This study utilizes machine learning and,more specifically,reinforcement learning(RL)to allow for an optimized,real-time operation of large numbers of decentral flexible assets on private household scale in the electr...This study utilizes machine learning and,more specifically,reinforcement learning(RL)to allow for an optimized,real-time operation of large numbers of decentral flexible assets on private household scale in the electricity domain.The potential and current obstacles of RL are demonstrated and a guide for interested practitioners is provided on how to tackle similar tasks without advanced skills in neural network programming.For the application in the energy domain it is demonstrated that state-of-the-art RL algorithms can be trained to control potentially millions of small-scale assets in private households.In detail,the applied RL algorithm outperforms common heuristic algorithms and only falls slightly short of the results provided by linear optimization,but at less than a thousandth of the simulation time.Thus,RL paves the way for aggregators of flexible energy assets to optimize profit over multiple use cases in a smart energy grid and thus also provide valuable grid services and a more sustainable operation of private energy assets.展开更多
基金funding by the German Federal Ministry of Education and Research(BMBF)obtained for the Kopernikus Project“ENSURE”(funding nos.03SFK1HO and 03SFK1C0-2)as well as helpful comments received from two anonymous reviewers.
文摘This study utilizes machine learning and,more specifically,reinforcement learning(RL)to allow for an optimized,real-time operation of large numbers of decentral flexible assets on private household scale in the electricity domain.The potential and current obstacles of RL are demonstrated and a guide for interested practitioners is provided on how to tackle similar tasks without advanced skills in neural network programming.For the application in the energy domain it is demonstrated that state-of-the-art RL algorithms can be trained to control potentially millions of small-scale assets in private households.In detail,the applied RL algorithm outperforms common heuristic algorithms and only falls slightly short of the results provided by linear optimization,but at less than a thousandth of the simulation time.Thus,RL paves the way for aggregators of flexible energy assets to optimize profit over multiple use cases in a smart energy grid and thus also provide valuable grid services and a more sustainable operation of private energy assets.
