Matchmaking model for bilateral trading decisions of load serving entity

Imran, K; Ullah, K; Khattak, A; Zhang, J; Pal, A; Rafique, MN; Baig, SM

Matchmaking model for bilateral trading decisions of load serving entity

Imran, K Ullah, K Khattak, A Zhang, J

Pal, A Rafique, MN Baig, SM

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Publisher:: ELSEVIER SCIENCE SA
Publication Type:: Journal Article
Citation:: Electric Power Systems Research, 2020, 183
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Imran, K
dc.contributor.author	Ullah, K
dc.contributor.author	Khattak, A
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722
dc.contributor.author	Pal, A
dc.contributor.author	Rafique, MN
dc.contributor.author	Baig, SM
dc.date.accessioned	2021-02-28T19:28:05Z
dc.date.available	2021-02-28T19:28:05Z
dc.date.issued	2020-06-01
dc.identifier.citation	Electric Power Systems Research, 2020, 183
dc.identifier.issn	0378-7796
dc.identifier.issn	1873-2046
dc.identifier.uri	http://hdl.handle.net/10453/146525
dc.description.abstract	© 2020 Matchmaking and bilateral negotiations are two distinct phases of practical market participants’ decision making for bilateral transactions. Agent-based models are naturally suitable for electricity markets in general and bilateral transactions in particular. This paper's contribution includes development of a novel matchmaking model that generates forward contracting power and utility curves. The matchmaking model enables a load serving entity agent to undertake its own matchmaking, to find optimal trading allocations over a range of prices, before engaging in bilateral negotiations with generation company agents. Open-source agent-based simulation platform allows combined simulation of bilateral transactions and day-ahead auction. In this research paper, matchmaking is achieved by direct-search without any organized bulletin board, broker, or matchmaker. Instead of random matchmaking, portfolio optimization based matchmaking systematically explores available electricity trading options throughout the market: local and non-local bilateral trades as well as day-ahead auctions. The matchmaking algorithm is unique because it scans all trading options over the entire range of negotiable prices. Depending on private profit-seeking goals, risk-aversion preferences and market price statistics, each load serving entity agent individually finds its matchmaking results. A set of case studies demonstrates how matchmaking model depends on transmission rights and performs for different risk aversion factors.
dc.language	English
dc.publisher	ELSEVIER SCIENCE SA
dc.relation.ispartof	Electric Power Systems Research
dc.relation.isbasedon	10.1016/j.epsr.2020.106281
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.title	Matchmaking model for bilateral trading decisions of load serving entity
dc.type	Journal Article
utslib.citation.volume	183
utslib.for	0906 Electrical and Electronic Engineering
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-28T19:28:03Z
pubs.publication-status	Published
pubs.volume	183

Abstract:

© 2020 Matchmaking and bilateral negotiations are two distinct phases of practical market participants’ decision making for bilateral transactions. Agent-based models are naturally suitable for electricity markets in general and bilateral transactions in particular. This paper's contribution includes development of a novel matchmaking model that generates forward contracting power and utility curves. The matchmaking model enables a load serving entity agent to undertake its own matchmaking, to find optimal trading allocations over a range of prices, before engaging in bilateral negotiations with generation company agents. Open-source agent-based simulation platform allows combined simulation of bilateral transactions and day-ahead auction. In this research paper, matchmaking is achieved by direct-search without any organized bulletin board, broker, or matchmaker. Instead of random matchmaking, portfolio optimization based matchmaking systematically explores available electricity trading options throughout the market: local and non-local bilateral trades as well as day-ahead auctions. The matchmaking algorithm is unique because it scans all trading options over the entire range of negotiable prices. Depending on private profit-seeking goals, risk-aversion preferences and market price statistics, each load serving entity agent individually finds its matchmaking results. A set of case studies demonstrates how matchmaking model depends on transmission rights and performs for different risk aversion factors.

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http://hdl.handle.net/10453/146525