{"type":"ScholarlyArticle","title":"Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract)","authors":[{"name":"Alvaro Perez Diaz","slug":"alvaro-perez-diaz","given":"Alvaro","family":"Perez-Diaz","affiliation":"Electronics and Computer Science , University of Southampton"},{"name":"Enrico H. Gerding","slug":"enrico-h-gerding","given":"Enrico H.","family":"Gerding","affiliation":"Electronics and Computer Science , University of Southampton"},{"name":"Frank McGroarty","slug":"frank-mcgroarty","given":"Frank","family":"McGroarty","affiliation":"Southampton Business School , University of Southampton"}],"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.","doi":"10.24963/ijcai.2020/714","url":"https://ijcai.org/proceedings/2020/714","pdf":"https://ijcai.org/proceedings/2020/0714.pdf","markdown":"https://ijcai.org/proceedings/2020/714.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/714","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Journal track","track":"Journal track","number":714,"pages":{"first":5095,"last":5099},"keywords":["Agent-based and Multi-agent Systems: Coordination and Cooperation","Multidisciplinary Topics and Applications: Transportation","Planning and Scheduling: Distributed;Multi-agent Planning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24484/file_24484.m3u8","length_sec":272,"provider":"hls"}],"references":[{"position":1,"raw":"[Boyd et al., 2010] Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. Distributed Opti- mization and Statistical Learning via the Alternating Di- rection Method of Multipliers. Foundations and Trends in Machine Learning, 3(1):1-122, 2010.","title":"Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers","authors":"Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein","year":"2010","venue":"Foundations and Trends in Machine Learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[International Energy Agency, 2019] International Energy Agency. Global EV Outlook 2019: Scaling-up the transition to electric mobility. https://www.iea.org/reports/global-ev-outlook-2019. 2019. Last accessed on 05/05/2020.","title":"Global EV Outlook 2019: Scaling-up the transition to electric mobility","authors":null,"year":"2019","venue":null,"doi":null,"url":"https://www.iea.org/reports/global-ev-outlook-2019.2019","internal_url":null},{"position":3,"raw":"[Kempton et al., 2001] Willett Kempton, Jasna Tomic, Steven Letendre, Alec Brooks, and Timothy Lipman. Vehicle-to-Grid Power: Battery, Hybrid, and Fuel Cell Vehicles as Resources for Distributed Electric Power in California. Fuel Cell, IUCD-ITS-R(June):95, 2001.","title":"Vehicle-to-Grid Power: Battery, Hybrid, and Fuel Cell Vehicles as Resources for Distributed Electric Power in California","authors":"Jasna Willett Kempton, Steven Tomic, Alec Letendre, Timothy Brooks, Lipman","year":"2001","venue":"Fuel Cell, IUCD-ITS-R","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Metz, 1978] C E Metz. Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8(4):283-298, 1978.","title":"Basic principles of ROC analysis","authors":"C E Metz","year":"1978","venue":"Seminars in Nuclear Medicine","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Perez-Diaz et al., 2018a] Alvaro Perez-Diaz, Enrico Gerd- ing, and Frank McGroarty. Coordination and Payment Mechanisms for Electric Vehicle Aggregators. Applied Energy, 212:185-195, 2018.","title":"Coordination and Payment Mechanisms for Electric Vehicle Aggregators","authors":"Perez-Diaz","year":"2018","venue":"Applied Energy","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Perez-Diaz et al., 2018b] Alvaro Perez-Diaz, Enrico Gerd- ing, and Frank McGroarty. Coordination of Electric Ve- hicle Aggregators: A Coalitional Approach. In Proceed- ings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), pages 676-684, 2018.","title":"Coordination of Electric Vehicle Aggregators: A Coalitional Approach","authors":"Perez-Diaz","year":"2018","venue":"Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Perez-Diaz et al., 2020] Alvaro Perez-Diaz, Enrico Gerd- ing, and Frank McGroarty. Catching Cheats: Detect- ing Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators. Journal of Artificial Intelli- gence Research, 67:437-470, 2020.","title":"Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators","authors":"Alvaro Perez-Diaz, Enrico Gerding, Frank Mcgroarty","year":"2020","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Rigas et al., 2015] Emmanouil S. Rigas, Sarvapali D. Ram- churn, and Nick Bassiliades. Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey. IEEE Transactions on Intelligent Transportation Systems, 16(4):1619-1635, 2015.","title":"Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey","authors":"S Emmanouil, Rigas, D Sarvapali, Nick Ramchurn, Bassiliades","year":"2015","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}