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Typical Source-Load Temporal Scenario Generation Method Based on Modification of Sampling Probability Interval
Author:
Affiliation:

1.Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology (China Electric Power Research Institute), Beijing 100192, China;2.Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China

Abstract:

In the background of the new power system, constructing typical source-load temporal scenarios can provide scenario support for the day-ahead or intra-day electricity production organizations. To further improve the quality of temporal scenarios generated by statistical methods, and reduce the number of typical source-load temporal scenarios, a method for generating typical source-load temporal scenarios based on the modification of sampling probability interval is proposed. Firstly, considering the historical fluctuation characteristics of renewable energy, the probability interval of the prediction error temporal sampling is modified, and the temporal scenarios are sampled and generated using Latin hypercube sampling at the first sampling point and using random sampling in modified probability interval at subsequent sampling points. Secondly, typical temporal scenarios are obtained based on clustering method. Then, the typical source-load temporal scenario combinations are reduced from the perspective of system security based on the source-load “AND” operation. Finally, based on the actual data from a province in Northwest China, the source-load temporal scenarios for an IEEE 39-bus system are constructed to verify the effectiveness and feasibility of the proposed method.

Keywords:

Foundation:

This work is supported by State Grid Corporation of China (No. 5100-202055331A-0-0-00).

Get Citation
[1]LYU Yan, LI Li, SUN Lüe, et al. Typical Source-Load Temporal Scenario Generation Method Based on Modification of Sampling Probability Interval[J]. Automation of Electric Power Systems,2024,48(17):141-150. DOI:10.7500/AEPS20240102003
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History
  • Received:January 02,2024
  • Revised:April 03,2024
  • Adopted:
  • Online: September 02,2024
  • Published: