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 and Changzheng Gao
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Yongchun Yang: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Yixuan Wang: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Yajing Gao: Carbon Neutrality Research Institute of China Huaneng Group Co., Ltd., Beijing 100031, China
Changzheng Gao: China Electric Power Enterprise Federation Power Construction Technology and Economic Advisory Center, Beijing 100053, China

Energies, 2022, vol. 15, issue 13, 1-15

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: uncertainty; stochastic production simulation; demand response; optimal peak shaving; carbon emissions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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