Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty

Farzad Hosseini, Amin Safari and Meisam Farrokhifar

Renewable Energy, 2020, vol. 161, issue C, 1130-1139

Abstract: Due to the spread and dispersed of load points and also growing penetration of wind farms to distribution systems, feeder reconfiguration strategy has been encountered with a high degree of uncertainty in such networks. In this way, efficient stochastic framework analysis based on cloud theory is proposed to handle the feeder reconfiguration problem considering uncertainty in load demands and wind turbine power. The cloud model, as a linguistic approach, comprises a correlation between randomness and fuzziness and provides good information to study the uncertain parameter effects on system performance through qualitative cloud models. According to qualitative-quantitative bidirectional transmission characteristic of the cloud theory through backward-forward cloud generator algorithm, a stochastic multi-objective feeder reconfiguration problem is formulated and solved utilizing powerful non-dominated sorting group search optimization algorithm. After obtaining Pareto fronts, the best compromise solution is determined by using the fuzzy decision-making technique. To demonstrate the applicability of the proposed method and to compare obtained results with the other literature, deterministic and stochastic analysis is implemented on the IEEE 33-bus and 69-bus radial distribution systems. The superiority and satisfying performance of the proposed algorithm can be inferred from the quality of simulation solutions.

Keywords: Cloud theory; Multi-objective feeder reconfiguration; Non-dominated sorting group search optimization; Uncertainty; Wind turbine power (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014812031212X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:161:y:2020:i:c:p:1130-1139

DOI: 10.1016/j.renene.2020.07.136

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().