考虑电网支撑能力的储换一体站容量优化配置

郭爱; 叶涵昌; 戴朝华; 王永强; 叶圣永; 陈维荣

doi:10.3969/j.issn.0258-2724.20220431

考虑电网支撑能力的储换一体站容量优化配置

doi: 10.3969/j.issn.0258-2724.20220431

1.
西南交通大学电气工程学院，四川成都 611756
2.
国网四川省电力公司经济技术研究院，四川成都 610041

基金项目: 四川省自然科学基金（2022NSFSC0269）；国家电网公司科技项目（SGSCJY00GHJS2200024）

详细信息

作者简介:
郭爱（1970—），女，讲师，博士，研究方向为新能源技术及应用、大功率燃料混合系统建模及控制等，E-mail：634576728@qq.com

通讯作者:
戴朝华（1973—），男，副教授，博士，博士生导师，研究方向为能源互联网规划与运行优化、轨道交通新能源技术等，E-mail：daichaohua@swjtu.edu.cn

中图分类号: TM715
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- 被引次数: 0
出版历程
- 收稿日期: 2022-06-16
- 修回日期: 2022-10-13
- 网络出版日期: 2023-09-02
- 刊出日期: 2022-10-13

Capacity Optimization Configuration of Electric Vehicle Swapping-Storage Integrated Station Considering Support Ability to Grid

1.
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
2.
State Grid Sichuan Economic Research Institute, Chengdu 610041, China

摘要

摘要:
电动汽车换电站同时作为储能电站，既可实现经济获利，又兼顾电网支撑，但目前缺乏这种储换一体站的容量配置研究. 为此，本文首先分析储换一体站工作模式及电价时段，构建一体站的运行模型；然后，基于用户出行模拟，建立电动汽车换电需求预测模型；接着，建立考虑全寿命周期收益和电网支撑能力的储换一体站容量双层规划模型，外层规划以全寿命周期总收益为目标，实现储换一体站的容量规划，内层规划以对电网支撑能力为目标，实现电池组充放电行为优化，内层获得最优充放电功率并返回外层，实现储换一体站容量最优配置；最后，在 IEEE33 节点系统上验证规划模型的有效性，为储换一体站建设提供理论支撑. 研究结果表明：与其他储换一体站模式相比，储换一体站投资收益率提高 1.51%～2.26%；基于双层规划的容量优化配置方法，在保证一体站经济性的同时，能够对支撑电网电压，使电压日方差降低 20%；随着参与换电的电动汽车数量增加，一体站的经济性进一步提高.
- 电动汽车换电站 /
- 储换一体站 /
- 全寿命周期收益 /
- 电网支撑能力 /
- 双层规划
Abstract:
Electric vehicle (EV) swapping stations can achieve economic benefits while also supporting the power grid by serving as energy storage stations. However, there is currently a lack of research on the capacity configuration of such EV storage and swapping integrated stations (EVSS-IS). To this end, the working mode and tariff period of EVSS-IS were firstly analyzed to build an operational model. Then, a predictive model for EV swapping demand was developed based on user travel simulations. Next, a bi-level capacity programming model of the EVSS-IS was established, which considered life cycle benefits and grid support capacity. Specifically, the outer planning aimed at the total revenue during the whole life cycle to optimize the capacity of the EVSS-IS; the inner planning aimed at supporting the power grid and optimizing the charging and discharging behaviors; the optimal charging and discharging power from the inner layer was returned to the outer layer to realize the optimal capacity programming of the EVSS-IS. Finally, the effectiveness of the planning model was verified on the IEEE33 node system, which provided theoretical support for the construction of the EVSS-IS. The results show that the return on investment of the EVSS-IS is 1.51%–2.26% higher compared with other models; the capacity optimization allocation method based on bi-level planning can support the grid voltage while ensuring the economy of the station, resulting in a 20% reduction in the daily variance of the voltage; as the number of EVs swapping batteries increases, the economics of the EVSS-IS is further improved.
- electric vehicle swapping station /
- electric vehicle storage-swapping integrated station /
- life cycle benefits /
- support ability to grid /
- bi-level programming

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图 1 储换一体站结构及能量流动关系

Figure 1. Structure and energy flows of EVSS-IS

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图 2 一个时间段储换一体站内能量流动关系

Figure 2. Energy flow of EVSS-IS in a time period

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图 3 电动汽车单位时间行驶距离概率分布

Figure 3. Distribution of EV mileage per unit time

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图 4 规划模型求解流程

Figure 4. Flow chart of solving bi-level planning model

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图 5 某地10 kV系统典型日负荷曲线及分时电价

Figure 5. Typical daily load curve of a 10 kV system and a time-of-use tariff

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图 6 优化收敛曲线

Figure 6. Optimized convergence curves for the case

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图 7 部分换电需求场景

Figure 7. Part of demand scenarios for battery swapping

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图 8 不同聚类数对应DB指标

Figure 8. DB index corresponding to different clustering numbers

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图 9 换电需求典型聚类场景

Figure 9. Typical clustering scenarios for battery swapping

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图 10 不同换电场景下规划结果

Figure 10. Optimization results under different demand scenarios for battery swapping

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图 11 不同接入节点储换一体站成本与收益

Figure 11. Costs and benefits of EVSS-IS at different nodes

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图 12 典型日负荷下测试系统电压取值范围

Figure 12. Range of system voltage under typical daily load

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表 1 储换一体站参数表

Table 1. Parameters of EVSS-IS

参数	数值	参数	数值
E_bat/（kW·h）	100	C_ES,E/（元·（kW·h）⁻¹）	1800
C_EV,E/（元·（kW·h）⁻¹）	1280	C_P/（元·kW⁻¹）	2400
C_OM/（元·（kW·年）⁻¹）	155	D_EV/（元·（kW·h）⁻¹）	1.47
D_ES,b/（元·（kW·h）⁻¹）	0.15	D_EV,b/（元·（kW·h）⁻¹）	0.1
D_rt/（元·年⁻¹）	10800	$ {\eta _{\text{c}}}{\text{, }}{\eta _{\text{d}}} $	0.9
r/%	5	N/a	15

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表 2 换电需求典型聚类场景统计结果

Table 2. Statistical results of demands for battery swapping on typical clustering scenarios

场景编号	总需求/次	概率
1	20	0.0785
2	25	0.4189
3	30	0.3265
4	35	0.1397
5	40	0.0364

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表 3 接入节点为11、18时不同情景下的规划结果

Table 3. Optimization results for different scenarios with the accessing 11th, 18th nodes

接入节点	方案编号	储能容量/（MW·h）	储能功率/MW	换电容量/（MW·h）	换电功率/MW	年均净利润/万元	电压日方差	年均成本/万元
11	1	10	2.00	3.5	0.60	60.590	6.0303	598.61
	2	13.5	2.70	0	0	42.961	5.3699	534.91
	3	0	0	3.5	0.61	17.016	5.9595	216.55
	4	10.0	2.00	3.5	0.60	53.510	5.6288	621.68
	5						7.6981
18	1	2.7	0.54	3.5	0.71	26.159	5.7167	320.57
	2	6.2	1.26	0	0	19.885	5.6637	245.51
	3	0	0	3.5	0.73	16.318	5.9594	214.94
	4	2.7	0.54	3.5	0.71	25.440	5.6904	336.27
	5						7.6981

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