Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Nineteenth Yale Workshop on Adaptive and Learning Systems |
| Number of pages | 8 |
| Publication date | 2019 |
| Publication status | Published - 2019 |
| Event | Nineteenth Yale Workshop on Adaptive and Learning Systems - Yale University, New Haven, United States Duration: 10 Jun 2019 → 12 Jun 2019 http://www.eng.yale.edu/css/ |
Workshop
| Workshop | Nineteenth Yale Workshop on Adaptive and Learning Systems |
|---|---|
| Location | Yale University |
| Country | United States |
| City | New Haven |
| Period | 10/06/2019 → 12/06/2019 |
| Internet address |
Keywords
- Electricity markets
- Peer-to-peer trading
- Order (online) matching
- Greedy algorithms
Cite this
}
, 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 proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Online matching and preferences in future electricity markets
AU - Esch, Helge Stefan
AU - Moret, Fabio
AU - Pinson, Pierre
AU - Marin Radoszynski, Andrea
PY - 2019
Y1 - 2019
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
M3 - Article in proceedings
BT - Proceedings of the Nineteenth Yale Workshop on Adaptive and Learning Systems
ER -