A Data-Driven Multi-Period Importance Sampling Strategy for Stochastic Economic Dispatch

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3 Scopus Citations

Abstract

Power systems with high penetrations of renewable energy (e.g., wind power) require sophisticated approaches to optimize system performance due to uncertainty in short-term system generation capacity. In this paper, we combine a data-driven analog scenario selection method with importance sampling to create a novel scenario construction approach for two-stage stochastic economic dispatch problems with a large number of wind farms on a network. The proposed method produces scenarios with realistic physics by finding high-fidelity analogs that can describe future states of the system. We show how to extend this method to multi-period operations and demonstrate the effectiveness of this technique by simulating economic dispatch operations on a synthetic test system over the course of a week.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2021
Event2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States
Duration: 26 Jul 202129 Jul 2021

Conference

Conference2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington
Period26/07/2129/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

NREL Publication Number

  • NREL/CP-2C00-78326

Keywords

  • data-driven forecasting
  • high penetrations of renewables
  • importance sampling
  • scenario-based optimization
  • Stochastic optimization

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