Electric vehicle charging pile configuration method, recording medium and system

Abstract

The present invention belongs to the field of electric vehicle charging technology and discloses a method for configuring electric vehicle charging piles. This method stratifies users and microgrid operators based on an electric vehicle charging load forecasting model. A multi-objective, two-tier optimization scheduling approach is employed to ensure user charging efficiency while fully considering the operating efficiency of the microgrid layer. A pricing mechanism is utilized to achieve spatiotemporal shifting of electric vehicle charging demand. A particle swarm algorithm is employed to solve the model, overcoming the limitations of traditional optimization methods and improving optimization performance. The present invention also provides a non-transient, readable recording medium storing an electric vehicle charging pile configuration program and a system containing the medium. This program can be invoked by a processing circuit to execute the aforementioned method, suitable for configuring electric vehicle charging piles.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging, and specifically discloses an electric vehicle charging pile configuration method, a non-transient readable recording medium, and a data processing system. Background Art

[0002] With the continuous depletion of fossil energy and the resulting increasingly severe environmental problems, electric vehicles (EVs) have garnered widespread attention and adoption as a low-carbon, environmentally friendly means of transportation. Charging piles, acting as the intermediary between EV loads and microgrids, have a significant impact on the safe and stable operation of the grid and on EV mobility. Therefore, analyzing the impact of large-scale, clustered EV charging on microgrid power ramping requirements, load fluctuation characteristics, and EV charging efficiency, and utilizing technical means to optimize the configuration of fast / slow charging piles across multiple microgrids, is of vital practical significance for ensuring the safe and stable operation of microgrids and improving EV charging efficiency.

[0003] Currently, a large number of researchers have conducted research on the spatiotemporal distribution of electric vehicle loads. However, these researchers have failed to thoroughly analyze the operational characteristics of fast and slow charging, and have not fully considered the time-shifting characteristics of slow charging across multiple regions, nor the spatial shift characteristics of fast charging. This has resulted in highly distorted electric vehicle load models, making it difficult to provide a sound basis for determining the number and capacity of fast and slow charging stations. Furthermore, extensive research has been conducted on the location and sizing of charging stations. However, these studies have configured charging stations based on the spatiotemporal distribution of user charging needs, failing to fully consider the impact of the EV clustering characteristics caused by the location and capacity configuration of charging stations on the safe and stable operation of microgrids, and thus failing to achieve optimal charging efficiency for both EV users and microgrid operation. Consequently, existing technologies fail to fully consider the impact of the charging characteristics and configuration capacity of charging stations within multiple microgrid regions on the safe and stable operation of microgrids and the charging efficiency of electric vehicles. This leads to a mismatch between the fast and slow charging configuration and capacity of charging stations, resulting in low EV charging efficiency and low microgrid operation efficiency. Summary of the Invention

[0004] In order to solve the problem in the background technology, the present invention provides a method for configuring an electric vehicle charging pile, comprising the following steps:

[0005] S1. Analyze multiple factors that affect the charging method, charging location, or charging time of electric vehicles, simulate and generate fast / slow charging load functions for electric vehicles in the study area, and describe the initial spatiotemporal distribution characteristics of electric vehicle loads;

[0006] S2. Develop a temporal and spatial distribution model for electric vehicle loads based on the electricity price and the initial temporal and spatial distribution characteristics, with the goal of maximizing user charging efficiency.

[0007] S3. Determine constraints in the spatiotemporal distribution model of the electric vehicle load, wherein the constraints include the state of charge of the electric vehicle battery, the upper and lower limits of the electricity price, and the charging time;

[0008] S4. Calculate the fast-charging and slow-charging loads of electric vehicles in each operator's microgrid and establish a net load model for each microgrid. The net load model describes the risk faced by the microgrid when the electric vehicle charging load is concentrated based on the net load fluctuation amplitude and the required ramping performance.

[0009] S5. Determine the output and ramp constraints of the microgrid power generation unit and the power balance constraints of each region in the net load model;

[0010] S6. Integrate the characteristics of the EV load temporal and spatial distribution model and the net load model to improve microgrid operating efficiency. Iteratively adjust the fast / slow charging electricity price of EVs to shift the temporal and spatial distribution of EV load demand.

[0011] S7. Substitute the adjusted electricity price into the net load model, where the electric vehicle charging load is represented by the capacity and number of fast / slow charging piles. Use a multi-objective particle swarm algorithm to solve and obtain an optimal configuration plan for the electric vehicle charging piles.

[0012] Preferably, the electric vehicle load spatiotemporal distribution law model uses user satisfaction to characterize user charging efficiency. User satisfaction is the sum of travel satisfaction and charging cost satisfaction. The specific expression is:

[0013]

[0014]

[0015]

[0016] Where f is the objective function; are the travel satisfaction and charging fee satisfaction of electric vehicle j going to charging station i; D max and D min are the farthest and shortest distances for electric vehicles in the road network from charging demand nodes to charging stations; is the distance from electric vehicle j to charging station i at time t; and are the highest and lowest charging costs for the corresponding vehicle type at charging station i; is the charging electricity price of electric vehicle j at charging station i at time t; For the jth car The remaining battery percentage at the time of arrival at the charging station.

[0017] Preferably, the net load model includes:

[0018] Microgrid net load fluctuation minimum sub-model:

[0019] in,

[0020]

[0021] Where, is the average net load of each microgrid, and are the charging power of electric vehicles, the power of basic loads, the power of energy storage equipment, and the output of wind / solar units at time t. and is the average value of the transmission power of each part in the scheduling period; T represents the scheduling period, and this paper takes 1 hour as the unit time period, that is, T = 24;

[0022] Minimum sub-model for comprehensive operation cost of microgrid:

[0023] Where S2 is the comprehensive operation cost of the microgrid; S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of microgrid (yuan / day); S i,2 is the operating cost of the output unit in each region; S i,3 The ramping cost of the microgrid output unit in each region;

[0024]

[0025] Where S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of the microgrid (yuan / day); and are the price and number of fast / slow charging piles, respectively; n is the operating life of the charging pile; d is the discount rate; and η is the percentage of operation and maintenance costs to investment costs;

[0026]

[0027]

[0028]

[0029]

[0030] Where, and They are the operating costs of diesel generator sets, energy storage equipment and distribution network tie lines in each region’s microgrid; and are respectively the operation and maintenance cost, fuel cost and environmental management cost of the diesel generator set; C de,om is the operation and maintenance coefficient of the diesel generator set, is the output power of the diesel generator set in area i at time t, a, b and c are the fuel coefficients, C k ,λ de,k are the cost of treating the kth type of pollutant and the emission of the kth type of pollutant generated by operation; C ES,om and are the energy storage equipment operating cost coefficient and the charging and discharging power of the energy storage equipment in area i at time t, respectively; and are the electricity transaction cost of the distribution network tie line and the environmental governance cost of the distribution network, is the electricity price of the distribution network at time t, is the power of the distribution network tie line. The positive and negative values ​​of the power represent the amount of electricity sold from the microgrid to the main grid. DN,k Emissions of type k pollutants generated by the operation of distribution network tie lines;

[0031]

[0032]

[0033]

[0034] Where, and are the ramping cost of diesel generator sets and tie line ramping cost of microgrids in each region; C de and C DN are the output ramp cost coefficients of diesel generator sets and distribution network tie lines, and are the transmission power of the diesel generator set and the distribution network tie line at time t in each regional microgrid; is the cost coefficient of spinning reserve for distribution network tie lines.

[0035] Preferably, the method of shifting the temporal and spatial distribution of electric vehicle load demand includes shifting the temporal distribution of the load by delaying slow charging; and shifting the spatial distribution of the load by switching the points of fast charging.

[0036] The present invention further provides an electric vehicle charging pile configuration system, comprising the following functional modules:

[0037] A charging demand analysis module is used to analyze multiple factors that affect the charging method, charging location or charging time of electric vehicles, simulate and generate fast / slow charging load functions of electric vehicles in the study area, and describe the initial spatiotemporal distribution characteristics of electric vehicle loads;

[0038] a charging demand model construction module, configured to establish a temporal and spatial distribution law model of electric vehicle loads based on the goal of maximizing user charging efficiency and combining electricity prices and the initial temporal and spatial distribution characteristics; and to determine constraints in the temporal and spatial distribution law model of electric vehicle loads, wherein the constraints include the state of charge of the electric vehicle battery, upper and lower limits of electricity prices, and charging time;

[0039] A microgrid load model construction module is used to calculate the fast-charging and slow-charging loads of electric vehicles in each operator's microgrid and establish a net load model for each microgrid. The net load model describes the risks faced by the microgrid when the electric vehicle charging load is concentrated based on the net load fluctuation amplitude and the climbing performance requirement. The net load model determines the output and climbing constraints of the microgrid's power generation units and the power balance constraints of each area.

[0040] The electricity price adjustment module is used to integrate the characteristics of the electric vehicle load spatiotemporal distribution model and the net load model to iteratively adjust the electricity price of electric vehicles for fast / slow charging with the goal of improving the operating efficiency of the microgrid and shift the spatiotemporal distribution of electric vehicle load demand;

[0041] The scheme integration module is used to substitute the adjusted electricity price into the net load model, in which the electric vehicle charging load is represented by the capacity and number of fast / slow charging piles, and solve it through a multi-objective particle swarm algorithm to obtain the optimal configuration scheme of electric vehicle charging piles.

[0042] Preferably, a temporal and spatial distribution law model of electric vehicle load is established in the charging demand model building module; the temporal and spatial distribution law model of electric vehicle load is characterized by user satisfaction to characterize user charging efficiency, and user satisfaction is the sum of travel satisfaction and charging cost satisfaction, and the specific expression is:

[0043]

[0044]

[0045]

[0046] Where f is the objective function; are the travel satisfaction and charging fee satisfaction of electric vehicle j going to charging station i; D max and D min are the farthest and shortest distances for electric vehicles in the road network from charging demand nodes to charging stations; is the distance from electric vehicle j to charging station i at time t; and are the highest and lowest charging costs for the corresponding vehicle type at charging station i; is the charging electricity price of electric vehicle j at charging station i at time t; For the jth car The remaining battery percentage at the time of arrival at the charging station.

