WO2022135473A1 - Method for evaluating acceptance capability of electric vehicle in urban distribution network - Google Patents

Method for evaluating acceptance capability of electric vehicle in urban distribution network Download PDF

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WO2022135473A1
WO2022135473A1 PCT/CN2021/140472 CN2021140472W WO2022135473A1 WO 2022135473 A1 WO2022135473 A1 WO 2022135473A1 CN 2021140472 W CN2021140472 W CN 2021140472W WO 2022135473 A1 WO2022135473 A1 WO 2022135473A1
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distribution network
electric vehicle
evaluation
charging
travel
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PCT/CN2021/140472
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French (fr)
Chinese (zh)
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李凡
吴裔
田英杰
郭乃网
张开宇
魏新迟
张美霞
徐立成
吴子敬
杨秀
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国网上海市电力公司
华东电力试验研究院有限公司
上海电力大学
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Priority to AU2021323387A priority Critical patent/AU2021323387B2/en
Publication of WO2022135473A1 publication Critical patent/WO2022135473A1/en

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    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

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  • the invention relates to the research field of electric vehicle charging load modeling and the influence of the charging load on a distribution network, in particular to a method for evaluating the acceptance capacity of an urban distribution network for electric vehicles.
  • the present invention proposes a method for evaluating the ability of the distribution network to accept electric vehicles based on the ideal point approximation method (TOPSIS, Technique for Order Preference by Similarity to an Ideal Solution).
  • TOPSIS ideal point approximation method
  • an evaluation index system is established to conduct an all-round evaluation of the acceptance capacity of the distribution network.
  • TOPSIS ideal point approximation method
  • entropy weight method to modify the comprehensive weighting method of the analytic hierarchy process to weight each evaluation index
  • the IEEE33 standard distribution network model Simulation and analysis of the acceptance capacity of the distribution network when electric vehicles are connected in different ways.
  • the rationality, safety and economy are selected as the criteria to select six evaluation indicators: the voltage excursion does not exceed the limit rate, the node reactive power failure rate, network security operation Indicators, load rate, network loss value, additional non-power consumption fee.
  • the acceptance evaluation method of TOPSIS is used to standardize the indicators in the original multi-attribute decision-making matrix. According to the order of each indicator, the optimal indicator value is selected to form a positive ideal solution, and the worst indicator value is selected to form a negative ideal solution.
  • the closeness to the positive and negative ideal solutions measures the closeness to the ideal value, and sorts the schemes according to the closeness, and evaluates the acceptance capacity of the distribution network when the charging load is connected in different ways.
  • the present invention has the following advantages:
  • TOPSIS ideal point approximation method
  • the Analytic Hierarchy Process is a subjective weighting method, which can combine qualitative concepts and quantitative data. Comparing and forming the judgment matrix of this layer, and then descending one layer to carry out the same influence degree judgment process until the bottom layer; by calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, the weight of each index is calculated.
  • the entropy weight method which defines the value and weight of the data according to the original discrete degree of the data.
  • Information is a measure of the degree of order of the system
  • entropy is a measure of the degree of disorder of the system. high weight value.
  • FIG. 1 is a framework diagram for evaluating the acceptance capacity of the distribution network for electric vehicles provided by the present invention.
  • FIG. 2 is a diagram of an evaluation index system for the acceptance capacity of a distribution network provided by the present invention.
  • FIG. 3 is a charging load curve diagram of each area under the hybrid chain provided by the present invention.
  • FIG. 4 is a topological structure diagram of an IEEE33 distribution network provided by the present invention.
  • FIG. 5 is the node voltage level of the electric vehicle charging load provided by the present invention under different access schemes.
  • the invention proposes a method for evaluating the ability of a distribution network to accept electric vehicles based on an ideal point approximation method (TOPSIS).
  • TOPSIS ideal point approximation method
  • the invention adopts the travel chain theory to study the time-space travel trajectories and travel characteristics of electric private vehicles.
  • the starting point and the end point of the travel chain proposed by the present invention mainly include residential areas, work areas, commercial areas, leisure areas and other areas.
  • W, C, R, O represent. It is assumed that the starting point of the user's first trip is a residential area, and t 0 is the starting trip time; is the travel time of the user from the starting point si to the end point d i ; is the dwell time at destination d i ; is the travel distance of the i-th trip.
  • G TC is the set of spatiotemporal feature quantities of electric vehicle travel, which can be described by equation (1):
  • the present invention simplifies the power consumption of the electric vehicle, ignores the influence of the user's driving habits and external factors on the power consumption of the vehicle battery during the actual driving process, and considers that the battery power consumption has a linear relationship with the mileage of the vehicle.
  • the battery power consumption and the battery power when reaching the destination can be determined by equations (2)-(4):
  • e 0 is the power consumption per unit mileage of the electric vehicle; is the total power consumption of the vehicle from si to si ; B ev is the battery capacity of the vehicle, is the remaining power of the electric vehicle when it reaches the destination i, is the state of charge of the electric vehicle when it reaches destination i.
  • the Monte Carlo method is used to build a model for all electric vehicles in the target area, and different charging decisions are taken for users with different charging needs.
  • the present invention considers the influence of electric vehicle access on the distribution network, and based on the traditional distribution network operation evaluation research, establishes an index system from the aspects of rationality, safety and economy. A comprehensive assessment of the receiving capacity of the power grid.
  • the voltage excursion not exceeding the limit rate T 1 refers to the ratio of the number of nodes in the distribution network whose node voltage does not exceed the limit to the total number of nodes after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate whether the voltage offset of each node meets the relevant technical standards after the electric vehicle charging load is connected.
  • 0.9-1.1 is regarded as the effective level range of the node voltage.
  • the node reactive power failure rate T2 refers to the ratio of the number of nodes to the total number of nodes whose power factor cannot reach the required standard for reactive power configuration after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate whether the reactive power of each node meets the standard after the electric vehicle charging load is connected. In this paper, the standard range of node power factor is set to 0.85-1.
  • the network safety operation index S 1 refers to the ratio of the number of lines to the total number of lines whose current value exceeds the safe current carrying capacity of the line after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate whether the single-circuit line in the network meets the safe operation standard after the charging load is connected.
  • L out and L are the number of lines and the total number of lines in the distribution network that exceed the maximum current safe operation range in the network, respectively.
  • P av and P max are the short-term average load and the generated maximum load value in the distribution network, respectively.
  • the network loss value E 1 refers to the sum of the active power losses of each line after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate the impact of the charging load on the operation economy of the distribution network.
  • P i and Q i are the active and reactive power of line i respectively;
  • R i is the resistance of line i and its connected equipment;
  • U i is the voltage of line i.
  • the additional non-power consumption fee E 2 refers to the additional fee generated by the reactive power compensation in order to ensure that the power factor is at a relatively reasonable value after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate the additional investment required for reactive power compensation for each node in the distribution network due to insufficient power factor.
  • ⁇ is the necessary investment to compensate the reactive power compensation of the unit capacity;
  • Q need is the reactive power compensation capacity required after the electric vehicle charging load is connected.
  • ⁇ is set to 0.01 million yuan/kvar.
  • the present invention firstly standardizes the index matrix of the evaluation scheme.
  • it also uses the gray correlation degree that describes the closeness of the relationship between the evaluation objects, and measures the degree of closeness between the evaluation objects.
