The electricity market rules determine the energy prices in the day-ahead market, matching offers from generators to bids from consumers. The unpredictability of renewable energy combined with the penalty deviations used in the regulation market makes it difficult for clean energy to play an important role in the electricity market. The high density of hydrogen as an energy storage system (ESS) appears to be one solution to the problems outlined. There is still not a perfect ESS, everyone has different limitations from the point of view of time autonomy, time response, degradation issues, or acquisition cost. The design of a hybrid energy storage management system emerges as a technological solution to the problems commented. The development of an optimal control for renewable energy microgrids with hybrid ESS is carried out using model predictive control (MPC). The MPC techniques allow maximizing the economical benefit of the microgrid, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The MPC problem is solved within mixed-integer quadratic programming.

This article presents the basic principles of operation for model predictive control (MPC), a control methodology that opens a new world of opportunities. MPC is a powerful technique that can fulfill the increased performance and higher efficiency demands of power converters today. The main features of this technique are presented as well as the MPC strategy and basic elements. The two main MPC methods for power converters [continuous-control-set MPC (CCS-MPC) and finite-control-set MPC (FCS-MPC)] are described, and their application to a voltage-source inverter (VSI) is shown to illustrate their capabilities. This article tries to bridge the gap between the powerful but sometimes abstract techniques developed by researchers in the control community and the empirical approach of power electronics practitioners.

Fuel cells represent an area of great industrial interest due to the possibility to generate clean energy for stationary and automotive applications. It is clear that the proper performance of these devices is closely related to the kind of control that is used; therefore, a study of improved control alternatives is fully justified. The air-supply control is widely used to guarantee safety and to achieve a high performance. This paper deals with this control loop, proposing and comparing two control objectives aimed at satisfying the oxygen starvation avoidance criterion and the maximum efficiency criterion, respectively. The control architecture is based on a constrained explicit model predictive control (MPC) law suitable for real-time implementation due to its low computational demands. The proposed controller is implemented and evaluated on a 1.2-kW polymer electrolyte membrane or proton exchange membrane fuel-cell test bench, thus obtaining real data which show that the maximum efficiency criterion does not conflict with the starvation avoidance criterion and allows system performance improvements of up to 3.46%. Moreover, experimental results utilizing the explicit MPC approach also show improved transient responses compared to those of the manufacturer's control law.

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.

This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid.