多变电所互联牵引供电系统光储容量优化配置

杨健维; 冯素华; 郭惠斌; 廖凯; 向悦萍

doi:10.3969/j.issn.0258-2724.20240028

多变电所互联牵引供电系统光储容量优化配置

doi: 10.3969/j.issn.0258-2724.20240028

1.
西南交通大学电气工程学院，四川成都 611752
2.
中国南方电网有限公司超高压输电公司广州局，广东广州 510663

基金项目: 国家重点研发计划（2022YFB2603100）；国家自然科学基金项目（51977180）

详细信息

作者简介:
杨健维（1983—），女，教授，博士，研究方向为新能源电力系统保护与控制以及牵引供电能量管理，E-mail：jwyang@swjtu.edu.com

通讯作者:
廖凯（1989—），男，教授，博士，研究方向为电力系统分析、稳定与控制以及交通能源融合，E-mail： liaokai@swjtu.edu.com

中图分类号: TM922
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出版历程
- 收稿日期: 2024-01-16
- 修回日期: 2024-04-25
- 网络出版日期: 2025-09-26

Optimal Configuration of Photovoltaic and Hybrid Energy Storage System Capacity in Multi-substation Interconnected Traction Power Supply System

1.
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611752, China
2.
Guangzhou Bureau of EHV Transmission Company, China Southern Power Grid, Guangzhou 510663, China

摘要

摘要:
随着我国电气化铁路规模持续扩大，光伏-储能装置接入牵引供电系统逐渐成为实现电气化铁路节能减排的有效方式. 为保障多变电所互联牵引供电系统能够平稳且经济地运行，提出一种基于技术-经济评价体系的光伏-储能系统容量优化配置方法. 该方法首先对牵引负荷运行特性与混合储能介质充放电特性开展分析，据此划分系统的运行工况，并对不同工况下的功率分配进行控制，进而给出考虑节能和三相电压不平衡度的能量管理策略；其次，在综合考虑系统稳定运行边界及经济效益的基础上，以全寿命周期净收益、能量利用率和负序容量为优化目标，定量评价系统运行的技术-经济效果；进一步建立基于所提能量管理策略的光伏-储能系统容量配置双层优化模型，以能量管理层日运行效果对容量优化配置层参数进行迭代修正；最后，以国内某高速铁路为例开展算例分析. 仿真结果显示：所提出方法能有效实现光伏-储能系统在多个互联变电所间的最优配置，总成本降低21.18%，能量利用率达74.61%，且三相电压不平衡度符合国标2%的上限要求.
- 电气化铁路 /
- 混合储能系统 /
- 光伏发电 /
- 容量配置 /
- 多目标优化
Abstract:
With the continuous expansion of China’s electrified railways, the integration of photovoltaic (PV) and hybrid energy storage systems (HESS) into the traction power supply system (TPSS) has gradually become an effective approach to achieve energy conservation and emission reduction in electrified railways. In order to ensure the stable and economical operation of multi-substation interconnected TPSS, the optimal configuration method of PV and HESS capacity based on a techno-economic evaluation system was proposed. By analyzing the operation characteristics of traction load and the charging and discharging characteristics of mixed energy storage media, the operation conditions of the system were divided, and the energy management strategy considering energy conservation and three-phase voltage imbalance was given by controlling the power allocation under different conditions. On the basis of comprehensively considering the boundaries of stable operation and economic benefits of the system, the technical and economic effects of the system operation were quantitatively evaluated with the net benefit throughout the full life cycle, energy utilization rate, and negative-sequence capacity as optimization objectives. Furthermore, a two-layer optimization model for PV and HESS capacity configuration based on the proposed energy management strategy was established, and the parameters of the capacity optimization layer were iteratively modified according to the daily operation effect of the energy management layer. China’s high-speed railway was taken as an example for analysis. Simulation results have shown that the proposed method can effectively realize the optimal configuration of PV and HESS in multiple interconnected substations, where the total cost is reduced by 21.18%; the energy utilization rate is up to 74.61%, and the three-phase voltage unbalance meets the upper limit of 2% in the national standard.
- electrified railway /
- hybrid energy storage system /
- photovoltaic generation /
- capacity configuration /
- multi-objective optimization

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图 1 多变电所互联牵引供电系统架构

Figure 1. Structure of multi-substation interconnected traction power supply system

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图 2 系统运行工况划分

Figure 2. Division of system operation conditions

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图 3 基于多阈值的功率分配策略示意

Figure 3. Power allocation strategy based on multi-threshold

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图 4 HESS及RPC参考功率计算流程

Figure 4. Reference power calculation process of HESS and RPC

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图 5 光储容量多目标优化配置求解流程

Figure 5. Solution process of multi-objective optimization configuration of PV and HESS capacity

