LAPSE:2023.12017
Published Article
LAPSE:2023.12017
Peak Shaving Analysis of Power Demand Response with Dual Uncertainty of Unit and Demand-Side Resources under Carbon Neutral Target
Yongchun Yang, Yixuan Wang, Yajing Gao, Changzheng Gao
February 28, 2023
Abstract
With the depletion of fossil energy and increasingly serious environmental problems, demand-side resources play an increasingly prominent role in peak shaving and valley filling, energy conservation, and emission reduction. Under the background of further promotion of the “double carbon” goal in China, considering the possible double uncertainty factors in the process of unit and demand response resources participating in the scheduling and the goal of minimum carbon emission, the uncertainty models of unit output and demand-side resource response are respectively constructed based on the sequential stochastic production simulation algorithm and the method of additional random variables. In the model, the influence of random forced outage on unit output and the uncertain influence of response deviation caused by the limitation of demand response resource information processing and response aging characteristics are considered, respectively. By analyzing the power supply and demand, considering demand response on two typical peak shaving days, the peak shaving cost, carbon emission reduction, and power limitation are obtained. An IEEE 30 bus 6-machine system example is used to verify the effectiveness of the dual uncertainty demand response model, which provides guidance for power dispatching decision-making.
Keywords
carbon emissions, demand response, optimal peak shaving, stochastic production simulation, uncertainty
Suggested Citation
Yang Y, Wang Y, Gao Y, Gao C. Peak Shaving Analysis of Power Demand Response with Dual Uncertainty of Unit and Demand-Side Resources under Carbon Neutral Target. (2023). LAPSE:2023.12017
Author Affiliations
Yang Y: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Wang Y: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China [ORCID]
Gao Y: Carbon Neutrality Research Institute of China Huaneng Group Co., Ltd., Beijing 100031, China
Gao C: China Electric Power Enterprise Federation Power Construction Technology and Economic Advisory Center, Beijing 100053, China
Journal Name
Energies
Volume
15
Issue
13
First Page
4588
Year
2022
Publication Date
2022-06-23
ISSN
1996-1073
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Original Submission
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PII: en15134588, Publication Type: Journal Article
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https://doi.org/10.3390/en15134588
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