The V2G Collaborative Optimization Strategy Driven by Electricity Prices Response Under the High Proportion of New Energy Penetration

向黎锋; 张鸿伟; 张晓东; 陈颉

doi:10.3969/j.issn.1008-0198.2025.06.001

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The V2G Collaborative Optimization Strategy Driven by Electricity Prices Response Under the High Proportion of New Energy Penetration

更新时间：2026-02-06

- The V2G Collaborative Optimization Strategy Driven by Electricity Prices Response Under the High Proportion of New Energy Penetration
- Vol. 45, Issue 6, Pages: 1-8(2025)
- 作者机构：
  
  1. 湘潭大学自动化与电子信息学院,湖南,湘潭,411105
  2. 广西电网有限责任公司百色供电局,广西,百色,533099
  3. 中国铁路广州局集团有限公司长沙机务段,湖南,长沙,410007
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.06.001
  CLC： TM732
- Online First：13 January 2026，
  
  Published：2025
- 稿件说明：
移动端阅览
向黎锋, 张鸿伟, 张晓东, 陈颉. 新能源高比例渗透下电价响应驱动的车网协同优化策略[J]. 湖南电力, 2025, 45(6): 1-8.

向黎锋, 张鸿伟, 张晓东, et al. The V2G Collaborative Optimization Strategy Driven by Electricity Prices Response Under the High Proportion of New Energy Penetration[J]. 2025, 45(6): 1-8.
向黎锋, 张鸿伟, 张晓东, 陈颉. 新能源高比例渗透下电价响应驱动的车网协同优化策略[J]. 湖南电力, 2025, 45(6): 1-8. DOI： 10.3969/j.issn.1008-0198.2025.06.001.

向黎锋, 张鸿伟, 张晓东, et al. The V2G Collaborative Optimization Strategy Driven by Electricity Prices Response Under the High Proportion of New Energy Penetration[J]. 2025, 45(6): 1-8. DOI： 10.3969/j.issn.1008-0198.2025.06.001.

摘要

针对车辆到电网(vehicle-to-grid

V2G)调度中

充电需求时间分布不确定性和高比例新能源渗透下出力不确定性导致电网负载难以平衡的问题

提出一种高比例新能源接入下电价响应驱动的车网协同优化策略。首先

构建一个面向高比例新能源接入场景的电价响应驱动车网协同优化模型

该模型通过调控电价引导V2G充电桩与电动汽车(electric vehicle

EV)的充放电行为

并以电网总成本最小化、电网负载峰谷差最小化和用户利益最大化为目标进行优化。其次

将第三代非支配遗传算法(non-dominated sorting genetic algorithm Ⅲ

NSGA-Ⅲ)与Wasserstein生成对抗网络(Wasserstein generative adversarial network

WGAN)融合

建立基于非支配排序和拥挤度距离选择精英解的奖励函数的动态调节算法

对模型进行求解。算例分析表明

相比于传统NSGA-Ⅲ算法

所提出的NSGA-Ⅲ+WGAN算法通过高效全局搜索与局部精调能力

能有效降低电网运行成本

提高电网的新能源消纳能力

平抑电网负载峰谷差

提高用户收益。

Abstract

Aiming at the uncertainty of the time distribution of charging demand in vehicle-to-grid (V2G) scheduling and the uncertainty of output under the penetration of high proportion of new energy

which makes it difficult to balance the load of the grid

a V2G collaborative optimization strategy driven by electricity price response under a high proportion of new energy access is proposed. Firstly

a V2G collaborative optimization model driven by electricity price response for high proportion new energy access scenarios is constructed

which guides the charging and discharging behavior of V2G charging piles and electric vehicles by regulating electricity prices

and is optimized with multiple goals to minimize the total cost of the power grid

minimize the peak-to-valley difference of grid load

and maximize user benefits. Secondly

the non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ) algorithm is fused with the Wasserstein generative adversarial network(WGAN) to establish a dynamic adjustment algorithm with non-dominated sorting and crowding distance selection elite solution reward function for solving the model. Case analysis shows that compared with the traditional NSGA-Ⅲ algorithm

the proposed NSGA-Ⅲ WGAN algorithm can effectively reduce the operating cost of the power grid

improve the new energy consumption capacity of the power grid

stabilize the peak-to-valley difference of the load of the power grid

and improve the benefits of users through efficient global search and local fine-tuning capabilities.

关键词

Keywords

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