Integrated demand response optimization strategy considering risk appetite under multi‐dimensional uncertain information

Abstract Integrated demand response (IDR) is deemed as an effective tool to balance energy supply and demand. User’s uncertain information containing prior uncertain information and posterior uncertain information is a key factor affecting the implementation effectiveness of IDR, but existing studies fail to consider the two types of uncertain information, response risk caused by the uncertain information, and risk appetite comprehensively. Based on the principal‐agent theory of optimal incentive contract under uncertain information and Markowitz's mean‐variance portfolio theory, a new IDR model is established in this paper, and an IDR optimization strategy considering risk appetite under uncertain information is proposed. By proposing the user model considering multi‐dimensional uncertain information and the risk appetite‐based integrated energy service providers (IESP) model based on the principal‐agent theory and Markowitz's mean‐variance portfolio theory, we have achieved effective modelling of the user’s uncertain information and the risk borne by IESP. The arithmetic examples have verified advantages of the model in enhancing the accuracy of user’s actual response prediction and the superiority of incentive strategies, which is beneficial to reduce the cost of IESPs and enhance the benefit of users participating in IDR.

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Publication Details

Journal
IET Generation Transmission & Distribution
Published
2024-08-15
DOI
https://doi.org/10.1049/gtd2.13245
Citations
1
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.21

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Integrated demand response optimization strategy considering risk appetite under multi‐dimensional uncertain information

Wangzhang Cao, Liming Feng, Yi Sun‡, Shunlin Zheng et al.
1 citations
IET Generation Transmission & Distribution
Smart Grid Energy Management
0.21
article

Integrated demand response optimization strategy considering risk appetite under multi‐dimensional uncertain information

Wangzhang Cao, Liming Feng, Yi Sun‡, Shunlin Zheng, Chao Quan, Yaliang Liu, Xinpeng Mo, Xinya Liu
article en
1 citations

Abstract

Abstract Integrated demand response (IDR) is deemed as an effective tool to balance energy supply and demand. User’s uncertain information containing prior uncertain information and posterior uncertain information is a key factor affecting the implementation effectiveness of IDR, but existing studies fail to consider the two types of uncertain information, response risk caused by the uncertain information, and risk appetite comprehensively. Based on the principal‐agent theory of optimal incentive contract under uncertain information and Markowitz's mean‐variance portfolio theory, a new IDR model is established in this paper, and an IDR optimization strategy considering risk appetite under uncertain information is proposed. By proposing the user model considering multi‐dimensional uncertain information and the risk appetite‐based integrated energy service providers (IESP) model based on the principal‐agent theory and Markowitz's mean‐variance portfolio theory, we have achieved effective modelling of the user’s uncertain information and the risk borne by IESP. The arithmetic examples have verified advantages of the model in enhancing the accuracy of user’s actual response prediction and the superiority of incentive strategies, which is beneficial to reduce the cost of IESPs and enhance the benefit of users participating in IDR.

IET Generation Transmission & DistributionVol. 18(18)
North China Electric Power University (CN), Shanghai Electric (China) (CN), China Southern Power Grid (China) (CN)
Fundamental Research Funds for the Central Universities
Affordable and clean energy
Openalex Percentile: Top 49%
Smart Grid Energy Management
0.21
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Integrated demand response optimization strategy considering risk appetite under multi‐dimensional uncertain information — Wangzhang Cao, Liming Feng, et al. · IET Generation Transmission & Distribution (2024) | TGRS Research Map | TGRS