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

Abstract

The invention provides a distribution network optimization and electric vehicle bearing capacity improvement method based on irradiation efficiency. Comprising the following steps: (1) distribution network node charging potential improvement utility calculation considering the multi-time scale characteristic of the charging capacity deviation of the electric vehicle; and (2) distribution network charging capacity configuration based on irradiation efficiency. And the positive deviation and negative deviation dominant force conversion is fully considered, so that the accuracy of distribution network node charging potential improvement utility calculation is improved, and long-term consideration of distribution network charging capability configuration planning is realized. According to the method, the concept of irradiation efficiency is introduced, so that the charging capacity of the power distribution network can cover more charging loads with 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] The charging capacity of the distribution network includes electric vehicle charging piles, energy storage, transformers, etc. It is an important resource for the distribution network to provide protection for electric vehicle charging. Whether the distribution network charging capacity configuration can be reasonably planned directly affects the stable operation of electric vehicles in the region. However, with the continuous expansion of the scale of electric vehicles connected to the power grid, how to efficiently improve the distribution network's carrying capacity for electric vehicles has become a major problem 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 and distribution networks, reasonably 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 power 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, and achieve a balance between the coverage rate and economy of the distribution network charging capacity. It 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, the traditional distribution network optimization and electric vehicle carrying capacity improvement methods do not consider the long-term configuration planning of the distribution network charging capacity, do not consider the electric vehicle charging capacity deviation, and lack the distribution characteristics of the 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 reasonable planning of the distribution network charging capacity.

[0005] Second, traditional distribution network optimization and electric vehicle carrying capacity improvement methods are mainly based on single-objective optimization, and do not consider 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 the 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 network and improving electric vehicle carrying capacity based on irradiation efficiency, which solves the following technical problems: How to accurately calculate the node charging potential improvement 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 irradiation efficiency is characterized by comprising the following steps:

[0009] (1) Calculation of the utility of improving the 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 trend 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 clarified;

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

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

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

[0014] First, according to the configuration planning, the utility of improving node charging potential is calculated for several distribution network nodes that have the need to configure charging capacity. Then, the distribution network nodes with high charging potential improvement utility are selected to configure charging capacity first.

[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 configured charging capacity is calculated.

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

[0017] Specific:

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

[0019] Considering that there are N nodes in the region, the set is expressed as Among them, 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, its set is defined as M = 12, where m represents the mth month, and the set of days in each month is represented 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 m At day t, the load of node n except electric vehicles is According to the regional distributed photovoltaic output data, the output of distributed photovoltaic at each node in the region at each time is obtained, and m / month / d is defined m The distributed photovoltaic output of node n at day t is Based on the current network planning and configuration status, 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] In the formula, Indicates m month d m The electric vehicle carrying capacity potential of node n at day t, represents the growth rate of distributed photovoltaic output of node n in month m, represents the growth rate of the load 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 power consumption of the load other than electric vehicles, as well as the changes in the actual photovoltaic output and the load other than electric vehicles on a monthly scale, and establish an electric vehicle carrying capacity potential model. By inputting the actual photovoltaic output and the actual power consumption of the load other than electric vehicles at different months, days and hours of a certain year at any node, as well as the growth rate of the actual photovoltaic output and the load other than electric vehicles in the current month, the electric vehicle carrying capacity potential of the node in a specific year, each month, each day and each hour is solved, which is expressed as:

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

[0026] In the formula, 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, 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 first travel time, etc. are generated, and the travel route and charging conditions of the electric vehicle 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 result output. The charging demand corresponding to each node at each time in 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] In the formula, is the time distribution of electric vehicle charging load at node n in month m, expressed as:

[0034]

[0035] In the formula, 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 a low 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. It is expressed as:

[0038]

[0039] In the formula, is the mth of node n. m The time of 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 improvement utility:

[0044] The utility of improving node charging potential requires examining the distribution characteristics of the electric vehicle charging capacity deviation of each node on multiple time scales of month and day, including mean, volatility, monthly mean trend and trend of trough value 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 it means that the electric vehicle carrying capacity potential is difficult 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 it means that the carrying capacity potential is sufficient to meet the load requirements, and no charging capacity configuration is 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 it means that the electric vehicle carrying capacity potential is more difficult to meet the load requirements, and charging capacity configuration is required, and the utility of improving node charging potential is high; the larger the monthly mean trend of positive deviation of electric vehicle charging capacity, the more it means that the electric vehicle carrying capacity potential can better meet the load requirements, and no charging capacity configuration is required, and the utility of improving node charging potential is low; the larger the trend of trough value of electric vehicle carrying capacity potential, the greater the growth of electric vehicle carrying capacity potential in the worst case, and no charging capacity configuration is required, and the utility of improving node charging potential is low. At the same time, since the configuration planning needs to be considered in the long term, the transformation of the dominant force of positive and negative deviations in the charging capacity of electric vehicles 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 m month d is defined m The mean positive deviation of daily electric vehicle charging capacity is The mean of negative deviations is Respectively expressed as:

[0046]

[0047] In the formula, is d m The set of hours that a day consists of.

[0048] Definition 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 m The trend of the low value of the carrying capacity potential of electric vehicles in Japan is It is expressed as:

[0051]

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

[0053] Considering the proportional relationship between positive deviation and negative deviation, we examine whether positive deviation or negative deviation is dominant in a day, and consider it from the perspectives of time and power, which can be expressed as:

[0054]

[0055] In the formula, 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 smaller the m The more dominant the positive deviation of the day node n is.

[0056] When the time scale is month, 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] In the formula, is the set of days contained in month m.

[0059] The volatility of the positive deviation of the 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 the 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] In the formula, 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 m months improves utility It is expressed as:

[0067]

[0068] In the formula, is the monthly scale positive deviation weight, is the negative deviation weight on a monthly scale, is the daily scale positive deviation weight, is the negative deviation weight on a daily basis, The weight of the daily electric vehicle carrying capacity potential valley value trend. is the weight of the dominant force on the monthly scale, is the dominant force weight of the daily scale, 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 means that the larger the mean of positive deviation of electric vehicle charging capacity, the smaller the volatility, the larger the monthly mean trend of positive deviation, and the lower the utility of node charging potential improvement; and It means that the larger the mean of negative deviation of electric vehicle charging capacity, the smaller the volatility, the larger the monthly mean trend of negative deviation, and 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 the electric vehicle carrying capacity potential is, the lower the utility of improving the 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 selected first to calculate the utility of improving charging potential of each node, and then 2L distribution network nodes with the highest charging potential are selected to configure charging capacity first, which is expressed as:

[0072]

[0073] In the formula, ψ m is the node set that prioritizes charging capabilities in month m, with 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.

[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 region 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] In the formula, 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 m,l,kis the decay index, which is related to the degree of repetition between the electric vehicle charging load covered by node k in month m and node l. The lower the repetition degree, the lower the repetition rate. 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 amount of electric vehicle charging load within the coverage area of ​​node l, expressed as:

[0078]

[0079] Where P m,l is the total amount of electric vehicle charging load 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, respectively expressed as:

[0080]

[0081] In the formula, 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 the long-term planning within one year, the irradiation efficiency of node l is expressed as:

[0083]

[0084] In the formula, 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] In the formula, ψ is the node set with configured charging capacity, Top L (·) is the function for selecting the first L nodes with the largest irradiation efficiency within the bracket.

[0088] Beneficial effects brought by the technical solution of the present invention:

[0089] (1) The present invention improves the accuracy of utility calculation for charging potential improvement of distribution network nodes by exploring 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) The present invention introduces the concept of irradiation efficiency, so that the charging capacity of the distribution network can 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 the figure, the method for optimizing the distribution network and improving the electric vehicle carrying capacity based on irradiation efficiency includes the following two steps: (1) calculating the utility of improving the charging potential of distribution network nodes taking into account the multi-time scale characteristics of the electric vehicle charging capacity deviation; and (2) configuring the charging capacity of the distribution network based on irradiation efficiency.

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

[0094] Considering that there are N nodes in the region, the set is expressed as Among them, 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, its set is defined as M = 12, where m represents the mth month, and the set of days in each month is represented 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 m At day t, the load of node n except electric vehicles is According to the regional distributed photovoltaic output data, the output of distributed photovoltaic at each node in the region at each time is obtained, and m / month / d is defined m The distributed photovoltaic output of node n at day t is Based on the current network planning and configuration status, 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] In the formula, Indicates m month d m The electric vehicle carrying capacity potential of node n at day t, represents the growth rate of distributed photovoltaic output of node n in month m, represents the growth rate of the load 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 power consumption of the load other than electric vehicles, as well as the changes in the actual photovoltaic output and the load other than electric vehicles on a monthly scale, and establish an electric vehicle carrying capacity potential model. By inputting the actual photovoltaic output and the actual power consumption of the load other than electric vehicles at different months, days and hours of a certain year at any node, as well as the growth rate of the actual photovoltaic output and the load other than electric vehicles in the current month, the electric vehicle carrying capacity potential of the node in a specific year, each month, each day and each hour is solved, which is expressed as:

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

[0101] In the formula, 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:

[0102]

[0103] S1.2 Spatiotemporal distribution of electric vehicle charging load;

[0104] The 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, and the first travel time are generated, and the travel path and charging conditions of the electric vehicle are set; secondly, different numbers of electric vehicles are set to participate in the Monte Carlo sampling simulation, and the charging probability of the electric vehicle at each node at each time is used as the result output. The charging demand corresponding to each node at each time in the day is analyzed, and the distribution of the electric vehicle charging load at each node at each time in the region is obtained. The spatial distribution of the electric vehicle charging load in month m obtained by the Monte Carlo sampling simulation is defined as:

[0105]

[0106] In the formula, is the time distribution of electric vehicle charging load at node n in month m, expressed as:

[0107]

[0108] In the formula, 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. It is expressed as:

[0111]

[0112] In the formula, is the mth of node n. m The time of 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 improvement utility:

[0117] The utility of improving node charging potential needs to examine the distribution characteristics of the electric vehicle charging capacity deviation of each node on multiple time scales of month and day, including mean, volatility, monthly mean trend and electric vehicle carrying capacity potential trough value trend. The larger the mean of negative deviation of electric vehicle charging capacity and the smaller the volatility, the more it means that the electric vehicle carrying capacity potential is difficult to meet the load requirements, and charging capacity configuration is required, and the utility of improving node charging potential is higher; the larger the mean of positive deviation of electric vehicle charging capacity and the smaller the volatility, the more it means that the carrying capacity potential is sufficient to meet the load requirements, and no charging capacity configuration is required, and the utility of improving node charging potential is lower; the larger the monthly mean trend of negative deviation of electric vehicle charging capacity, the more it means that the electric vehicle carrying capacity potential is more difficult to meet the load requirements, and charging capacity configuration is required, and the utility of improving node charging potential is higher; the larger the monthly mean trend of positive deviation of electric vehicle charging capacity, the more it means that the electric vehicle carrying capacity potential can better meet the load requirements, and no charging capacity configuration is required, and the utility of improving node charging potential is lower; the larger the trend of trough value of electric vehicle carrying capacity potential, the greater the growth of electric vehicle carrying capacity potential in the worst case, and no charging capacity configuration is required, and the utility of improving node charging potential is lower. At the same time, since the configuration planning needs to be considered in the long term, the transformation of the dominant force of positive and negative deviations in the charging capacity of electric vehicles 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 m month d is defined m The mean positive deviation of daily electric vehicle charging capacity is The mean of negative deviations is Respectively expressed as:

[0119]

[0120] In the formula, is d m The set of hours that a day consists of.

[0121] Definition 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 m The trend of the low value of the carrying capacity potential of electric vehicles in Japan is It is expressed as:

[0124]

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

[0126] Considering the proportional relationship between positive deviation and negative deviation, we examine whether positive deviation or negative deviation is dominant in a day, and consider it from the perspectives of time and power, which can be expressed as:

[0127]

[0128] In the formula, 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 smaller the m The more dominant the positive deviation of the day node n is.

[0129] When the time scale is month, 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] In the formula, is the set of days contained in month m.

[0132] The volatility of the positive deviation of the 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 the 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] In the formula, 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 m months improves utility It is expressed as:

[0141]

[0142] In the formula, is the monthly scale positive deviation weight, is the negative deviation weight on a monthly scale, is the daily scale positive deviation weight, is the negative deviation weight on a daily basis, The weight of the daily electric vehicle carrying capacity potential valley value trend. is the weight of the dominant force on the monthly scale, 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 can reflect the influence of factors such as holidays. Considering only the monthly scale will have a poor effect. Setting the monthly scale weight and the daily scale weight at the same time can satisfy the economic efficiency 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 means that the larger the mean of positive deviation of electric vehicle charging capacity, the smaller the volatility, the larger the monthly mean trend of positive deviation, and the lower the utility of node charging potential improvement; and It means that the larger the mean of negative deviation of electric vehicle charging capacity, the smaller the volatility, the larger the monthly mean trend of negative deviation, and 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 the electric vehicle carrying capacity potential is, the lower the utility of improving the 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 selected first to calculate the utility of improving charging potential of each node, and then the 2L distribution network nodes with the highest charging potential improvement utility are selected to configure charging capacity first, which is expressed as:

[0146]

[0147] In the formula, ψ m is the node set that prioritizes charging capabilities in month m, with 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.

[0148] S2.2 Irradiation efficiency:

[0149] To calculate the radiation efficiency of configuring charging capacity at a certain node, it is necessary to consider the total amount and change trend of electric vehicle charging load within the coverage area, as well as the line transformation cost of configuring charging capacity at the node. The greater the total amount of electric vehicle charging load within the coverage area, the more significant the growth trend, and the lower the line transformation cost, 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 electric vehicle charging load covered by the node with newly configured charging capacity overlaps with the node with the original configuration of charging capacity, the radiation efficiency of the node with the original configuration of charging capacity will be reduced accordingly.

[0150] Considering that electric vehicles generally do not go to very far places 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 region 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] In the formula, 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 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 in month m and node l. The lower the repetition degree, the lower the repetition rate. 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 amount of electric vehicle charging load within the coverage area of ​​node l, expressed as:

[0153]

[0154] Where P m,l is the total amount of electric vehicle charging load 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, respectively expressed as:

[0155]

[0156] In the formula, 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 the long-term planning within one year, the irradiation efficiency of node l is expressed as:

[0158]

[0159] In the formula, 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] In the formula, ψ is the node set with configured charging capacity, 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; 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. 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, 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 its lowest 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 m-th d-th day is The volatility of the negative deviation is They are respectively expressed as: Define m month 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, n represents 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 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 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. 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: 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 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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