  • the group utility value of the overall closeness of the scheme to the ideal solution and the individual deviation value describing the deviation degree of the worst indicators in each scheme are comprehensively evaluated, and the program acceptance ability is prioritized according to the comprehensive evaluation criteria.
  • the decision matrix X is normalized according to formulas (11)-(13), and the comprehensive weight of the above comprehensive indicators is multiplied by the standardized decision matrix to obtain the weighted normalization matrix Y
  • a max,j and a min,j are the maximum and minimum values of the jth index; aij represents the jth index in scheme i; bij is the standardized form of the jth index in scheme i; q 1 , q 2 is the boundary value of the interval where the intermediate indicator is located.
  • (y ij ) m ⁇ n is the weighted normalized decision matrix
  • (c ij ) m ⁇ n is the original decision matrix
  • (k j ) m ⁇ n is the original comprehensive index comprehensive weight matrix.
  • y ij is the element of the i-th row and the j-th column in the decision matrix
  • y + is the maximum value of the element in the decision matrix
  • y - is the minimum value of the element in the decision matrix
  • Euclidean distance used to calculate the distance between different solutions and the ideal solution.
  • D i + is the Euclidean distance between the ith estimated value and the positive ideal solution y +
  • D i - is the Euclidean distance between the ith estimated value and the negative ideal solution y - .
  • Grey correlation degree used to calculate the degree of correlation between different schemes and ideal solutions.
  • g ij + is the positive gray correlation coefficient
  • g ij - is the negative gray correlation coefficient
  • ⁇ is the resolution coefficient
  • G i + is the positive grey relational degree
  • G i - is the negative grey relational degree
  • n is the number of grey relational coefficients.
  • Group utility value S i used to calculate the closeness of different schemes to the positive ideal solution.
  • Individual deviation value B i used to calculate the degree of deviation between the worst index and the ideal index under each scheme.
  • the Euclidean distance and the gray correlation degree can be integrated.
  • the positive and negative Euclidean distance and the gray correlation degree are combined in pairs according to the user's judgment preference to obtain the positive ideal distance. and negative ideal distance
  • the calculation formula is shown in formula (21)-formula (22).
  • ⁇ and ⁇ are the preference coefficients of the user when evaluating.
  • the positive ideal distance when the Euclidean distance from the negative ideal solution is farther and the correlation degree with the positive ideal solution is higher, that is, The larger the value, the higher the similarity between the solution to be evaluated and the ideal solution; otherwise, the negative ideal distance (Including the Euclidean distance from the positive ideal solution and the degree of association with the negative ideal solution), the greater the degree of similarity between the scheme to be evaluated and the negative ideal solution, the worse the acceptance capacity of the distribution network under this scheme.
  • the positive and negative ideal distances are synthesized to obtain the relative distances Ri between different schemes and the ideal solution, as shown in formula (23).
  • the group utility value and individual bias value can be synthesized to obtain the compromise coefficient Q i of the two, and the acceptance ability can be measured by the compromise coefficient, as shown in formula (24).
  • R i is the ideal distance
  • S i is the group utility value
  • v is the weight ratio of the group utility value
  • the compromise coefficient not only reflects the degree of closeness between the scheme and the ideal scheme, but also reflects the degree of deviation between the worst individual index and the project approval index.
  • the charging load of urban electric private vehicles in the corresponding space-time area is calculated, and the charging load curve of each area under the hybrid chain is obtained, as shown in Figure 3.
  • the present invention adopts the IEEE33 node distribution network system for simulation (topological structure is shown in Figure 4.
  • the reference power of the distribution network is set as 10MVA
  • the reference voltage at the head end of the network is 12.66kV
  • the total network load is 3715+j2300kVA.
  • Option 1 Consider 5,000 vehicles to be connected to all nodes according to the conventional load ratio
  • Option 3 Consider 5,000 vehicles connected to multiple nodes in the form of charging stations in proportion, and connected to the end nodes of the distribution network in each functional area (nodes 22, 18, 32, and 25 are selected in this article);
  • Option 4 Consider 5,000 vehicles connected to multiple nodes in the form of charging stations in proportion, and connected to the head node of the distribution network in each functional area (nodes 19, 7, 26, and 23 are selected in this paper).
  • the full-node, partial-node and single-node access schemes for electric vehicles of different scales are selected as the evaluation objects.
  • IEEE33 node distribution network carry out the power flow calculation considering the charging load, and obtain the node voltage level of the electric vehicle charging load under different access schemes, as shown in Figure 5.
  • the degree of fit between each index and the ideal point in different schemes is calculated from the three aspects of technical rationality, safety reliability and operation economy, and the schemes are ranked according to the evaluation results.
  • the initial data in Table 1 constitute the original index matrix X.
  • the weighted normalization matrix is obtained by matrix normalization and weight determination:
  • the Euclidean distance is used to measure the distance between each scheme and the ideal solution.
  • the smaller the deviation between the inferior index and the ideal index the higher the acceptance capacity of the distribution network.
  • Table 6 sorts the schemes according to different index values

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Abstract

A method for evaluating the acceptance capability of an electric vehicle in a urban distribution network, comprising the following steps: 1) on the basis of a trip-chaining theory and a Monte Carlo method, performing electric vehicle charging load prediction on an electric vehicle charging load space-time distribution in a target area; 2) by taking into consideration the rationality, reliability and economy of a distribution network, setting up a comprehensive acceptance capability evaluation index system; 3) proposing an analytic hierarchy process (AHP) and entropy weight method-based comprehensive weighting method for evaluation indices, and using a technique for order of preference by similarity to ideal solution (TOPSIS) to evaluate the acceptance capability of the distribution network when the charging load is accessed in different ways; and 4) by taking a typical IEEE33 standard distribution network as an example for simulation, analyzing the space-time distribution of the charging load in the target area and the impact thereof on the distribution network. Simulation results show that when selecting some nodes of the distribution network to access electric vehicles in an appropriate amount, the closer the evaluation indices are to an ideal point, and the better the acceptance capability under the aforementioned solution.

Description

城市配电网对电动汽车的接纳能力评估方法Evaluation method for the acceptance capacity of electric vehicles in urban distribution network 技术领域technical field
本发明涉及电动汽车充电负荷建模以及充电负荷对配电网影响研究领域，尤其是涉及城市配电网对电动汽车的接纳能力评估方法。The invention relates to the research field of electric vehicle charging load modeling and the influence of the charging load on a distribution network, in particular to a method for evaluating the acceptance capacity of an urban distribution network for electric vehicles.
背景技术Background technique
随着日益严峻的能源和环境问题，具有高效、清洁等优点的电动汽车得到了全球各国政府的大力推广。然而由于电动汽车充电负荷在时空分布上具有一定的随机性和聚集性，大规模电动汽车的接入会对配电网的安全经济运行和电能质量带来不良影响，主要体现在充电负荷的接入导致线路过载、变压器过载、电力器件老化、电压下降、谐波污染以及***网损增加等方面。由于电动汽车接入配网的节点以及电动汽车数量不同，不同接入情境下对配网的影响不同，因此需要对配电网接纳电动汽车的能力进行评估，这也成为进一步推广电动汽车的重要前提。With increasingly severe energy and environmental problems, electric vehicles with the advantages of high efficiency and cleanliness have been vigorously promoted by governments around the world. However, due to the randomness and aggregation of electric vehicle charging loads in space and time distribution, the access of large-scale electric vehicles will have adverse effects on the safe and economic operation and power quality of the distribution network, which is mainly reflected in the connection of charging loads. The input causes line overload, transformer overload, aging of power devices, voltage drop, harmonic pollution and increased system network loss. Due to the different nodes connected to the distribution network and the number of electric vehicles, the impact on the distribution network under different access scenarios is different. Therefore, it is necessary to evaluate the ability of the distribution network to accept electric vehicles, which has become an important factor in the further promotion of electric vehicles. premise.
