Risk-based Distributionally Robust Energy and Reserve Dispatch with Wasserstein-Moment Metric

Li Yao, Xiuli Wang, Chao Duan, Xiong Wu, Wentao Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

With resorting to the tool of distributionally robust optimization, this paper proposes a risk-based distributionally robust approach to address the wind power uncertainty in the energy and reserve dispatch. The proposed model minimizes the total cost including dispatch cost and risk cost. The dispatch cost refers to the cost of scheduling energy and spinning reserve while the risk cost is the expected cost of load shedding and wind spillage. Unlike the previous approach which predefined distribution of random variables, the proposed approach takes the ambiguity of distributions into account. It extracts probabilistic information from historical data of random variables, and constructs ambiguity set to contain possible distributions. Then the worst-case distribution over ambiguity set is used to evaluate risks to hedge against the ambiguity. In this paper, a novel metric - Wasserstein-moment metric (WM-metric) is introduced to construct ambiguity set. Compared with Wasserstein-metric and Moment-metric, WM-metric considers more probabilistic information and thus can further mitigate the conservativeness of ambiguity set. The performance of the proposed approach is tested by a 6-bus system for illustrative purpose.

Original languageEnglish (US)
Title of host publication2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538677032
DOIs
StatePublished - Dec 21 2018
Event2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States
Duration: Aug 5 2018Aug 10 2018

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2018-August
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Country/TerritoryUnited States
CityPortland
Period8/5/188/10/18

Keywords

  • Distributionally robust optimization
  • Economic dispatch
  • Risk-based approach

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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