As the energy landscape evolves towards sustainability,the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid.One significant aspect of...As the energy landscape evolves towards sustainability,the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid.One significant aspect of this issue is the notable increase in net load variability at the grid edge.Transactive energy,implemented through local energy markets,has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized,indirect demand response on a community level.Model-free control approaches,such as deep reinforcement learning(DRL),show promise for the decentralized automation of participation within this context.Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics,overlooking the crucial goal of reducing community-level net load variability.This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven,autonomous local energy market(ALEX).In this setting,agents do not share information and only prioritize individual bill optimization.The study unveils a clear correlation between bill reduction and reduced net load variability.The impact on net load variability is assessed over various time horizons using metrics such as ramping rate,daily and monthly load factor,as well as daily average and total peak export and import on an open-source dataset.To examine the performance of the proposed DRL method,its agents are benchmarked against a optimal near-dynamic programming method,using a no-control scenario as the baseline.The dynamic programming benchmark reduces average daily import,export,and peak demand by 22.05%,83.92%,and 24.09%,respectively.The RL agents demonstrate comparable or superior performance,with improvements of 21.93%,84.46%,and 27.02%on these metrics.This demonstrates that DRL can be effectively employed for such tasks,as they are inherently scalable with near-optimal performance in decentralized grid management.展开更多
基金supported by the Natural Sciences and Engineering Research Council(NSERC)of Canada grant RGPIN-2024-04565by the NSERC/Alberta Innovates grant ALLRP 561116-20+5 种基金Part of this work has taken place in the Intelligent Robot Learning(IRL)Lab at the University of Alberta,which is supported in part by research grants from the Alberta Machine Intelligence Institute(Amii),Canadaa Canada CIFAR AI Chair,AmiiDigital Research Alliance of CanadaHuaweiMitacs,Canadaand NSERC,Canada.
文摘As the energy landscape evolves towards sustainability,the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid.One significant aspect of this issue is the notable increase in net load variability at the grid edge.Transactive energy,implemented through local energy markets,has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized,indirect demand response on a community level.Model-free control approaches,such as deep reinforcement learning(DRL),show promise for the decentralized automation of participation within this context.Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics,overlooking the crucial goal of reducing community-level net load variability.This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven,autonomous local energy market(ALEX).In this setting,agents do not share information and only prioritize individual bill optimization.The study unveils a clear correlation between bill reduction and reduced net load variability.The impact on net load variability is assessed over various time horizons using metrics such as ramping rate,daily and monthly load factor,as well as daily average and total peak export and import on an open-source dataset.To examine the performance of the proposed DRL method,its agents are benchmarked against a optimal near-dynamic programming method,using a no-control scenario as the baseline.The dynamic programming benchmark reduces average daily import,export,and peak demand by 22.05%,83.92%,and 24.09%,respectively.The RL agents demonstrate comparable or superior performance,with improvements of 21.93%,84.46%,and 27.02%on these metrics.This demonstrates that DRL can be effectively employed for such tasks,as they are inherently scalable with near-optimal performance in decentralized grid management.
