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Chance-constrained Programming Based Congestion Dispatching Optimization of Power System with Large-scale Wind Power Integration
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1. State Grid Economic and Technology Research Institute Co., Ltd., Beijing 102206, China;2. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;3. State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China;4. Maintenance Company of State Grid Chongqing Electric Power Company, Chongqing 400039, China

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    Abstract:

    In the power market, the uncertainty of wind power integration increases the probability of system transmission congestion. The existing deterministic methods for transmission congestion dispatching will lead to some transmission lines close to the thermal stability limit. Furthermore, when the forecasting errors of wind power exist, the system would be re-blocked in real-time operation. In order to eliminate the system transmission congestion and make the system be able to resist re-congestion, a new congestion dispatching model is put forward, in which the forecasting errors of wind power are considered as random variables, and chance-constrained conditions for simulating the uncertainty is formed in real-time operation. Meanwhile, the indices of expected demand not supplied and risk of abandoning wind power are taken into account for co-optimizing power adjustment and spinning reserves in congestion dispatching. Based on Monte Carlo method, method and indices are presented for measuring the system re-congestion caused by the uncertainty of wind power integration. Based on IEEE RTS 24-bus system, the effectiveness of the proposed model is illustrated.

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WANG Zhidong, LOU Suhua, FAN Zhen,et al.Chance-constrained Programming Based Congestion Dispatching Optimization of Power System with Large-scale Wind Power Integration[J].Automation of Electric Power Systems,2019,43(23):147-154.DOI:10.7500/AEPS20181219003

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History
  • Received:December 19,2018
  • Revised:September 18,2019
  • Adopted:June 20,2019
  • Online: August 27,2019
  • Published: