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

CSEE Journal of Power and Energy Systems 2023年第1期9卷 28-36页

作者： adedasola a.ademola Shutang You Yilu Liu Bredesen Centre and Centre of Ultra-Wide Area Resilient Electrical Energy Transmission NetworksUniversity of TennesseeKnoxvilleTN 37996 USA Centre of Ultra-Wide Area Resilient Electrical Energy Transmission Networks University of TennesseeKnoxvilleTN 37996 USA

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 *** paper proposes an automated two-stage approach to solve ACPF formulated from DCOPF dispatch *** 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 *** 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 *** datasets with hourly dispatches for the 243-bus reduced WECC system were used to test the proposed *** algorithm was able to convert 100%of the first set of dispatch cases to solved ACPF *** 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 *** 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.

关键词： AC power flow DC power flow hot-starting algorithm homotopy continuation power flow convergence reactive power compensation voltage setpoint tuning

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

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High Voltage 2021年第6期6卷 924-937页

作者： Lin Zhu Yinfeng Zhao Yi Cui Shutang You Wenpeng Yu Shengyuan Liu He Yin Chang Chen Yuru Wu Wei Qiu Mirka Mandich Hongyu Li adedasola ademola Chengwen Zhang Chujie Zeng Xinlan Jia Weikang Wang Haoyu Yuan Huaiguang Jiang Jin Tan Yilu Liu The University of Tennessee KnoxvilleTennesseeUSA National Renewable Energy Laboratory GoldenColoradoUSA Oak Ridge National Laboratory Oak RidgeTennesseeUSA

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 *** 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 *** study provides an overview of the latest progress of FNET/*** sensors,communication,and data servers are upgraded to handle ultra‐high density synchrophasor and point‐on‐wave data to monitor system dynamics with 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.

关键词： artificial handle inverter

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