Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty. (December 2020)

• Record Type: Journal Article
• Authors: Hosseini, Farzad; Safari, Amin; Farrokhifar, Meisam
• Is Part Of: Renewable energy. Volume 161(2020)
• Page Start: 1130
• Page End: 1139
• Publication Date: 2020-12
• Subjects: Cloud theory -- Multi-objective feeder reconfiguration -- Non-dominated sorting group search optimization -- Uncertainty -- Wind turbine power; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.07.136
• Languages: English
• ISSNs: 0960-1481

Abstract

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. Highlights: Uncertainty associated with wind energy is proposed by a Weibull cloud model. Load variations are represented by a normal cloud model. Stochastic multi-objective feeder reconfiguration is solved utilizing non-dominated sorting GSO algorithm. Simulations have extensively demonstrated the effectiveness of the proposed model.