Smart grid operational strategies for power distribution systems with large penetration of distributed energy resources

Abstract

Power distribution systems are transitioning from traditional centralized-control distribution grids to the modern distribution grids that are more customer-interactive and include microgrids (MGs) as well as various unpredictable and multi-scale distributed energy resources (DERs). However, power fueled by renewable DERs such as wind and solar is highly variable and high penetration of renewable DERs in distribution system may potentially degrade the grid reliability and power quality. Moreover, the growth of such generation sources will increase the number of variables and cause scalability concerns for distribution system operators (DSOs) in handling system optimization problems. Further, with development of MGs, DSO and MG may have different owners and schedule renewable and non-renewable DERs based on their own economic rules and policies while secure and economic operation of the entire system is necessary. The widespread integration of wind and solar and deployment of MGs in distribution system make the task of distribution system operation management quite challenging especially from the viewpoint of variability, scalability, and multi-authority operation management. This research develops unique models and methodologies to overcome such issues and make distribution grid operation, optimization and control more robust against renewable intermittency, intractability, and operation complexity. The objectives of this research are as follows: 1) to develop a three-phase unbalanced large-scale distribution system to serve as a benchmark for studying challenges related to integration of DERs, such as scalability concerns in optimization problems, incremental power losses, voltage rise, voltage fluctuations, volt/var control, and operation management; 2) to develop a novel hierarchical and multilevel distributed optimization for power loss minimization via optimal reactive power provisioning from rooftop PVs which addresses the scalability issues with widespread DER integration in large-scale networks; 3) to develop a dynamic operational scheme for residential PV smart inverters to mitigate the fluctuations from rooftop PV integration under all-weather-condition (fully sunny, overcast and transient cloudy days) while increasing network efficiency in terms of power losses, and number of transformer load tap changer (LTC) operation; 4) to develop a stochastic energy management model for multi-authority distribution system operating under uncertainty from load and wind generation, which is able to precisely account interactions between DSO and MGs.