Automated Two-stage Conversion of Hourly Optimal DC Flow Solution to AC Power Flow

Conversion of hourly dispatch cases derived using DC optimal power flow(DCOPF)to AC power flow(ACPF)case is often challenging and requires arduous human analysis and intervention.This paper proposes an automated two-stage approach to solve ACPF formulated from DCOPF dispatch cases.The first stage involved the use of the conventional Newton Raphson method to solve the ACPF from flat start,then ACPF cases that are unsolvable in the first stage are subjected to a hotstarting incremental method,based on homotopy continuation,in the second stage.Critical tasks such as the addition of reactive power compensation and tuning of voltage setpoints that typically require human intervention were automated using a criteriabased selection method and optimal power flow respectively.Two datasets with hourly dispatches for the 243-bus reduced WECC system were used to test the proposed method.The algorithm was able to convert 100%of the first set of dispatch cases to solved ACPF cases.In the second dataset with suspect dispatch cases to represent an extreme conversion scenario,the algorithm created solved ACPF cases that satisfied a defined success criterion for 77.8%of the dispatch cases.The average run time for the hotstarting algorithm to create a solved ACPF case for a dispatch was less than 1 minute for the reduced WECC system.

Recent Developments of FNET/GridEye—A Situational Awareness Tool for Smart Grid

Wide-area monitoring systems(WAMS)are becoming increasingly vital for enhancing power grid operators’situational awareness capabilities.As a pilot WAMS that was initially deployed in 2003,the frequency monitoring network FNET/GridEye uses GPS-time-synchronized monitors called frequency disturbance recorders(FDRs)to capture dynamic grid behaviors.Over the past ten years,a large number of publications related to FNET/GridEye have been reported.In this paper,the most recent developments of FNET/GridEye sensors,data centers,and data analytics applications are reviewed.These works demonstrate that FNET/GridEye will become a costeffective situational awareness tool for the future smart grid.

Impact of Simultaneous Activities on Frequency Fluctuations:Comprehensive Analyses Based on Real Measurement Data from the FNET/GridEye

Simultaneous human activities,such as the Super Bowl game,would cause certain impacts on frequency fluctuations in power systems.With the help of FNET/GridEye measurements,this paper aims to give comprehensive analyses on the frequency fluctuations during Super Bowl LIV held on Feb.2,2020,so as to better understand several phenomena caused by simultaneous activities which will help system operations and controls.First,recent developments of the FNET/GridEye are briefly introduced.Second,the frequency fluctuations of the Eastern Interconnection(El),western electricity coordinating council(WECC),and electric reliability council of Texas(ERCOT)power systems during Super Bowl LIV are analyzed.Third,frequency fluctuations of Super Bowl Sunday and ordinary Sundays in 2020 are compared.Finally,the differences of frequency fluctuations among different years during the Super Bowl and their change trends are also given.Furthermore,several possible explanations,including the simultaneity of electricity consumption at the beginning of commercial breaks and the halftime show,the increasing usage of the Internet,and the increasing size of TV screens,are illustrated in detail in this paper.

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Large‐scale power systems exhibit more complex dynamics due to the increasing inte-gration of inverter‐based resources(IBRs).Therefore,there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs.As a pioneering Wide‐Area Measurement System,FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large‐scale power grids.This study provides an overview of the latest progress of FNET/GridEye.The sensors,communication,and data servers are upgraded to handle ultra‐high density synchrophasor and point‐on‐wave data to monitor system dynamics with more details.More importantly,several artificial intelligence(AI)‐based advanced appli-cations are introduced,including AI‐based inertia estimation,AI‐based disturbance size and location estimation,AI‐based system stability assessment,and AI‐based data authentication.