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Scarcity events analysis in adequacy studies using CN2 rule mining

Energy and AI 2022年第2期8卷 116-123页

作者： fabian heymann Ricardo Bessa Mario Liebensteiner Konstantinos Parginos Juan Carlos Martin Hinojar Pablo Duenas Center for Power and Energy Systems INESC TEC Porto Portugal School of Business Economics and Society Friedrich-Alexander-Universitat Erlangen-Nuremberg NurembergGermany System Development ENTSO-E Brussels Belgium MIT Energy Initiative Massachusetts Institute of Technology Cambridge(MA) United States PERSEE Mines ParisTech

Dealing with scarcity events is nowadays gaining relevance in electricity market studies, as traditionally predictable generation and consumption patterns are fading. Policymakers and system planners use therefore adequacy studies to a) understand if the current market design will attract sufficient generation capacity to meet electricity demand in the future and b) to comprehend what drives system inadequacy or resource scarcity when future scenarios lack adequate capacity. This work addressed the latter and showcases a first in-its-kind rulebased methodology that filters scarcity events from a large set of electricity market simulations. In this proof-of-concept, a rule-mining algorithm is applied to outputs from ENTSO-E’s Pan-European electricity market model, which is run for 700 model scenarios, each covering 8760 time steps. The developed methodology shows how to unveil potential reasons behind scarcity events in an automated, interpretable, and scalable manner.

关键词： Electricity markets Data mining Security of supply Renewable energy Interpretability

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A 3D indicator for guiding AI applications in the energy sector

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Energy and AI 2022年第3期9卷 65-77页

作者： Hugo Quest Marine Cauz fabian heymann Christian Rod Lionel Perret Christophe Ballif Alessandro Virtuani Nicolas Wyrsch École Polytechnique Fédérale de Lausanne(EPFL) Institute of Electrical and Micro Engineering(IEM)Photovoltaics and Thin-Film Electronics Laboratory(PV-LAB)NeuchâtelSwitzerland S Swiss Solar Solutions AG ThunSwitzerland Planair SA Yverdon-les-BainsSwitzerland Swiss Federal Office for Energy(SFOE) Digital Innovation OfficeIttigenSwitzerland Swiss Center for Electronics and Microtechnology(CSEM) Sustainable Energy CenterNeuchâtelSwitzerland

The utilisation of Artificial Intelligence (AI) applications in the energy sector is gaining momentum, withincreasingly intensive search for suitable, high-quality and trustworthy solutions that displayed promisingresults in research. The growing interest comes from decision makers of both the industry and policydomains, searching for applications to increase companies’ profitability, raise efficiency and facilitate theenergy transition. This paper aims to provide a novel three-dimensional (3D) indicator for AI applicationsin the energy sector, based on their respective maturity level, regulatory risks and potential benefits. Casestudies are used to exemplify the application of the 3D indicator, showcasing how the developed frameworkcan be used to filter promising AI applications eligible for governmental funding or business *** addition, the 3D indicator is used to rank AI applications considering different stakeholder preferences(risk-avoidance, profit-seeking, balanced). These results allow AI applications to be better categorised in theface of rapidly emerging national and intergovernmental AI strategies and regulations that constrain the useof AI applications in critical infrastructures.

关键词： Artificial intelligence Digitalisation AI application Big data AI policy Energy sector

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