Multi‐objective optimisation‐based active distribution system planning with reconfiguration, intermittent RES, and DSTATCOM

The enormous load growth in recent times has forced distribution companies to undertake comprehensive planning of the active distribution system (ADS) to maintain superior service to their consumers. Under different critical situations in the restructured power system, reconfiguration in combination...

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Vydáno v:IET renewable power generation Ročník 13; číslo 13; s. 2418 - 2429
Hlavní autoři: Sannigrahi, Surajit, Roy Ghatak, Sriparna, Acharjee, Parimal
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Institution of Engineering and Technology 07.10.2019
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ISSN:1752-1424, 1752-1416, 1752-1424
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Shrnutí:The enormous load growth in recent times has forced distribution companies to undertake comprehensive planning of the active distribution system (ADS) to maintain superior service to their consumers. Under different critical situations in the restructured power system, reconfiguration in combination with the incorporation of renewable energy sources (RESs) and distributed static compensator (DSTATCOM) must be utilised for accurate system planning. In addition, from a practical viewpoint, the time‐variant load demand of different consumers and the intermittency of RES units must be considered. This study proposes a modified multi‐objective particle swarm optimisation (m‐MOPSO) technique for ADS planning considering reconfiguration, RES, and DSTATCOM to enhance voltage stability, reduce pollution, improve reliability, and maximise financial benefits. In the proposed m‐MOPSO, a novel non‐dominant sorting strategy is used to maintain diversity among the non‐dominated solutions. The time‐varying system load, yearly load growth, and intermittent power generation of RES are considered to construct a realistic planning model. The proposed technique is tested on the 33‐bus ADS considering different planning schemes to provide the most suitable planning scheme to the ADS planners. Moreover, the accuracy of the proposed algorithm is confirmed by comparing it with other multi‐objective algorithms.
ISSN:1752-1424
1752-1416
1752-1424
DOI:10.1049/iet-rpg.2018.6060