| 高少希,张达敏,陈伟川,陈鼎圣.计及供需两侧的电动汽车有序充放电优化算法研究[J].电子测量与仪器学报,2020,34(11):140-147 |
| 计及供需两侧的电动汽车有序充放电优化算法研究 |
| Optimization algorithm research on ordered charge and discharge of electric vehicles considering supply and demand sides |
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| DOI： |
| 中文关键词: 电动汽车 充放电优化 NSGA-Ⅱ 粒子群 用户成本 峰谷差率 |
| 英文关键词:electric vehicle charge and discharge optimization NSGA-II particle swarm drivers’ cost peak-to-valley difference |
| 基金项目:2018 年福建省中青年教师教育科研项目(JT180427)、福建省科技厅引导性项目(2019H0039)资助 |
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| 摘要点击次数: 96 |
| 全文下载次数: 288 |
| 中文摘要: |
| 大量电动汽车进行无序充电将给电网的安全运行带来“峰上加峰”的运行风险,作为一种移动的储能设备,大量电动汽
车的无序放电也会对电网的稳定性造成重要影响,因此对电动汽车的充放电行为进行有序引导十分必要。 首先,分析了某小区
电动汽车无序充放电的负荷情况,并以峰谷分时电价为引导,研究不同响应度的下的日负荷情况;在此基础上综合考虑用户侧
和电网侧利益,以电动汽车用户成本最低和小区日负荷峰谷差率最小为优化目标,选择峰谷分时区间为优化变量,构建电动汽
车最优充放电模型,分别采用基于 Pareto 最优的多目标遗传算法 NSGA-Ⅱ和基于 Pareto 最优的粒子群算法求解,得到最优充放
电时段,并对二者的优化结果进行比较。 最后利用蒙特卡洛算法对算例进行仿真和分析验证,结果表明,利用所提出的有序充
放电优化算法,用户可通过放电补偿充电费用,且 NSGA-Ⅱ算法更优。 |
| 英文摘要: |
| The disorderly charging of large-scale electric vehicles (EVs) will increase the operation risk of peak-up-peak to the power
grid. In addition, as a mobile energy storage device, the disordered discharge of a large number of EVs will also have an important
impact on the stability of the power grid. Therefore, it is necessary to guide the charge and discharge behavior of EVs in an orderly
manner. First of all, the general loads of disordered charge and discharge of electric vehicles in a residential area are analyzed, and the
daily load under different response is studied under the guidance of peak-valley time-of-use electricity price. On this basis, considering
both the benefits of the divers and the power grid, the optimal charge and discharge model of the EVs are constructed, which takes the
lowest charging cost of the EVs and the minimum peak-valley difference of the daily load in the community as the optimization objectives,
and selecting the peak-valley time-sharing interval as the optimization variable. The optimal charge and discharge time intervals are found
by Pareto-based optimization multi-objective genetic NSGA-Ⅱ and Pareto-based optimization particle swarm (PSO), respectively. The
results by the different optimization algorithms are compared. Finally, the Monte Carlo algorithm is used to simulate and analyze the
model. Simulation results show that users can reduce the charging cost to some extent through the discharge compensation, and NSGA-Ⅱ
algorithm is better than PSO. |
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