An Integrated Energy Management System for Nearly Zero Energy Buildings

With the development of the nearly zero energy buildings (nZEB), the most challenging problem is the optimal energy management of buildings with respect to multiple and conflicting objectives. In this paper, a genetic algorithm (GA) is developed for optimal electric energy use in a building, considering a balance between energy saving and a comfortable lifetime in combination with maximizing the exploitation of the excess energy of the renewable energy sources (RES). The proposed control algorithm is based on a mixed objective function that considers the real time electricity price, the state of charge (SoC) of the battery-based energy storage system (ESS), the weather forecast, the system constraints and the user preferences ensuring reduced utility bills and optimal task scheduling for programmable loads and energy sources. Also, the proposed GA-based control scheme has generalized utilization at smart building applications and can be used either if the feed-in tariff policy is adopted by the electric energy provider or not. To verify the efficiency of the proposed algorithm, several simulations were performed under different scenarios using real data and the obtained results were compared in terms of total energy consumption cost and users' comfort level.