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图 6 光伏−储能系统容量优化配置方法

Figure 6. Optimal configuration method of PV and HESS capacity

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图 7 牵引变电所实测数据

Figure 7. Measured data of traction substation

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图 8 典型日光伏出力预测数据

Figure 8. Forecast data of PV output on typical days

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图 9 Pareto解分布情况

Figure 9. Distribution of Pareto solutions

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图 10 PV-HESS接入前、后变电所的有功功率

Figure 10. Active power of substation before and after PV and HESS integration

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图 11 HESS的SOC变化

Figure 11. Variation of SOC in HESS

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图 12 功率缺额工况下HESS及RPC运行结果

Figure 12. Operation results of HESS and RPC under power deficit conditions

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图 13 HESS及RPC工作情况

Figure 13. Operation conditions of HESS and RPC

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表 1 系统运行模式

Table 1. System operation modes

运行模式	判据	变电所 1	变电所 2
功率缺额	${P_{{\text{GS}}}} \gt {P_{\text{T}}}$	牵引	牵引
		牵引	制动
		制动	牵引
功率过剩	${P_{{\text{GS}}}} \lt {P_{\text{B}}}$	制动	制动
功率过剩	${P_{{\text{GS}}}} \lt {P_{\text{B}}}$	牵引	制动
功率平衡	${P_{\text{B}}} \lt {P_{{\text{GS}}}} \lt {P_{\text{T}}}$	牵引	制动
功率平衡	${P_{\text{B}}} \lt {P_{{\text{GS}}}} \lt {P_{\text{T}}}$	制动	牵引

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表 2 各运行工况能量流

Table 2. Energy flow of each operation condition

工况	运行状态	能量流
1	${S_1} = 1,{S_2} = 1$	PV 不工作，HESS 放电供能
2	${S_1} = 1,{S_2} = 2$	PV 和 HESS 不工作，电网供能
3	${S_1} = 1,{S_2} = 3$	HESS 和 PV 同时供能
4	${S_1} = 1,{S_2} = 4$	HESS 不工作，限制 PV 供能
5	${S_1} = 1,{S_2} = 5$	限制 PV 供能，HESS 吸收 PV
6	${S_1} = 3,{S_2} = 1$	HESS 吸收 PV 能量
7	${S_1} = 3,{S_2} = 2$	HESS 和 PV 均不工作
8	${S_1} = 2,{S_2} = 1$	HESS 吸收 RBE 和 PV 能量
9	${S_1} = 2,{S_2} = 2$	HESS 不工作，RBE 反送电网
10	${S_1} = 2,{S_2} = 3$	PV 不工作，HESS 吸收 RBE

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表 3 HESS工作状态

Table 3. Operative modes of HESS

区域工况	HESS 工作状态
牵引工况 1	电池和超级电容均可最大功率放电
牵引工况 2	电池和超级电容均可放电
牵引工况 3	仅超级电容放电
制动工况 1	仅超级电容充电
制动工况 2	电池和超级电容均可充电
制动工况 3	电池和超级电容均可最大功率充电

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表 4 系统仿真参数

Table 4. Simulation parameters of system

类别	参数	数值	类别	参数	数值
超级电容	SOC 范围	[0.05,0.95]	收益参数	电度电费/（元·（kW·h）⁻¹）	0.6
	功率成本/（元·kW⁻¹）	600		需量电费/（元·（kWh·月）⁻¹）	40
	充放电效率	0.95		附加电费/（元·（kW·h）⁻¹）	0.1
锂电池	SOC 范围	[0.2,0.8]	其他参数	设备残值率/%	7
	功率成本/（元·kW⁻¹）	1000		通货膨胀率/%	5
	容量成本/（元·（kW·h）⁻¹）	1200		资本贴现率/%	3.5
	充放电效率	0.85		项目全寿命周期/年	15
成本参数	PV 投资成本/（元·kW⁻¹）	9000	决策变量限制参数	超级电容功率/kW	[1000,15000]
	PV 运维成本/（元·（kW·年）⁻¹）	45		锂电池功率/kW	[1000,6000]
	RPC 投资成本/（元·kW⁻¹）	460		锂电池容量/（kW·h）	[2000,10000]
	配套设备成本/（元·（kW·月）⁻¹）	200		PV 装机容量/kW	[2000,10000]
	HESS 运维成本/（元·（kW·月）⁻¹）	0.1		放电阈值/kW	[2000,6000]
				充电阈值/kW	[−5000,−500]

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表 5 多目标优化配置方案

Table 5. Schemes of multi-objective optimization configuration

参数	方案 1	方案 2	方案 3	方案 4
超级电容功率/MW	5.96	11.56	12.57	13.37
超级电容容量/（kW·h⁻¹）	59.62	115.68	125.72	133.77
锂电池功率/MW	2.19	4.85	5.50	5.16
锂电池容量/（MW·h）	2.89	4.26	5.27	10.00
PV 装机容量/MW	10.00	9.85	10.00	9.91
RPC 容量/MW	13.81	14.47	14.59	14.21
放电阈值/MW	40.74	42.89	43.33	42.07
能量利用率/%	74.61	79.94	80.14	81.28
总负序容量/MW	48.51	48.81	48.39	47.70
变电所 1 负序容量/MW	27.33	27.14	27.13	27.35
变电所 2 负序容量/MW	21.18	21.67	21.26	20.35
净收益/万元	25695	21986	20447	12441

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表 6 4种方案效果对比

Table 6. Comparison of effect of four schemes

方案	$ \eta $/%	$ {S^{( - )}} $/MW	$ {C_{{\text{total}}}} $/万元	$ R $/万元
方案 A	0	74.89	23.56	0
方案 B	60.96	50.78	21.50	1.41
方案 C	65.75	51.55	21.34	2.37
本文方案	74.61	48.51	18.57	4.69
优化率/%		35.22	21.18

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