FIS synthesis by clustering for microgrid energy management systems
Microgrids (MGs) are the most affordable solution for the development of smart grid infrastructures. They are conceived to intelligently integrate the generation from Distributed Energy Resources (DERs), to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well. In this work it is proposed a novel Fuzzy Inference System (FIS) synthesis procedure as the core inference engine of an Energy Management System (EMS) for a grid-connected MG equipped with a photovoltaic power plant, an aggregated load and an Energy Storage System (ESS). The EMS is designed to operate in real time by defining the ESS energy flow in order to maximize the revenues generated by the energy trade with the distribution grid considering a Time Of Use (TOU) energy prices policy. The FIS adopted is a first order Tagaki-Sugeno type, designed through a data driven approach. In particular, multidimensional Membership Functions (MFs) are modelled by a K-Means clustering algorithm. Successively, each cluster is used to define both the antecedent and the consequent parts of a tailored fuzzy rule, by estimating a multivariate Gaussian MF and the related interpolating hyperplane. Results have been compared with benchmark references obtained by a Linear Programming (LP) optimization. The best solution found is characterized by a small number of MFs, namely a limited number of fuzzy rules. Its performances are close to the optimum solution in terms of profit generated and, moreover, it shows a smooth exploitation of the ESS.