A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration: Article No. 036301

Binghui Li, Kwami Sedzro, Xin Fang, Bri-Mathias Hodge, Jie Zhang

Research output: Contribution to journalArticlepeer-review

18 Scopus Citations

Abstract

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.
Original languageAmerican English
Number of pages17
JournalJournal of Renewable and Sustainable Energy
Volume12
Issue number3
DOIs
StatePublished - 2020

NREL Publication Number

  • NREL/JA-5D00-77549

Keywords

  • data visualization
  • energy forecasting
  • Gaussian processes
  • Monte Carlo methods
  • optimization problems
  • stochastic processes
  • wind energy

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