Energy management strategy of integrated photovoltaic-storage-swapping on highways considering influence of photovoltaic uncertainty
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摘要: 针对光伏出力不确定因素影响下的高速公路能源管理，分别从确定性光伏出力和不确定性光伏出力2个维度，研究了夏季晴朗日、黄金周和冬季雨雪日3种不同条件的出行场景在并网单一电价和并网分时电价下的光储换一体化的服务区电动汽车换电问题；以光伏自洽率最大和经济效益最高为目标函数，以微电网功率平衡以及供需两端用能特性为约束，建立了考虑光伏不确定因素的高速公路光储换一体化能源管理优化问题模型；考虑到传统遗传算法收敛速率慢、局部搜素能力差与易陷入“早熟”的缺点，提出了一种基于精英保留策略和快速非支配排序策略的改进多目标量子遗传算法。研究结果表明：夏季晴朗日、黄金周和冬季雨雪日的出行场景在考虑不确定性和确定性影响的光伏出力2种情形下皆可保证电动车换电需求，在场景气象条件限制下可再生能源利用率达到10.31%~78.27%，并取得较好的日经济效益，同时在夏季晴朗日、黄金周和冬季雨雪日光储换一体化服务区的CO2排放量分别下降了62.5%、41.3%和10.3%；从并网用电模式角度考量，分时电价与单一电价相比，其光伏自洽率和碳减排量无显著差异，但日经济效益提升10%，因而分时电价方案比单一单价方案具有更高的性价比。Abstract: In view of the energy management of highways under the influence of uncertain factors of photovoltaic power generation, the issue of swapping electric vehicles in the service area ensuring integrated photovoltaic-storage-swapping was studied under three scenarios, featuring summer sunny days, golden weeks, and winter snowy days in terms of the grid-connected single electricity price and grid-connected time-of-use electricity price from two dimensions: deterministic and uncertain photovoltaic power generation. Taking the maximum photovoltaic self-consistency rate and highest economic benefit as the objective function, constrained by the microgrid power balance and energy consumption characteristics at both supply and demand ends, an optimization model for energy management of integrated photovoltaic-storage-swapping on highways was established by considering the uncertain factors of photovoltaic power generation. To address the shortcomings of traditional genetic algorithms, such as slow convergence rate, poor local search ability, and easy falling into prematurity, an improved multi-objective quantum genetic algorithm based on elite retention strategy and fast non-dominated sorting strategy was proposed. Research results show that the three scenarios featuring summer sunny days, golden weeks, and winter snowy days, can ensure the charging and swapping demands of electric vehicles with consideration for both deterministic and uncertain photovoltaic power generation. Under the constraint of weather conditions in each scenario, the renewable energy utilization rates can reach 10.31%-78.27% with better daily economic benefit. In addition, CO2 emissions in service areas ensuring integrated photovoltaic-storage-swapping on three scenarios reduce by 62.5%, 41.3%, and 10.3%, respectively. From the perspective of grid-connected electricity consumption mode, the photovoltaic self-consistency rate and carbon emission reduction have no significant difference in terms of single electricity price and time-of-use electricity price. However, the daily economic benefit of time-of-use electricity price increases by 10%, so the time-of-use electricity price scheme has a higher cost performance than the single electricity price scheme. 9 tabs, 11 figs, 31 refs.
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图 2 光储换一体化能源管理系统运行优化流程
Figure 2. Optimization process of integrated photovoltaic-storge- swapping for energy management system
图 4 光储换一体化高速公路能源多目标优化流程
Figure 4. Multi-objective optimization process of energy for integrated photovoltaic-storage-swapping on highways
图 6 并网单一电价下夏季晴朗日HSA运行情况
Figure 6. Summer sunny days' HSA operation conditions under grid-connected single electricity price
图 7 并网单一电价下黄金周HSA运行情况
Figure 7. Golden weeks' HSA operation conditions under grid-connected single electricity price
图 8 并网单一电价下冬季雨雪日HSA运行情况
Figure 8. Winter snow days' HSA operation conditions under grid-connected single electricity price
图 9 并网分时电价下夏季晴朗日HSA运行情况
Figure 9. Summer sunny days' HSA operation conditions under grid-connected time-of-use electricity price
图 10 并网分时电价下黄金周HSA运行情况
Figure 10. Golden weeks' HSA operation conditions under grid-connected time-of-use electricity price
图 11 并网分时电价下冬季雨雪日HSA运行情况
Figure 11. Winter snow days' HSA operation conditions under grid-connected time-of-use electricity price
表 1 旋转角更新策略
Table 1. Update strategies of rotation angle
xi bi f(xi)>f(bi) Δθi s(αi, βi) αiβi>0 αiβi < 0 αi=0 βi=0 0 0 假 0 0 0 0 0 0 0 真 0 0 0 0 0 0 1 假 Δθ －1 +1 ±1 0 0 1 真 Δθ +1 －1 0 ±1 1 0 假 Δθ +1 －1 0 ±1 1 0 真 Δθ －1 +1 ±1 0 1 1 假 0 0 0 0 0 1 1 真 0 0 0 0 0 
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表 2 算法性能对比
Table 2. Performance comparison of algorithms
测试函数 指标 NSGA-Ⅱ MOEA/D TVMOPSO IMOQGA ZDT2 GD 0.000 4 0.000 6 0.000 5 0.000 4 CPF 0.662 4 0.795 3 0.801 5 0.836 9 HV 0.444 1 0.444 7 0.446 5 0.462 4 ZDT3 GD 0.000 1 0.000 5 0.000 4 0.000 1 CPF 0.819 6 0.460 5 0.687 1 0.857 2 HV 0.599 6 0.595 0 0.585 1 0.621 5 ZDT4 GD 0.000 2 0.000 4 0.000 5 0.000 3 CPF 0.672 4 0.633 7 0.567 6 0.720 7 HV 0.707 2 0.693 4 0.678 5 0.719 7 ZDT6 GD 0.000 2 0.000 4 0.000 5 0.000 2 CPF 0.692 3 0.774 9 0.741 8 0.808 1 HV 0.718 9 0.710 6 0.727 1 0.736 5 DTLZ1 GD 0.000 7 0.001 4 0.001 3 0.000 8 CPF 0.314 3 0.442 8 0.513 4 0.522 9 HV 0.807 3 0.795 4 0.798 3 0.813 3 DTLZ3 GD 0.001 7 0.002 3 0.003 7 0.002 2 CPF 0.382 1 0.378 2 0.209 3 0.465 0 HV 0.502 9 0.340 7 0.437 1 0.517 6 DTLZ5 GD 0.000 4 0.000 5 0.000 6 0.000 4 CPF 0.533 0 0.690 3 0.715 4 0.741 7 HV 0.194 1 0.190 4 0.197 9 0.199 1 DTLZ7 GD 0.055 2 0.059 2 0.069 1 0.055 2 CPF 0.168 4 0.151 2 0.155 4 0.257 5 HV 0.268 2 0.258 8 0.263 2 0.270 2
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表 3 电价参数
Table 3. Electricity price parameters
情景 电价类型 每kW·h成本/元 时段 并网单一电价 平价 0.599 5 00:00~24:00 并网分时电价 低谷价 0.322 9 23:00~8:00 平价 0.599 5 12:00~18:00 高峰价 0.876 1 8:00~12:00，18:00~23:00
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表 4 HSA内PV电池板参数
Table 4. Parameters of PV panel in HSA
额定功率/W 开路电压/V 短路电流/A 尺寸/mm2 每块单价/元 250 37 8.33 1 640×992 1 125
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表 5 HSA内磷酸铁锂电池参数
Table 5. Parameters of LiFePO4 battery in HSA
额定功率/ kW 额定容量/ (kW·h) 充放电效率/ % 自放电系数/ % 每块单价/ 元 10 55 96 1 45 000
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表 6 并网单一电价下确定性光伏出力下的求解结果
Table 6. Solution results of deterministic PV power generation under grid-connected single electrictity price
场景 购置电池/ 块 租赁电池/ 块 日经济效益/ 元 光伏自洽率/ % 夏季晴朗日 41 0 3 867.9 70.91 黄金周 41 58 6 786.6 33.54 冬季雨雪日 34 0 2 231.8 11.12
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表 7 并网单一电价下考虑不确定性光伏出力的求解结果
Table 7. Solution results of uncertain PV power generation under grid-connected single electrictity price
场景 购置电池/ 块 租赁电池/ 块 日经济效益/ 元 光伏自洽率/ % 夏季晴朗日 46 0 3 235.1 53.50 黄金周 46 53 4 033.4 21.64 冬季雨雪日 34 0 2 200.6 10.31
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表 8 并网分时电价下确定性光伏出力下的求解结果
Table 8. Solution results of deterministic PV power generation under grid-connected time-of-use electricity price
场景 购置电池/ 块 租赁电池/ 块 日经济效益/ 元 光伏自洽率/ % 夏季晴朗日 46 0 4 278.4 78.27 黄金周 46 120 7 229.6 34.05 冬季雨雪日 34 0 2 418.1 11.66
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表 9 并网分时电价下考虑不确定性光伏出力的求解结果
Table 9. Solution results of uncertain PV power generation under grid-connected time-of-use electricity price
场景 购置电池/块 租赁电池/块 日经济效益/元 光伏自洽率/% 夏季晴朗日 69 0 3 460.3 52.49 黄金周 69 121 6 464.4 21.73 冬季雨雪日 69 37 2 390.7 10.81
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