Coordinated flexible energy and self-healing management according to the multi-agent system-based restoration scheme in active distribution network

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Leila Bagherzadeh (Author) Other; Hossein Shayeghi (Author) Other; Sasan Pirouzi (Author) Other; Miadreza Shafie-khah (Author) Other; João P. S. Catalão (Author) FEUP

Journal

Title: IET Renewable Power Generation

Vol. 15 No. 8

Pages: 1765-1777

ISSN: 1752-1416

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 8 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00T-NS1

DOI: 10.1049/rpg2.12145

Abstract (EN): This study presents the optimal model of the coordinated flexible energy and self-healing management (C-FE&SH-M) in the active distribution network (ADN) including renewable energy sources (RESs), electric vehicles (EVs) and demand response program (DRP).The flexible energy management (FEM) is extracted using coordination between the RESs, EVs and DRP. The self-healing method (SHM) is related to multi-agent system-based restoration process (MAS-based RP) that finds the optimal restoration pattern at the fault condition according to the different zone agents (ZAs) distributing along with the network. This method minimizes the difference between energy cost and flexibility benefit related to the FEM part and difference between the number of switching operation and priority loads restored based on the SHM part. Also, this problem subjects to power flow equations, RESs and active loads constraints, restoration process formulation and system operation limits. Stochastic programming is used to model the uncertainty of loads, energy prices, RESs and EVs. Hereupon, the suggested strategy is implemented on the 33-bus radial distribution network and it is solved by the crow search algorithm (CSA). Ultimately, the obtained results imply the high flexibility and security of the operation, incorporating the proposed strategy, and delineate the optimal restoration scheme for the ADN.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13