Open Access

ARTICLE

Low-Carbon Day-Ahead Scheduling Strategy for Active Distribution Network Considering Flexible Response of V2G

Sichang Xiao¹, Tiantian Song¹, Sihang Qin¹, Hengrui Ma^2,3,*, Bo Wang²
1 Wuhan Power Supply Company, State Grid Hubei Electric Power Co., Ltd., Wuhan, China
2 School of Electrical Engineering and Automation, Wuhan University, Wuhan, China
3 School of Automation, Wuhan University of Technology, Wuhan, China
* Corresponding Author: Hengrui Ma. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.088272

Received 01 July 2026; Accepted 28 August 2026; Published online 11 September 2026

Abstract

The high penetration of distributed renewable energy and flexible loads brings prominent challenges to the low-carbon economic dispatch of active distribution networks (ADNs). Existing vehicle-to-grid (V2G)-aided dispatch studies often fail to precisely characterize the bidirectional charging-discharging feasible region of large-scale electric vehicle clusters and suffer from either over-conservatism or heavy computational burden when handling source-load uncertainties. To fill these research gaps and improve renewable energy accommodation while cutting system carbon emissions, this paper proposes a day-ahead low-carbon scheduling strategy for ADNs considering the flexible response capability of V2G. First, an energy-power feasible region is constructed to accurately describe the energy-storage flexible characteristics of aggregated V2G units, with constraints on battery discharge depth and cycle life incorporated for practicality. Second, a stepped carbon-trading model is introduced to build the low-carbon dispatch framework, which imposes differentiated economic penalties on high-carbon links including grid-purchased power and distributed gas turbines. Third, fuzzy chance constraints are employed to quantify the dual uncertainties of distributed generation and loads so as to mitigate the conservatism of scheduling decisions. An improved particle swarm optimization algorithm fused with teaching-learning-based optimization (TLBO-PSO) is further developed to tackle the complex optimization problem with numerous binary variables and multiple constraints. Finally, an improved IEEE 33-node test system is utilized for numerical validation. Simulation results demonstrate that compared with the baseline scheme without V2G participation, the proposed coordinated dispatch strategy reduces daily system carbon emissions by up to 37.1% and cuts total scheduling costs by 6.03%. Meanwhile, it achieves a renewable-energy accommodation rate of 97.3% with competitive computational efficiency. The results verify the effectiveness of the proposed framework and highlight the necessity of exploiting V2G flexibility for low-carbon operation of ADNs.

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Keywords

Active distribution networks (ADNs); distributed renewable energy; low-carbon economic dispatch; vehicle-to-grid (V2G)

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