Architecture,Control,and Implementation of Networked Microgrids for Future Distribution Systems

Microgrid(MG)is a small-scale,self-sufficient power system that accommodates various distributed energy resources(DERs),controllable loads,and future distribution systems.Networked microgrids(NMGs)are clusters of MGs,which are physically interconnected and functionally coordinated to enhance distribution systems in terms of economics,resilience,and reliability.This paper introduces the architecture and control of NMGs including nanogrid(NG)and MG.To accommodate variable DERs in NMGs,master and distributed control strategies are adopted to manage the high penetration of DERs,where master control focuses on economic operation,while distributed control focuses on reliability and resilience through active power sharing and voltage and frequency regulation.The initial practices of NG,MG,and NMG in the networked Illinois Institute of Technology(IIT)campus microgrid(ICM)and Bronzeville community microgrid(BCM)in the U.S.are presented.The applications of the master and distributed control strategies are illustrated for the networked ICM-BCM to show their benefits to economics,resilience,and reliability.

State identification of home appliance with transient features in residential buildings

Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.