Illegitimate Trade Detection for Electricity Energy Markets /Author=Sun, Hongbo; Shoichi, Kitamura; Nikovski, Daniel N.; Mori, Kazuyuki; Hashimoto, Hiroyuki /CreationDate=November 25, 2020 /Subject=Electric Systems

This paper proposes a novel algorithm for detecting trade activities suspected to be illegitimate across day-ahead and real-time energy markets. Illegitimate trades bring severe risks to the financial health of energy markets and the operation stability of power grids. It is of critical importance for the operators to identify such activities in a sound and timely manner. The proposed algorithm firstly generates a set of legitimate trade feature samples using historical trade profiles of energy markets based on the comparison of day-ahead and real-time trade profiles and related environmental impacts. Then a set of illegitimate trade feature samples are created using a Genetic-algorithms based negative selection procedure based on those legitimate trade feature samples. The created illegitimate samples are further labeled with specific illegitimate trade types by comparing with pre-defined typical trade feature samples for each illegitimate trade type. After that, deep learning is employed to learn the relationship between the trade features and associated legitimacy labels from the sets of legitimate and illegitimate trade feature samples, and predict the legitimacy status for incoming trade profiles according to corresponding trade features. The effectiveness of the proposed algorithm has been demonstrated using sample trade profiles obtained from New England ISO International Conference on Smart Grids and Energy Systems c © 2020 MERL. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. 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Mitsubishi Electric Research Laboratories, Inc. 201 Broadway, Cambridge, Massachusetts 02139 Illegitimate Trade Detection for Electricity Energy Markets Hongbo Sun Data Analytics Group Mitsubishi Electric Research Labs Cambridge, MA 02139, USA hongbosun@merl.com Shoichi Kitamura Advanced Technology Center Mitsubishi Electric Corporation Amagasaki 661-8661, Japan kitamura.shoichi@dw.mitsubishielectric. co.jp Daniel Nikovski Data Analytics Group Mitsubishi Electric Research Labs Cambridge, MA 02139, USA nikovski@merl.com Kazuyuki Mori Advanced Technology Center Mitsubishi Electric Corporation Amagasaki 661-8661, Japan mori.kazuyuki@ab.mitsubishielectric. co.jp Hiroyuki Hashimoto Data Analytics Group Mitsubishi Electric Research Labs Cambridge, MA 02139, USA hashimoto@merl.com Abstract—This paper proposes a novel algorithm for detecting trade activities suspected to be illegitimate across day-ahead and real-time energy markets. Illegitimate trades bring severe risks to the financial health of energy markets and the operation stability of power grids. It is of critical importance for the operators to identify such activities in a sound and timely manner. The proposed algorithm firstly generates a set of legitimate trade feature samples using historical trade profiles of energy markets based on the comparison of day-ahead and real-time trade profiles and related environmental impacts. Then a set of illegitimate trade feature samples are created using a Genetic-algorithms based negative selection procedure based on those legitimate trade feature samples. The created illegitimate samples are further labeled with specific illegitimate trade types by comparing with pre-defined typical trade feature samples for each illegitimate trade type. After that, deep learning is employed to learn the relationship between the trade features and associated legitimacy labels from the sets of legitimate and illegitimate trade feature samples, and predict the legitimacy status for incoming trade profiles according to corresponding trade features. The effectiveness of the proposed algorithm has been demonstrated using sample trade profiles obtained from New England ISO. Keywords— anomaly detection, electricity energy markets, genetic algorithms, illegitimate trade, negative selectionThis paper proposes a novel algorithm for detecting trade activities suspected to be illegitimate across day-ahead and real-time energy markets. Illegitimate trades bring severe risks to the financial health of energy markets and the operation stability of power grids. It is of critical importance for the operators to identify such activities in a sound and timely manner. The proposed algorithm firstly generates a set of legitimate trade feature samples using historical trade profiles of energy markets based on the comparison of day-ahead and real-time trade profiles and related environmental impacts. Then a set of illegitimate trade feature samples are created using a Genetic-algorithms based negative selection procedure based on those legitimate trade feature samples. The created illegitimate samples are further labeled with specific illegitimate trade types by comparing with pre-defined typical trade feature samples for each illegitimate trade type. After that, deep learning is employed to learn the relationship between the trade features and associated legitimacy labels from the sets of legitimate and illegitimate trade feature samples, and predict the legitimacy status for incoming trade profiles according to corresponding trade features. The effectiveness of the proposed algorithm has been demonstrated using sample trade profiles obtained from New England ISO. Keywords— anomaly detection, electricity energy markets, genetic algorithms, illegitimate trade, negative selection