Multi-objective distributed wind generation planning in an unbalanced distribution system

Yu Zheng; Zhao Yang Dong; Ke Meng; Hongming Yang; Mingyong Lai; Kit Po Wong

doi:10.17775/CSEEJPES.2017.0023

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Multi-objective distributed wind generation planning in an unbalanced distribution system

更新时间：2025-12-18

- Multi-objective distributed wind generation planning in an unbalanced distribution system
- CSEE Journal of Power and Energy Systems Vol. 3, Issue 2, Pages: 186-195(2017)
- 作者机构：
  
  1. Electric Power Research Institute, China Southern Power Grid,Guangzhou,China,510080
  2. The University of Sydney, Sydney, NSW, AU
  3. Changsha University of Science and Technology,,, CN,Hunan,Changsha
  4. University of Western Australia, Perth, WA, AU
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2017.0023
  CLC：
- Published：2017
- 稿件说明：
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Yu Zheng, Zhao Yang Dong, Ke Meng, et al. Multi-objective distributed wind generation planning in an unbalanced distribution system[J]. CSEE Journal of Power and Energy Systems, 2017, 3(2): 186-195.
DOI：

Yu Zheng, Zhao Yang Dong, Ke Meng, et al. Multi-objective distributed wind generation planning in an unbalanced distribution system[J]. CSEE Journal of Power and Energy Systems, 2017, 3(2): 186-195. DOI： 10.17775/CSEEJPES.2017.0023.

摘要

Abstract

In addition to increasing penetration of distributed generation (DG)

the distribution system power flow may be significantly impacted by direction and magnitude. This paper proposes a method for optimal placement of wind DG considering the unbalanced operation of distribution systems. The objective function includes static voltage stability index

three-phase unbalance index

system reliability index

and DG investment cost. The untransposed distribution lines and unbalanced load are modelled

and corresponding static voltage stability index and system reliability considering DG penetrations are derived. The expected and stochastic daily distributed generation and demand profiles in four seasons are calculated to improve the accuracy. To solve this multi-objective optimization model

a fuzzy membership function is used to integrate the four individual objectives

and a sensitivity-based method is proposed to solve the model efficiently. Case study on IEEE 13-bus distribution 3-phase networks and 123-node test feeder successfully verifies the performance of the proposed approach.

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