Assessment Method for Dynamic Adjustable Capacity of Distribution Network Feeder Load Based on CNN-LSTM Source–Load Forecasting

Youzhuo Zheng, Zhi Long (), Hengrong Zhang, Yutao Xu, Yongxiang Cai, Fengming Shi, Nuoqing Shen and Siyang Liao
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Youzhuo Zheng: Electric Power Science Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Zhi Long: Electric Power Science Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Hengrong Zhang: Electric Power Science Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Yutao Xu: Electric Power Science Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Yongxiang Cai: Electric Power Science Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Fengming Shi: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Nuoqing Shen: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Siyang Liao: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2025, vol. 18, issue 21, 1-21

Abstract: In response to the demand for flexible regulation resources in distribution networks with high proportion of new energy integration, this study explores the regulation potential of feeder loads. It controls the power of feeder loads through various types of voltage regulation equipment, treating these loads as a key component of virtual power plants (VPPs) to participate in grid security and stability control, demand response, and other fields, thereby enhancing the operational flexibility of the system. This paper focuses on the research of dynamic adjustable capacity evaluation for feeder loads, aiming to provide capacity constraints for their participation in grid interaction. Firstly, a CVR coefficient model is established based on the voltage–power coupling characteristics of feeder loads to characterize their regulation properties. Secondly, an analytical expression for voltage sensitivity is derived using an improved Zbus linearized power flow model, and a system-wide node voltage prediction model is constructed by combining the source–load prediction results from the CNN-LSTM model. On this basis, the dynamic regulation boundaries of each node’s voltage are solved with the constraint of system-wide voltage security. The adjustable capacity for the next 3 h is calculated iteratively by integrating the CVR coefficients of each feeder load, realizing the dynamic evaluation of the regulation capability of feeder loads.

Keywords: CVR coefficient; CNN-LSTM; source-load forecasting; voltage security; feeder load adjustable capacity (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: 2025
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