The use of electric power by wind generation in actual grids is hampered by its inherent stochastic nature and the penalty deviations adopted in several electricity regulation markets with respect to power quality requirements. Coupling wind farms with advanced Energy Storage Systems (ESS) can help their integration within grids. In this direction, several studies have been conducted, but the problem is still open due to the constraints and limitations regarding the ESSs time autonomy, time response, degradation issues and overall costs. In order to take into account these relevant aspects, advanced control algorithms are needed. In this paper, a Model-Based Predictive Controller (MPC) is presented. Such a controller minimizes the degradation of the ESS and the load tracking error while fulfilling the operational constraints and dynamics. The ESS considered is hydrogen-based and the study has been developed within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS aiming at building and integrating advanced control strategies for a hydrogen-based ESS within a wind farm fence. Numerical simulations show the feasibility and the effectiveness of the proposed approach.

This paper proposes an output power smoothing strategy for a grid-connected wind-hydrogen plant. An Energy Storage System (ESS) composed of an electrolyzer and a fuel cell is used to smooth the fluctuating output power of the wind plant. The aim of this study is to propose a multi-objective optimization model for joint wind farm and energy storage operation, to smooth the wind power output, and to track a load demand subject to a variety of constraints on the system model. Based on a modeling adopting mixed-integer constraints and dynamics, the problem of output power fluctuations and smooth tracking of load demand is solved through the implementation of an optimal controller which follows a sequential optimization technique. We illustrate the effectiveness of the proposed controller with a simulation example, employing real equipment data and wind profiles.