[0047] Preferably, the microgrid load model building module establishes a net load model of each microgrid, and the net load model includes:

[0048] Microgrid net load fluctuation minimum sub-model:

[0049] in,

[0050]

[0051] Where, is the average net load of each microgrid, and are the charging power of electric vehicles, the power of basic loads, the power of energy storage equipment, and the output of wind / solar units at time t. and is the average value of the transmission power of each part in the scheduling period; T represents the scheduling period, and this paper takes 1 hour as the unit time period, that is, T = 24;

[0052] Minimum sub-model for comprehensive operation cost of microgrid:

[0053] Where S2 is the comprehensive operation cost of the microgrid; S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of microgrid (yuan / day); S i,2 is the operating cost of the output unit in each region; S i,3 The ramping cost of the microgrid output unit in each region;

[0054]

[0055] Where S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of the microgrid (yuan / day); and are the price and number of fast / slow charging piles, respectively; n is the operating life of the charging pile; d is the discount rate; and η is the percentage of operation and maintenance costs to investment costs;

[0056]

[0057]

[0058]

[0059]

[0060] Where, and They are the operating costs of diesel generator sets, energy storage equipment and distribution network tie lines in each region’s microgrid; and are respectively the operation and maintenance cost, fuel cost and environmental management cost of the diesel generator set; C de,om is the operation and maintenance coefficient of the diesel generator set, is the output power of the diesel generator set in area i at time t, a, b and c are the fuel coefficients, C k ,λ de,k are the cost of treating the kth type of pollutant and the emission of the kth type of pollutant generated by operation; C ES,om and are the energy storage equipment operating cost coefficient and the charging and discharging power of the energy storage equipment in area i at time t, respectively; and are the electricity transaction cost of the distribution network tie line and the environmental governance cost of the distribution network, is the electricity price of the distribution network at time t, is the power of the distribution network tie line. The positive and negative values ​​of the power represent the amount of electricity sold from the microgrid to the main grid. DN,k Emissions of type k pollutants generated by the operation of distribution network tie lines;

[0061]

[0062]

[0063]

[0064] Where, and are the ramping cost of diesel generator sets and tie line ramping cost of microgrids in each region; C de and C DN are the output ramp cost coefficients of diesel generator sets and distribution network tie lines, and are the transmission power of the diesel generator set and the distribution network tie line at time t in each regional microgrid; is the cost coefficient of spinning reserve for distribution network tie lines.

[0065] Preferably, the electricity price adjustment module involves a method for transferring the spatiotemporal distribution of electric vehicle load demand, and the method for transferring the spatiotemporal distribution of electric vehicle load demand includes transferring the time distribution of the load through the delay of slow charging; and transferring the spatial distribution of the load through point switching of fast charging.

[0066] Another embodiment of the present invention is to provide a non-transitory readable recording medium for storing one or more programs containing multiple instructions. When the instructions are executed, the processing circuit will execute the steps included in the electric vehicle charging pile configuration method.

[0067] Another embodiment of the present invention is to provide a data processing device, including a processing circuit and a memory electrically coupled thereto, characterized in that the memory is configured to store at least one program, the program including multiple instructions, and the processing circuit runs the program to execute the steps included in the electric vehicle charging pile configuration method.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] (1) The method of the present invention fully considers the spatiotemporal distribution of charging demand of different types of electric vehicles, establishes an electric vehicle charging load prediction model, and more accurately describes the changes in the fast / slow charging demand of electric vehicles through the electric vehicle fast / slow charging scheduling strategy. By utilizing the respective operating characteristics of fast / slow charging, the fast / slow charging load demand of each region is optimized. The formed electric vehicle load model can provide a good reference basis for the configuration capacity of charging piles.

[0070] (2) The method of the present invention structurally divides users and microgrid operators into layers, adopts a multi-objective two-layer optimization scheduling method, and fully considers the operating efficiency of the microgrid layer on the basis of ensuring the charging efficiency of users. The power ramp risk brought by the huge impact of electric vehicle load on the microgrid is coupled into the formulation of charging electricity prices. The time and space transfer characteristics of electric vehicles are utilized to fully absorb the wind / solar output of each region, reduce the ramp output of units in each region, and improve the charging efficiency of electric vehicle users and the operating efficiency of multiple microgrids.

[0071] (3) The method of the present invention uses the fast charging mode to select charging stations in different areas and the slow charging mode to select different charging times as variables for simulation, and uses the particle swarm algorithm to solve the model, which overcomes the shortcomings of using optimization methods that are prone to falling into local optimal solutions and improves the optimization performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A framework diagram of an embodiment of the present invention;

[0073] Figure 2 A road network diagram according to an embodiment of the present invention;

[0074] Figure 3 A schematic diagram of a flow chart of an embodiment of the present invention;

[0075] Figure 4 This is a comparison chart of fast / slow charging load and net load in each area before and after optimization in an embodiment of the present invention;

[0076] Figure 5 The dynamic electricity price of the microgrid in each region in the embodiment of the present invention;

[0077] Figure 6 2 is a comparison chart of charging efficiency in embodiments of the present invention. DETAILED DESCRIPTION

[0078] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings. The described embodiments are part of the embodiments of the present invention, but not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without making any innovative efforts shall fall within the scope of protection of the present invention.

