An Optimization Method for Determining the Proportion between Year and Day-ahead Procurement of Frequency Response Reserve

With the continuous increase in the penetration rate of new energy units, the power system needs to purchase frequency response reserve to cope with the shortage of frequency response resources. The ratio of long-term to short-term procurement for frequency response reserve is generally determined based on experience, lacking theoretical basis. This article establishes a year and day-ahead joint model and proposes a method for determining the ratio. The case study shows that the proposed method can determine the optimal procurement ratio between the year and day-ahead, and achieve optimal allocation of frequency response resources at different time scales.

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Publication Details

Published
2024-11-29
DOI
https://doi.org/10.1109/ei264398.2024.10991713
Primary Topic
Smart Grid and Power Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

An Optimization Method for Determining the Proportion between Year and Day-ahead Procurement of Frequency Response Reserve

Weidong Li, Zeng Liu, Mingze Zhang, Huanhuan Luo
Smart Grid and Power Systems
article

An Optimization Method for Determining the Proportion between Year and Day-ahead Procurement of Frequency Response Reserve

Weidong Li, Zeng Liu, Mingze Zhang, Huanhuan Luo
article en

Abstract

With the continuous increase in the penetration rate of new energy units, the power system needs to purchase frequency response reserve to cope with the shortage of frequency response resources. The ratio of long-term to short-term procurement for frequency response reserve is generally determined based on experience, lacking theoretical basis. This article establishes a year and day-ahead joint model and proposes a method for determining the ratio. The case study shows that the proposed method can determine the optimal procurement ratio between the year and day-ahead, and achieve optimal allocation of frequency response resources at different time scales.

Harbin University of Science and Technology (CN), Dalian University of Technology (CN), Shanghai Electric (China) (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 72%
Smart Grid and Power Systems
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