An Optimal Energy Management System Using Day-ahead Prediction Algorithm

Renewable energy sources (RES) are currently being deployed worldwide with the focus to reduce grid load, secure tax incentives, and promote RES. Meanwhile, the Dutch government has introduced a new regulation for net-metering policy and nearly zero energy building (n-ZEB). To combat these new regulations, wind turbines and photovoltaics (PV) panels along with a battery energy storage (BES) are placed to improve the tuning possibilities of RES and load demand. An energy management system (EMS) is required to optimize the performance and lifecycle of the BES and simultaneously reducing the electricity bill. In this paper, a mixed-integer linear programming (MILP) optimization problem is formulated to schedule energy profile for the BES considering the day-ahead predicted RES and load demand. Henceforth, a prediction algorithm is designed to forecast the day-ahead prediction of RES and load demand from the day-ahead weather forecast. A case study has been performed for 130 house apartment building in Alkmaar, the Netherlands. The simulation results show at least 90% correlation between the weather parameters, RES, and load demand. Further, results demonstrate the effectiveness of the proposed EMS for different seasons throughout the year.