[0079] like Figure 1 As shown, an embodiment of a method for configuring an electric vehicle charging pile includes the following steps:

[0080] Analyze various factors that affect electric vehicle charging methods, charging locations, and charging times, use Monte Carlo simulation to generate fast / slow charging loads for electric vehicles in various regions, and describe the initial spatiotemporal distribution characteristics of electric vehicle loads;

[0081] This paper mainly studies the charging load requirements of three types of vehicles: private cars, taxis, and buses. It specifically analyzes the charging mode, charging location, and charging time of the corresponding types of vehicles, and sets various parameters of the load forecasting model based on this.

[0082] Initial charging demand: The charging load of different types of electric vehicles is random in both time and space. Based on NHTS statistical analysis, it is approximately assumed that the initial charging demand of the three user groups in different time periods satisfies the normal distribution, and the state of charge at the initial charging demand also satisfies the normal distribution:

[0083]

[0084] Where t is the initial moment of charging demand, that is, at this moment, the electric vehicle user considers going to the charging station; σ s and μ s (s=1,2), σ1 and μ1 are the expectation and standard deviation of the charging demand at the initial moment, and σ2 and μ2 are the expectation and standard deviation of the state of charge at the initial moment of charging demand.

[0085] like Figure 2As shown, network nodes 1-12 are residential areas, of which 6 nodes are microgrid charging station nodes in residential areas; network nodes 13-19 are commercial areas, of which 14 nodes are microgrid charging station nodes in commercial areas; network nodes 20-25 are office areas, of which 6 nodes are microgrid charging station nodes in office areas;

[0086] Private car load: Private cars have the largest number of users and have charging needs at different times and locations. Charging locations can be in work areas, commercial areas, and residential areas, with the charging probabilities set to 0.4, 0.2, and 0.4, respectively. In the work area, there is a 0.2 probability of slow charging between 06:00 and 18:00, and a 0.2 probability of fast charging between 07:00 and 11:00, and 12:00 and 16:00, respectively. The initial charging demand follows the normal distribution N(12, 3 2 )、N(9,1 2 )、N(14,1 2 ); In the commercial area, there is a 0.1 probability of choosing fast charging between 06:00 and 18:00, and a 0.1 probability of choosing fast charging between 17:00 and 21:00. The charging demand at the initial time follows the normal distribution N(12,3 2 )、N(19,1 2 ); In residential areas, there is a probability of 0.1 to choose fast charging between 08:00 and 20:00, and the main choice is slow charging between 17:00 and 03:00 the next day. The initial charging demand follows the normal distribution N(14,3 2 )、N(23,2 2 ); The charging demand of private cars at the initial moment of charge state obeys the normal distribution N(0.3, 0.1 2 );

[0087] Taxi load: Taxis operate all day, so they are usually charged twice a day. The charging time is chosen during the periods with less traffic, namely noon and midnight. The charging periods for taxis are 01:00-05:00 and 10:00-14:00, which follow the normal distribution N(3,1 2 ) and N(12,1 2 Since the taxi’s itinerary is not fixed, the charging demand nodes are randomly distributed. After considering the distance and charging price, the fast charging pile is selected for charging. The initial state of charge of the taxi’s charging demand follows the normal distribution N(0.2, 0.1 2 );

[0088] Bus load: The operating hours of buses are roughly from 06:00 to 22:00. The operating hours and routes of buses are relatively concentrated, so they can be charged in a centralized manner. The charging nodes are fixed and usually charged twice a day. Fast charging is performed at noon and slow charging is performed after get off work in the evening. The charging periods of buses are from 12:00 to 16:00 and from 18:00 to 02:00 the next day, which follow the normal distribution N(14, 1 2 ) and N(22,2 2 During the noon period, fast charging is performed at the fast charging pile in the commercial area, and during the night period, slow charging is performed at the charging station in the residential area; the charging demand of the bus at the initial moment of charge state follows the normal distribution N(0.5, 0.1 2 ).

[0089] Table 1 Electric vehicle charging load prediction parameters

[0090]

[0091] Analyze the impact of fast / slow charging of electric vehicles on the safe and stable operation of each microgrid, and use load time transfer and space transfer technology to maximize user charging efficiency to establish an upper-level electric vehicle load temporal and spatial distribution law model;

[0092] Electric vehicle fast / slow charging scheduling strategy: Some private cars choose to slow charge in work areas during the day and in residential areas at night. Due to the long slow charging time, these electric vehicles are considered time-shiftable loads. Due to the shorter fast charging time, scheduling is more flexible. Private cars and taxis can fast charge at different charging stations within the charging period and be considered spatially shiftable loads. In addition, since the charging time and location of buses are relatively fixed, their charging mode is switched within relevant constraints, and the charging time can be flexibly arranged within a fixed period.

