Energy Management Algorithm in Smart Microgrids with Renewable Energies That Uses Battery Degradation Cost and Short-Term Consumption Prediction

Abstract

This paper develops a day-ahead energy management algorithm, based on mixed integer linear programming (MILP), dedicated to the efficient operation of island microgrids (without connection to the national grid). The proposed algorithm integrates two essential components: a short-term (24-hour) prediction method of the consumption curve of the loads connected to the microgrid, as well as a rigorous method of estimating the costs associated with battery degradation, resulting from charge/discharge cycles. The analyzed microgrid is composed of a geothermal generator, a biomass generator, a photovoltaic (PV) system, a battery energy storage system and an energy management system. The load forecasting method allows to anticipate consumption variations with increased accuracy, using historical data measured in the microgrid. This prediction is essential for making optimal decisions within the MILP algorithm, which aims to minimize the total operating costs of the microgrid, including those resulting from battery degradation. By correlating meteorological data and those obtained from the consumption prediction with a detailed model of battery behavior, a local resource scheduling strategy is obtained that maximizes battery life, ensures energy balance and contributes to system sustainability. The results obtained highlight the benefits of an integrated and adaptive approach to energy management in the context of island microgrids.