RESUMEN
Abstract: Microgrids (MD) is a new technology to improve efficiency, resilience, and reliability in the electricity sector. MD are most likely to have a clean energy generation, but the increase of microgrids with this kind of generation brings new challenges for energy management (EMS), especially concerning load uncertainties and variation of energy generation. In this context, this study has the main objective to propose a method of how to attend this matter, verifying the difference between the day before and real-time. The EMS proposed analyses the MD in real-time, calculating the deviation between dispatched and what was predicted to happen in the operation point in a three-dimensional analysis approach, considering renewable energy generation, battery State of Charge (SOC) and load curve. The system categorized the deviation in three possible quantities (small, medium, or high) and it acts accordingly. For the Next Operation Point predictor are used an artificial neural network (ANN) methodology. For the Decision Support System, it's used a fuzzy logic system to adjust the next operation point, and it uses a mixed-integer linear programming (MILP) approach when the deviation is too high, and the dispatched operation is unfeasible. Simulations with real data and information of a pilot project of MD are carried out to test and validate the proposed method. Results show that the methodology used to attend the matters of uncertainties and variation of energy generation. A reduction of operational cost is observed in the simulations.