Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract)

Alvaro Perez-Diaz, Enrico H. Gerding, Frank McGroarty

IJCAI 2020 pp. 5095-5099

doi:10.24963/IJCAI.2020/714 /ijcai/2020/perezdiaz2020ijcai-catching/

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.

PDF IJCAI Semantic Scholar

Cite

Text

Perez-Diaz et al. "Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/714

Markdown

[Perez-Diaz et al. "Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/perezdiaz2020ijcai-catching/) doi:10.24963/IJCAI.2020/714

BibTeX

@inproceedings{perezdiaz2020ijcai-catching,
  title     = {{Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract)}},
  author    = {Perez-Diaz, Alvaro and Gerding, Enrico H. and McGroarty, Frank},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {5095-5099},
  doi       = {10.24963/IJCAI.2020/714},
  url       = {https://mlanthology.org/ijcai/2020/perezdiaz2020ijcai-catching/}
}