Distribution network optimization and electric vehicle carrying capacity improvement method based on irradiation efficiency

Abstract

The application provides a distribution network optimization and electric vehicle carrying capacity improvement method based on irradiation efficiency, comprising the following steps: (1) distribution network node charging potential improvement utility calculation considering electric vehicle charging capacity deviation multi-time scale characteristics; (2) distribution network charging capacity configuration based on irradiation efficiency; the application improves the accuracy of distribution network node charging potential improvement utility calculation by excavating the multi-time scale characteristics of electric vehicle charging capacity deviation and fully considering the conversion of positive deviation and negative deviation dominant force, and realizes long-term consideration of distribution network charging capacity configuration planning. The application introduces the concept of irradiation efficiency, so that the distribution network charging capacity can cover more charging loads at a lower cost.

Description

Technical Field

[0001] The present invention provides a method for optimizing a distribution network and improving the carrying capacity of an electric vehicle based on radiation efficiency, and belongs to the technical field of power distribution. Background Art

[0002] Distribution network charging capacity, including EV charging stations, energy storage, and transformers, is a crucial resource for ensuring EV charging. The rational planning of distribution network charging capacity directly impacts the stable operation of EVs in a region. However, as the scale of EVs connected to the grid continues to expand, efficiently improving the distribution network's capacity to support EVs has become a major challenge in distribution network optimization and charging capacity planning.

[0003] The irradiation efficiency of electric vehicles is the ratio of the node configuration charging capacity to the economic cost. It is a key abstract indicator that reflects the coverage rate of electric vehicle charging loads and the cost of line transformation. Through the research on distribution network optimization and electric vehicle carrying capacity improvement, electric vehicle charging can provide a basis for the planning, construction, and operation of charging and discharging loads such as electric vehicles and electric vehicle charging piles, as well as distribution networks, rationally plan the configuration of charging capacity, scientifically guide the access of electric vehicle loads to the power grid, avoid excessive construction and waste of resources, improve the overall efficiency of the system, and promote the healthy and orderly development of the electric vehicle industry. In the electricity market, effective improvement of electric vehicle carrying capacity can also help optimize the electricity price mechanism and promote the consumption of new energy. Therefore, it is urgent to propose a distribution network optimization and electric vehicle carrying capacity improvement method to effectively improve the electric vehicle carrying capacity potential and charging capacity irradiation efficiency of the distribution network, achieve a balance between the distribution network charging capacity in terms of coverage and economy, and is the key to the reasonable configuration planning and efficient and stable operation of the distribution network charging capacity. However, the distribution network optimization and electric vehicle carrying capacity improvement method still need to solve the following technical challenges:

[0004] First, traditional methods for optimizing distribution networks and improving electric vehicle carrying capacity do not consider the long-term configuration planning of distribution network charging capacity, do not consider the deviation of electric vehicle charging capacity, and lack the distribution characteristics of electric vehicle charging capacity deviation from multiple time scales. As a result, the electric vehicle carrying capacity potential cannot be effectively improved, affecting the long-term and reasonable planning of distribution network charging capacity.

[0005] Second, traditional methods for optimizing distribution networks and improving electric vehicle carrying capacity are mainly based on single-objective optimization, without considering multiple objectives such as charging capacity coverage and economy for comprehensive optimization. As a result, the evaluation results cannot effectively guide the selection of actual solutions, and it is difficult to balance charging capacity coverage and economy, which affects the expansion of charging capacity irradiation efficiency and the improvement of electric vehicle carrying capacity potential.

[0006] Therefore, there is an urgent need to design a method for optimizing distribution networks and improving electric vehicle carrying capacity based on irradiation efficiency. Summary of the Invention

[0007] The present invention provides a method for optimizing distribution networks and improving electric vehicle carrying capacity based on irradiation efficiency, which solves the following technical problems: how to accurately calculate the node charging potential to improve utility, improve the electric vehicle carrying capacity potential, and realize long-term and reasonable planning of distribution network charging capacity.

[0008] The method for optimizing the distribution network and improving the carrying capacity of electric vehicles based on radiation efficiency is characterized by comprising the following steps:

[0009] (1) Calculation of the utility improvement of charging potential of distribution network nodes taking into account the multi-time scale characteristics of electric vehicle charging capacity deviation:

[0010] First, considering the growth trends of regional load and distributed photovoltaics, the temporal and spatial distribution of the electric vehicle carrying capacity potential of the distribution network and the temporal and spatial distribution of the electric vehicle charging load are calculated, and the time period and value of the low carrying capacity potential are determined.

[0011] Secondly, the electric vehicle charging capacity deviation of each node is calculated, including positive deviation and negative deviation.

[0012] Finally, by calculating the distribution characteristics of electric vehicle charging capacity deviation on multiple time scales, including the mean, volatility, monthly average trend, and the dominance of positive and negative deviations, and considering the trend of low values ​​of electric vehicle carrying capacity, the utility of node charging potential improvement is calculated;

[0013] (2) Distribution network charging capacity configuration based on irradiation efficiency:

[0014] First, according to the configuration planning, the charging potential improvement utility of several distribution network nodes that have charging capacity configuration requirements is calculated respectively; then the distribution network nodes with high charging potential improvement utility are selected to prioritize charging capacity configuration.

[0015] Secondly, based on the distribution and growth trend of electric vehicle charging load within the coverage area of ​​the node, as well as the line transformation cost of configuring charging capacity, and considering the attenuation effect of the newly configured charging capacity of other nodes on the irradiation efficiency of the node, the irradiation efficiency of the node configuration charging capacity is calculated.

[0016] Finally, nodes with high irradiation efficiency are selected for charging capacity configuration.

