An Interpretable Data-Driven Dynamic Operating Envelope Calculation Method Based on an Improved Deep Learning Model

Yun Li, Tunan Chen, Jianzhao Liu, Zhaohua Hu, Yuchen Qi () and Ye Guo
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Yun Li: Shenzhen Power Supply Co., Ltd., Shenzhen 518103, China
Tunan Chen: Shenzhen Power Supply Co., Ltd., Shenzhen 518103, China
Jianzhao Liu: Shenzhen Power Supply Co., Ltd., Shenzhen 518103, China
Zhaohua Hu: Shenzhen Power Supply Co., Ltd., Shenzhen 518103, China
Yuchen Qi: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Ye Guo: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China

Energies, 2025, vol. 18, issue 10, 1-16

Abstract: As the integration of distributed energy resources (DERs) continues to rise, the simultaneous import and export of energy can lead to excessive voltage violations. Therefore, calculating dynamic operating envelopes (DOEs), which represent time-varying export restrictions, is essential for ensuring the safe operation of distribution networks. Traditional methods for calculating DOEs rely on complete distribution network parameters for power-flow calculations. However, acquiring accurate parameters and network topology is often challenging, which limits the practical implementation of these traditional approaches. This paper proposes an interpretable model-free DOE calculation method that leverages smart meter data to address this issue. We train a CNN-LSTM-Attention neural network for voltage estimation, where we employ the whale optimization algorithm (WOA) to adjust hyperparameters automatically. Additionally, this paper employs the SHAP algorithm to interpret the deep learning model, providing insights into the relationship between the bus voltage and the condition of each bus, which enhances the model’s transparency and helps identify the key factors influencing voltage levels. The proposed method is validated through simulations on the IEEE 33−bus distribution network model, demonstrating favorable results.

Keywords: convolutional neural networks; distributed energy resources; dynamic operating envelopes; long short-term memory networks; SHAP (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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