A distribution network reconstruction method with DG and EV based on improved gravitation algorithm. (3rd May 2021)
- Record Type:
- Journal Article
- Title:
- A distribution network reconstruction method with DG and EV based on improved gravitation algorithm. (3rd May 2021)
- Main Title:
- A distribution network reconstruction method with DG and EV based on improved gravitation algorithm
- Authors:
- Sun, Qi
Yu, Yongjin
Li, Debing
Hu, Xiangqian - Abstract:
- Abstract : In order to solve the problem of distribution network reconstruction with distributed generation (DG) and electric vehicle (EV), a multi-objective distribution network reconstruction model with DG and EV is established in this study. Two rules for opening the loop are proposed to reduce the probability of infeasible solutions. Some measures are proposed to improve traditional gravitational algorithm (GSA). Firstly, the particle swarm algorithm (PSO) is combined to improves the update formula of speed and position. In this way, the global search capability of the GSA is enhanced, which gives the best performance with respect to jump out of the local traps. Furthermore, the processing method for agents that cross the boundary is improved, which increases the diversity of samples while generating elite particles. Hence, this method can improve the efficiency of the algorithm. Finally, the variability of load, DG and EV is considered for dynamic reconstruction. The validity of the optimization algorithm and refactoring strategy are demonstrated by case studies in the paper.
- Is Part Of:
- Systems science & control engineering. Volume 9(2021)Supplement 2
- Journal:
- Systems science & control engineering
- Issue:
- Volume 9(2021)Supplement 2
- Issue Display:
- Volume 9, Issue 2 (2021)
- Year:
- 2021
- Volume:
- 9
- Issue:
- 2
- Issue Sort Value:
- 2021-0009-0002-0000
- Page Start:
- 6
- Page End:
- 13
- Publication Date:
- 2021-05-03
- Subjects:
- Gravity algorithm -- particle swarm algorithm -- reconfiguration of distribution network -- DG -- EV
System theory -- Periodicals
Automatic control -- Periodicals
003.05 - Journal URLs:
- http://www.tandfonline.com/ ↗
http://www.tandfonline.com/toc/tssc20/current ↗ - DOI:
- 10.1080/21642583.2020.1833781 ↗
- Languages:
- English
- ISSNs:
- 2164-2583
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16620.xml