[0017] Specifically:

[0018] 1. Calculation of the utility of improving the charging potential of distribution network nodes taking into account the multi-timescale characteristics of electric vehicle charging capacity deviation:

[0019] Considering that there are N nodes in the region, the set is expressed as Where n represents the nth node. For 24 hours in a day, the set is expressed as Among them, t represents the tth hour. For the 12 months in a year, the set is defined as M=12, where m represents the mth month, and the set of days in each month is expressed as Among them, d m Indicates the dth day of month m m God, D m The number of days in month m.

[0020] Specifically include:

[0021] S1.1 Spatiotemporal distribution of electric vehicle carrying capacity potential;

[0022] According to the regional load data, the temporal and spatial distribution of loads other than electric vehicles in the region is obtained, and m / d is defined as m The load of node n except electric vehicles at day t is According to the regional distributed photovoltaic output data, the output of distributed photovoltaic at each node in the region at each moment is obtained, and m / month / d is defined. m The distributed photovoltaic output of node n at time t is Based on the current status of network planning and configuration, a hierarchical iterative optimization method is used to calculate the electric vehicle carrying capacity potential, taking into account the growth rate of photovoltaic output and the growth of loads other than electric vehicles. m The electric vehicle carrying capacity potential of node n at day t is expressed as:

[0023]

[0024] Where, Indicates m month d m The electric vehicle carrying capacity potential of node n at time t on day, represents the growth rate of distributed photovoltaic output of node n in month m, represents the growth rate of loads other than electric vehicles at node n in month m, and f(·) represents the hierarchical iterative optimization algorithm, which is not limited here. When performing hierarchical iterative optimization, it is necessary to consider the hourly situation of the actual photovoltaic output and the actual electricity consumption of loads other than electric vehicles, as well as the monthly changes of the actual photovoltaic output and loads other than electric vehicles. An electric vehicle carrying capacity potential model is established. By inputting the actual photovoltaic output and actual electricity consumption of loads other than electric vehicles at any node in different months, days, and hours of a certain year, as well as the growth rates of the actual photovoltaic output and loads other than electric vehicles in the current month, the electric vehicle carrying capacity potential of the node is solved for each hour, month, day, and year, expressed as:

[0025] γ m =[γ m,1 ,…γ m,n,…,γ m,N ] (2)

[0026] Where, γ m represents the spatial distribution of electric vehicle carrying capacity potential in month m, γ m,n is the time distribution of electric vehicle carrying capacity potential at node n in month m, expressed as:

[0027]

[0028] S1.2 Spatiotemporal distribution of electric vehicle charging load;

[0029] Monte Carlo sampling simulation method is used:

[0030] First, the urban road network is modeled, basic vehicle information is initialized, and characteristic quantities such as electric vehicle battery capacity, initial battery state of charge, and first travel time are generated. The electric vehicle travel path and charging conditions are set.

[0031] Secondly, different numbers of electric vehicles are set to participate in the Monte Carlo sampling simulation, and the charging probability of electric vehicles at each node at each time is used as the output result. The charging demand corresponding to each node at each time within the day is analyzed, and the distribution of electric vehicle charging load at each node in the region at each time is obtained. The spatial distribution of electric vehicle charging load in month m obtained by Monte Carlo sampling simulation is defined as:

[0032]

[0033] Where, is the time distribution of electric vehicle charging load at node n in month m, expressed as:

[0034]

[0035] Where, Indicates m month d m Electric vehicle charging load at node n at time t on day.

[0036] S1.3 Electric vehicle carrying capacity potential is at its lowest point:

[0037] Based on the spatiotemporal distribution of electric vehicle carrying capacity potential, for node n, the hour with the lowest electric vehicle carrying capacity potential on each day of month m is extracted to form the low time period set of electric vehicle carrying capacity potential of node n in month m. Expressed as:

[0038]

[0039] Where, is the mth month d of node n m The time period of the day when electric vehicle carrying capacity potential is lowest.

[0040] S1.4 Electric vehicle charging capacity deviation:

[0041] Based on the above electric vehicle carrying capacity potential and electric vehicle charging load, the electric vehicle charging capacity deviation of each node is obtained, including positive deviation and negative deviation. m The positive deviation of the electric vehicle charging capacity at node n at day t is Negative deviation is Respectively expressed as:

[0042]

[0043] S1.5 node charging potential improves utility:

[0044] The utility of improving node charging potential requires examining the distribution characteristics of the electric vehicle charging capacity deviation at each node on multiple time scales, including the mean, volatility, monthly mean trend, and the trend of the trough value of the electric vehicle carrying capacity potential. The larger the mean of the negative deviation of the electric vehicle charging capacity and the smaller the volatility, the more difficult it is for the electric vehicle carrying capacity potential to meet the load requirements, and charging capacity configuration is needed. The utility of improving node charging potential is high. The larger the mean of the positive deviation of the electric vehicle charging capacity and the smaller the volatility, the more sufficient the carrying capacity potential is to meet the load requirements, and no charging capacity configuration is needed. The utility of improving node charging potential is low. The larger the monthly mean trend of the negative deviation of the electric vehicle charging capacity, the more difficult it is for the electric vehicle carrying capacity potential to meet the load requirements, and charging capacity configuration is needed. The utility of improving node charging potential is high. The larger the monthly mean trend of the positive deviation of the electric vehicle charging capacity, the more able the electric vehicle carrying capacity potential is to meet the load requirements, and no charging capacity configuration is needed. The utility of improving node charging potential is low. The larger the trend of the trough value of the electric vehicle carrying capacity potential, the greater the increase in the electric vehicle carrying capacity potential in the worst case, and no charging capacity configuration is needed. The utility of improving node charging potential is low. At the same time, since configuration planning needs to be considered in the long term, the transformation of the dominant forces of positive and negative deviations in electric vehicle charging capacity should also be considered. When the positive deviation tends to transform into a negative deviation, the node charging capacity should be improved.

