Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II

被引：65

作者：

Teo, Tiong Teck ^{[1
]}

Logenthiran, Thillainathan ^{[2
]}

Woo, Wai Lok ^{[3
]}

Abidi, Khalid ^{[1
]}

John, Thomas ^{[4
]}

Wade, Neal S. ^{[4
]}

Greenwood, David M. ^{[4
]}

Patsios, Charalampos ^{[4
]}

Taylor, Philip C. ^{[4
]}

机构：

[1] Newcastle Univ Singapore, Elect Power Engn Program, Singapore, Singapore

[2] Univ Washington, Sch Engn & Technol, Tacoma, WA 98402 USA

[3] Northumbria Univ, Dept Comp & Informat Sci, Newcastle Upon Tyne NE1 8QH, Tyne & Wear, England

[4] Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2021年 / 51卷 / 11期

基金：

英国工程与自然科学研究理事会;

关键词：

Microgrids; Biological cells; Finite element analysis; Energy management; Optimization; Mathematical model; Linear programming; Energy storage management; membership function (MF) tuning; microgrid; multiobjective evolutionary algorithm (MOEA); MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS; STORAGE SYSTEM; GENETIC ALGORITHM; ALLOCATION; OPERATION; DISPATCH;

D O I：

10.1109/TCYB.2020.3031109

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.

引用

页码：5375 / 5386

页数：12

共 45 条

[31] SYSWERDA G, 1989, PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P2
[32] Tee JEC, 2017, TENCON IEEE REGION, P2973, DOI 10.1109/TENCON.2017.8228371
[33] Teo TT, 2015, 2015 IEEE INNOVATIVE SMART GRID TECHNOLOGIES - ASIA (ISGT ASIA)
[34] Teo T. T., 2018, 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). Proceedings, P1257, DOI 10.1109/ISGT-Asia.2018.8467921
[35] Teo TT, 2016, PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON), P455, DOI 10.1109/TENCON.2016.7848040
[36] Teo TT, 2016, 2016 IEEE INNOVATIVE SMART GRID TECHNOLOGIES - ASIA (ISGT-ASIA), P65, DOI 10.1109/ISGT-Asia.2016.7796362
[37] Intelligent Controller for Energy Storage System in Grid-Connected Microgrid
Teo, Tiong Teck
Logenthiran, Thillainathan
Woo, Wai Lok
Abidi, Khalid
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (01): : 650 - 658
[38] Advanced control strategy for an energy storage system in a grid-connected microgrid with renewable energy generation
Teo, Tiong Teck
Logenthiran, Thillainathan
Woo, Wai Lok
Abidi, Khalid
[J]. IET SMART GRID, 2018, 1 (03) : 96 - 103
[39] Localized Weighted Sum Method for Many-Objective Optimization
Wang, Rui
Zhou, Zhongbao
Ishibuchi, Hisao
Liao, Tianjun
Zhang, Tao
[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2018, 22 (01) : 3 - 18
[40] Design and advanced control strategies of a hybrid energy storage system for the grid integration of wind power generations
Wang, Shu
Tang, Yuejin
Shi, Jing
Gong, Kang
Liu, Yang
Ren, Li
Li, Jingdong
[J]. IET RENEWABLE POWER GENERATION, 2015, 9 (02) : 89 - 98

← 1 2 3 4 5 →