This is the final version. Available on open access from MDPI via the DOI in this recordData Availability Statement:
The data is available from the lead or the corresponding author upon reasonable requestsThis paper proposes an Energy Management System (EMS) for battery storage systems in grid connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is
minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimization problem over a 24-hours horizon and solved using mixed integer
linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 hours. To achieve this, a long short-term memory (LSTM) network is proposed. The Receding Horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day, the Receding Horizon (RH) strategy is suggested. At each hour, the LSTM predicts generation and load data for the next 24 hours, the dispatch problem is then solved, and the battery charging or discharging command for only the first hour is applied in real-time. Real data is then used to update the LSTM input, and the process is repeated. Simulation results show
that the proposed real-time strategy outperforms the offline optimisation strategy reducing the operating cost by 3.3%.Petroleum Technology Development Fund of Nigeri