[0093] With the goal of maximizing user charging efficiency, a temporal and spatial distribution model of upper-level electric vehicle load is established, and the objective function is as follows:

[0094] The upper layer uses user satisfaction to characterize user charging efficiency. User satisfaction is the sum of travel satisfaction and charging cost satisfaction. The specific expression is:

[0095]

[0096]

[0097]

[0098] Where f is the objective function; are the travel satisfaction and charging fee satisfaction of electric vehicle j going to charging station i; D max and Dmin are the farthest and shortest distances for electric vehicles in the road network from charging demand nodes to charging stations; is the distance from electric vehicle j to charging station i at time t; and are the highest and lowest charging costs for the corresponding vehicle type at charging station i; is the charging electricity price of electric vehicle j at charging station i at time t; For the jth car The remaining battery percentage at the time of arrival at the charging station.

[0099] Determine the electric vehicle battery SOC, electricity price upper and lower limits, and charging time constraints:

[0100] (1) Battery SOC constraint

[0101] In order to prevent the electric vehicle from running out of power, a lower limit of SOC is set to ensure that the electric vehicle meets the energy consumption requirements of traveling from the charging demand node to the selected charging station; at the same time, in order to prevent overcharging from having a serious adverse effect on the battery life, an upper limit of SOC is set, namely:

[0102]

[0103] Where, and are the maximum and minimum values ​​of the battery SOC allowed respectively;

[0104] (2) Electricity price upper and lower limits

[0105] In order to protect the interests of electric vehicle users and microgrid operators, the charging price of electric vehicles must be kept within reasonable upper and lower limits, namely:

[0106]

[0107] Where, and are the minimum and maximum charging prices of charging station i, respectively;

[0108] (3) Charging time constraints

[0109] In order to prevent users from charging for too long, which would reduce their satisfaction with the charging time, an upper limit on the charging time is set, namely:

[0110] 0≤T i f,t ≤T i f,max

[0111] 0≤T i s,t ≤Ti s,max

[0112] Where, T i f,max and T i s,max are the minimum and maximum fast / slow charging times of charging station i, respectively; T i f,t and T i s,t are the fast charging / slow charging time of charging station i at time t respectively.

[0113] Taking into account the fast-charging and slow-charging loads of electric vehicles, a net load model for each microgrid is established. Based on the net load fluctuation amplitude and the required ramping performance, the risks faced by the microgrid when the electric vehicle charging load is concentrated are described;

[0114] Electric vehicle charging electricity price pricing strategy based on the operating status of the microgrid: When an electric vehicle generates a charging demand, it will go to the nearest charging station to charge. At this time, the electric vehicle does not participate in the optimized scheduling, and the charging electricity price is set to the peak electricity price. When the electric vehicle selects the charging location and charging start time based on the charging electricity price, the electric vehicle participates in the optimized scheduling. The electric vehicle charging electricity price pricing strategy based on the operating status of the microgrid sets a dynamic electricity price.

[0115] The peak and valley electricity prices are divided into two intervals:

[0116]

[0117] Where C 1,i and C 2,i They are electricity price range 1 and electricity price range 2, C v 、C n and C p They are the valley value, average value and peak value of electricity price respectively; is the net load size of each microgrid at time t;

[0118] First, determine whether the net load at each moment is negative. If it is negative, it means that there is wind / solar power that has not been absorbed. At this time, the electricity price is mapped in electricity price range 1 according to the proportion of wind / solar power that has not been absorbed:

[0119]

[0120] Where, is the net load valley value within each microgrid dispatch period;

[0121] If the net load is positive, it means that all wind / solar output has been absorbed. At this time, the electricity price is mapped to the electricity price range 2 according to the net load ramp rate:

[0122]

[0123]

[0124]

[0125] Where, and are the size of the net load ramp of each microgrid at time t and the maximum value of the net load ramp within the scheduling period, respectively.

[0126] Considering the microgrid operation risks caused by the concentrated charging of electric vehicles, taking into account factors such as net load fluctuation, ramping demand and new energy consumption, and aiming at the highest operating efficiency of each microgrid, a lower-level multi-microgrid fast / slow charging pile configuration model is established. The objective function is as follows:

[0127] (1) The net load fluctuation of the microgrid is minimal:

[0128]

[0129]

[0130]

[0131] Where, is the average net load of each microgrid, and are the charging power of electric vehicles, the power of basic loads, the power of energy storage equipment, and the output of wind / solar units at time t. and is the average value of the transmission power of each part in the scheduling period; T represents the scheduling period, and this paper takes 1 hour as the unit time period, that is, T = 24;

[0132] (2) The comprehensive operating cost of the microgrid is minimal:

[0133]

[0134] Where S2 is the comprehensive operation cost of the microgrid; S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of microgrid (yuan / day); S i,2 is the operating cost of the output unit in each region; S i,3 The ramping cost of the microgrid output unit in each region;

[0135]

[0136] Where S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of the microgrid (yuan / day); and are the price and number of fast / slow charging piles, respectively; n is the operating life of the charging pile; d is the discount rate; and η is the percentage of operation and maintenance costs to investment costs;

[0137]

[0138]

[0139]

[0140]

