Optimization strategies for microgrid energy management systems by genetic algorithms

Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.