Energy management system for distribution networks integrating photovoltaic and storage units

<span>The concept of the optimization energy management system, developed in this work, is to determine optimal combination from several generation sources and schedule their commitment, while optimizing cost energy, power losses voltage drops. In order achieve these objectives, non-dominated sorting genetic Algorithm II (NSGA-II) was modified applied an IEEE 33-bus test network containing 10 photovoltaic plants 4 battery storage systems placed at points network. To evaluate system performance, resolution performed under conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), TOPSIS entropy-TOPSIS, compared each other for more accurate results. The simulation results obtained by NSGA-II entropy-TOPSIS showed a significant considerable reduction terms cost, drops successfully meeting all constraints. addition, diversity proved once again robustness effectiveness algorithm. A graphical interface also display decisions made algorithm, information such as states systems, profiles, alarms, history.</span>