An Evaluation and Optimization Method of the Consistency Between Self-consistent Energy Systems and Highway Development Levels Considering Intelligent Facilities and New Energy Vehicles
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摘要: 高速公路用能需求与能源供给的不适配，会造成了交通系统运行效率低、不稳定，以及可再生能源自然禀赋难消纳等问题。为此，考虑智能化设施与新能源汽车普及的影响，分析高速公路发展水平与自洽能源系统的相互影响关系，构建高速公路自洽能源系统动态演化的系统动力学（system dynamics，SD）模型，并筛选出节能增效、负荷匹配、高效通行、灵活调度、自然禀赋匹配5个方面的15个定量指标，构建高速公路发展水平与自洽能源系统的适配性评估指标体系；根据SD模型的评价指标影响因素分析提出了熵值法的改进策略，并对指标权重赋权方法的兼容性与区分性进行验证，检验了该赋权方法和改进策略的有效性。然后基于层次分析法以及TOPSIS的综合评估方法计算适配性评估指标权重，并以适配性评估模型为目标函数，自洽率、负荷匹配与基础设施稳定运行为约束条件，提出了基础设施能耗优化模型；最后基于SD模型模拟的高速公路交能融合场景开展案例分析，对高速公路发展水平与自洽能源系统适配性评估方法以及优化模型进行验证。研究结果表明：该方法可以针对考虑智能化设施与新能源汽车的高速公路发展水平与自洽能源系统的适配性情况进行推演与评价，与实际情况相符。通过对关键指标的优化，将供电裕度提高了34%，充电桩利用效率上升了49.2%，自洽率增长了64%，适配性综合得分提升了75%。Abstract: Inconsistency between energy demand and energy supply of highways leads to issues such as low and unstable operational efficiency of the transportation system and challenges in accommodating natural endowments of renewable energies. Therefore, the mutual influences between the development levels of highways and self-consistent energy systems are analyzed, with the consideration of intelligent facilities and new energy vehicles. A simulation model for dynamic evolution of highway transportation and its energy systems based on system dynamics (SD) is built. A set of evaluation indicators for evaluating the consistency level between the development level of highways and self-consistent energy systems are proposed. The indicators consist 15 quantitative parameters covering 5 aspects including energy-saving and efficiency-enhancing, load-demand matching, efficient transportation, flexible resource dispatching, and natural endowments matching. An improvement strategy for calculating weighting parameters of the evaluation indicators based on the influencing factors analyzed in the SD model is proposed, and the effectiveness of the improved weighting method is examined considering its compatibility and discrimination. An integrated evaluation method combining analytic hierarchy process (AHP), improved entropy evaluation and TOPSIS is used to calculate the weights of the evaluation indicators. And then an energy consumption optimization model for transportation infrastructure is established with the consistency evaluation model as the objective function, considering constraints of rate of self-consistent, load-demand matching and stable operation of infrastructures. Finally, a smart highway scenario based on the SD model is used as a case study to verify and analyze the evaluation method and optimization model. The results demonstrate that the proposed method can predict or evaluate the consistency between Self-consistent energy systems and highway development levels considering the integration of intelligent facilities and new energy vehicles and the evaluation results agree with the actual situation. Besides, optimization of key indicators using the optimization model demonstrates that the power supply margin could be increased by 34%, the utilization efficiency of charging stations could be improved by 49.2%, the self-consistency rate could be increased by 64%, and the comprehensive consistency score could be improved by 75%.
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图 1 高速公路发展水平与自洽能源系统的适配性评估与优化方法整体研究思路
Figure 1. Research framework for evaluation and optimization of the consistency between the development levels of highways and self-consistent energy systems
图 2 高速公路发展水平与自洽能源系统交互影响因果关系
Figure 2. The Causality relationships of self-consistent energy system and the development levels for highway transportation
图 3 高速公路交通自洽能源系统的系统动力学模型
Figure 3. System dynamics model for self-consistent energy system of highway transportation
图 8 高速公路发展水平与自洽能源系统的适配性评估标准
Figure 8. The standards for evaluating the consistency between the development levels of highways and self-consistent energy systems
图 9 场景1直接相乘法得分排序
Figure 9. The ordered scores of directly multiplicative method in case 1
图 10 场景2直接相乘法得分排序
Figure 10. The ordered scores of directly multiplicative method in case 2
图 15 典型场景适配性综合得分
Figure 15. Comparison of the comprehensive evaluation scores of typical scenarios
图 21 优化前后适配性综合得分对比
Figure 21. Comparison of consistency scores before and after optimization
表 1 SD模型参数及其在案例场景下的初始值
Table 1. Initial values of the parameters in the SD model for case study scenarios
参数名称 场景1取值 场景2取值 汽车保有量/万辆 328.99 1449.68 传统汽车保有量/万辆 396.237 1430.48 新能源汽车保有量/万辆 0.4628 19.2 自动驾驶汽车保有量/万辆 0.353 1.634 汽车强制报废标准/年 15 15 服务区数量/个 8 21 变电所数量$ /$个 32 51 桥梁长度/km 4.6 0.44 收费站数量/个 19 30 隧道长度/km 2.1 10.9 充电桩数量/台 37 92 充电桩额定功率/kW $ · \mathrm{h} $ 120 120 公路沿线风力装机容量/MW 0.809 4.157 公路沿线光伏装机容量/MW 0.486 3.471 理论最大光伏装机容量/MW 139.29 390.1 理论最大风力装机容量/MW 213.63 467.2 地区太阳能辐射量/（kW $· \mathrm{h} /\mathrm{m}^{2} $） 1511.21 1169.114 地区风能资源量/h 4392 3734 光伏储能配比/% 20 10 风力储能配比/% 20 10 光伏发电建设时间/年 2023 2018 风力发电建设时间/年 2024 2018 交通流量/（万辆/年） 520.21 3539.67 公路场景里程/km 256.8 367 智能基础设施单位能耗/kW $ \cdot \mathrm{h} $ 6500 5254 智能供配电设施单位能耗$ /\mathrm{kW} \cdot \mathrm{h} $ 487.8 487.8 交通事故发生率/$ \% $ 0.0261 0.0203 最高限制速度/（km/h） 90 120 $ \alpha $ 0.5 0.5 $ \beta $ 0.1 0.1 基准通行能力/（pcu/h） 2100 2100 
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表 2 多层次评价指标体系
Table 2. Multi-level evaluation indicators
目标层 准则层 指标层 指标方向 高速公路智能和绿色化发展与自洽能源系统的适配性 节能增效A 新能源汽车渗透率A1/% 正向 自动驾驶车辆渗透率A2/% 正向 智能交通基础设施能耗密度A3/（组/km） 正向 负荷匹配B 公路电网电压波动率B1/% 逆向 公路电网供电裕度B2/% 中间型 充电桩平均利用率B3/% 中间型 高效通行C 交通流量C1/% 正向 平均车速C2/（km/h） 正向 道路服务水平C3/% 逆向 灵活调度D 公路沿线储能设备总容量D1/kW·h 正向 公路沿线光伏发电总量D2/kW·h 正向 公路沿线风力发电总量D3/kW·h 正向 自然禀赋匹配E 弃风率E1/% 逆向 弃光率E2/% 逆向 自洽率E3/% 正向
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表 3 标度的含义
Table 3. Denotations of the scaler factors
标度 定义（比较因素q与p） 1 第q个指标第p个指标的影响相同 3 第q个指标比第p个指标的影响稍强 5 第q个指标比第p个指标的影响强 7 第q个指标比第p个指标的影响明显地强 9 第q个指标比第p个指标的影响绝对地强 2、4、6、8 2个相邻判断指标的中间值 倒数 因q与p比较得判断矩阵 apq，则因p与q相比的判断为 apq= 1/aqp则各评价指标之间的判断矩阵 A可表示为
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表 4 场景1斯皮尔曼相关性均值
Table 4. Means of Spearman correlation for the case 1
方法 直接相乘 TOPSIS VIKOR 各权重方法均值 改进的熵值法 0.992 0.992 0.995 0.993 熵值法 0.8215 0.816 0.6485 0.762 变异系数法 0.904 0.904 0.808 0.872 独立权重系数法 0.783 0.783 0.566 0.71 CRITIC权重法 0.654 0.698 0.418 0.59
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表 5 场景2斯皮尔曼相关性均值
Table 5. Means of Spearman correlation for the case 2
方法 直接相乘 TOPSIS VIKOR 各权重方法均值 改进的熵值法 0.9975 0.9975 0.995 0.997 熵值法 0.7965 0.7965 0.593 0.729 变异系数法 0.901 0.913 0.932 0.915 独立权重系数法 0.8025 0.816 0.6235 0.747 CRITIC权重法 0.75 0.819 0.651 0.74
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表 6 评估值离差平方和
Table 6. Evaluations of the sum of squares of deviation
方法 场景1 场景2 相乘直接相乘 TOPSIS VIKOR 直接相乘 TOPSIS VIKOR 改进的熵值法 0.041 0.384 1.336 0.147 0.435 1.467 熵值法 0.042 0.346 0.840 0.100 0.371 0.855 变异系数法 0.039 0.321 1.031 0.158 0.564 1.153 独立权重系数法 0.026 0.317 0.756 0.096 0.379 0.942 CRITIC权重法 0.022 0.159 0.990 0.089 0.166 0.744
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表 7 赋权方法标准贴进度排序
Table 7. Sorting of weighting method based on degrees of close
方法 斯皮尔曼相关性 离差平方和 MEAN 改进的熵值法 1 1 1 熵值法 3 3 3 变异系数法 2 2 2 独立权重系数法 4 4 4 CRITIC权重法 5 5 5
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表 8 组合权重最终结果
Table 8. The final result of combination weight
准则层 准则层权重 指标层 wh，j/% we，j/% 最终权重/% 节能增效 0.096 新能源汽车渗透率 4.6 8.067 5.99 自动驾驶汽车渗透率 3.35 8.209 5.29 智能网联设备密度 1.61 7.885 4.12 负荷匹配 0.443 公路电压波动率 6.3 9.692 7.66 供电裕度 19 7.025 14.21 充电桩利用效率 19 6.476 13.99 高效通行 0.05 最低通行效率 3 5.679 4.07 高峰期速度 1 5.709 2.88 交通安全事故发生数量 1 4.596 2.44 灵活调度 0.14 公路沿线储能设备总容量 5.6 7.236 6.25 公路沿线光伏发电总量 4.2 7.133 5.37 公路沿线风力发电总量 4.2 7.358 5.46 自然禀赋匹配 0.271 弃风率 5.4 5.275 5.35 弃光率 5.4 5.648 5.53 自洽率 16.3 4.012 11.39
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表 9 各年份优化后的参数设置
Table 9. Parameters after optimization for each year
年份 风力装机量x1 /MW 光伏装机量x2 /MW 充电桩密度x3 /（个/km） 2024 42.22 35.705 0.62 2025 53.709 45.417 0.78 2026 64.450 54.450 0.965 2027 80.218 67.834 1.24 2028 105.884 89537 1.71 2029 142.945 120.876 2.33 2030 192.8 163.032 3.19
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