Open Access

ARTICLE

Congestion Management Strategy for Distribution Networks Considering Flexible Loads and Advanced Energy Storage under Renewable Energy Integration

Chuan Yuan¹, Zhu Liang¹, Ke Xu^1,2, Yufan Chen^1,2, Chang Liu¹, Jian Zeng¹, Hao Li³, Weiting Xu^1,2,*
1 New Power System Research Institute of State Grid Sichuan Electric Power Company, Chengdu, China
2 Economic and Technological Research Institute of State Grid Sichuan Electric Power Company, Chengdu, China
3 College of Electrical Engineering, Sichuan University, Chengdu, China
* Corresponding Author: Weiting Xu. Email:
(This article belongs to the Special Issue: Collaborative Operation and Market Participation Mechanisms of Distributed Energy Resources in New Power Systems)

Energy Engineering https://doi.org/10.32604/ee.2026.084558

Received 25 April 2026; Accepted 16 June 2026; Published online 15 July 2026

Abstract

Large-scale integration of customer-side flexible resources and distributed resources can aggravate line congestion and voltage violations in active distribution networks, particularly under power supply guarantee scenarios. This paper develops a bi-level congestion management method that coordinates heterogeneous flexible resources through a Stackelberg game framework. Distributed energy storage, electric vehicles, interruptible loads, and time-shiftable loads are scheduled, and vehicle-to-grid capability is explicitly incorporated to enhance operational flexibility during critical supply periods. The model captures the interaction between the load aggregator (LA) and the distribution system operator (DSO): the LA optimizes the dispatch of aggregated flexible resources in response to price signals, while the DSO seeks to maximize social welfare subject to network security constraints. To solve the nested bi-level problem, an improved grey wolf optimizer (IGWO) with Tent chaotic initialization and nonlinear convergence control is employed. Simulations on a modified IEEE 33-bus system show that the proposed method can relieve line overloading, keep nodal voltages within allowable limits, smooth net-load fluctuations, and improve peak-shaving and valley-filling performance, thereby reducing social welfare losses. The results indicate that the method provides practical support for the secure and economic operation of active distribution networks and facilitates the effective integration of renewable generation and distributed storage.

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Keywords

Congestion management; power supply guarantee scenario; multiple flexible loads; Stackelberg game; improved grey wolf optimizer

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