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 A. Felder

doi:10.1016/j.ijepes.2015.05.003

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 A. Felder

Rutgers University

Research output: Contribution to journal › Article › peer-review

14 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 language	English
Pages (from-to)	309-317
Number of pages	9
Journal	International Journal of Electrical Power and Energy Systems
Volume	73
DOIs	https://doi.org/10.1016/j.ijepes.2015.05.003
Publication status	Published - Dec 1 2015

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

Access to Document

10.1016/j.ijepes.2015.05.003

Fingerprint Dive into the research topics of 'Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures'. Together they form a unique fingerprint.

Cite this

Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures. / Tekiner-Mogulkoc, Hatice; Coit, David W.; Felder, Frank A.

In: International Journal of Electrical Power and Energy Systems, Vol. 73, 01.12.2015, p. 309-317.

Research output: Contribution to journal › Article › peer-review

@article{3c36c7b1d7d4499e87fc5b1a3976705b,

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 Felder, {Frank A.}",

year = "2015",

month = dec,

day = "1",

doi = "10.1016/j.ijepes.2015.05.003",

language = "English",

volume = "73",

pages = "309--317",

journal = "International Journal of Electrical Power and Energy Systems",

issn = "0142-0615",

publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures

AU - Tekiner-Mogulkoc, Hatice

AU - Coit, David W.

AU - Felder, Frank A.

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.

KW - Conditional-value-at-risk

KW - Electricity demand uncertainty

KW - Generation expansion

KW - Maximum regret

KW - Risk aversion

UR - http://www.scopus.com/inward/record.url?scp=84930006602&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84930006602&partnerID=8YFLogxK

U2 - 10.1016/j.ijepes.2015.05.003

DO - 10.1016/j.ijepes.2015.05.003

M3 - Article

AN - SCOPUS:84930006602

VL - 73

SP - 309

EP - 317

JO - International Journal of Electrical Power and Energy Systems

JF - International Journal of Electrical Power and Energy Systems

SN - 0142-0615

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this