Online matching and preferences in future electricity markets

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Abstract

Electricity markets are to be rethought in view of the context of deployment of distributed energy resources, new enabling technologies and evolving business models. Future market mechanisms should have no barrier to entry, while being scalable and giving the possibility to accommodate asynchronicity. Consequently, we propose here to use online matching algorithms, relying on various types of continuous double auctions. They allow agents to trade electricity forward contracts while expressing preferences and being continuously matched as new orders come. Such markets can accommodate agents and trades of any size and characteristics. We eventually concentrate on naive greedy and pro-rata matching algorithms. A discrete double-auction is used as a benchmark. The double auctions are generalized to account for preferences. A case-study application allows us to discuss the computational properties and optimality of the various approaches. An upper bound on the sub-optimality of online matching algorithms, compared to an offline double auction, is also provided.
Original languageEnglish
Title of host publicationProceedings of the Nineteenth Yale Workshop on Adaptive and Learning Systems
Number of pages8
Publication date2019
Publication statusPublished - 2019
EventNineteenth Yale Workshop on Adaptive and Learning Systems
- Yale University, New Haven, United States
Duration: 10 Jun 201912 Jun 2019
http://www.eng.yale.edu/css/

Workshop

WorkshopNineteenth Yale Workshop on Adaptive and Learning Systems
LocationYale University
CountryUnited States
CityNew Haven
Period10/06/201912/06/2019
Internet address

Keywords

  • Electricity markets
  • Peer-to-peer trading
  • Order (online) matching
  • Greedy algorithms

Cite this

Esch, H. S., Moret, F., Pinson, P., & Marin Radoszynski, A. (2019). Online matching and preferences in future electricity markets. In Proceedings of the Nineteenth Yale Workshop on Adaptive and Learning Systems
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Esch, HS, Moret, F, Pinson, P & Marin Radoszynski, A 2019, Online matching and preferences in future electricity markets. in Proceedings of the Nineteenth Yale Workshop on Adaptive and Learning Systems. Nineteenth Yale Workshop on Adaptive and Learning Systems
, New Haven, United States, 10/06/2019.

Online matching and preferences in future electricity markets. / Esch, Helge Stefan; Moret, Fabio; Pinson, Pierre; Marin Radoszynski, Andrea.

Proceedings of the Nineteenth Yale Workshop on Adaptive and Learning Systems. 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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T1 - Online matching and preferences in future electricity markets

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AU - Pinson, Pierre

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N2 - Electricity markets are to be rethought in view of the context of deployment of distributed energy resources, new enabling technologies and evolving business models. Future market mechanisms should have no barrier to entry, while being scalable and giving the possibility to accommodate asynchronicity. Consequently, we propose here to use online matching algorithms, relying on various types of continuous double auctions. They allow agents to trade electricity forward contracts while expressing preferences and being continuously matched as new orders come. Such markets can accommodate agents and trades of any size and characteristics. We eventually concentrate on naive greedy and pro-rata matching algorithms. A discrete double-auction is used as a benchmark. The double auctions are generalized to account for preferences. A case-study application allows us to discuss the computational properties and optimality of the various approaches. An upper bound on the sub-optimality of online matching algorithms, compared to an offline double auction, is also provided.

AB - Electricity markets are to be rethought in view of the context of deployment of distributed energy resources, new enabling technologies and evolving business models. Future market mechanisms should have no barrier to entry, while being scalable and giving the possibility to accommodate asynchronicity. Consequently, we propose here to use online matching algorithms, relying on various types of continuous double auctions. They allow agents to trade electricity forward contracts while expressing preferences and being continuously matched as new orders come. Such markets can accommodate agents and trades of any size and characteristics. We eventually concentrate on naive greedy and pro-rata matching algorithms. A discrete double-auction is used as a benchmark. The double auctions are generalized to account for preferences. A case-study application allows us to discuss the computational properties and optimality of the various approaches. An upper bound on the sub-optimality of online matching algorithms, compared to an offline double auction, is also provided.

KW - Electricity markets

KW - Peer-to-peer trading

KW - Order (online) matching

KW - Greedy algorithms

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Esch HS, Moret F, Pinson P, Marin Radoszynski A. Online matching and preferences in future electricity markets. In Proceedings of the Nineteenth Yale Workshop on Adaptive and Learning Systems. 2019