[0045] For node n, the time scale is day and hour, and we define m month d m The mean positive deviation of daily electric vehicle charging capacity is The mean of negative deviations is Respectively expressed as:

[0046]

[0047] Where, d m The set of hours that a day consists of.

[0048] Definition of m月d m The volatility of the positive deviation of daily electric vehicle charging capacity is The volatility of negative deviation is Respectively expressed as:

[0049]

[0050] Definition of m月d m The trend of the low value of the carrying capacity potential of electric vehicles in Japan is Expressed as:

[0051]

[0052] Where, Indicates m month d m The potential low value of electric vehicle carrying capacity in Japan, Indicates m month d m -1-day low value of electric vehicle carrying capacity potential.

[0053] Considering the proportional relationship between positive and negative deviations, we examine whether positive or negative deviations dominate within a day. From the perspectives of time and power, we can express it as:

[0054]

[0055] Where, for m month d m The ratio of positive deviation to negative deviation of daily node n, for m month d m The duration of positive deviation at day node n, for m month d m The duration of negative deviation of daily node n, ω t and ω P is the weight coefficient. The larger the size, the more likely it is to be m m The more dominant the positive deviation of the day node n is.

[0056] When the time scale is monthly, the mean of the positive deviation of the electric vehicle charging capacity in month m is defined as The mean of negative deviations is Respectively expressed as:

[0057]

[0058] Where, is the set of days contained in month m.

[0059] The volatility of the positive deviation of electric vehicle charging capacity in month m is defined as The volatility of negative deviation is Respectively expressed as:

[0060]

[0061] The monthly average trend of the positive deviation of electric vehicle charging capacity in month m is defined as The monthly average trend of negative deviation is Respectively expressed as:

[0062]

[0063] Define the ratio of positive deviation to negative deviation of node n in month m as H m,n , expressed as:

[0064]

[0065] Where, is the duration of positive deviation of node n in month m, is the duration of negative deviation of node n in month m. m,n The larger it is, the more dominant the positive deviation of node n in month m is.

[0066] In summary, the node charging potential of node n in month m increases the utility Expressed as:

[0067]

[0068] Where, is the monthly scale positive deviation weight, is the monthly negative deviation weight, is the daily scale positive deviation weight, is the daily negative deviation weight, The weight of the low value trend of the potential carrying capacity of electric vehicles on a daily basis. is the monthly dominant force weight, is the daily scale dominant force weight, 1[·] is the indicative function, and takes 1 when the conditions in the brackets are met, otherwise it takes 0. In this formula, and It indicates that the larger the mean of the positive deviation of the electric vehicle charging capacity, the smaller the volatility, the larger the monthly mean trend of the positive deviation, and the lower the utility of improving the node charging potential; and The larger the mean negative deviation of electric vehicle charging capacity, the smaller the volatility; the larger the monthly mean trend of negative deviation, the higher the utility of node charging potential improvement; and It means that the more dominant the positive deviation is, the lower the utility of node charging potential improvement is; It means that the larger the trough value of electric vehicle carrying capacity potential is, the lower the utility of improving node charging potential is.

[0069] 2. Distribution network charging capacity configuration based on irradiation efficiency:

[0070] S2.1 Charging capacity configuration node selection:

[0071] According to the configuration planning, if charging capacity is to be configured at L nodes, 4L distribution network nodes with demand are first selected to calculate the utility of their node charging potential improvement. Then, the 2L distribution network nodes with the highest charging potential improvement utility are selected to prioritize the charging capacity, which can be expressed as:

[0072]

[0073] Where, ψ m The node set that prioritizes charging capabilities in month m has a total of 2L elements. is the set of distribution network nodes with demand in month m, with a total of 4L elements. 2L (·) is the function for selecting the first 2L nodes with the largest charging potential improvement utility value within the bracket.

[0074] S2.2 Irradiation efficiency:

[0075] Set the charging distance threshold to S th , for the 2L nodes, define their set as For node l, according to the urban road network model, the shortest distance between it and other nodes b in the area is defined as S l,b , if node b satisfies S l,b ≤S th , which is considered to be within the coverage of node l, thus obtaining the node set within the coverage of node l If charging capacity is configured at node l, the irradiation efficiency of node l in month m is expressed as:

[0076]

[0077] Where, f m,l is the irradiation efficiency of node l in month m, C m,l is the line reconstruction cost of node l in month m, is the attenuation term, which represents the attenuation of the radiation efficiency of node l caused by the newly configured charging capacity of other nodes after node l. It is used to accurately reflect the attenuation effect of multi-node configuration charging capacity on radiation efficiency. The more the electric vehicle charging load covered by the nodes with newly configured charging capacity after node l overlaps with node l, the lower the radiation efficiency of node l. l,k For node l and set The shortest distance to other nodes k in the network, ρ m,l,kis the decay index, which is related to the degree of repetition between the electric vehicle charging load covered by node k and node l in month m. The lower the repetition, the lower the repetition. m,l,k The larger the α m,l is the radiation efficiency increase index of node l in month m, reflecting the growth of the total electric vehicle charging load within the coverage area of ​​node l, expressed as:

[0078]

[0079] Where, P m,l is the total charging load of electric vehicles at node l in month m, is the total amount of electric vehicle charging load within the coverage area of ​​node l in month m, is the average total amount of electric vehicle charging load within the coverage area of ​​node l in the previous m-1 months, which can be expressed as:

[0080]

[0081] Where, for m month d m The electric vehicle charging load at node l at time t on day, for m month d m The electric vehicle charging load at node g at time t on day, is the total amount of electric vehicle charging load within the coverage area of ​​node l in month j.

[0082] Considering long-term planning within one year, the irradiation efficiency of node l is expressed as:

[0083]

[0084] Where β is the time factor.

[0085] Finally, select L nodes with the largest irradiation efficiency to configure the charging capacity, which is expressed as:

[0086] ψ=Top L (f l ) (28)

[0087] Where, ψ is the node set that configures the charging capability, Top L (·) is the function for selecting the first L nodes with the largest irradiation efficiency within the bracket.

[0088] The beneficial effects brought about by the technical solution of the present invention are:

[0089] (1) The present invention improves the accuracy of utility calculation for charging potential improvement of distribution network nodes by exploiting the multi-time scale characteristics of electric vehicle charging capacity deviation and fully considering the conversion of the dominant forces of positive deviation and negative deviation, thus realizing long-term consideration of distribution network charging capacity configuration planning.

[0090] (2) By introducing the concept of irradiation efficiency, the present invention enables the charging capacity of the distribution network to cover more charging loads at a lower cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0092] like Figure 1 As shown in FIG, the distribution network optimization and electric vehicle carrying capacity improvement method based on irradiation efficiency includes the following two steps: (1) calculation of the charging potential improvement utility of distribution network nodes taking into account the multi-time scale characteristics of electric vehicle charging capacity deviation; (2) configuration of distribution network charging capacity based on irradiation efficiency.

[0093] 1. Calculation of the utility of improving the charging potential of distribution network nodes taking into account the multi-timescale characteristics of electric vehicle charging capacity deviation:

[0094] Considering that there are N nodes in the region, the set is expressed as Where n represents the nth node. For 24 hours in a day, the set is expressed as Among them, t represents the tth hour. For the 12 months in a year, the set is defined as M=12, where m represents the mth month, and the set of days in each month is expressed as Among them, d m Indicates the dth day of month m m God, D m The number of days in month m.

[0095] Specifically include:

[0096] S1.1 Spatiotemporal distribution of electric vehicle carrying capacity potential;

[0097] According to the regional load data, the temporal and spatial distribution of loads other than electric vehicles in the region is obtained, and m / d is defined as m The load of node n except electric vehicles at day t is According to the regional distributed photovoltaic output data, the output of distributed photovoltaic at each node in the region at each moment is obtained, and m / month / d is defined. m The distributed photovoltaic output of node n at time t is Based on the current status of network planning and configuration, a hierarchical iterative optimization method can be used to calculate the electric vehicle carrying capacity potential, taking into account the growth rate of photovoltaic output and the growth of loads other than electric vehicles. m The electric vehicle carrying capacity potential of node n at day t is expressed as:

[0098]

[0099] Where, denotes the mth month d m the electric vehicle carrying capacity potential of node n at time t, denotes the growth rate of distributed photovoltaic output of node n in the mth month, denotes the growth rate of load other than electric vehicles of node n in the mth month, f(·) denotes a hierarchical iterative optimization algorithm, which is not limited here, and when hierarchical iterative optimization is performed, the actual output of photovoltaic and the actual electricity consumption of load other than electric vehicles at the hourly level, as well as the monthly changes of the actual output of photovoltaic and the actual electricity consumption of load other than electric vehicles, are taken into account, an electric vehicle carrying capacity potential model is established, by inputting the actual output of photovoltaic and the actual electricity consumption of load other than electric vehicles of any node at different months, different days, different hours of a year, as well as the growth rate of the actual output of photovoltaic and the actual electricity consumption of load other than electric vehicles in the current month, the electric vehicle carrying capacity potential of the node at each month, each day and each hour of a specific year is solved, and is denoted as:

[0100] γ m = [γ m,1 ,…γ m,n ,…,γ m,N ] (2)

[0101] In the formula, γ m denotes the spatial distribution of electric vehicle carrying capacity potential in the mth month, γ m,n is the time distribution of electric vehicle carrying capacity potential of node n in the mth month, and is denoted as:

[0102]

[0103] S1.2 Electric vehicle charging load spatiotemporal distribution

[0104] A Monte Carlo sampling simulation method is used to study the spatiotemporal distribution of electric vehicle charging load. First, the urban road network is modeled, the basic information of the vehicle is initialized, the characteristic quantities such as the battery capacity of the electric vehicle, the initial battery state of charge, the initial travel time, etc. are generated, and the electric vehicle travel path and charging conditions are set. Second, different numbers of electric vehicles are set to participate in Monte Carlo sampling simulation, and the charging probability of the electric vehicle at each node at each time is taken as the result output. The charging demand corresponding to each node at each time within a day is analyzed, and the distribution of electric vehicle charging load at each node at each time within a region is obtained. The spatial distribution of electric vehicle charging load in the mth month obtained by Monte Carlo sampling simulation is defined as:

[0105]

[0106] In the formula, is the time distribution of electric vehicle charging load of node n in the mth month, and is denoted as:

[0107]

[0108] Where, Indicates m month d m Electric vehicle charging load at node n at time t on day.

[0109] S1.3 Electric vehicle carrying capacity potential is at a low point:

[0110] Based on the spatiotemporal distribution of electric vehicle carrying capacity potential, for node n, the hour with the lowest electric vehicle carrying capacity potential on each day of month m is extracted to form the low time period set of electric vehicle carrying capacity potential of node n in month m. Expressed as:

[0111]

[0112] Where, is the mth month d of node n m The time period of the day when electric vehicle carrying capacity potential is lowest.

[0113] S1.4 Electric vehicle charging capacity deviation:

[0114] Based on the above electric vehicle carrying capacity potential and electric vehicle charging load, the electric vehicle charging capacity deviation of each node is obtained, including positive deviation and negative deviation. m The positive deviation of the electric vehicle charging capacity at node n at day t is Negative deviation is Respectively expressed as:

[0115]

[0116] S1.5 node charging potential improves utility:

[0117] The utility of improving node charging potential requires examining the distribution characteristics of the electric vehicle charging capacity deviation at each node on multiple time scales, including the mean, volatility, monthly mean trend, and the trend of the trough value of the electric vehicle carrying capacity potential. The larger the mean of the negative deviation of the electric vehicle charging capacity and the smaller the volatility, the more difficult it is for the electric vehicle carrying capacity potential to meet the load requirements, and charging capacity configuration is needed. The utility of improving node charging potential is high. The larger the mean of the positive deviation of the electric vehicle charging capacity and the smaller the volatility, the more sufficient the carrying capacity potential is to meet the load requirements, and no charging capacity configuration is needed. The utility of improving node charging potential is low. The larger the monthly mean trend of the negative deviation of the electric vehicle charging capacity, the more difficult it is for the electric vehicle carrying capacity potential to meet the load requirements, and charging capacity configuration is needed. The utility of improving node charging potential is high. The larger the monthly mean trend of the positive deviation of the electric vehicle charging capacity, the more able the electric vehicle carrying capacity is to meet the load requirements, and no charging capacity configuration is needed. The utility of improving node charging potential is low. The larger the trend of the trough value of the electric vehicle carrying capacity potential, the greater the increase in the electric vehicle carrying capacity potential in the worst case, and no charging capacity configuration is needed. The utility of improving node charging potential is low. At the same time, since configuration planning needs to be considered in the long term, the transformation of the dominant forces of positive and negative deviations in electric vehicle charging capacity should also be considered. When the positive deviation tends to transform into a negative deviation, the node charging capacity should be improved.

[0118] For node n, the time scale is day and hour, and we define m month d m The mean positive deviation of daily electric vehicle charging capacity is The mean of negative deviations is Respectively expressed as:

[0119]

[0120] Where, d m The set of hours that a day consists of.

[0121] Definition of m月d m The volatility of the positive deviation of daily electric vehicle charging capacity is The volatility of negative deviation is Respectively expressed as:

[0122]

[0123] Definition of m月d m The trend of the low value of the carrying capacity potential of electric vehicles in Japan is Expressed as:

[0124]

[0125] Where, Indicates m month d m The potential low value of electric vehicle carrying capacity in Japan, Indicates m month d m -1-day low value of electric vehicle carrying capacity potential.

[0126] Considering the proportional relationship between positive and negative deviations, we examine whether positive or negative deviations dominate within a day. From the perspectives of time and power, we can express it as:

[0127]

[0128] Where, for m month d m The ratio of positive deviation to negative deviation of daily node n, for m month d m The duration of positive deviation at day node n, for m month d m The duration of negative deviation of daily node n, ω t and ω P is the weight coefficient. The larger the size, the more likely it is to be m m The more dominant the positive deviation of the day node n is.

[0129] When the time scale is monthly, the mean of the positive deviation of the electric vehicle charging capacity in month m is defined as The mean of negative deviations is Respectively expressed as:

[0130]

[0131] Where, is the set of days contained in month m.

[0132] The volatility of the positive deviation of electric vehicle charging capacity in month m is defined as The volatility of negative deviation is Respectively expressed as:

[0133]

[0134]

[0135] The monthly average trend of the positive deviation of electric vehicle charging capacity in month m is defined as The monthly average trend of negative deviation is Respectively expressed as:

[0136]

[0137] Define the ratio of positive deviation to negative deviation of node n in month m as H m,n , expressed as:

[0138]

[0139] Where, is the duration of positive deviation of node n in month m, is the duration of negative deviation of node n in month m. m,n The larger it is, the more dominant the positive deviation of node n in month m is.

[0140] In summary, the node charging potential of node n in month m increases the utility Expressed as:

[0141]

[0142] Where, is the monthly positive deviation weight, is the monthly negative deviation weight, is the daily scale positive deviation weight, is the daily negative deviation weight, The weight of the low value trend of the potential carrying capacity of electric vehicles on a daily basis. is the monthly dominant force weight, is the dominant force weight at the daily scale. Setting the weight at the daily scale can reflect the daily situation of the node charging potential improvement utility and the influence of factors such as holidays. Considering only the monthly scale will have a poor effect. Setting the monthly and daily scale weights at the same time can satisfy both the economy of calculating the node charging potential improvement utility at a large time scale of months and the accuracy of calculating the node charging potential improvement utility at a small time scale of days. 1[·] is an indicative function. It takes 1 when the conditions in the brackets are met, otherwise it takes 0. In this formula, and It indicates that the larger the mean of the positive deviation of the electric vehicle charging capacity, the smaller the volatility, the larger the monthly mean trend of the positive deviation, and the lower the utility of improving the node charging potential; and The larger the mean negative deviation of electric vehicle charging capacity, the smaller the volatility; the larger the monthly mean trend of negative deviation, the higher the utility of node charging potential improvement; and It means that the more dominant the positive deviation is, the lower the utility of node charging potential improvement is; It means that the larger the trough value of electric vehicle carrying capacity potential is, the lower the utility of improving node charging potential is.

[0143] 2. Distribution network charging capacity configuration based on irradiation efficiency:

[0144] S2.1 Charging capacity configuration node selection:

[0145] According to the configuration planning, assuming that charging capacity is to be configured at L nodes, 4L distribution network nodes with demand are first selected to calculate the utility of their node charging potential improvement. Then, the 2L distribution network nodes with the highest charging potential improvement utility are selected to prioritize the charging capacity configuration, which is expressed as:

[0146]

[0147] Where, ψ m The node set that prioritizes charging capabilities in month m has a total of 2L elements. is the set of distribution network nodes with demand in month m, with a total of 4L elements. 2L (·) is the function for selecting the first 2L nodes with the largest charging potential improvement utility value within the bracket.

[0148] S2.2 Irradiation efficiency:

[0149] When calculating the radiation efficiency of configuring charging capacity at a node, it is necessary to consider the total amount and changing trend of the EV charging load within the coverage area, as well as the cost of line modification to configure charging capacity at that node. The greater the total amount of EV charging load within the coverage area, the more significant the growth trend, and the lower the cost of line modification, the higher the radiation efficiency. At the same time, the attenuation effect of configuring charging capacity at multiple nodes on coverage efficiency should also be considered. If the EV charging load covered by the newly configured charging capacity node overlaps with the node originally configured with charging capacity, the radiation efficiency of the node originally configured with charging capacity will be reduced accordingly.

[0150] Considering that electric vehicles generally do not go to a very far place to charge, the charging distance threshold is set to S th , for the 2L nodes mentioned above, define their set as For node l, according to the urban road network model, the shortest distance between it and other nodes b in the area is defined as S l,b , if node b satisfies S l,b ≤S th , which is considered to be within the coverage of node l, thus obtaining the node set within the coverage of node l If charging capacity is configured at node l, the irradiation efficiency of node l in month m is expressed as:

[0151]

[0152] Where, f m,l is the irradiation efficiency of node l in month m, C m,l is the line reconstruction cost of node l in month m, is the attenuation term, which represents the attenuation of the radiation efficiency of node l caused by the newly configured charging capacity of other nodes after node l. It is used to accurately reflect the attenuation effect of multi-node configuration charging capacity on radiation efficiency. The more the electric vehicle charging load covered by the nodes with newly configured charging capacity after node l overlaps with node l, the lower the radiation efficiency of node l. l,k For node l and set The shortest distance to other nodes k in the network, ρ m,l,k is the decay index, which is related to the degree of repetition between the electric vehicle charging load covered by node k and node l in month m. The lower the repetition, the lower the repetition. m,l,k The larger the α m,l is the radiation efficiency increase index of node l in month m, reflecting the growth of the total electric vehicle charging load within the coverage area of ​​node l, expressed as:

[0153]

[0154] Where, P m,l is the total charging load of electric vehicles at node l in month m, is the total amount of electric vehicle charging load within the coverage area of ​​node l in month m, is the average total amount of electric vehicle charging load within the coverage area of ​​node l in the previous m-1 months, which can be expressed as:

[0155]

[0156] Where, for m month d m The electric vehicle charging load at node l at time t on day, for m month d m The electric vehicle charging load at node g at time t on day, is the total amount of electric vehicle charging load within the coverage area of ​​node l in month j.

[0157] Considering long-term planning within one year, the irradiation efficiency of node l is expressed as:

[0158]

[0159] Where β is the time factor.

[0160] Finally, select L nodes with the largest irradiation efficiency to configure the charging capacity, which is expressed as:

[0161] ψ=Top L (f l ) (28)

[0162] Where, ψ is the node set that configures the charging capability, Top L (·) is the function for selecting the first L nodes with the largest irradiation efficiency within the bracket.

Claims

1. A method for optimizing distribution networks and improving electric vehicle carrying capacity based on irradiation efficiency, characterized in that: The following steps are involved: (1) Calculation of the utility improvement of charging potential of distribution network nodes taking into account the multi-time scale characteristics of electric vehicle charging capacity deviation: First, considering the growth trends of regional load and distributed photovoltaics, the temporal and spatial distribution of the electric vehicle carrying capacity potential of the distribution network and the temporal and spatial distribution of the electric vehicle charging load are calculated, and the time period and value of the low carrying capacity potential are determined. Secondly, the electric vehicle charging capacity deviation of each node is calculated, including positive deviation and negative deviation; Specifically, based on the electric vehicle carrying capacity potential and the electric vehicle charging load, the electric vehicle charging capacity deviation of each node is obtained, including positive deviation and negative deviation; define m / d m The positive deviation of the electric vehicle charging capacity at node n at day t is Negative deviation is Respectively expressed as: Indicates m month d m The electric vehicle carrying capacity potential of node n at time t on day; Indicates m month d m The electric vehicle charging load at node n at time t on day; Finally, by calculating the distribution characteristics of electric vehicle charging capacity deviation on multiple time scales, including the mean, volatility, monthly average trend, and the dominance of positive and negative deviations, and considering the trend of low values ​​of electric vehicle carrying capacity, the utility of node charging potential improvement is calculated; (2) Distribution network charging capacity configuration based on irradiation efficiency: First, based on the configuration planning, the utility of improving charging potential of several distribution network nodes that need to be equipped with charging capacity is calculated. Then, the distribution network nodes with high charging potential improvement utility are selected to configure charging capacity first. Secondly, the irradiation efficiency of the node's charging capacity is calculated based on the distribution and growth trend of electric vehicle charging load within the node's coverage area, the line modification cost of configuring charging capacity, and the attenuation effect of the newly configured charging capacity at other nodes on the node's irradiation efficiency. If charging capacity is configured at node l, the irradiation efficiency of node l in month m is expressed as: Where, f m,l is the irradiation efficiency of node l in month m, C m,l is the line reconstruction cost of node l in month m, is the attenuation term; S l,k For node l and set The shortest distance to other nodes k in the network, ρ m,l,k is the decay exponent, α m,l The irradiation efficiency of node l in month m is increased exponentially; Finally, nodes with high irradiation efficiency are selected for charging capacity configuration.

2. The method for optimizing distribution network and improving electric vehicle carrying capacity based on irradiation efficiency according to claim 1 is characterized in that: The specific method of step (1) is: Considering that there are N nodes in the region, the set is expressed as Where n represents the nth node. For 24 hours in a day, the set is expressed as Among them, t represents the tth hour. For the 12 months in a year, the set is defined as Among them, m represents the mth month, and the set of several days in each month is expressed as Among them, d m Indicates the dth day of month m m God, D m is the number of days in month m; Specifically include: S1.1 Spatiotemporal distribution of electric vehicle carrying capacity potential; According to the regional load data, the temporal and spatial distribution of loads other than electric vehicles in the region is obtained, and m / d is defined as m The load of node n except electric vehicles at day t is According to the regional distributed photovoltaic output data, the output of distributed photovoltaic at each node in the region at each moment is obtained, and m / month / d is defined. m The distributed photovoltaic output of node n at time t is Based on the current status of network planning and configuration, a hierarchical iterative optimization method is used to calculate the electric vehicle carrying capacity potential, taking into account the growth rate of photovoltaic output and the growth of loads other than electric vehicles. m The electric vehicle carrying capacity potential of node n at day t is expressed as: Where, γ m,dm,t,n Indicates m month d m The electric vehicle carrying capacity potential of node n at time t on day, represents the growth rate of distributed photovoltaic output of node n in month m, represents the growth rate of loads other than electric vehicles at node n in month m, and f(·) represents the hierarchical iterative optimization algorithm, which is not limited here. When performing hierarchical iterative optimization, it is necessary to consider the hourly situation of the actual photovoltaic output and the actual electricity consumption of loads other than electric vehicles, as well as the monthly changes of the actual photovoltaic output and loads other than electric vehicles. An electric vehicle carrying capacity potential model is established. By inputting the actual photovoltaic output and actual electricity consumption of loads other than electric vehicles at any node in different months, days, and hours of a certain year, as well as the growth rates of the actual photovoltaic output and loads other than electric vehicles in the current month, the electric vehicle carrying capacity potential of the node is solved for each hour, month, day, and year, expressed as: c m =[γ m,1 ,…c m,n ,…,c m,N ] (2) Where, γ m represents the spatial distribution of electric vehicle carrying capacity potential in month m, γ m,n is the time distribution of electric vehicle carrying capacity potential at node n in month m, expressed as: S1.2 Spatiotemporal distribution of electric vehicle charging load; Monte Carlo sampling simulation method is used: First, the urban road network is modeled, basic vehicle information is initialized, and characteristic quantities such as electric vehicle battery capacity, initial battery state of charge, and first travel time are generated. The electric vehicle travel path and charging conditions are set. Secondly, different numbers of electric vehicles are set to participate in the Monte Carlo sampling simulation. The charging probability of electric vehicles at each node at each time is used as the result output. The charging demand corresponding to each node at each time within the day is analyzed, and the distribution of electric vehicle charging load at each node in the region at each time is obtained. The spatial distribution of electric vehicle charging load in month m obtained by Monte Carlo sampling simulation is defined as: Where, is the time distribution of electric vehicle charging load at node n in month m, expressed as: Where, Indicates m month d m The electric vehicle charging load at node n at time t on day; S1.3 Electric vehicle carrying capacity potential is at a low point: Based on the spatiotemporal distribution of electric vehicle carrying capacity potential, for node n, the hour with the lowest electric vehicle carrying capacity potential on each day of month m is extracted to form the low time period set of electric vehicle carrying capacity potential of node n in month m. Expressed as: Where, is the mth month d of node n m The time period of the day when electric vehicle carrying capacity potential is lowest; S1.4 Electric vehicle charging capacity deviation: Based on the above electric vehicle carrying capacity potential and electric vehicle charging load, the electric vehicle charging capacity deviation of each node is obtained, including positive deviation and negative deviation; define m / d m The positive deviation of the electric vehicle charging capacity at node n at day t is Negative deviation is Respectively expressed as: S1.5 node charging potential improves utility: The utility of improving node charging potential requires examining the distribution characteristics of the electric vehicle charging capacity deviation of each node at multiple time scales of month and day, including mean, volatility, monthly mean trend and trough value trend of electric vehicle carrying capacity potential. The larger the mean of negative deviation of electric vehicle charging capacity and the smaller the volatility, the more difficult it is for electric vehicle carrying capacity potential to meet the load requirements, and charging capacity configuration is required, and the utility of improving node charging potential is high; the larger the mean of positive deviation of electric vehicle charging capacity and the smaller the volatility, the more difficult it is for electric vehicle carrying capacity potential to meet the load requirements, and charging capacity configuration is not required, and the utility of improving node charging potential is low; the larger the monthly mean trend of negative deviation of electric vehicle charging capacity, the more difficult it is for electric vehicle carrying capacity potential to meet the load requirements, and charging capacity configuration is not required, and the utility of improving node charging potential is low. To meet the load requirements, charging capacity configuration is required, and the utility of node charging potential improvement is high; the larger the monthly average trend of the positive deviation of electric vehicle charging capacity, the more the electric vehicle carrying capacity potential can meet the load requirements, and no charging capacity configuration is required, and the utility of node charging potential improvement is low; the larger the trend of the trough value of electric vehicle carrying capacity potential, the greater the increase in electric vehicle carrying capacity potential under the worst case scenario, and no charging capacity configuration is required, and the utility of node charging potential improvement is low; at the same time, since configuration planning should be considered in the long term, the transformation of the dominant force of positive and negative deviations of electric vehicle charging capacity should also be considered. When the positive deviation tends to transform into a negative deviation, the node charging capacity should be improved; For node n, the time scale is day and hour, and we define m month d m The mean positive deviation of daily electric vehicle charging capacity is The mean of negative deviations is Respectively expressed as: Where, d m The set of hours that a day contains; Define m d m The volatility of the positive deviation of the electric vehicle charging capacity on the dth day of the mth month is The volatility of the negative deviation is They are respectively expressed as: Define m d m The numerical trend of the low valley of the bearing capacity potential of electric vehicles on the dth day of the mth month is Expressed as: Where, Indicates m month d m The potential low value of electric vehicle carrying capacity in Japan, Indicates m month d m -1 day low value of electric vehicle carrying capacity potential; Considering the proportional relationship between positive and negative deviations, we examine whether positive or negative deviations dominate within a day. From the perspectives of time and power, we can express it as: Where, for m month d m The ratio of positive deviation to negative deviation of daily node n, for m month d m The duration of positive deviation at day node n, for m month d m The duration of negative deviation of daily node n, ω t and ω P is the weight coefficient; The larger the size, the more likely it is to be m m The more dominant the positive deviation of the day node n is; When the time scale is monthly, the mean of the positive deviation of the electric vehicle charging capacity in month m is defined as The mean of negative deviations is Respectively expressed as: Where, is the set of days contained in month m; The volatility of the positive deviation of electric vehicle charging capacity in month m is defined as The volatility of negative deviation is Respectively expressed as: The monthly average trend of the positive deviation of electric vehicle charging capacity in month m is defined as The monthly average trend of negative deviation is Respectively expressed as: Define the ratio of positive deviation to negative deviation of node n in month m as H m,n , expressed as: Where, is the duration of positive deviation of node n in month m, is the duration of negative deviation of node n in month m; H m,n The larger it is, the more dominant the positive deviation of node n in month m is; In summary, the node charging potential of node n in month m increases the utility Expressed as: Where, is the monthly positive deviation weight, is the monthly negative deviation weight, is the daily scale positive deviation weight, is the daily negative deviation weight, The weight of the low value trend of the potential carrying capacity of electric vehicles on a daily basis. is the monthly dominant force weight, is the daily dominant force weight, 1[·] is the indicative function, and takes 1 when the conditions in the brackets are met, otherwise it takes 0; In this formula, and It indicates that the larger the mean of the positive deviation of the electric vehicle charging capacity, the smaller the volatility, the larger the monthly mean trend of the positive deviation, and the lower the utility of improving the node charging potential; and The larger the mean negative deviation of electric vehicle charging capacity, the smaller the volatility; the larger the monthly mean trend of negative deviation, the higher the utility of node charging potential improvement; and It means that the more dominant the positive deviation is, the lower the utility of node charging potential improvement is; It means that the larger the trough value of electric vehicle carrying capacity potential is, the lower the utility of improving node charging potential is.

3. The method for optimizing distribution network and improving electric vehicle carrying capacity based on irradiation efficiency according to claim 1, characterized in that: Step (2) specifically includes the following sub-steps: S2.1 Charging capacity configuration node selection: According to the configuration planning, if charging capacity is to be configured at L nodes, 4L distribution network nodes with demand are first selected to calculate the utility of their node charging potential improvement, and then the 2L distribution network nodes with the highest charging potential improvement utility are selected to prioritize the charging capacity, which is expressed as: Where, ψ m The node set that prioritizes charging capabilities in month m has a total of 2L elements. is the set of distribution network nodes with demand in month m, with a total of 4L elements. 2L (·) is a function for selecting the first 2L nodes with the largest charging potential improvement utility value within the bracket; S2.2 Irradiation efficiency: Set the charging distance threshold to S th , for the 2L nodes, define their set as For node l, according to the urban road network model, the shortest distance between it and other nodes b in the area is defined as S l,b , if node b satisfies S l,b ≤S th , which is considered to be within the coverage of node l, thus obtaining the node set within the coverage of node l If charging capacity is configured at node l, the irradiation efficiency of node l in month m is expressed as: Where, f m,l is the irradiation efficiency of node l in month m, C m,l is the line reconstruction cost of node l in month m, is the attenuation term, which represents the attenuation of the radiation efficiency of node l caused by the newly configured charging capacity of other nodes after node l. It is used to accurately reflect the attenuation effect of multi-node configuration charging capacity on radiation efficiency. The more the electric vehicle charging load covered by the nodes with newly configured charging capacity after node l overlaps with node l, the lower the radiation efficiency of node l. l,k For node l and set The shortest distance to other nodes k in the network, ρ m,l,k is the decay index, which is related to the degree of repetition between the electric vehicle charging load covered by node k and node l in month m. The lower the repetition, the lower the repetition. m,l,k The larger the α m,l is the radiation efficiency increase index of node l in month m, reflecting the growth of the total electric vehicle charging load within the coverage area of ​​node l, expressed as: Where, P m,l is the total charging load of electric vehicles at node l in month m, is the total amount of electric vehicle charging load within the coverage area of ​​node l in month m, is the average total amount of electric vehicle charging load within the coverage area of ​​node l in the previous m-1 months, which can be expressed as: Where, for m month d m The electric vehicle charging load at node l at time t on day, for m month d m The electric vehicle charging load at node g at time t on day, is the total amount of electric vehicle charging load within the coverage area of ​​node l in month j; Considering long-term planning within one year, the irradiation efficiency of node l is expressed as: Where β is the time factor; Finally, select L nodes with the largest irradiation efficiency to configure the charging capacity, which is expressed as: ψ=Top L (f l ) (28) Where, ψ is the node set that configures the charging capability, Top L (·) is the function for selecting the first L nodes with the largest irradiation efficiency within the bracket.

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