[0141] Where, and They are the operating costs of diesel generator sets, energy storage equipment and distribution network tie lines in each region’s microgrid; and are respectively the operation and maintenance cost, fuel cost and environmental management cost of the diesel generator set; C de,om is the operation and maintenance coefficient of the diesel generator set, is the output power of the diesel generator set in area i at time t, a, b and c are the fuel coefficients, C k ,λ de,k are the cost of treating the kth type of pollutant and the emission of the kth type of pollutant generated by operation; C ES,om and are the energy storage equipment operating cost coefficient and the charging and discharging power of the energy storage equipment in area i at time t, respectively; and are the electricity transaction cost of the distribution network tie line and the environmental governance cost of the distribution network, is the electricity price of the distribution network at time t, is the power of the distribution network tie line. The positive and negative values ​​of the power represent the amount of electricity sold from the microgrid to the main grid. DN,k Emissions of type k pollutants generated by the operation of distribution network tie lines;

[0142]

[0143]

[0144]

[0145] Where, and are the ramping cost of diesel generator sets and tie line ramping cost of microgrids in each region; C de and C DN are the output ramp cost coefficients of diesel generator sets and distribution network tie lines, and are the transmission power of the diesel generator set and the distribution network tie line at time t in each regional microgrid; is the cost coefficient of spinning reserve for distribution network tie lines.

[0146] Determine the output and ramp constraints of the microgrid generation units, as well as the power balance constraints of each area:

[0147] (1) Power generation unit output constraints

[0148]

[0149] Where, and and and are the minimum and maximum outputs of the diesel generator set, energy storage equipment, and distribution network tie line, respectively;

[0150] (2) Power generation unit climbing constraints

[0151]

[0152] Where, are the maximum values ​​of output ramp of diesel generator set and distribution network tie line respectively;

[0153] (3) Power balance constraints

[0154]

[0155] Based on the temporal and spatial distribution characteristics of electric vehicle loads, the microgrid operation risks caused by concentrated electric vehicle charging are considered, and factors such as net load fluctuation, ramping demand, and new energy consumption are taken into account. With the goal of improving microgrid operation efficiency, electric vehicle charging electricity prices are formulated, and a multi-microgrid fast / slow charging pile capacity optimization configuration model is established. The model is solved using a multi-objective particle swarm algorithm to obtain an optimized charging pile configuration plan, improving user charging efficiency and microgrid operation efficiency.

[0156] like Figure 3 As shown in the figure, the model is solved using Monte Carlo simulation and a multi-objective particle swarm algorithm. The upper layer uses Monte Carlo simulation to generate charging demand nodes for each type of user, the initial charging demand moment, and the state of charge at that moment. The load curves of all users are superimposed to obtain the fast / slow charging load curves for each region, which are passed to the lower layer. The electricity price is determined based on the operating status of each regional microgrid. Different charging stations are selected for fast charging mode, and charging time points for slow charging mode are selected as variables. The multi-objective particle swarm algorithm is used to solve the two-layer model, obtaining the fast / slow charging loads that maximize user charging efficiency and the optimal configuration of fast / slow charging piles for each region with the highest operating efficiency.

[0157] This paper sets up two control scenarios for case analysis. Scenario 1: Without considering the temporal and spatial distribution of EV charging demand guided by charging electricity prices, the fast / slow charging demand of EV users is obtained based on the load forecasting parameters of this invention. Scenario 2: The method proposed in this invention considers the guidance of charging electricity prices in various regions on the fast / slow charging load and optimizes the fast / slow charging demand of EV users.

[0158] Parameter setting: Assume that 1,000 private cars, 100 taxis, and 50 buses are charged daily in the planning area; the average vehicle speed is 30 km / h; the battery parameters of various electric vehicles are shown in Table 2.

[0159] Table 2 Parameters of batteries for different types of electric vehicles

[0160]

[0161] The fast charging piles in the microgrid are DC charging piles with a power of 60 kW and a unit price of 50,000 yuan. The slow charging piles are AC charging piles with a power of 12 kW and a unit price of 10,000 yuan. The operating life is 10 years, the discount rate is 10%, and the percentage of operation and maintenance costs to construction costs η is 10%. The peak, valley and flat electricity prices of the microgrid are 1.3 yuan / kWh, 0.8 yuan / kWh and 0.3 yuan / kWh respectively. The equipment parameters of the diesel generator sets, distribution network interconnection lines, energy storage equipment, wind / solar generator sets and basic loads in each area are shown in Tables 3, 4 and 5. Equipment numbers 1 / 2 / 3 are microgrid equipment in the office area, commercial area and residential area respectively.

[0162] Table 3 Parameters of microgrid diesel generator sets and distribution network tie lines

[0163]

[0164] Table 4 Microgrid energy storage equipment parameters

[0165]

[0166] Table 5 Microgrid wind / solar unit and base load parameters

[0167]

