智能小区电动汽车充电动态电价策略设计

潘廷哲; 靳丰源; 陆泳昊; 曹望璋; 阳浩; 于鹤洋; 赵勃扬

doi:10.11930/j.issn.1004-9649.202503024

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智能小区电动汽车充电动态电价策略设计

推进全国统一电力市场建设关键技术与机制 | 更新时间：2025-12-20

- 智能小区电动汽车充电动态电价策略设计
- Design of Dynamic Pricing Strategy for Electric Vehicles Charging in Smart Communities
- 中国电力 2025年58卷第11期页码：14-24
- 作者机构：
  
  1. 南方电网科学研究院有限责任公司,广东,广州,510663
  2. 西安交通大学电气工程系,陕西,西安,710049
  3. 中国南方电网有限责任公司,广东,广州,510663
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（52177113）;中国南方电网有限责任公司重点科技项目（ZBKJXM20232273）。
- DOI：10.11930/j.issn.1004-9649.202503024
  中图分类号：
- 网络出版：2025-12-01，
  
  纸质出版：2025-12-01
- 稿件说明：
移动端阅览
潘廷哲, 靳丰源, 陆泳昊, 曹望璋, 阳浩, 于鹤洋, 赵勃扬. 智能小区电动汽车充电动态电价策略设计[J]. 中国电力, 2025, 58(11): 14-24, 37.

PAN Tingzhe, JIN Fengyuan, LU Yonghao, et al. Design of Dynamic Pricing Strategy for Electric Vehicles Charging in Smart Communities[J]. 2025, 58(11): 14-24.
潘廷哲, 靳丰源, 陆泳昊, 曹望璋, 阳浩, 于鹤洋, 赵勃扬. 智能小区电动汽车充电动态电价策略设计[J]. 中国电力, 2025, 58(11): 14-24, 37. DOI： 10.11930/j.issn.1004-9649.202503024.

PAN Tingzhe, JIN Fengyuan, LU Yonghao, et al. Design of Dynamic Pricing Strategy for Electric Vehicles Charging in Smart Communities[J]. 2025, 58(11): 14-24. DOI： 10.11930/j.issn.1004-9649.202503024.

摘要

智能小区利用需求响应对电动汽车和光储系统进行综合管理。然而，传统静态电价因忽视负荷实际响应，易引发新负荷高峰，导致等效负荷波动性增加。为此，提出一种动态电价策略。该动态电价不仅随时间变化，还与小区内部的净负荷水平相关。首先，运营者预测调度周期内的光伏出力与基础负荷，并据此构建动态电价的初始模型。其次，构建主从博弈框架：运营者作为领导者，在上层模型中以最小化等效负荷波动为目标，制定并发布动态电价，且安排储能出力；电动汽车作为跟随者，在下层模型中响应动态电价，并以最小化充电成本为目标，优化其充电策略。进一步，在该下层模型中，动态电价的引入使得电动汽车充电决策相互依赖，形成聚合博弈结构，其最优充电负荷通过纳什均衡确定。最后，通过遗传算法实现对最优动态电价策略的求解。仿真结果表明，所提模型在保障电动汽车经济利益的同时，可有效规避负荷新高峰，平抑等效负荷曲线波动，实现小区运营者的管理目标。

Abstract

Smart communities utilize demand response to achieve integrated management of electric vehicles (EVs) and photovoltaic-storage systems. However

traditional static pricing

which disregards actual load responses

tends to induce new load peaks and increases the volatility of the equivalent load. To address these challenges

this paper proposes a dynamic pricing strategy

wherein the price not only varies over time but is also correlated with the internal net load of the community. Firstly

the operator forecasts the photovoltaic generation and the baseline load within the scheduling horizon

and establishes the initial model of dynamic pricing. Secondly

a stackelberg game framework is constructed: the operator

as the leader in the upper-level model

aims to minimize the volatility of the equivalent load by formulating and publishing the dynamic prices as well as scheduling the energy storage output; EVs

as followers in the lower-level model

respond to the dynamic prices and optimize their charging strategies with the objective of minimizing charging costs. Furthermore

in the lower-level model

the introduction of dynamic pricing makes the charging decision of EVs mutually dependent

thereby forming an aggregative game structure

in which the optimal charging load of each EV is determined through the Nash equilibrium. Finally

the optimal dynamic pricing is determined through a genetic algorithm. Simulation results demonstrate that the proposed model effectively reduces load volatility

avoids new load peaks

and achieves the management objectives of the community operator while safeguarding the economic interests of EV users.

关键词

Keywords

references

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