以往研究在电动汽车接纳能力评估指标选取时，通常考虑节点电压水平是否越线、配电变压器负载路率、线路潮流是否超过安全约束条件、网络功率损耗情况以及其他因素。这些研究注重对评估对象进行综合多方面综合评价，从技术合理性、安全可靠性以及经济性三个方面提出评估配电网承载能力的7项指标，将模糊理论与层次分析法相结合形成模糊层次法进行多目标决策，实现不同方案下配电网的承载能力的评估，该方法为工程中常用的评估方法。然而这在配电网接纳电动汽车能力评估指标的选取上有所欠缺，缺乏一定的全面性；其次在指标权重的处理上，主观性较强，会对评估结果产生一定的偏差。In the past research, when selecting the evaluation index of electric vehicle acceptance capacity, it usually considers whether the node voltage level crosses the line, whether the load rate of the distribution transformer, whether the line power flow exceeds the safety constraint, the network power loss and other factors. These studies focus on comprehensive multi-faceted evaluation of the evaluation object, and put forward seven indicators to evaluate the carrying capacity of the distribution network from three aspects: technical rationality, safety and reliability, and economy. The multi-objective decision-making method is used to realize the evaluation of the carrying capacity of the distribution network under different schemes. This method is a commonly used evaluation method in engineering. However, this lacks in the selection of the evaluation indicators for the ability of the distribution network to accept electric vehicles, and lacks a certain comprehensiveness; secondly, in the processing of the indicator weights, the subjectivity is strong, which will cause certain deviations in the evaluation results.
因此，本发明提出了一种基于理想点逼近法(TOPSIS，Technique for Order Preference by Similarity to an Ideal Solution)的配电网接纳电动汽车能力评估方法，从配电网运行的合理性、安全性以及经济性方面建立评估指标体系，对配电网的接纳能力进行全方位评估。利用理想点逼近法(TOPSIS)对配电网接纳电动汽车的能力进行评估；利用熵权法修正层次分析法的综合赋权方法对各评估指标进行赋权，最后借助IEEE33的标准配电网模型对电动汽车不同接入方式接入时配 电网的接纳能力进行仿真分析。Therefore, the present invention proposes a method for evaluating the ability of the distribution network to accept electric vehicles based on the ideal point approximation method (TOPSIS, Technique for Order Preference by Similarity to an Ideal Solution). In terms of economy, an evaluation index system is established to conduct an all-round evaluation of the acceptance capacity of the distribution network. Using the ideal point approximation method (TOPSIS) to evaluate the ability of the distribution network to accept electric vehicles; using the entropy weight method to modify the comprehensive weighting method of the analytic hierarchy process to weight each evaluation index, and finally using the IEEE33 standard distribution network model Simulation and analysis of the acceptance capacity of the distribution network when electric vehicles are connected in different ways.
发明内容SUMMARY OF THE INVENTION
在传统配电网接纳能力评估的基础上，分别选取合理性、安全性以及经济性三方面为准则选取六项评估指标：电压偏移不越限率、节点无功不达标率、网络安全运行指标、负载率、网损值、附加无功耗费。采用TOPSIS的接纳评估法对原始多属性决策矩阵中的各项指标进行标准化处理，根据各指标顺序选取最优指标值组成正理想解，并选取最劣指标值组成负理想解，然后以各方案与正、负理想解的贴近程度衡量与理想值的接近度，并按照贴近度对方案进行排序，对以不同方式接入充电负荷时的配电网接纳能力进行评估。On the basis of the traditional distribution network receiving capacity evaluation, the rationality, safety and economy are selected as the criteria to select six evaluation indicators: the voltage excursion does not exceed the limit rate, the node reactive power failure rate, network security operation Indicators, load rate, network loss value, additional non-power consumption fee. The acceptance evaluation method of TOPSIS is used to standardize the indicators in the original multi-attribute decision-making matrix. According to the order of each indicator, the optimal indicator value is selected to form a positive ideal solution, and the worst indicator value is selected to form a negative ideal solution. The closeness to the positive and negative ideal solutions measures the closeness to the ideal value, and sorts the schemes according to the closeness, and evaluates the acceptance capacity of the distribution network when the charging load is connected in different ways.
与现有技术相比，本发明具有以下优点：Compared with the prior art, the present invention has the following advantages:
一、利用理想点逼近法(TOPSIS)对以不同方式接入充电负荷时的配电网接纳能力进行评估，在传统配电网接纳能力评估的基础上，选取全方位综合评价指标，分别涵盖合理性、安全性以及经济性三方面。能够对配电网的接纳能力进行全方位准确评估。1. Use the ideal point approximation method (TOPSIS) to evaluate the acceptance capacity of the distribution network when the charging load is connected in different ways. On the basis of the traditional acceptance capacity evaluation of the distribution network, comprehensive evaluation indicators are selected, covering reasonable safety, security and economy. It can conduct a comprehensive and accurate assessment of the receiving capacity of the distribution network.
二、采用层次分析法(AHP，Analytic Hierarchy Process)是一种主观赋权法，可以将定性概念和定量数据结合起来，通过建立的指标体系，将每个指标对上一层指标的影响程度作对比形成该层的判断矩阵，接着下降一层进行同样的影响程度判断过程，直至最底层；通过求取判断矩阵的最大特征值与其对应的特征向量，计算出每个指标的权重。2. The Analytic Hierarchy Process (AHP, Analytic Hierarchy Process) is a subjective weighting method, which can combine qualitative concepts and quantitative data. Comparing and forming the judgment matrix of this layer, and then descending one layer to carry out the same influence degree judgment process until the bottom layer; by calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, the weight of each index is calculated.
三、引入一种客观赋权方法——熵权法，该方法根据数据原本的离散程度来定义其价值和权重。信息是***有序程度的度量，熵则是***无序程度的度量，各指标的信息熵越小，其所含的信息量就越大，在评估过程中价值也就越大，应该赋予较高的权重值。Third, introduce an objective weighting method, the entropy weight method, which defines the value and weight of the data according to the original discrete degree of the data. Information is a measure of the degree of order of the system, and entropy is a measure of the degree of disorder of the system. high weight value.
附图说明Description of drawings
图1为本发明提供的配电网对电动汽车的接纳能力评估框架图。FIG. 1 is a framework diagram for evaluating the acceptance capacity of the distribution network for electric vehicles provided by the present invention.
图2为本发明提供的配电网接纳能力评估指标体系图。FIG. 2 is a diagram of an evaluation index system for the acceptance capacity of a distribution network provided by the present invention.
图3为本发明提供的混合链下各区域充电负荷曲线图。FIG. 3 is a charging load curve diagram of each area under the hybrid chain provided by the present invention.
图4为本发明提供的IEEE33配电网拓扑结构图。FIG. 4 is a topological structure diagram of an IEEE33 distribution network provided by the present invention.
图5为本发明提供的电动汽车充电负荷在不同接入方案下的节点电压水平。FIG. 5 is the node voltage level of the electric vehicle charging load provided by the present invention under different access schemes.
具体实施方式Detailed ways
下面结合接纳能力评估框架图和评估指标体系图以及具体实施例对本发明进行详细说明。The present invention will be described in detail below with reference to the acceptance capability evaluation framework diagram, the evaluation index system diagram and specific embodiments.
本发明提出一种基于理想点逼近法(TOPSIS)的配电网接纳电动汽车能力评估方法。The invention proposes a method for evaluating the ability of a distribution network to accept electric vehicles based on an ideal point approximation method (TOPSIS).
1)基于出行链和蒙特卡洛方法的城市电动私家车充电负荷建模1) Modeling of urban electric private vehicle charging load based on travel chain and Monte Carlo method
①出行链模型生成①Travel chain model generation
本发明采用出行链理论来研究电动私家车的时空出行轨迹和出行特征，本发明所提出行链中起点和终点主要包括住宅区、工作区、商业区、休闲区以及其他区域，分别由H、W、C、R、O表示。假设用户首次出行起始点都为住宅区，t 0为起始出行时刻；
Figure PCTCN2021140472-appb-000001
为用户从起点s i行驶至终点d i的行驶时间；
Figure PCTCN2021140472-appb-000002
为在目的地d i的驻留时间；
Figure PCTCN2021140472-appb-000003
为第i次行程的行驶距离。G TC为电动汽车出行时空特征量的集合，可由式(1)描述： The invention adopts the travel chain theory to study the time-space travel trajectories and travel characteristics of electric private vehicles. The starting point and the end point of the travel chain proposed by the present invention mainly include residential areas, work areas, commercial areas, leisure areas and other areas. W, C, R, O represent. It is assumed that the starting point of the user's first trip is a residential area, and t 0 is the starting trip time;
Figure PCTCN2021140472-appb-000001
is the travel time of the user from the starting point si to the end point d i ;
Figure PCTCN2021140472-appb-000002
is the dwell time at destination d i ;
Figure PCTCN2021140472-appb-000003
is the travel distance of the i-th trip. G TC is the set of spatiotemporal feature quantities of electric vehicle travel, which can be described by equation (1):
Figure PCTCN2021140472-appb-000004
Figure PCTCN2021140472-appb-000004
i∈{1,2,3,4,5}；s i,d i∈{H,W,C,R,O} i∈{1,2,3,4,5}; s i ,d i ∈{H,W,C,R,O}
②电动汽车耗电量② Electric vehicle power consumption
本发明将电动汽车耗电量作简化处理，忽略实际行驶过程中用户驾驶习惯以及外界因素对车辆电池耗电量的影响，认为电池耗电量与车辆行驶里程呈线性关系，车辆行驶过程中其电池耗电量以及达到目的地时的电池电量可由式(2)-(4)确定：The present invention simplifies the power consumption of the electric vehicle, ignores the influence of the user's driving habits and external factors on the power consumption of the vehicle battery during the actual driving process, and considers that the battery power consumption has a linear relationship with the mileage of the vehicle. The battery power consumption and the battery power when reaching the destination can be determined by equations (2)-(4):
Figure PCTCN2021140472-appb-000005
Figure PCTCN2021140472-appb-000005
Figure PCTCN2021140472-appb-000006
Figure PCTCN2021140472-appb-000006
Figure PCTCN2021140472-appb-000007
Figure PCTCN2021140472-appb-000007
式中，e 0为电动汽车单位里程耗电量；
Figure PCTCN2021140472-appb-000008
为车辆从s i行驶至s i的总耗电量；B ev为车辆电池容量,
Figure PCTCN2021140472-appb-000009
为到达目的地i时电动汽车剩余电量，
Figure PCTCN2021140472-appb-000010
为到达目的地i时电动汽车荷电状态。 In the formula, e 0 is the power consumption per unit mileage of the electric vehicle;
Figure PCTCN2021140472-appb-000008
is the total power consumption of the vehicle from si to si ; B ev is the battery capacity of the vehicle,
Figure PCTCN2021140472-appb-000009
is the remaining power of the electric vehicle when it reaches the destination i,
Figure PCTCN2021140472-appb-000010
is the state of charge of the electric vehicle when it reaches destination i.
③电动汽车用户充电决策模型及充电负荷计算③ Electric vehicle user charging decision model and charging load calculation
根据电动汽车用户当前所在位置电池电量SOC的多少，若剩余SOC无法满足下一段行程的电量需求，应及时充电；若SOC相对充足，可根据当前时刻充电需求安 排充电计划。According to the current location of the battery power SOC of the electric vehicle user, if the remaining SOC cannot meet the power demand of the next trip, it should be charged in time; if the SOC is relatively sufficient, the charging plan can be arranged according to the current charging demand.
采用蒙特卡洛法对目标区域内所有电动汽车进行模型建立，针对不同充电需求的用户采取不同的充电决策，分别统计其充电时长及充电负荷，继而得到总的充电需求时空分布。The Monte Carlo method is used to build a model for all electric vehicles in the target area, and different charging decisions are taken for users with different charging needs.
2)配电网对电动汽车接纳能力评估体系的建立2) Establishment of an evaluation system for the acceptance of electric vehicles by the distribution network
在电动汽车充电负荷建模的基础上，本发明考虑电动汽车接入对配电网的影响，基于传统配电网运行评估研究，从合理性、安全性以及经济性方面建立指标体系，对配电网的接纳能力进行全方位评估。On the basis of the electric vehicle charging load modeling, the present invention considers the influence of electric vehicle access on the distribution network, and based on the traditional distribution network operation evaluation research, establishes an index system from the aspects of rationality, safety and economy. A comprehensive assessment of the receiving capacity of the power grid.
为体现方法的客观性与合理性，本文结合层次分析法与熵权法对不同决策方案下的多种指标进行综合赋权。最终利用理想点逼近法(TOPSIS)对以不同方式接入充电负荷时的配电网接纳能力进行评估。接纳能力评估构架如图1所示，在传统配电网接纳能力评估的基础上，分别选取合理性、安全性以及经济性三方面为准则选取六项评估指标，如图2所示。In order to reflect the objectivity and rationality of the method, this paper combines the analytic hierarchy process and the entropy weight method to comprehensively weight various indicators under different decision-making schemes. Finally, using the ideal point approximation method (TOPSIS) to evaluate the acceptance capacity of the distribution network when the charging load is connected in different ways. The acceptance capacity evaluation framework is shown in Figure 1. On the basis of the traditional distribution network acceptance capacity assessment, six evaluation indicators are selected from the three aspects of rationality, safety and economy, as shown in Figure 2.
①电压偏移不越限率T 1； ① The voltage offset does not exceed the limit rate T 1 ;
电压偏移不越限率T 1是指配电网接入电动汽车充电负荷后配电网中节点电压不越限的节点数与节点总数的比例。该指标用于评估电动汽车充电负荷接入后各节点的电压偏移是否满足相关技术标准。本发明中以0.9-1.1视为节点电压的有效水平范围。 The voltage excursion not exceeding the limit rate T 1 refers to the ratio of the number of nodes in the distribution network whose node voltage does not exceed the limit to the total number of nodes after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate whether the voltage offset of each node meets the relevant technical standards after the electric vehicle charging load is connected. In the present invention, 0.9-1.1 is regarded as the effective level range of the node voltage.
Figure PCTCN2021140472-appb-000011
Figure PCTCN2021140472-appb-000011
其中，N v、N分别为配电网中满足电压偏移标准的节点数和***的节点总数。 Among them, N v and N are the number of nodes in the distribution network that meet the voltage offset standard and the total number of nodes in the system, respectively.
②节点无功不达标率T 2； ② Node reactive power failure rate T 2 ;
节点无功不达标率T 2是指配电网接入电动汽车充电负荷后各节点的功率因数无法达到无功配置所需标准的节点数与节点总数的比例。该指标用于评估电动汽车充电负荷接入后各节点的无功是否达标。本文中将节点功率因素标准范围设置为0.85-1。 The node reactive power failure rate T2 refers to the ratio of the number of nodes to the total number of nodes whose power factor cannot reach the required standard for reactive power configuration after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate whether the reactive power of each node meets the standard after the electric vehicle charging load is connected. In this paper, the standard range of node power factor is set to 0.85-1.
Figure PCTCN2021140472-appb-000012
Figure PCTCN2021140472-appb-000012
其中，N q、N分别为配电网中达到无功标准的节点数和节点总数。 Among them, N q and N are the number of nodes and the total number of nodes in the distribution network that reach the reactive power standard, respectively.
③网络安全运行指标S 1； ③Network security operation index S 1 ;
网络安全运行指标S 1是指配电网接入电动汽车充电负荷后产生的电流值超越该线路的安全载流量的线路数量与线路总数的比例。该指标用来评估充电负荷接入 后网络中单回线路是否满足安全运行标准。 The network safety operation index S 1 refers to the ratio of the number of lines to the total number of lines whose current value exceeds the safe current carrying capacity of the line after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate whether the single-circuit line in the network meets the safe operation standard after the charging load is connected.
Figure PCTCN2021140472-appb-000013
Figure PCTCN2021140472-appb-000013
其中，L out、L分别为配电网中超出网络中最大电流安全运行区间的线路数和线路总数。 Among them, L out and L are the number of lines and the total number of lines in the distribution network that exceed the maximum current safe operation range in the network, respectively.
④负载率S 2； ④ Load rate S 2 ;
负载率S 2是指配电网接入电动汽车充电负荷后配电变压器或线路在短时间内的平均负荷与最大负荷的比值。该指标用于评估充电负荷接入后短时间内对配电网安全运行的影响。 The load ratio S2 refers to the ratio of the average load to the maximum load of the distribution transformer or line in a short period of time after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate the impact on the safe operation of the distribution network within a short period of time after the charging load is connected.
Figure PCTCN2021140472-appb-000014
Figure PCTCN2021140472-appb-000014
其中，P av、P max分别为配电网中短时平均负荷与产生的最大负荷值。 Among them, P av and P max are the short-term average load and the generated maximum load value in the distribution network, respectively.
⑤网损值E 1； ⑤ Network loss value E 1 ;
网损值E 1是指配电网接入电动汽车充电负荷后各条线路的有功损耗之和。该指标用于评估充电负荷接入后对配电网运行经济性的影响。 The network loss value E 1 refers to the sum of the active power losses of each line after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate the impact of the charging load on the operation economy of the distribution network.
Figure PCTCN2021140472-appb-000015
Figure PCTCN2021140472-appb-000015
其中，P i、Q i分别为线路i的有功与无功功率；R i为线路i的及其相连设备的电阻；U i为线路i的电压。 Among them, P i and Q i are the active and reactive power of line i respectively; R i is the resistance of line i and its connected equipment; U i is the voltage of line i.
⑥附加无功耗费E 2； ⑥ Additional non-power consumption fee E 2 ;
附加无功耗费E 2是指配电网接入电动汽车充电负荷后为了保证功率因数在一个相对合理的值进行无功补偿所产生的的额外费用。该指标用于评估配电网中各节点由于功率因数不足而进行无功补偿时所需要的额外投资。 The additional non-power consumption fee E 2 refers to the additional fee generated by the reactive power compensation in order to ensure that the power factor is at a relatively reasonable value after the distribution network is connected to the electric vehicle charging load. This indicator is used to evaluate the additional investment required for reactive power compensation for each node in the distribution network due to insufficient power factor.
E 2＝η·Q need      (10) E 2 =η·Q need (10)
其中，η为补偿单位容量的无功补偿时所必需投资；Q need为电动汽车充电负荷接入后所需的无功补偿容量，本文中设置η为0.01万元/kvar。 Among them, η is the necessary investment to compensate the reactive power compensation of the unit capacity; Q need is the reactive power compensation capacity required after the electric vehicle charging load is connected. In this paper, η is set to 0.01 million yuan/kvar.
3)基于理想点逼近法(TOPSIS)的接纳能力评估方法研究3) Research on the acceptance ability evaluation method based on the ideal point approximation method (TOPSIS)
本发明首先对评估方案的指标矩阵进行标准化处理，在衡量与理想值的接近程度的度量方面除了采用欧氏距离进行度量外，还采用描述评价对象之间关系紧密程度的灰色关联度、衡量各方案与理想解整体贴近程度的群体效用值以及描述各方案中最劣指标偏离程度的个体偏差值进行综合评估，并根据综合评估标准进行方案接纳能力优先级排序。The present invention firstly standardizes the index matrix of the evaluation scheme. In the aspect of measuring the degree of closeness to the ideal value, in addition to using the Euclidean distance to measure, it also uses the gray correlation degree that describes the closeness of the relationship between the evaluation objects, and measures the degree of closeness between the evaluation objects. The group utility value of the overall closeness of the scheme to the ideal solution and the individual deviation value describing the deviation degree of the worst indicators in each scheme are comprehensively evaluated, and the program acceptance ability is prioritized according to the comprehensive evaluation criteria.
①构建加权规范化矩阵①Construct a weighted normalization matrix
将决策矩阵X按照式(11)-式(13)进行规范化，并将上述综合指标综合权重与标准化的决策矩阵相乘，得到加权规范化矩阵YThe decision matrix X is normalized according to formulas (11)-(13), and the comprehensive weight of the above comprehensive indicators is multiplied by the standardized decision matrix to obtain the weighted normalization matrix Y
Figure PCTCN2021140472-appb-000016
Figure PCTCN2021140472-appb-000016
Figure PCTCN2021140472-appb-000017
Figure PCTCN2021140472-appb-000017
Figure PCTCN2021140472-appb-000018
Figure PCTCN2021140472-appb-000018
其中，a max,j、a min,j为第j个指标的最大值和最小值；a ij表示方案i中第j个指标；b ij为方案i中第j个指标的标准化形式；q 1、q 2为中间型指标所在区间的边界值。 Among them, a max,j and a min,j are the maximum and minimum values of the jth index; aij represents the jth index in scheme i; bij is the standardized form of the jth index in scheme i; q 1 , q 2 is the boundary value of the interval where the intermediate indicator is located.
Y＝(y ij) m×n＝(k jc ij) m×n     (14) Y=(y ij ) m×n =(k j c ij ) m×n (14)
式中：(y ij) m×n为加权规范化的决策矩阵，(c ij) m×n为原始的决策矩阵，(k j) m×n为原始综合指标综合权重矩阵。 In the formula: (y ij ) m×n is the weighted normalized decision matrix, (c ij ) m×n is the original decision matrix, and (k j ) m×n is the original comprehensive index comprehensive weight matrix.
