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

被引：36

作者：

Kampouropoulos, Konstantinos ^{[1
,2
,3
]}

Andrade, Fabio ^{[1
,2
,3
]}

Sala, Enric ^{[1
,2
,3
]}

Garcia Espinosa, Antonio ^{[1
,2
,3
]}

Romeral, Luis ^{[1
,2
,3
]}

机构：

[1] Fundacio CTM Ctr Tecnol, Manresa 08243, Spain

[2] Univ Puerto Rico, Mayaguez, PR 00681 USA

[3] Univ Politecn Cataluna, MCIA Res Ctr, Terrassa 08222, Spain

来源：

IEEE TRANSACTIONS ON SMART GRID | 2018年 / 9卷 / 03期

关键词：

Energy optimization; multicarrier systems; dynamic optimization; mixed-integer programming; multiobjective problem; energy hub; energy prediction; optimal control; manufacturing plants; SIDE MANAGEMENT GAME; OPTIMAL POWER-FLOW; HUBS; CARRIERS;

D O I：

10.1109/TSG.2016.2609740

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];

摘要：

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.

引用

页码：2276 / 2283

页数：8

共 31 条

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On Decisive Storage Parameters for Minimizing Energy Supply Costs in Multicarrier Energy Systems [J].