Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynam...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 9; no. 3; pp. 2276 - 2283
Main Authors: Kampouropoulos, Konstantinos, Andrade, Fabio, Sala, Enric, Espinosa, Antonio Garcia, Romeral, Luis
Format: Journal Article Publication
Language:English
Published: IEEE 01.05.2018
Institute of Electrical and Electronics Engineers (IEEE)
Subjects:
Dynamic optimization
Electrical engineering
Energy hub
Energy optimization
Energy prediction
Enginyeria elèctrica
Genetic algorithms
Indexes
Linear programming
Manufacturing plants
Mathematical model
Mixed-integer programming
Multicarrier systems
Multiobjective problem
Optimal control
Optimization
Power demand
Predictive models
Àrees temàtiques de la UPC
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment's thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost, and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2016.2609740