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Perspectives on Future Power System Control Centers for Energy Transition

Journal of Modern Power Systems and Clean Energy 2022年第2期10卷 328-344页

作者： antoine marot Adrian Kelly Matija Naglic Vincent Barbesant Jochen Cremer Alexandru Stefanov Jan Viebahn Reseau de Transport d'Electricite ParisFrance Electric Power Research Institute DublinIreland TenneT Amhemthe Netherlands TU Delft Delftthe Netherlands

Today's power systems are seeing a paradigm shift under the energy transition,sparkled by the electrification of demand,digitalisation of systems,and an increasing share of decarbonated power *** of these changes have a direct impact on their control centers,forcing them to handle weather-based energy resources,new interconnections with neighbouring transmission networks,more markets,active distribution networks,micro-grids,and greater amounts of available ***,these changes have translated during the past decade to small,incremental changes,mostly centered on hardware,software,and human *** assert that more transformative changes are needed,especially regarding human-centered design approaches,to enable control room operators to manage the future power *** paper discusses the evolution of operators towards continuous operation planners,monitoring complex time horizons thanks to adequate real-time *** upcoming challenges as well as emerging technologies for power systems,we present our vision of a new evolutionary architecture for control centers,both at backend and frontend *** propose a unified hypervision scheme based on structured decision-making concepts,providing operators with proactive,collaborative,and effective decision support.

关键词： Artificial intelligence cyber-phvsical system decision-making digital architecture digital twin energy transition hypervision.

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Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

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Energy and AI 2023年第4期14卷 283-293页

作者： Malte Lehna Jan Viebahn antoine marot Sven Tomforde Christoph Scholz Fraunhofer Institute for Energy Economics and Energy System Technology(IEE) Germany Kassel University:Intelligent Embedded Systems Germany TenneT TSO BV Netherlands AI Lab Reseau de Transport d’Electricite(RTE)France Kiel University:Intelligent Systems Germany

The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy *** a consequence,active grid management is reaching its limits with conventional *** the context of the Learning to Run a Power Network(L2RPN)challenge,it has been shown that Reinforcement Learning(RL)is an efficient and reliable approach with considerable potential for automatic grid *** this article,we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent,both for the RL and the rule-based *** main improvement is a N-1 strategy,where we consider topology actions that keep the grid stable,even if one line is ***,we also propose a topology reversion to the original grid,which proved to be *** improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%.In direct comparison between rule-based and RL agent we find similar ***,the RL agent has a clear computational *** also analyse the behaviour in an exemplary case in more detail to provide additional ***,we observe that through the N-1 strategy,the actions of both the rule-based and the RL agent become more diversified.

关键词： Deep reinforcement learning Electricity grids Learning to run a power network Topology control Proximal policy optimisation

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