To improve the controllability and utilization of distributed energy resources(DERs),distribution-level electricity markets based on consumers’bidding and offers have been proposed.However,the transaction costs will ...To improve the controllability and utilization of distributed energy resources(DERs),distribution-level electricity markets based on consumers’bidding and offers have been proposed.However,the transaction costs will dramatically increase with the rapid development of DERs.Therefore,in this paper,we develop an energy sharing scheme that allows users to share DERs with neighbors,and design a novel incentive mechanism for benefit allocation without users’bidding on electricity prices.In the energy sharing scheme,an aggregator organizes a number of electricity users,and trades with the connected power grid.The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads.A novel index,termed as sharing contribution rate(SCR),is presented to evaluate different users’contributions to the energy sharing.Then,based on users’SCRs,an efficient benefit allocation mechanism is implemented to determine the aggregator’s payment to users that incentivize their participation in energy sharing.To avoid users’bidding,we propose a decentralized framework for the energy sharing and incentive mechanism.Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme,and the incentive mechanism allocates the benefits according to users’contributions.展开更多
The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution gr...The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.展开更多
基金supported by National Natural Science Foundation of China(No.51777102,No.51537005)Chinese Association of Science and Technology Young Elite Scientists Sponsorship Program(No.YESS20170206)the State Grid Corporation of China(No.5210EF18000G).
文摘To improve the controllability and utilization of distributed energy resources(DERs),distribution-level electricity markets based on consumers’bidding and offers have been proposed.However,the transaction costs will dramatically increase with the rapid development of DERs.Therefore,in this paper,we develop an energy sharing scheme that allows users to share DERs with neighbors,and design a novel incentive mechanism for benefit allocation without users’bidding on electricity prices.In the energy sharing scheme,an aggregator organizes a number of electricity users,and trades with the connected power grid.The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads.A novel index,termed as sharing contribution rate(SCR),is presented to evaluate different users’contributions to the energy sharing.Then,based on users’SCRs,an efficient benefit allocation mechanism is implemented to determine the aggregator’s payment to users that incentivize their participation in energy sharing.To avoid users’bidding,we propose a decentralized framework for the energy sharing and incentive mechanism.Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme,and the incentive mechanism allocates the benefits according to users’contributions.
文摘The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.