Enhancing stochastic unit commitment to include nodal wind power uncertainty

Rui Pinto, Leonel Carvalho, Jean Sumaili, Vladimiro Miranda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The Unit Commitment (UC) problem consists on the day-ahead scheduling of thermal generation units. The scheduling process is based on a forecast for the demand, which adds uncertainty to the decision of starting or shutting down units. With the increasing penetration of renewable energy sources, namely wind power, the level of uncertainty is such that deterministic UC approaches that rely uniquely on point forecasts are no longer appropriate. The UC approach reported in this paper considers a stochastic formulation and includes constraints for the technical limits of thermal generation units, like ramp-rates and minimum and maximum power output, and also for the power flow equations by integrating the DC model in the optimization process. The objective is to assess the ability of the stochastic UC approach to decrease the expected value of load shedding and wind power loss when compared to the deterministic UC approach. A case study based on IEEE-RTS 79 system, which has 24 buses and 32 thermal generation units, for two different penetrations of wind power and a 24-hour horizon is carried out. The computational performance of the methodology proposed is also discussed to show that considerable performance gains without compromising the robustness of the stochastic UC approach can be achieved.

Original languageEnglish
Title of host publication2016 13th International Conference on the European Energy Market, EEM 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781509012978
DOIs
Publication statusPublished - 25 Jul 2016
Externally publishedYes
Event13th International Conference on the European Energy Market, EEM 2016 - Porto, Portugal
Duration: 6 Jun 20169 Jun 2016

Publication series

NameInternational Conference on the European Energy Market, EEM
Volume2016-July
ISSN (Print)2165-4077
ISSN (Electronic)2165-4093

Conference

Conference13th International Conference on the European Energy Market, EEM 2016
Country/TerritoryPortugal
CityPorto
Period6/06/169/06/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Stochastic Unit Commitment
  • Uncertainty Modeling
  • Wind Power

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