②确定正、负理想解②Determine the positive and negative ideal solutions
根据加权规范化矩阵确定正、负理想解y +和y -，其中正、负理想解参考值的选取方式如下： Determine the positive and negative ideal solutions y + and y - according to the weighted normalization matrix, where the reference values of the positive and negative ideal solutions are selected as follows:
Figure PCTCN2021140472-appb-000019
Figure PCTCN2021140472-appb-000019
式中：y ij为决策矩阵中的第i行第j列元素，y +为决策矩阵中的元素最大值，y -为决策矩阵中的元素最小值。 In the formula: y ij is the element of the i-th row and the j-th column in the decision matrix, y + is the maximum value of the element in the decision matrix, and y - is the minimum value of the element in the decision matrix.
③计算评估方案与正、负理想解之间的贴近度③ Calculate the closeness between the evaluation scheme and the positive and negative ideal solutions
通过分别计算欧式距离、灰色关联度、群体效用值以及个体偏差值用来衡量各方案到正、负理想解的贴近程度，将各方案按照贴近优先程度进行排序。By calculating the Euclidean distance, grey correlation degree, group utility value and individual deviation value, the closeness of each scheme to the positive and negative ideal solutions is measured, and the schemes are sorted according to the closeness priority.
欧氏距离：用以计算不同方案与理想解之间的距离。Euclidean distance: used to calculate the distance between different solutions and the ideal solution.
Figure PCTCN2021140472-appb-000020
Figure PCTCN2021140472-appb-000020
式中：D i +为第i个预估值与正理想解y +之间的欧氏距离，D i -为第i个预估值与负理想解y -之间的欧氏距离。 In the formula: D i + is the Euclidean distance between the ith estimated value and the positive ideal solution y + , and D i - is the Euclidean distance between the ith estimated value and the negative ideal solution y - .
灰色关联度：用以计算不同方案与理想解之间的关联程度。Grey correlation degree: used to calculate the degree of correlation between different schemes and ideal solutions.
灰色关联系数g ij： Grey correlation coefficient g ij :
Figure PCTCN2021140472-appb-000021
Figure PCTCN2021140472-appb-000021
式中：g ij +为正灰色关联系数，g ij -为负灰色关联系数，ε为分辨系数。 In the formula: g ij + is the positive gray correlation coefficient, g ij - is the negative gray correlation coefficient, and ε is the resolution coefficient.
灰色关联度：Gray Relevance:
Figure PCTCN2021140472-appb-000022
Figure PCTCN2021140472-appb-000022
式中：G i +为正灰色关联度，G i -为负灰色关联度，n为灰色关联系数个数。 In the formula: G i + is the positive grey relational degree, G i - is the negative grey relational degree, and n is the number of grey relational coefficients.
群体效用值S i：用以计算不同方案与正理想解方案的接近程度。 Group utility value S i : used to calculate the closeness of different schemes to the positive ideal solution.
Figure PCTCN2021140472-appb-000023
Figure PCTCN2021140472-appb-000023
个体偏差值B i：用以计算每个方案下最劣指标与理想指标之间的偏差程度。 Individual deviation value B i : used to calculate the degree of deviation between the worst index and the ideal index under each scheme.
Figure PCTCN2021140472-appb-000024
Figure PCTCN2021140472-appb-000024
4)确定综合评估指标4) Determine the comprehensive evaluation index
从距离和相似度层面上看，可将欧式距离和灰色关联度进行综合，首先将正、负欧式距离和灰色关联度根据用户评判偏好进行两两综合得到正理想距离
Figure PCTCN2021140472-appb-000025
和负理想距离
Figure PCTCN2021140472-appb-000026
计算公式如式(21)-式(22)所示。 From the perspective of distance and similarity, the Euclidean distance and the gray correlation degree can be integrated. First, the positive and negative Euclidean distance and the gray correlation degree are combined in pairs according to the user's judgment preference to obtain the positive ideal distance.
Figure PCTCN2021140472-appb-000025
and negative ideal distance
Figure PCTCN2021140472-appb-000026
The calculation formula is shown in formula (21)-formula (22).
Figure PCTCN2021140472-appb-000027
Figure PCTCN2021140472-appb-000027
Figure PCTCN2021140472-appb-000028
Figure PCTCN2021140472-appb-000028
其中，α、β为用户进行评估时的偏好系数。Among them, α and β are the preference coefficients of the user when evaluating.
正理想距离
Figure PCTCN2021140472-appb-000029
中，当距离负理想解的欧氏距离越远且与正理想解的关联度越高，即
Figure PCTCN2021140472-appb-000030
越大，说明待评估方案与理想解的相似程度越高；反之，负理想距离
Figure PCTCN2021140472-appb-000031
(包含与正理想解的欧氏距离和与负理想解的关联程度)越大，说明待评估方案与负理想解的相似程度越近，此方案下配电网的接纳能力越差。将正、负理想距离进行综合得到不同方案与理想解的相对距离R i，如式(23)所示。 positive ideal distance
Figure PCTCN2021140472-appb-000029
, when the Euclidean distance from the negative ideal solution is farther and the correlation degree with the positive ideal solution is higher, that is,
Figure PCTCN2021140472-appb-000030
The larger the value, the higher the similarity between the solution to be evaluated and the ideal solution; otherwise, the negative ideal distance
Figure PCTCN2021140472-appb-000031
(Including the Euclidean distance from the positive ideal solution and the degree of association with the negative ideal solution), the greater the degree of similarity between the scheme to be evaluated and the negative ideal solution, the worse the acceptance capacity of the distribution network under this scheme. The positive and negative ideal distances are synthesized to obtain the relative distances Ri between different schemes and the ideal solution, as shown in formula (23).
Figure PCTCN2021140472-appb-000032
Figure PCTCN2021140472-appb-000032
从贴近度和个体偏差的角度上来看，可将群体效用值和个体偏差值综合得到两者的折衷系数Q i，通过折衷系数衡量接纳能力，如式(24)所示。 From the perspective of closeness and individual bias, the group utility value and individual bias value can be synthesized to obtain the compromise coefficient Q i of the two, and the acceptance ability can be measured by the compromise coefficient, as shown in formula (24).
Figure PCTCN2021140472-appb-000033
Figure PCTCN2021140472-appb-000033
式中：R i为理想距离，S i为群体效用值，v为群体效用值权重占比。 In the formula: R i is the ideal distance, S i is the group utility value, and v is the weight ratio of the group utility value.
折衷系数在体现方案与理想方案之间的贴近程度的同时，反映出最劣的个体指标与立项指标之间的偏差程度，折衷系数越小说明此方案与理想方案之间的贴近程度越近，个体偏差程度越小，此方案下配电网的接纳能力越高。The compromise coefficient not only reflects the degree of closeness between the scheme and the ideal scheme, but also reflects the degree of deviation between the worst individual index and the project approval index. The smaller the trade-off coefficient, the closer the degree of closeness between the scheme and the ideal scheme is. The smaller the individual deviation, the higher the acceptance capacity of the distribution network under this scheme.
