Demand-side management program planning using stochastic load forecasting with extreme value theory

Young Min Wi; Seongbae Kong; Jaehee Lee; Sung Kwan Joo

doi:10.5370/JEET.2016.11.5.1093

Demand-side management program planning using stochastic load forecasting with extreme value theory

Young Min Wi, Seongbae Kong, Jaehee Lee, Sung Kwan Joo

School of Electrical Engineering

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

2 Citations (Scopus)

Abstract

Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.

Original language	English
Pages (from-to)	1093-1099
Number of pages	7
Journal	Journal of Electrical Engineering and Technology
Volume	11
Issue number	5
DOIs	https://doi.org/10.5370/JEET.2016.11.5.1093
Publication status	Published - 2016 Sept

Bibliographical note

Funding Information:
This research was supported by Korea Electric Power Corporation through Korea Electrical Engineering & Science Research Institute. (No. R15XA03-45) This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2015R1C1A1A01051928)

Publisher Copyright:
© The Korean Institute of Electrical Engineers.

Keywords

Demand-side management program
Generalized extreme value distribution
Load forecasting
Temperature stochastic process

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.5370/JEET.2016.11.5.1093

Cite this

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title = "Demand-side management program planning using stochastic load forecasting with extreme value theory",

abstract = "Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.",

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author = "Wi, {Young Min} and Seongbae Kong and Jaehee Lee and Joo, {Sung Kwan}",

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AU - Lee, Jaehee

AU - Joo, Sung Kwan

N1 - Funding Information: This research was supported by Korea Electric Power Corporation through Korea Electrical Engineering & Science Research Institute. (No. R15XA03-45) This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2015R1C1A1A01051928) Publisher Copyright: © The Korean Institute of Electrical Engineers.

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N2 - Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.

AB - Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.

KW - Demand-side management program

KW - Generalized extreme value distribution

KW - Load forecasting

KW - Temperature stochastic process

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Demand-side management program planning using stochastic load forecasting with extreme value theory

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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