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 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. The ANFIS EMS is synthesized through a data driven approach that relies on a clustering algorithm which defines the MFs and the rule consequent hyperplanes. Moreover, three clustering algorithms are investigated. Results show that the adoption of k-medoids based on Mahalanobis (dis)similarity measure is more efficient with respect to the k-means, although affected by some variety in clusters composition.

ANFIS synthesis by clustering for microgrids EMS design / Leonori, Stefano; Martino, Alessio; Rizzi, Antonello; Frattale Mascioli, Fabio Massimo. - Proceedings of the 9th International Joint Conference on Computational Intelligence - IJCCI, (2017), pp. 328-337. (IJCCI 2017 - 9th International Joint Conference on Computational Intelligence, Funchal, Madeira, Portugal, 1-3 November 2017). [10.5220/0006514903280337].

ANFIS synthesis by clustering for microgrids EMS design

Martino, Alessio;
2017

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 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. The ANFIS EMS is synthesized through a data driven approach that relies on a clustering algorithm which defines the MFs and the rule consequent hyperplanes. Moreover, three clustering algorithms are investigated. Results show that the adoption of k-medoids based on Mahalanobis (dis)similarity measure is more efficient with respect to the k-means, although affected by some variety in clusters composition.
2017
978-989-758-274-5
smart grids
microgrids
energy management system
ANFIS
data clustering
decision making system
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/214585
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