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Catching cheats: detecting strategic manipulation in distributed optimisation of electric vehicle aggregators

Catching cheats: detecting strategic manipulation in distributed optimisation of electric vehicle aggregators
Catching cheats: detecting strategic manipulation in distributed optimisation of electric vehicle aggregators
Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark.
electric vehicles, decentralisation, mechanism design
1076-9757
Perez-Diaz, Alvaro
dc83bca5-5108-4448-878f-23e73dec4c88
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
Perez-Diaz, Alvaro
dc83bca5-5108-4448-878f-23e73dec4c88
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072

Perez-Diaz, Alvaro, Gerding, Enrico and McGroarty, Frank (2020) Catching cheats: detecting strategic manipulation in distributed optimisation of electric vehicle aggregators. Journal of Artificial Intelligence Research, 67. (doi:10.1613/jair.1.11573).

Record type: Article

Abstract

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark.

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Accepted/In Press date: 29 November 2019
Published date: 5 March 2020
Keywords: electric vehicles, decentralisation, mechanism design

Identifiers

Local EPrints ID: 437680
URI: http://eprints.soton.ac.uk/id/eprint/437680
ISSN: 1076-9757
PURE UUID: db8b5804-28e8-4cc9-95f0-885761571e91
ORCID for Alvaro Perez-Diaz: ORCID iD orcid.org/0000-0001-8081-0772
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X
ORCID for Frank McGroarty: ORCID iD orcid.org/0000-0003-2962-0927

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Date deposited: 11 Feb 2020 17:32
Last modified: 30 Jul 2020 01:36

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Contributors

Author: Alvaro Perez-Diaz ORCID iD
Author: Enrico Gerding ORCID iD
Author: Frank McGroarty ORCID iD

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