5)实例分析5) Case analysis
根据出行链理论和蒙特卡洛模拟，计算相应时空区域内城市电动私家车充电负荷，得到混合链下各区域充电负荷曲线图，如图3所示。本发明采用IEEE33节点配网***进行仿真(拓扑结构如图4所示。设置配网的基准功率为10MVA,网络首端的基准电压为12.66kV，网络总负荷为3715+j2300kVA。According to the travel chain theory and Monte Carlo simulation, the charging load of urban electric private vehicles in the corresponding space-time area is calculated, and the charging load curve of each area under the hybrid chain is obtained, as shown in Figure 3. The present invention adopts the IEEE33 node distribution network system for simulation (topological structure is shown in Figure 4. The reference power of the distribution network is set as 10MVA, the reference voltage at the head end of the network is 12.66kV, and the total network load is 3715+j2300kVA.
本文按照电动汽车接入数量以及不同接入方式设置如下4种评估方案：This paper sets the following four evaluation schemes according to the number of electric vehicles connected and different access methods:
方案1：考虑5000辆车按常规负荷比例全节点接入；Option 1: Consider 5,000 vehicles to be connected to all nodes according to the conventional load ratio;
方案2：考虑5000辆车以充电站形式全部单节点接入(本文中选取靠近电源点的2号节点)Option 2: Consider 5000 vehicles to be connected to all single nodes in the form of charging stations (in this paper, the No. 2 node close to the power point is selected)
方案3：考虑5000辆车按比例以充电站形式多节点接入，接在各功能区配网末端节点(本文中选取22、18、32、25号节点)；Option 3: Consider 5,000 vehicles connected to multiple nodes in the form of charging stations in proportion, and connected to the end nodes of the distribution network in each functional area ( nodes 22, 18, 32, and 25 are selected in this article);
方案4：考虑5000辆车按比例以充电站形式多节点接入，接在各功能区配网首端节点(本文中选取19、7、26、23号节点)。Option 4: Consider 5,000 vehicles connected to multiple nodes in the form of charging stations in proportion, and connected to the head node of the distribution network in each functional area ( nodes 19, 7, 26, and 23 are selected in this paper).
考虑到目前电动汽车充电地区分散且目前电动汽车充电负荷对于整体配电网的影响不够显著，本文实例分析中将不同规模电动汽车进行全节点、部分节点以及单节点接入方案为评估对象，选取IEEE33节点配电网，进行计及充电负荷的潮流计算，得到电动汽车充电负荷在不同接入方案下的节点电压水平，如图5所示。其次从技术合理性、安全可靠性以及运行经济性三方面计算不同方案中各指标与理想点之间的贴合度，并根据评估结果对方案进行排序。Considering that the current electric vehicle charging areas are scattered and the current electric vehicle charging load does not have a significant impact on the overall distribution network, in the case analysis of this paper, the full-node, partial-node and single-node access schemes for electric vehicles of different scales are selected as the evaluation objects. IEEE33 node distribution network, carry out the power flow calculation considering the charging load, and obtain the node voltage level of the electric vehicle charging load under different access schemes, as shown in Figure 5. Secondly, the degree of fit between each index and the ideal point in different schemes is calculated from the three aspects of technical rationality, safety reliability and operation economy, and the schemes are ranked according to the evaluation results.
以本文中设立的4种方案为基础，按照上述构建的接纳能力评估指标体系计算指标如表1所示。Based on the four schemes established in this paper, the indicators calculated according to the above-mentioned acceptance capacity evaluation index system are shown in Table 1.
表1充电负荷3中接入方案下的配电网接纳能力评估指标初始值Table 1 Initial value of the evaluation index of the acceptance capacity of the distribution network under the access scheme in the charging load 3
Figure PCTCN2021140472-appb-000034
Figure PCTCN2021140472-appb-000034
Figure PCTCN2021140472-appb-000035
Figure PCTCN2021140472-appb-000035
将表1中的初始数据构成原始指标矩阵X。The initial data in Table 1 constitute the original index matrix X.
Figure PCTCN2021140472-appb-000036
Figure PCTCN2021140472-appb-000036
进行指标规范化处理，具体结果如表2所示。The indicators are normalized, and the specific results are shown in Table 2.
计算所得的客观和综合权重值如表3和表4所示。The calculated objective and comprehensive weight values are shown in Tables 3 and 4.
表2不同方案下得出的规范化指标Table 2 Normalized indicators obtained under different schemes
Figure PCTCN2021140472-appb-000037
Figure PCTCN2021140472-appb-000037
表3客观权重值Table 3 Objective Weight Values
Figure PCTCN2021140472-appb-000038
Figure PCTCN2021140472-appb-000038
表4综合权重值Table 4 Comprehensive weight values
Figure PCTCN2021140472-appb-000039
Figure PCTCN2021140472-appb-000039
通过矩阵规范化和权重确定得到加权规范化矩阵：The weighted normalization matrix is obtained by matrix normalization and weight determination:
Figure PCTCN2021140472-appb-000040
Figure PCTCN2021140472-appb-000040
各方案下的正、负理想解为：The positive and negative ideal solutions under each scheme are:
Y +＝(1,1,1,1,1,1)     (25) Y + = (1,1,1,1,1,1) (25)
Y -＝(0,0,0,0,0,0)    (26) Y - = (0,0,0,0,0,0) (26)
在上述研究内容的基础上，根据上述公式计算各方案下的不同指标与正负理想解之间的加权欧氏距离、灰色关联度、群体效用值以及个体偏差值，通过不同角 度度量不同方案的各指标与理想指标之间的贴近程度，计算结果如表5所示。On the basis of the above research content, the weighted Euclidean distance, gray correlation degree, group utility value and individual deviation value between different indicators under each scheme and the positive and negative ideal solutions are calculated according to the above formula, and the performance of different schemes is measured from different angles. The closeness between each index and the ideal index, the calculation results are shown in Table 5.
表5不同方案下个指标与理想指标之间的距离评估值Table 5 The distance evaluation value between the next index and the ideal index in different schemes
Figure PCTCN2021140472-appb-000041
Figure PCTCN2021140472-appb-000041
从上述六项度量标准来看，欧式距离是用来度量各方案与理想解的距离，
Figure PCTCN2021140472-appb-000042
越小，距正理想解的欧氏距离越近，
Figure PCTCN2021140472-appb-000043
越大，距负理想解的欧氏距离越远，配电网的接纳能力越好；灰色关联度可应用于衡量不同方案与理想方案之间的相似程度，
Figure PCTCN2021140472-appb-000044
越大，说明该方案与理想方案的相似程度越高，
Figure PCTCN2021140472-appb-000045
越小，则该方案与负理想解的相似程度越低，该方案的接纳能力越好；群体效用值是用来量化不同方案与正理想方案之间的整体贴近程度，S i越小，说明该方案与理想方案的贴近度越近，此时接纳程度越好；个体效用值是用来度量方案中个体指标与最优指标之间的偏差值，R i越小，说明不同方案下的最劣指标与理想指标之间的偏差度越小，此时配电网的接纳能力越高。 From the above six metrics, the Euclidean distance is used to measure the distance between each scheme and the ideal solution.
Figure PCTCN2021140472-appb-000042
The smaller is, the closer the Euclidean distance is to the positive ideal solution,
Figure PCTCN2021140472-appb-000043
The larger the value, the farther the Euclidean distance from the negative ideal solution, and the better the acceptance capacity of the distribution network; the gray correlation degree can be used to measure the similarity between different schemes and the ideal scheme,
Figure PCTCN2021140472-appb-000044
The larger the value, the higher the similarity between the scheme and the ideal scheme.
