Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures

Hatice Tekiner-Mogulkoc, David W. Coit, Frank Felder

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper focused on solving electricity generation expansion planning problems where there are uncertainties associated with the electricity demand forecasts. The electricity expansion plans are long-term commitments and affects people's living conditions and business prosperity deeply. Therefore it is reasonable that the decision makers may be risk averse. In this paper, mathematical models are developed to incorporate the risk aversion into the generation expansion planning problems. We use the conditional-value-at-risk and maximum regret as risk measures and the results shows that the investment plans are affected when the risk is considered.

Original languageEnglish
Pages (from-to)309-317
Number of pages9
JournalInternational Journal of Electrical Power and Energy Systems
Volume73
DOIs
Publication statusPublished - Dec 1 2015

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Electricity
Planning
Uncertainty
Mathematical models
Industry

Keywords

  • Conditional-value-at-risk
  • Electricity demand uncertainty
  • Generation expansion
  • Maximum regret
  • Risk aversion

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

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title = "Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures",
abstract = "This paper focused on solving electricity generation expansion planning problems where there are uncertainties associated with the electricity demand forecasts. The electricity expansion plans are long-term commitments and affects people's living conditions and business prosperity deeply. Therefore it is reasonable that the decision makers may be risk averse. In this paper, mathematical models are developed to incorporate the risk aversion into the generation expansion planning problems. We use the conditional-value-at-risk and maximum regret as risk measures and the results shows that the investment plans are affected when the risk is considered.",
keywords = "Conditional-value-at-risk, Electricity demand uncertainty, Generation expansion, Maximum regret, Risk aversion",
author = "Hatice Tekiner-Mogulkoc and Coit, {David W.} and Frank Felder",
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AU - Tekiner-Mogulkoc, Hatice

AU - Coit, David W.

AU - Felder, Frank

PY - 2015/12/1

Y1 - 2015/12/1

N2 - This paper focused on solving electricity generation expansion planning problems where there are uncertainties associated with the electricity demand forecasts. The electricity expansion plans are long-term commitments and affects people's living conditions and business prosperity deeply. Therefore it is reasonable that the decision makers may be risk averse. In this paper, mathematical models are developed to incorporate the risk aversion into the generation expansion planning problems. We use the conditional-value-at-risk and maximum regret as risk measures and the results shows that the investment plans are affected when the risk is considered.

AB - This paper focused on solving electricity generation expansion planning problems where there are uncertainties associated with the electricity demand forecasts. The electricity expansion plans are long-term commitments and affects people's living conditions and business prosperity deeply. Therefore it is reasonable that the decision makers may be risk averse. In this paper, mathematical models are developed to incorporate the risk aversion into the generation expansion planning problems. We use the conditional-value-at-risk and maximum regret as risk measures and the results shows that the investment plans are affected when the risk is considered.

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KW - Electricity demand uncertainty

KW - Generation expansion

KW - Maximum regret

KW - Risk aversion

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