目前对电动汽车充电负荷预测的研究未解决实际应用中无法实时获得起迄点(origin destination, OD)数据并考虑车主的现实感知决策心理的问题。针对此问题,在综合考虑动态交通信息、环境温度、实时车流量、排队论等方法的基础上,建立一种城市交通系统OD流量预测的新型深度学习架构,完成电动汽车充电负荷时空分布预测。首先分析城市交通路网信息、日类型和天气等多种因素对电动汽车行驶规律的影响,通过双向长短期记忆递归神经网络算法分别获得相应私家车和出租车驾驶行为的起讫点。其次引入考虑动态交通信息及交通路口流量的路段阻抗与节点阻抗模型和考虑环境温度和车辆实时速度的空调能耗模型,采用实时Dijkstra算法为电动汽车起讫点规划最小出行成本的行驶路径,模拟电动汽车用户的驾驶行为。最终在不同应用场景下完成不同类型电动汽车的路径规划试验和充电需求预测试验。结果表明,所得充电需求时空分布特征与客观需求相符合。

At present, research on predicting the charging load of electric vehicles has not solved the problem of not being able to obtain real-time origin destination (OD) data in practical applications and considering the actual perception and decision-making psychology of car owners. In response to this issue, based on comprehensive consideration of dynamic traffic information, environmental temperature, real-time vehicle flow, queuing theory, and other methods, this paper establishes a new in-depth learning framework for urban transportation system OD flow prediction, and completes the spatiotemporal distribution prediction of electric vehicle charging load. This paper firstly analyzes the impact of various factors such as urban traffic network information, day type, and weather on the driving rules of electric vehicles. The starting and ending points of corresponding private car and taxi driving behaviors are obtained through a bidirectional short-term memory recursive neural network algorithm. Secondly, a link impedance and node impedance model considering dynamic traffic information and intersection flow is introduced, as well as an air conditioning energy consumption model considering ambient temperature and vehicle real-time speed. The real-time Dijkstra algorithm is used to plan the minimum travel cost travel path for the starting and ending points of electric vehicles, simulating the driving behavior of electric vehicle users. Finally, path planning experiments and charging demand prediction experiments for different types of electric vehicles are completed in different application scenarios. The results show that the spatiotemporal distribution characteristics of the charging demand obtained are consistent with the objective demand.

为缩短电动汽车充电行驶距离,提升车主充电便利性,建立了基于流量需求模型(flow capturing location model,FCLM)的电动汽车充电网络随机规划模型。在充电站建造数目给定的前提下,通过优化充电站建设地址,在保证电动汽车充电行驶距离满足机会约束的同时,最小化整个交通网络中的电动汽车平均充电行驶里程。所建立的规划模型为考虑机会约束的0-1整数规划问题,采用基于可行性法则的遗传算法(genetic algorithm,GA)对其进行求解,为提高求解性能对遗传算法中的交叉、变异算子进行改进。最后,采用基于25节点交通网络的算例验证所提模型与求解方法的有效性,并对不同规划边界条件下充电行驶距离概率分布特性、置信度与充电站建造数目对规划结果的影响进行了分析。

In order to shorten the charging distance of electric vehicles and improve the charging convenience of car owners, a stochastic planning model for charging electric vehicle electrical network based on flow capturing location model(FCLM) is established. Under the premise of a given number of charging stations, by optimizing the construction address of charging stations, while ensuring that the charging distance of electric vehicles meets opportunity constraints, the average charging distance of electric vehicles in the entire transportation network is minimized. The established programming model is a 0-1 integer programming problem with opportunity constraints. The genetic algorithm based on the feasibility rule is used to solve it. In order to improve the solution performance, the crossover and mutation operators in the genetic algorithm(GA)are improved. Finally, an example based on a 25 node transportation network is used to validate the effectiveness of the proposed model and solution method. The probability distribution characteristics of charging distance under different planning boundary conditions, as well as the impact of confidence and the number of charging stations built on the planning results, are analyzed.

电网设备成本核算精细化、价值管理精益化、投资精准化的要求不断提高。在此背景下,考虑自建造至退役期间变电站设备所涉及的成本消耗,构建准确反映全寿命周期成本的各个指标,选取110 kV户外、户内变电站进行实例验证,比较不同类型变电站在全寿命周期成本各个子指标的差异。

The requirements of power grid refined equipment cost accounting,lean value management and precise investment are constantly improving. Under this background, considering the cost consumption of substation equipment during the period from construction to decommissioning, each index that accurately reflects the life cycle cost is constructed, to compare the difference of each sub-index of the life cycle cost of different types of substations.

新能源电动汽车是汽车产业升级的主要方向,对保障能源安全、改善生态环境有重要作用,因此,开展新能源电动汽车发展研究有一定的理论和现实意义。对电动汽车产业发展的影响因素进行系统分析,建立因果关系模型、存量流量模型,确定动力学方程并进行参数赋值。在模型有效性得到验证的基础上,进行系统仿真和分析,探索新能源电动汽车发展趋势及主要影响因素,并从政府补贴、关键技术突破、充换电基础设施建设、消费市场培育等方面提出了电动汽车产业发展对策建议。

New energy electric vehicle are the main trend of automobile industry upgrading. It plays an important role in ensuring the energy security and improving the ecological environment. It is of theoretical and practical significance to carry out research on the development of new energy electric vehicles<em>. The affecting factors are systematically analyzed, the causal relationship model and stock flow model are established, and the dynamic equation of the model are determined and the parameter assignments are made</em>. The model is verified and the system simulation and analysis are performed. The development trend and main influencing factors of new energy electric vehicles are explored. The countermeasures for the development of electric vehicle industry are put forward from the aspects of government subsidies, key technological breakthroughs, charging and replacement infrastructure construction, and consumer market cultivation.