Neural ODE-Based Dynamic Modeling and Predictive Control for Power Regulation in Distribution Networks

Libin Wen, Jinji Xi, Hong Hu, Li Xiong, Guangling Lu and Tannan Xiao ()
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Libin Wen: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530032, China
Jinji Xi: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530032, China
Hong Hu: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530032, China
Li Xiong: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530032, China
Guangling Lu: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530032, China
Tannan Xiao: State Key Laboratory of Power System Operation and Control, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Energies, 2025, vol. 18, issue 13, 1-23

Abstract: The increasing penetration of distributed energy resources (DERs) and power electronic loads challenges the modeling and control of modern distribution networks (DNs). The traditional models often fail to capture the complex aggregate dynamics required for advanced control strategies. This paper proposes a novel framework for DN power regulation based on Neural Ordinary Differential Equations (NODEs) and Model Predictive Control (MPC). NODEs are employed to develop a data-driven, continuous-time dynamic model capturing the aggregate relationship between the voltage at the point of common coupling (PCC) and the network’s power consumption, using only PCC measurements. Building upon this NODE model, an MPC strategy is designed to regulate the DN’s active power by manipulating the PCC voltage. To ensure computational tractability for real-time applications, a local linearization technique is applied to the NODE dynamics within the MPC, transforming the optimization problem into a standard Quadratic Programming (QP) problem that can be solved efficiently. The framework’s efficacy is comprehensively validated through simulations. The NODE model demonstrates high accuracy in predicting the dynamic behavior in a DN against a detailed simulator, with maximum relative errors below 0.35% for active power. The linearized NODE-MPC controller shows effective tracking performance, constraint handling, and computational efficiency, with typical QP solve times below 0.1 s within a 0.1 s control interval. The validation includes offline tests using the NODE model and online co-simulation studies using CloudPSS and Python via Redis. Application scenarios, including Conservation Voltage Reduction (CVR) and supply–demand balancing, further illustrate the practical potential of the proposed approach for enhancing the operation and efficiency of modern distribution networks.

Keywords: distribution network modeling; neural ordinary differential equations (NODEs); model predictive control (MPC); power control; data-driven modeling; conservation voltage reduction (CVR); dynamic equivalents (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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