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Applications of Multiobjective Particle Swarm Optimization Algorithms in Smart Grid: a Comprehensive Survey

Journal from gdlhub / 2016-11-11 07:32:38
Oleh : Ting Li, Bo Yang, Dong Liu, Telkomnika
Dibuat : 2016-09-01, dengan 1 file

Keyword : multiobjective optimization, particle swarm optimization, smart grid, distributed generation
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4397

Multiobjective optimization problems (MOP) emerging in smart grid, such as optimal operation of distributed generation (DG) and microgrid, are very complex because of conflicting objectives, high dimension variables, and numerous operational or security constraints, and difficult to be solved. Multiobjective particle swarm optimization (MOPSO) has powerful potential for obtaining Pareto optimal solutions of these MOPs in a run because it has advantages of parallel computation, faster convergence, and easier implementation. This paper summarizes general procedure of MOPSO at first and then well categorizes MOPSO improvements according to parameter adjusting method, archive update scheme, flying guidance selection, diversity preservation approach, and hybridization with other algorithms. Moreover, it also provides a comprehensive survey on MOPSO applications in smart grid, and gives valuable MOPSO design suggestions to solve MOP in smart grid. This paper can serve a very useful purpose by providing a good reference source of MOPSO design to those interested in multiobjective optimization issues in smart grid.

Deskripsi Alternatif :

Multiobjective optimization problems (MOP) emerging in smart grid, such as optimal operation of distributed generation (DG) and microgrid, are very complex because of conflicting objectives, high dimension variables, and numerous operational or security constraints, and difficult to be solved. Multiobjective particle swarm optimization (MOPSO) has powerful potential for obtaining Pareto optimal solutions of these MOPs in a run because it has advantages of parallel computation, faster convergence, and easier implementation. This paper summarizes general procedure of MOPSO at first and then well categorizes MOPSO improvements according to parameter adjusting method, archive update scheme, flying guidance selection, diversity preservation approach, and hybridization with other algorithms. Moreover, it also provides a comprehensive survey on MOPSO applications in smart grid, and gives valuable MOPSO design suggestions to solve MOP in smart grid. This paper can serve a very useful purpose by providing a good reference source of MOPSO design to those interested in multiobjective optimization issues in smart grid.

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