Catching cheats: detecting strategic manipulation in distributed optimisation of electric vehicle aggregators
Catching cheats: detecting strategic manipulation in distributed optimisation of electric vehicle aggregators
We consider a scenario where self-interested Electric Vehicle (EV) aggregators compete in the day-ahead electricity market in order to purchase the electricity needed to meet EV requirements. We propose a novel decentralised bidding coordination algorithm based on the Alternating Direction Method of Multipliers (ADMM). Our simulations using real market and driver data from Spain show that the algorithm is able to significantly reduce energy costs for all participants. Furthermore, we postulate that strategic manipulation by deviating agents is possible in decentralised algorithms like ADMM. Hence, we describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Our simulations show that our ADMM-based algorithm can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. At the same time, our proposed manipulation detection algorithm achieves very high accuracy.
5095-5099
International Joint Conferences on Artificial Intelligence
Perez-Diaz, Alvaro
dc83bca5-5108-4448-878f-23e73dec4c88
Gerding, Enrico H.
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
1 December 2020
Perez-Diaz, Alvaro
dc83bca5-5108-4448-878f-23e73dec4c88
Gerding, Enrico H.
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
Perez-Diaz, Alvaro, Gerding, Enrico H. and McGroarty, Frank
(2020)
Catching cheats: detecting strategic manipulation in distributed optimisation of electric vehicle aggregators.
Bessiere, Christian
(ed.)
In Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020: Journal Track.
vol. 2021-January,
International Joint Conferences on Artificial Intelligence.
.
(doi:10.24963/ijcai.2020/714).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We consider a scenario where self-interested Electric Vehicle (EV) aggregators compete in the day-ahead electricity market in order to purchase the electricity needed to meet EV requirements. We propose a novel decentralised bidding coordination algorithm based on the Alternating Direction Method of Multipliers (ADMM). Our simulations using real market and driver data from Spain show that the algorithm is able to significantly reduce energy costs for all participants. Furthermore, we postulate that strategic manipulation by deviating agents is possible in decentralised algorithms like ADMM. Hence, we describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Our simulations show that our ADMM-based algorithm can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. At the same time, our proposed manipulation detection algorithm achieves very high accuracy.
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Published date: 1 December 2020
Venue - Dates:
29th International Joint Conference on Artificial Intelligence, IJCAI 2020, , Yokohama, Japan, 2021-01-01
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Local EPrints ID: 453401
URI: http://eprints.soton.ac.uk/id/eprint/453401
ISSN: 1045-0823
PURE UUID: 8e25d455-d393-489f-b878-3b8e598dea56
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Date deposited: 13 Jan 2022 18:23
Last modified: 17 Mar 2024 03:03
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