[0168] According to the load prediction parameters of various electric vehicles, the Monte Carlo method is used to sample the charging location, the initial time of charging demand and SOC and other parameters to simulate the fast / slow charging load of each area. The results are as follows: Figure 4 As shown in Scenario 1; Figure 4Scenario 2 takes into account the guidance of charging electricity prices on fast / slow charging loads, and optimizes the fast / slow charging needs of electric vehicle users. Through the comparison chart of fast / slow charging loads and net loads in each area, it can be seen that during the period of 07:00-11:00, the fast / slow charging loads in the office area in Scenario 1 are increasing rapidly, resulting in a large increase in the net load of the work area. At this time, the fast charging load is guided to the commercial and residential areas for fast charging through electricity prices, and the slow charging load is transferred in the time and space distribution by changing the charging start time. The optimization results show that the net load fluctuation of the work area in Scenario 2 is greatly reduced, and the impact on the net load fluctuation of the commercial and residential areas is small; during the period of 11:00-18:00, there is a lot of wind / solar output in the residential microgrid in Scenario 1 that is not absorbed. At this time, part of the fast charging load in the office and commercial areas chooses the residential area for fast charging. Scenario 2 achieves effective absorption of wind / solar output through load transfer, and at the same time, it has a great impact on the commercial area. The net load fluctuation in the office area also decreased, but the net load fluctuation in the office area increased slightly; during the period of 18:00-24:00, photovoltaic power generation stopped, wind power output decreased, and the basic load of each area was at its peak. In scenario one, the net load in the commercial area climbed the most. During this period, some fast-charging loads in the commercial area chose to fast charge in the work area and residential area. In scenario two, the net load fluctuation in the office area and residential area increased slightly, but effectively alleviated the impact of the net load fluctuation in the commercial area on the microgrid; during the period of 24:00-07:00, wind power output gradually increased, and the basic load power consumption decreased. Some slow-charging loads in the work area and residential area were transferred from the period of 18:00-24:00 to this period for charging, and some fast-charging loads in the commercial area were also transferred to the work area and residential area for fast charging; through analysis of each time period, the fast / slow-charging loads were transferred according to the electricity price set according to the operating status of each area, achieving the optimal operating efficiency of multiple microgrid areas.

[0169] Table 6 Comparison of net load data in each region

[0170]

[0171] Table 6 compares the net load data of the microgrids before and after charging optimization. The peak-to-valley differences in net load in each region were reduced by 28.3%, 15.4%, and 22.4%, respectively, and the net load variance was reduced by 24.5%, 15.7%, and 2.1%, respectively. It can be seen that by guiding the spatiotemporal distribution of electric vehicle charging demand through electricity prices, the net load fluctuations and peak-to-valley differences of the microgrids in each region have been reduced to varying degrees, achieving safe and stable operation of multiple microgrid regions.

[0172] According to the electric vehicle charging electricity price pricing strategy based on the microgrid operating status proposed in this invention, the charging electricity price for each region is formulated. The optimized electricity price results are as follows: Figure 5As shown in the figure; combined with the analysis of the net load curves of each region, the office area only has the situation where wind / solar power is not fully absorbed at 17:00, so the electricity price at 17:00 is in interval 1. The net load at other times is positive, so it is mapped in electricity price interval 2 according to the net load ramp; similarly, in commercial and residential areas, when the net load is negative, the electricity price is mapped in interval 1, guiding the electric vehicle load to absorb the wind / solar output. When the net load is positive, the electricity price is mapped in interval 2, guiding the electric vehicle load to charge in areas with smaller output ramps. The electricity price strategy proposed in this paper further optimizes the peak, valley and flat electricity prices. According to the heterogeneity of the microgrid output and load in each region, the electricity price is used to guide the temporal and spatial distribution of the electric vehicle load, so that the impact of large-scale electric vehicle load access on the microgrids in each region is minimized, which is conducive to the safe and stable operation of multi-regional microgrids.

[0173] Table 7 Fast / slow charging pile configuration results

[0174]

[0175] The results of configuring charging piles according to the fast / slow charging load before and after optimization are shown in Table 7. By optimizing the charging time of slow-charging vehicles in work areas and residential areas, the peak of slow-charging load is reduced. Therefore, the number of slow-charging piles in work areas and residential areas is reduced from 68 and 87 to 66 and 80 respectively. On the basis of meeting user needs, the reduction in the number of slow-charging piles can not only reduce the idle rate of slow-charging piles, but also reduce the configuration cost. The fast-charging loads in the three areas flexibly select charging locations. The peak of fast-charging loads in work areas and commercial areas is reduced. The number of fast-charging piles configured is reduced from 18 and 87 respectively. The number of fast charging piles in scenario 1 is reduced from 18 to 13 and 16; since there is no slow charging load in the residential area during the lunch period and the basic load itself is relatively small, the number of fast charging vehicles in the residential area increases during the lunch period, and the peak fast charging load in the residential area increases. The number of fast charging piles configured increases from 10 to 13, but the overall number of fast charging piles decreases from 46 to 42. Compared with the configuration of fast charging piles in scenario 1, the configuration of fast charging piles in scenario 2 is more balanced and reasonable, and fully considers the impact of the electric vehicle aggregation characteristics caused by the location and capacity configuration of charging piles on the safe and stable operation of the microgrid, and realizes the overall optimized configuration of fast / slow charging piles.

[0176] User satisfaction Figure 6 As shown in the figure, in scenario one, electric vehicle users go to the nearest charging station to charge after generating charging needs. Since such users do not participate in electricity price demand response, their satisfaction with charging costs is low, but their travel satisfaction is the highest, with a user satisfaction rate of 871.5. In scenario two, when users participate in electricity price demand response, they need to choose different charging times and locations. Their travel satisfaction is greatly reduced, but due to the reduction in charging electricity prices, their cost satisfaction is greatly improved compared with scenario one, and their overall satisfaction is improved by 22.1% compared with scenario one.

