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Modeling and Scenario Generation Method of Annual Load Series for Evaluation of Renewable Energy Accommodation Capacity
Author:
Affiliation:

1.School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2.State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute), Beijing 100192, China

Abstract:

Annual load series is the basis of evaluation of renewable energy accommodation capacity in provincial power grid of China. In this paper, modeling and scenario generation methods of annual load series are proposed based on the cluster analysis and Markov chain technology. First, the self-organizing map technology is used on the historical load data for cluster analysis of typical days, and the discrete Markov chain is adopted to describe the state transition characteristics between different typical days. For each type of typical days, the kernel density estimation and t-Copula function are utilized to construct the joint probability distribution model of daily load characteristics. Then, the indices of the typical daily states and daily load characteristics are generated by Markov chain and Monte Carlo random sampling. Finally, through the construction of optimization model for daily load series, the optimization reconstruction of daily load series is realized until the annual load series scenario is generated. The case studies are conducted based on annual load data of a provincial power grid in China. The scenarios of load series generated by the proposed method are also used to evaluate renewable energy accommodation in the next year. The testing results verify the effectiveness and practicality of the proposed method.

Keywords:

Foundation:

This work is supported by Foundation:State Grid Corporation of China (No. 4000-201955194A-0-0-00).

Get Citation
[1]QU Kai, LI Pai, HUANG Yuehui, et al. Modeling and Scenario Generation Method of Annual Load Series for Evaluation of Renewable Energy Accommodation Capacity[J]. Automation of Electric Power Systems,2021,45(1):123-131. DOI:10.7500/AEPS20200201002
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History
  • Received:February 01,2020
  • Revised:June 27,2020
  • Adopted:
  • Online: January 05,2021
  • Published: