Day-Ahead Optimal Scheduling of Co-phase Traction Power Supply System with Photovoltaic and Hybrid Energy Storage
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摘要:
既有牵引供电系统中以负序为主的电能质量问题以及电分相环节严重制约了其安全、高效运行，目前理想的解决方案是基于对称补偿理论的同相供电技术. 通过同相补偿装置中的直流母线接入光伏发电系统以及混合储能装置，进一步实现再生回馈能量利用和牵引负荷削峰填谷，提高光伏渗透率. 因此，建立了一种同相牵引供电系统优化运行模型，该模型以同相牵引变电所日运行成本最低为目标，以混合储能装置充放电策略、光伏出力以及潮流控制器功率为决策变量，尤其考虑了电网侧三相电压不平衡度约束；进一步将原始优化模型中非线性约束进行线性化处理，得到混合整数线性规划模型，并利用商业规划求解器CPLEX进行求解. 算例分析结果表明：接入光伏与混合储能装置后日运行成本可节省36.45%，且三相电压不平衡度满足国标上限2%的要求.
Abstract:Power quality issues represented by voltage unbalance and the electrical sectioning issues have severely restricted the safe and efficient operation of the traction power supply system. At present, the ideal solution is the co-phase power supply technology based on symmetrical compensation theory. By integrating the photovoltaic power generation system and the hybrid energy storage system with the DC bus of power flow controller, the utilization of regenerative braking energy, and peak-shaving and valley-filling of traction load can be further achieved to improve photovoltaic penetration rate. For this purpose, the optimal operation model of co-phase traction power supply system is established, which sets the minimum daily operation cost of traction substation as the objective, and takes the charging and discharging strategy of hybrid energy storage, photovoltaic output and power flow controller power as decision variables, and also takes into account the three-phase voltage unbalance constraint. The nonlinear constraints are linearized to formulate the mixed-integer linear programming model, which can be solved by programming solver CPLEX. The case study results show that the integration of photovoltaic and hybrid energy storage can effectively reduce 36.45% of daily operating cost, while the three-phase voltage unbalance meets the upper limit of 2% in the national standard.
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图 3 牵引负荷及最大光伏出力日前预测数据
Figure 3. Day-ahead forecast values of traction load and maximum photovoltaic output
图 6 反馈电能计费方案 a、b 和 c 结果对比
Figure 6. Comparison of feedback power billing schemes a, b and c
表 1 模型输入参数
Table 1. Input parameters of model
项目 参数 参数取值 电网 US/kV 220.0 Scap/（MV·A） 750 $ {{\overline \varepsilon _{\text{U}}}} $/% 2 牵引变压器 N1 4 N2 4$/ {\sqrt 3 }$ UT/kV 27.5 潮流控制器 Uα/kV 27.5 Uβ/kV 27.5 Sα,cap/（MV·A） 10 Sβ,cap/ （MV·A） 10 混合储能装置 电池 超级电容 SOC 范围 [0.20, 0.80] [0.05, 0.95] 初始 SOC 0.5 0.5 效率（充/放电） 0.80/0.80 0.95/0.95 额定容量/（MW·h） 5.00 0.25 额定功率/MW 2 10 日最大循环数/次 15 不限 电价 峰时 平时 谷时 电度/ （元·（kW·h）−1） 1.252 0.782 0.370 需量/
（元·（kW·月−1）−1）42.000 42.000 42.000 时间段 8：00—11：00,
18：00—21：007：00—8：00,
12：00—17：000：00—6：00,
22：00—0：00反馈电能
计费方案方案 a cfed = 0 方案 b cfed = cbuy 方案 c cfed = −0.8cbuy 
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表 2 方案Ⅰ与方案Ⅱ优化结果对比
Table 2. Comparison of scheme Ⅰ and scheme Ⅱ
指标 方案Ⅰ 方案Ⅱ 优化率/% 经济 电度电费/元 67 077.81 44 674.85 33.40 需量电费/元 17 129.90 10 489.85 38.76 回馈电能计费/元 14 759.58 26 36.13 82.14 光伏运维费用/元 0 12 56.80 储能运维费用/元 0 3 833.72 总成本/元 98 967.29 62 891.35 36.45 技术 制动能量
利用率/%0 80.27 80.27 最大电压
不平衡度/%2.79 2.00 28.32
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表 3 反馈电能计费方案a、b和c优化结果对比
Table 3. Optimal result comparison of feedback power billing schemes a, b and c
项目 方案 a 方案 b 方案 c 总弃光量/（MW·h） 1.43 1.47 0 电池日循环数 15 15 6 电度电费/元 44 674.85 44 674.85 53 149.54 需量电费/元 10 489.85 10 489.85 10 489.85 回馈电能收费/元 0 2 636.13 −12 704.86 光伏运维费用/元 1 261.25 1 256.80 1 409.51 储能运维费用/元 3 770.44 3 833.72 1 713.61 总成本/元 60 196.39 62 891.35 5 4057.66 总成本节省率
（相较方案Ⅰ） /%28.51 36.45 25.33
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