Optimal implementation of consumer demand response program with consideration of uncertain generation in a microgrid

Alikhani, M; Moghaddam, MP; Moazzen, F; Azadi, A

Optimal implementation of consumer demand response program with consideration of uncertain generation in a microgrid

Alikhani, M Moghaddam, MP Moazzen, F

Azadi, A

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Electric Power Systems Research, 2023, 225, pp. 109859
Issue Date:: 2023-12-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0378779623007472-main.pdf	Published version	7.02 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Alikhani, M
dc.contributor.author	Moghaddam, MP
dc.contributor.author	Moazzen, F https://orcid.org/0000-0002-1834-662X
dc.contributor.author	Azadi, A
dc.date.accessioned	2024-05-09T01:26:17Z
dc.date.available	2024-05-09T01:26:17Z
dc.date.issued	2023-12-01
dc.identifier.citation	Electric Power Systems Research, 2023, 225, pp. 109859
dc.identifier.issn	0378-7796
dc.identifier.uri	http://hdl.handle.net/10453/178756
dc.description.abstract	Power systems encounter frequent challenges related to imbalances between load and generation, prompting the exploration of Demand Side Management (DSM) techniques, notably through the adoption of Demand Response Programs (DRPs). Nevertheless, integrating DRPs into smart grids presents complexities. This research paper proposes an enhanced Weighted Fuzzy Average (WFA) K-means clustering algorithm to effectively group consumers' load patterns. Furthermore, the behaviors of DRP implementations for each consumption cluster are modeled in both linear and nonlinear fashions, utilizing the Coefficient of Participation (CoP) as the foundation for these models. To identify the most optimum DRP program, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is employed, with decision indicators weighted using Shannon entropy. The proposed method is then evaluated by simulating a 33-bus Microgrid (MG) with uncertain generation, incorporating wind turbine, photovoltaic, and Battery Energy Storage System (BESS) components. The outcomes demonstrate that through the optimal application of DRPs in a microgrid, diverse objectives of consumers and utility companies can be successfully attained.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Electric Power Systems Research
dc.relation.isbasedon	10.1016/j.epsr.2023.109859
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.title	Optimal implementation of consumer demand response program with consideration of uncertain generation in a microgrid
dc.type	Journal Article
utslib.citation.volume	225
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-09T01:26:15Z
pubs.publication-status	Published
pubs.volume	225

Abstract:

Power systems encounter frequent challenges related to imbalances between load and generation, prompting the exploration of Demand Side Management (DSM) techniques, notably through the adoption of Demand Response Programs (DRPs). Nevertheless, integrating DRPs into smart grids presents complexities. This research paper proposes an enhanced Weighted Fuzzy Average (WFA) K-means clustering algorithm to effectively group consumers' load patterns. Furthermore, the behaviors of DRP implementations for each consumption cluster are modeled in both linear and nonlinear fashions, utilizing the Coefficient of Participation (CoP) as the foundation for these models. To identify the most optimum DRP program, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is employed, with decision indicators weighted using Shannon entropy. The proposed method is then evaluated by simulating a 33-bus Microgrid (MG) with uncertain generation, incorporating wind turbine, photovoltaic, and Battery Energy Storage System (BESS) components. The outcomes demonstrate that through the optimal application of DRPs in a microgrid, diverse objectives of consumers and utility companies can be successfully attained.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178756