension on the MG operation modes and prevent the reported failures in the literature. The experimental platform developed will provide the valuable knowledge and solid guidelines for future test centers and demonstration plants. The MG, located in Seville, Spain, incorporates an electrolyzer, metal hydride storage, fuel cell, and a battery bank as main components. The developed MG laboratory has been successfully tested. The results indicate reliable operation incorporating the hydrogen and batteries as energy storage.

Power management using model predictive control in a hydrogen-based microgrid

Due to the intermittent feature of renewable energies, microgrids usually require additional energy sources to operate when the stored energy or the renewable energy supply is scarce or are not present. Then, hybrid systems obtained by combining several sources and means of storage can be used. In order to draw the best performance of such systems, a proper energy management is essential. A key factor is to try to adapt the energy production to the demand, where Model Predictive Control (MPC) and supervision techniques can play an important role. The problem is more interesting when several possibilities for using the produced energy exist, and it is necessary to select how the energy is distributed among subsystems. Hence different objectives can arise (minimizing the use of conventional energy sources, energy saving, economic and quality aspects...). In this paper a supervisory MPC for optimal power management and control in a hydrogen-based Micro-Grid (MG) is designed. The system operation under appropriate constraints is also taken into account in the problem formulation. Simulations are carried out over experimentally validated models to demonstrate the goodness of the controller.

Periodic Economic Control of a Nonisolated Microgrid

This paper presents the application of economic predictive control to minimize the cost of operating a nonisolated microgrid connected to an electric utility (EU) subject to a periodic internal demand. The microgrid considered is composed of a set of photovoltaic panels and two storage systems and it can buy and sell energy to an EU. The first storage system is composed of a cluster of batteries of lead acid, and the second storage system is based on hydrogen storage. A function that describes the economic cost of operating the plant taking into account aspects such as electric market costs, degradation of the microgrid, and amortization costs is proposed. Based on this cost and considering the periodic nature of the plant, an economic predictive controller capable of adapting to sudden changes on the cost function while guaranteeing stability and recursive feasibility has been successfully tested on a realistic nonlinear model of an experimental configurable testbed located in the laboratories of the University of Seville.

Predictive control of a renewable energy microgrid with operational cost optimization

A proposal for optimal control of a microgrid powered by solar energy with mixed temporary storage based on a battery bank and hydrogen is presented and evaluated here by means of simulations. The proposal is based on a Predictive Controller that optimizes the operational costs by taking into account the value of the energy generated, the cost of locally storing energy, the aging of the components and the operational constraints. The Constrained Mixed-Integer Predictive Control problem obtained is then solved using a formulation based on the duration of the states, which simplifies the numerical solution. Some simulation results are presented to show the validity of the proposed approach.

Optimal Load Sharing of Hydrogen-Based Microgrids With Hybrid Storage Using Model-Predictive Control

Real operational scenario in renewable energy microgrids typically differs from the forecast computed by the economic dispatch, making difficult to achieve the contracted schedule agreed with the grid Market/System operator. Each energy storage system (ESS) has its own capabilities referred to the relationship between energy and power density. In addition, degradation issues or anomalous working conditions should be considered. Advanced control algorithms come up as a technological solution to these problems, taking advantage of each storage system and avoiding the degradation and/or limitations to provide optimal operation of a hybrid ESS. The high number of constraints and variables to be optimized increases the complexity of the control problem, being the rationale to deploy advanced control algorithms. In this paper, optimal load sharing of a real scenario in a renewable energy microgrid with hydrogen/batteries/ultracapacitor hybrid ESS is developed through an advanced control system based on model-predictive control techniques. The presence of logical states such as the start-up/shut-down of the fuel cell and electrolyzer or charge/discharge states in the batteries and ultracapacitor introduces logical variables. In order to model both continuous/discrete dynamics, the plant is modeled in the mixed logic dynamic framework.

Economic model predictive control of a smartgrid with hydrogen storage and PEM fuel cell

This paper presents a control scheme in order to manage a laboratory-scale microgrid in a economic way. The main aim of this paper is the comparison between different control strategies. This microgrid is located in Seville (Spain) and incorporates an electrolyzer, metal hybrid storage, fuel cell and a battery set, as well as electronic load and generator as main components. This allows us to study smartgrids with different renewable sources (emulated by the electronic generator) and energy storage based on hydrogen under realistic scenarios. The control objective is to regulate the plant fulfilling the energy demand. This is posed as a tracking problem to be solved. A first approach is a two layer controller composed by a scheduler layer and an offset free LQR layer with output feedforward. This controller provides nice results and although its control layer does not take into account the operational limits of the plant, it gets the best performance from a optimal point of view. A one layer model predictive control for tracking has also been designed and tested in order to increase the performance of the first approach. The obtained results show that this last approach is a good control technique for this class of plants.