In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one‐day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms‐based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.

The increasing presence of green energy on grid needs using of high control strategy and good power quality. For that reason this paper presents analysis and control of three phase space vector pulse width modulated (SVPWM) two level voltage source inverters (VSI) with constant switching frequency using a direct power control (DPC). In order to improve the power quality by reducing harmonic distortion, a design of LCL filter by genetic algorithm (GA) and particle swarm optimization (PSO) is also described. Comparative studies between these two methods are discussed to show the most efficient one in terms of generation and harmonics injected to the grid.