Multi-objective Metaheuristics' Challenges in the Optimization of Microgrids Planning and Management

被引：0

作者：

Salgueiro, Yamisleydi ^{[1
]}

Rivera, Marco ^{[2
]}

Napoles, Gonzalo ^{[3
]}

机构：

[1] Univ Talca, Fac Ingn, Dept Ciencias Comp, Campus Curico, Curico, Maule, Chile

[2] Univ Talca, Fac Ingn, Dept Electromecan & Convers Energia, Campus Curico, Curico, Maule, Chile

[3] Hasselt Univ, Fac Business Econ, Hasselt, Belgium

来源：

2019 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (CHILECON) | 2019年

关键词：

Microgrid; Multi-objective Optimization; P-metaheuristics; Renewable Energy; EVOLUTIONARY ALGORITHMS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Microgrids are essentially an aggregation concept in low-voltage grids with a high penetration of renewable energy sources. During the last years, we observed a rise in the use of multi-objective metaheuristics in the optimization of microgrids' planning and operation. In this paper, we survey the technical elements of multi-objective metaheuristics, essential for the construction of robust solutions and often ignored by researchers. We identified the main problems of the multi-objective metaheuristics applications in the microgrids' planning and management, and provide explanatory comments and recommendations based on experimental studies and previous works. Additionally, we offer suggestions for comparisons between multi-objective metaheuristics. This work serves as a guide for researchers in the area and will allow obtaining increasingly robust solutions.

引用

页数：6

共 24 条

[1] A review of optimal power flow studies applied to smart grids and microgrids [J].

Abdi, Hamdi ;

Beigvand, Soheil Derafshi ;

La Scala, Massimo .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 71 :742-766

[2]

[Anonymous], 2014, MICROGRIDS ARCHITECT

[3]

[Anonymous], 2014, SEARCH METHODOLOGIES

[4] A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems [J].

Cheng, Lefeng ;

Yu, Tao .

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2019, 43 (06) :1928-1973

[5] A fast and elitist multiobjective genetic algorithm: NSGA-II [J].

Deb, K ;

Pratap, A ;

Agarwal, S ;

Meyarivan, T .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197

[6]

Deb K, 2014, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, P403, DOI DOI 10.1007/978-1-4614-6940-7_15

[7]

Ferreira A, 2014, 2014 IEEE INTERNATIONAL WORKSHOP ON INTELLIGENT ENERGY SYSTEMS (IWIES), P6, DOI 10.1109/IWIES.2014.6957039

[8] Computational optimization techniques applied to microgrids planning: A review [J].

Gamarra, Carlos ;

Guerrero, Josep M. .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 48 :413-424

[9]

Heliodore F., 2017, METAHEURISTICS INTEL, P1

[10] Modified Distance Calculation in Generational Distance and Inverted Generational Distance [J].

Ishibuchi, Hisao ;

Masuda, Hiroyuki ;

Tanigaki, Yuki ;

Nojima, Yusuke .

EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT II, 2015, 9019 :110-125

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