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Distribution network management under electricity deregulation using evolutionary many‐objective optimization
Author(s) -
Sekizaki Shinya,
Nishizaki Ichiro,
Hayashida Tomohiro
Publication year - 2017
Publication title -
journal of multi‐criteria decision analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 47
eISSN - 1099-1360
pISSN - 1057-9214
DOI - 10.1002/mcda.1627
Subject(s) - electricity , deregulation , mathematical optimization , computer science , transformer , operator (biology) , electricity market , pareto principle , evolutionary algorithm , multi objective optimization , electric power distribution , operations research , topology (electrical circuits) , economics , engineering , mathematics , electrical engineering , voltage , biochemistry , chemistry , repressor , gene , transcription factor , macroeconomics
A distribution network operator can reconfigure the network topology by operating section switches to improve multiple objective functions, that is, the line losses and the lifespan of transformers. In the context of long term network management, the impact of future electricity deregulation should be taken into account. Because the related literature has not addressed the above problems, this paper presents a multicriteria optimization approach for efficient distribution network management under electricity market deregulation. The main contributions of this paper can be summarized as follows: (a) the time‐variable (time dependent) and nonconvex issues for the distribution network management are formulated such that the time series variations of the line losses and the transformers mechanical operations are modelled, (b) an electricity consumption model under the deregulation, in which consumers flexibly respond to electricity prices, is taken into account, and (c) from the practical viewpoint, the proposed evolutionary many‐objective optimization approach is applied to find approximated Pareto optimal solutions of the nonconvex large‐scale problem within practical operating time. It can be seen from the computational experiment that the proposed method enables the network operator to easily identify a better network topology, which adequately meets the multiple requirements of the operator.