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 generation.Most of th...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 generation.Most 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 data.Unfortunately,these changes have translated during the past decade to small,incremental changes,mostly centered on hardware,software,and human factors.We assert that more transformative changes are needed,especially regarding human-centered design approaches,to enable control room operators to manage the future power system.This paper discusses the evolution of operators towards continuous operation planners,monitoring complex time horizons thanks to adequate real-time automation.Reviewing 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 levels.We propose a unified hypervision scheme based on structured decision-making concepts,providing operators with proactive,collaborative,and effective decision support.展开更多
The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production.As a consequence,active grid management is reaching its limits with conven...The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production.As a consequence,active grid management is reaching its limits with conventional approaches.In 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 operation.In 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 approach.The main improvement is a N-1 strategy,where we consider topology actions that keep the grid stable,even if one line is disconnected.More,we also propose a topology reversion to the original grid,which proved to be beneficial.The 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 performance.However,the RL agent has a clear computational advantage.We also analyse the behaviour in an exemplary case in more detail to provide additional insights.Here,we observe that through the N-1 strategy,the actions of both the rule-based and the RL agent become more diversified.展开更多
Achieving carbon neutrality by 2050 will require power-grid operators to absorb unprecedented volumes of variable solar and wind generation while maintaining reliability.To tackle this systems-level bottleneck,Ré...Achieving carbon neutrality by 2050 will require power-grid operators to absorb unprecedented volumes of variable solar and wind generation while maintaining reliability.To tackle this systems-level bottleneck,Réseau de Transport d’Électricité(RTE)and the research community launched Learn To Run A Power Network(L2RPN),a crowd-sourced competition aiming to accelerate the integration of intermittent renewables into power-grid operations.L2RPN is based on 16 years of weekly scenarios(832 in total)on a 118-node grid under realistic constraints,and casts real-time grid operation as a Markov-Decision-Process.The six participating teams tackled the challenge by developing autonomous agents with various strategies blending heuristics,optimization,data scaling,supervised learning,and reinforcement learning.We provide a detailed overview of all six participants’performance under the competition’s demanding design.In addition,we present an in-depth analysis of the winning solution-made publicly available-which achieves consistent decision making across scenarios,executes real-time multimodal actions in under five seconds,and performs efficient topology control via action-space reduction and a neural policy that predicts useful grid actions with over 80%accuracy.In parallel,we trained a neural alert module on 315,000 samples derived from top agents,achieving 93.9%recall in flagging dangerous states and allowing agents to predict future failure.Finally,this work not only demonstrates AI’s promise and current limits in real-time grid management but also lays a transparent foundation for more robust,trustworthy systems in the energy transition.展开更多
文摘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 generation.Most 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 data.Unfortunately,these changes have translated during the past decade to small,incremental changes,mostly centered on hardware,software,and human factors.We assert that more transformative changes are needed,especially regarding human-centered design approaches,to enable control room operators to manage the future power system.This paper discusses the evolution of operators towards continuous operation planners,monitoring complex time horizons thanks to adequate real-time automation.Reviewing 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 levels.We propose a unified hypervision scheme based on structured decision-making concepts,providing operators with proactive,collaborative,and effective decision support.
基金This work was supported by the Competence Centre for Cognitive Energy Systems of the Fraunhofer IEE and the research group Rein-forcement Learning for cognitive energy systems(RL4CES)from the Intelligent Embedded Systems of the University Kassel.
文摘The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production.As a consequence,active grid management is reaching its limits with conventional approaches.In 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 operation.In 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 approach.The main improvement is a N-1 strategy,where we consider topology actions that keep the grid stable,even if one line is disconnected.More,we also propose a topology reversion to the original grid,which proved to be beneficial.The 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 performance.However,the RL agent has a clear computational advantage.We also analyse the behaviour in an exemplary case in more detail to provide additional insights.Here,we observe that through the N-1 strategy,the actions of both the rule-based and the RL agent become more diversified.
文摘Achieving carbon neutrality by 2050 will require power-grid operators to absorb unprecedented volumes of variable solar and wind generation while maintaining reliability.To tackle this systems-level bottleneck,Réseau de Transport d’Électricité(RTE)and the research community launched Learn To Run A Power Network(L2RPN),a crowd-sourced competition aiming to accelerate the integration of intermittent renewables into power-grid operations.L2RPN is based on 16 years of weekly scenarios(832 in total)on a 118-node grid under realistic constraints,and casts real-time grid operation as a Markov-Decision-Process.The six participating teams tackled the challenge by developing autonomous agents with various strategies blending heuristics,optimization,data scaling,supervised learning,and reinforcement learning.We provide a detailed overview of all six participants’performance under the competition’s demanding design.In addition,we present an in-depth analysis of the winning solution-made publicly available-which achieves consistent decision making across scenarios,executes real-time multimodal actions in under five seconds,and performs efficient topology control via action-space reduction and a neural policy that predicts useful grid actions with over 80%accuracy.In parallel,we trained a neural alert module on 315,000 samples derived from top agents,achieving 93.9%recall in flagging dangerous states and allowing agents to predict future failure.Finally,this work not only demonstrates AI’s promise and current limits in real-time grid management but also lays a transparent foundation for more robust,trustworthy systems in the energy transition.
