Stochastic Operation Framework for Distribution Networks Hosting High Wind Penetrations

M. R. Dorostkar-Ghamsari, M. Fotuhi-Firuzabad, M. Lehtonen, A. Safdarian, A. S. Hoshyarzadeh

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

Abstract

In this paper, a stochastic framework including two hierarchical stages is presented for the operation of distribution networks with high penetrations of wind power. In the first stage termed Day Ahead Market Stage (DAMS), the power purchases from the day-ahead market and commitment of distributed generations (DGs) are determined. The DAMS model is formulated as a mixed integer linear programming optimization problem. The uncertainty in predictions of wind generation, real time prices, and load profile are included in the optimization problem according to a scenario-based stochastic programming approach. The risk encountered due to the uncertainties is also taken into account. The objective is to minimize the expected operation cost while satisfying the acceptable level of risk. In the second stage named Real Time Market Stage (RTMS), the power purchases from the real time market, dispatch of committed DGs, load curtailment invocations, and hourly reconfigurations are determined. In each hour, the RTMS problem is solved based on the information of that hour and next few hours. To prevent large numbers of switching operations during a day, the switching cost of reconfiguration is considered. The RTMS is modeled as a mixed integer conic programming problem. To analyze the proposed framework, the IEEE 33-bus DN is used as a case study.

Original languageEnglish
Article number8064667
Pages (from-to)344-354
Number of pages11
JournalIEEE Transactions on Sustainable Energy
Volume10
Issue number1
Early online date10 Oct 2017
DOIs
Publication statusPublished - 1 Jan 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Cone programming
  • Distribution network
  • Load modeling
  • Optimization
  • Reconfiguration
  • Stochastic optimization
  • Stochastic processes
  • Switches
  • Uncertainty
  • Wind forecasting
  • Wind power generation

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