Authors:
Stefano Leonori
;
Alessio Martino
;
Antonello Rizzi
and
Fabio Massimo Frattale Mascioli
Affiliation:
University of Rome "La Sapienza", Italy
Keyword(s):
Smart Grids, Microgrids, Energy Management System, ANFIS, Data Clustering, Decision Making System.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Systems
;
Learning and Adaptive Fuzzy Systems
;
Soft Computing
Abstract:
Microgrids (MGs) play a crucial role for the development of Smart Grids. They are conceived to intelligently
integrate the generation from Distributed Energy Resources, 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 synthesis procedure for modelling an Adaptive Neuro-Fuzzy Inference System
(ANFIS) featured by multivariate Gaussian Membership Functions (MFs) and first order Takagi-Sugeno
rules. The Fuzzy Rule Base is the core inference engine of an Energy Management System (EMS) for a gridconnected
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. The ANFIS EMS is synthesized through
a data driven approach that relies on a clust
ering algorithm which defines the MFs and the rule consequent
hyperplanes. Moreover, three clustering algorithms are investigated. Results show that the adoption of kmedoids
based on Mahalanobis (dis)similarity measure is more efficient with respect to the k-means, although
affected by some variety in clusters composition.
(More)