Supply-side gaming on electricity markets with physical constrained transmission network
Eric Guerci
(1, 2)
,
Mohammad Ali Rastegar
(3)
,
Silvano Cincotti
(4)
,
Federico Delfino
(5)
,
Renato Procopio
(5)
,
Marco Ruga
(3)
1
GREDEG -
Groupe de Recherche en Droit, Economie et Gestion
2 GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille
3 Chercheur indépendant
4 DIME - Dipartimento di ingegneria meccanica, energetica, gestionale e dei trasporti
5 DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture
2 GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille
3 Chercheur indépendant
4 DIME - Dipartimento di ingegneria meccanica, energetica, gestionale e dei trasporti
5 DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture
Eric Guerci
- Fonction : Auteur
- PersonId : 177620
- IdHAL : eric-guerci
- ORCID : 0000-0003-4862-4405
- IdRef : 219669961
Résumé
This paper proposes an agent-based computational approach to study physical constrained electricity markets. The computational model consists of repeated day-ahead market sessions and a two-zone transmission network. Different inelastic load serving entities configurations are considered for studying how producers learn to strategically decommit their units and how they exercise market power by profiting from transmission network constraints. Learning producers are modeled by different multi-agent learning algorithms, such as the Q-Learning, the EWA learning and the GIGA-WoLF. Computational results point out that all learning models considered are able to learn to appropriately decommit their units and to sustain the exertion of zonal market power.
Format du dépôt | Notice |
---|---|
Type de dépôt | Communication dans un congrès |
Titre |
en
Supply-side gaming on electricity markets with physical constrained transmission network
|
Résumé |
en
This paper proposes an agent-based computational approach to study physical constrained electricity markets. The computational model consists of repeated day-ahead market sessions and a two-zone transmission network. Different inelastic load serving entities configurations are considered for studying how producers learn to strategically decommit their units and how they exercise market power by profiting from transmission network constraints. Learning producers are modeled by different multi-agent learning algorithms, such as the Q-Learning, the EWA learning and the GIGA-WoLF. Computational results point out that all learning models considered are able to learn to appropriately decommit their units and to sustain the exertion of zonal market power.
|
Auteur(s) |
Eric Guerci
1, 2
, Mohammad Ali Rastegar
3
, Silvano Cincotti
4
, Federico Delfino
5
, Renato Procopio
5
, Marco Ruga
3
1
GREDEG -
Groupe de Recherche en Droit, Economie et Gestion
( 185786 )
- GREDEG - Bâtiment 2 - Campus Azur du CNRS - 250 rue Albert Einstein - CS 10269 - F
06905 SOPHIA ANTIPOLIS Cedex
- France
2
GREQAM -
Groupement de Recherche en Économie Quantitative d'Aix-Marseille
( 199934 )
- Centre de la Vieille Charité, 2 rue de la Charité, 13236 Marseille cedex 02
- France
3
Chercheur indépendant
( 107396 )
- France
4
DIME -
Dipartimento di ingegneria meccanica, energetica, gestionale e dei trasporti
( 233219 )
- Via all'Opera Pia, 15 16145 Genova, Italia
- Italie
5
DITEN -
Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture
( 222856 )
- Via all'Opera Pia, 11 16145 Genova GE
- Italie
|
Langue du document |
Anglais
|
Vulgarisation |
Non
|
Actes |
Oui
|
Comité de lecture |
Oui
|
Invité |
Non
|
Audience |
Internationale
|
Date de publication |
2008
|
Page/Identifiant |
1-6
|
Titre du congrès |
Electricity Market, 2008. EEM 2008. 5th International Conference on European
|
Date début congrès |
2008-05-28
|
Ville |
Lisboa
|
Pays |
Portugal
|
Domaine(s) |
|
Collaboration/Projet |
|
Mots-clés |
en
Electricity markets, agent-based computational economics, multi-agent learning
|
DOI | 10.1109/EEM.2008.4579076 |
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