Figure PCTCN2021140472-appb-000045
The smaller the value, the lower the similarity between the scheme and the negative ideal solution, and the better the acceptance ability of the scheme; the group utility value is used to quantify the overall degree of closeness between different schemes and the positive ideal solution. The closer the program is to the ideal program, the better the degree of acceptance; the individual utility value is used to measure the deviation between the individual indicators and the optimal indicators in the program. The smaller the deviation between the inferior index and the ideal index, the higher the acceptance capacity of the distribution network.
根据上述分析和表6中度量值结算结果，通过个体指标对不同方案进行排序：According to the above analysis and the metric settlement results in Table 6, the different schemes are sorted by individual indicators:
表6根据不同指标值进行方案排序Table 6 sorts the schemes according to different index values
Figure PCTCN2021140472-appb-000046
Figure PCTCN2021140472-appb-000046
计算不同方案下与正、负理想解的相对距离和同时考虑贴近程度与个体偏差的折衷系数，结果如表7所示。The relative distances to the positive and negative ideal solutions under different schemes and the compromise coefficients considering both the degree of closeness and the individual deviation are calculated, and the results are shown in Table 7.
表7不同方案下各指标与理想指标之间的相对距离和折衷系数表Table 7 Relative distance and compromise coefficient between each indicator and ideal indicator under different schemes
Figure PCTCN2021140472-appb-000047
Figure PCTCN2021140472-appb-000047
由以上两项综合指标计算结果表明，不论从相对距离层面还是考虑偏差值的贴近程度方面来看，方案4(按比例以充电站形式多节点接入，接在各功能区配网末端节点)情况下配电网的接纳能力是最优的。The calculation results of the above two comprehensive indicators show that, no matter from the perspective of relative distance or considering the closeness of the deviation value, scheme 4 (multi-node access in the form of charging stations in proportion, connected to the end nodes of the distribution network in each functional area) In this case, the receiving capacity of the distribution network is optimal.
上述实施方式仅为例举，不限定本发明的应用范围。这些实施方式还能以其它 各种方式来实施，且能在不脱离本发明技术思想的范围内作不同的假设、替换。The above-described embodiments are only examples, and do not limit the scope of application of the present invention. These embodiments can also be implemented in other various ways, and different assumptions and substitutions can be made within the scope of not departing from the technical idea of the present invention.

Claims (1)

  1. 一种城市配电网对电动汽车的接纳能力评估方法，包括以下步骤：A method for evaluating the acceptance capacity of electric vehicles in a city distribution network, comprising the following steps:
    1)通过采用出行链理论来研究电动私家车的时空出行轨迹和出行特征，出行链是将不同的出行目的以特定的时间顺序进行连接就构成了电动汽车的出行链，其中包含了不同类型的出行特征信息，可以很好地描述用户的出行过程，同时反映了不同行程之间的连贯性，进一步确立了基于出行链理论的时空特征量集合；1) By using the travel chain theory to study the time-space travel trajectory and travel characteristics of electric private vehicles, the travel chain is to connect different travel purposes in a specific time sequence to form a travel chain of electric vehicles, which includes different types of travel chains. Travel feature information can well describe the user's travel process, and at the same time reflect the continuity between different itineraries, further establishing a spatiotemporal feature set based on travel chain theory;
    2)建立电动汽车耗电量模型将电动汽车耗电量作简化处理，忽略实际行驶过程中用户驾驶习惯以及外界因素对车辆电池耗电量的影响，认为电池耗电量与车辆行驶里程呈线性关系，车辆行驶过程中其电池耗电量以及达到目的地时的电池电量可由下式确定：2) Establish an electric vehicle power consumption model to simplify the electric vehicle power consumption, ignore the influence of the user's driving habits and external factors on the vehicle battery power consumption during the actual driving process, and consider that the battery power consumption is linear with the vehicle mileage relationship, the battery power consumption of the vehicle during driving and the battery power when reaching the destination can be determined by the following formula:
    Figure PCTCN2021140472-appb-100001
    Figure PCTCN2021140472-appb-100001
    Figure PCTCN2021140472-appb-100002
    Figure PCTCN2021140472-appb-100002
    Figure PCTCN2021140472-appb-100003
    Figure PCTCN2021140472-appb-100003
    式中，e 0为电动汽车单位里程耗电量；
    Figure PCTCN2021140472-appb-100004
    为车辆从s i行驶至s i的总耗电量；B ev为车辆电池容量；     In the formula, e 0 is the power consumption per unit mileage of the electric vehicle;
    Figure PCTCN2021140472-appb-100004
    is the total power consumption of the vehicle from si to si ; B ev is the battery capacity of the vehicle;
    3)建立电动汽车用户充电决策模型并进行充电负荷计算；根据电动汽车用户当前所在位置电池电量SOC的多少，选取不同的充电决策；若当前剩余SOC无法满足下一段行程的电量需求，应及时充电；若SOC相对充足，可根据当前时刻充电成本安排充电计划；采用蒙特卡洛法对目标区域内所有电动汽车进行模型，针对不同充电需求的用户，分别统计其充电时长及充电负荷，继而得到总的充电需求时空分布；3) Establish a charging decision model for electric vehicle users and calculate the charging load; select different charging decisions according to the current location of the battery power SOC of the electric vehicle user; if the current remaining SOC cannot meet the power demand of the next trip, it should be charged in time ; If the SOC is relatively sufficient, the charging plan can be arranged according to the charging cost at the current moment; the Monte Carlo method is used to model all electric vehicles in the target area, and for users with different charging needs, the charging time and charging load are counted separately, and then the total The spatiotemporal distribution of charging demand;
    4)配电网对电动汽车接纳能力评估体系的建立；在电动汽车充电负荷建模的基础上，考虑电动汽车接入对配电网的影响，基于传统配电网运行评估研究，从合理性、安全性以及经济性方面建立指标体系，结合层次分析法与熵权法对不同决策方案下的多种指标进行综合赋权；4) The establishment of an evaluation system for the acceptance capacity of electric vehicles in the distribution network; on the basis of the charging load modeling of electric vehicles, considering the impact of electric vehicle access on the distribution network, based on the traditional distribution network operation evaluation research, from the rationality Establish an indicator system in terms of safety, security and economy, and combine AHP and entropy weight method to comprehensively weight various indicators under different decision-making schemes;
    5)基于理想点逼近法(TOPSIS)的接纳能力评估方法研究；首先对评估方案 的指标矩阵进行标准化处理，在衡量与理想值的接近程度的度量方面除了采用欧氏距离进行度量外，还采用描述评价对象之间关系紧密程度的灰色关联度、衡量各方案与理想解整体贴近程度的群体效用值以及描述各方案中最劣指标偏离程度的个体偏差值进行综合评估，并根据综合评估标准进行方案接纳能力优先级排序。5) Research on the evaluation method of acceptance capacity based on the ideal point approximation method (TOPSIS); first, standardize the index matrix of the evaluation plan. In terms of measuring the closeness to the ideal value, in addition to using the Euclidean distance to measure, it also uses The grey correlation degree, which describes the closeness of the relationship between the evaluation objects, the group utility value, which measures the overall closeness of each scheme to the ideal solution, and the individual deviation value, which describes the degree of deviation of the worst indicators in each scheme, are comprehensively evaluated, and the evaluation is carried out according to the comprehensive evaluation standard. Prioritize the program acceptance capacity.
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