Building upon prior research that highlighted the need for standardizing environments for building controlresearch, and inspired by recently introduced challenges for real life reinforcement learning (RL) control, her...Building upon prior research that highlighted the need for standardizing environments for building controlresearch, and inspired by recently introduced challenges for real life reinforcement learning (RL) control, herewe propose a non-exhaustive set of nine real world challenges for RL control in grid-interactive buildings(GIBs). We argue that research in this area should be expressed in this framework in addition to providing astandardized environment for repeatability. Advanced controllers such as model predictive control (MPC) andRL control have both advantages and disadvantages that prevent them from being implemented in real worldproblems. Comparisons between the two are rare, and often biased. By focusing on the challenges, we caninvestigate the performance of the controllers under a variety of situations and generate a fair comparison.展开更多
End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed r...End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed renewable generation.This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling.We consider services delivered to(1)the high-voitage bulk power system through the coordinated action of many,distributed building loads working together,and(2)targeted support provided to the operation of low-voltage electric distribution grids.Although an exhaustive exploration is not possible,we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling(BEM)tools,suggesting pathways for future research and development.展开更多
文摘Building upon prior research that highlighted the need for standardizing environments for building controlresearch, and inspired by recently introduced challenges for real life reinforcement learning (RL) control, herewe propose a non-exhaustive set of nine real world challenges for RL control in grid-interactive buildings(GIBs). We argue that research in this area should be expressed in this framework in addition to providing astandardized environment for repeatability. Advanced controllers such as model predictive control (MPC) andRL control have both advantages and disadvantages that prevent them from being implemented in real worldproblems. Comparisons between the two are rare, and often biased. By focusing on the challenges, we caninvestigate the performance of the controllers under a variety of situations and generate a fair comparison.
基金This work was authored in part by the National Renewable Energy Laboratory,operated by Alliance for Sustainable Energy,LLC,for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308Funding provided by the National Renewable Energy Laboratory(NREL)Laboratory Directed Research and Development(LDRD)program.
文摘End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed renewable generation.This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling.We consider services delivered to(1)the high-voitage bulk power system through the coordinated action of many,distributed building loads working together,and(2)targeted support provided to the operation of low-voltage electric distribution grids.Although an exhaustive exploration is not possible,we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling(BEM)tools,suggesting pathways for future research and development.