[0177] In the above method, users and microgrid operators are stratified, and a multi-objective two-layer optimization scheduling method is adopted. The spatiotemporal distribution of charging demand of different types of electric vehicles is taken into consideration, and an electric vehicle charging load prediction model is established. On the basis of ensuring the charging efficiency of users, the operating efficiency of the microgrid layer is fully considered, and the power ramp risk brought by the huge impact of electric vehicle load on the microgrid is coupled into the formulation of charging electricity prices. The spatiotemporal transfer characteristics of electric vehicles are utilized to fully absorb the wind / solar output of each region and reduce the ramp output of units in each region. The optimal configuration of multi-microgrid fast / slow charging piles with the highest efficiency for electric vehicle users and microgrid operators is obtained.

[0178] An embodiment of an electric vehicle charging pile configuration system includes a charging demand analysis module, a charging demand model construction module, a microgrid load model construction module, an electricity price adjustment module, and a solution integration module:

[0179] The charging demand analysis module is used to analyze the factors that affect the charging method, charging location and charging time of electric vehicles, use Monte Carlo simulation to generate the fast / slow charging load of electric vehicles in each region, and describe the initial spatiotemporal distribution characteristics of electric vehicle loads.

[0180] This paper mainly studies the charging load requirements of three types of vehicles: private cars, taxis, and buses. It specifically analyzes the charging mode, charging location, and charging time of the corresponding types of vehicles, and sets various parameters of the load forecasting model based on this.

[0181] Initial charging demand: The charging load of different types of electric vehicles is random in both time and space. Based on NHTS statistical analysis, it is approximately assumed that the initial charging demand of the three user groups in different time periods satisfies the normal distribution, and the state of charge at the initial charging demand also satisfies the normal distribution:

[0182]

[0183] Where t is the initial moment of charging demand, that is, at this moment, the electric vehicle user considers going to the charging station; σ s and μ s (s=1,2), σ1 and μ1 are the expectation and standard deviation of the charging demand at the initial moment, and σ2 and μ2 are the expectation and standard deviation of the state of charge at the initial moment of charging demand.

[0184] like Figure 2 As shown, network nodes 1-12 are residential areas, of which 6 nodes are microgrid charging station nodes in residential areas; network nodes 13-19 are commercial areas, of which 14 nodes are microgrid charging station nodes in commercial areas; network nodes 20-25 are office areas, of which 6 nodes are microgrid charging station nodes in office areas;

[0185] Private car load: Private cars have the largest number of users and have charging needs at different times and locations. Charging locations can be in work areas, commercial areas, and residential areas, with the charging probabilities set to 0.4, 0.2, and 0.4, respectively. In the work area, there is a 0.2 probability of slow charging between 06:00 and 18:00, and a 0.2 probability of fast charging between 07:00 and 11:00, and 12:00 and 16:00, respectively. The initial charging demand follows the normal distribution N(12, 3 2 )、N(9,1 2 )、N(14,1 2 ); In the commercial area, there is a 0.1 probability of choosing fast charging between 06:00 and 18:00, and a 0.1 probability of choosing fast charging between 17:00 and 21:00. The charging demand at the initial time follows the normal distribution N(12,3 2 )、N(19,1 2 ); In residential areas, there is a probability of 0.1 to choose fast charging between 08:00 and 20:00, and the main choice is slow charging between 17:00 and 03:00 the next day. The initial charging demand follows the normal distribution N(14,3 2 )、N(23,2 2 ); The charging demand of private cars at the initial moment of charge state obeys the normal distribution N(0.3, 0.1 2 );

[0186] Taxi load: Taxis operate all day, so they are usually charged twice a day. The charging time is chosen during the periods with less traffic, namely noon and midnight. The charging periods for taxis are 01:00-05:00 and 10:00-14:00, which follow the normal distribution N(3,1 2 ) and N(12,1 2 Since the taxi’s itinerary is not fixed, the charging demand nodes are randomly distributed. After considering the distance and charging price, the fast charging pile is selected for charging. The initial state of charge of the taxi’s charging demand follows the normal distribution N(0.2, 0.1 2 );

[0187] Bus load: The operating hours of buses are roughly from 06:00 to 22:00. The operating hours and routes of buses are relatively concentrated, so they can be charged in a centralized manner. The charging nodes are fixed and usually charged twice a day. Fast charging is performed at noon and slow charging is performed after get off work in the evening. The charging periods of buses are from 12:00 to 16:00 and from 18:00 to 02:00 the next day, which follow the normal distribution N(14, 1 2 ) and N(22,2 2During the noon period, fast charging is performed at the fast charging pile in the commercial area, and during the night period, slow charging is performed at the charging station in the residential area; the charging demand of the bus at the initial moment of charge state follows the normal distribution N(0.5, 0.1 2 ).

[0188] Table 1 Electric vehicle charging load prediction parameters

[0189]

[0190]

[0191] The charging demand model construction module is used to analyze the impact of fast / slow charging of electric vehicles on the safe and stable operation of each microgrid. By using load time transfer and space transfer technology, with the goal of maximizing user charging efficiency, an upper-level electric vehicle load spatiotemporal distribution model is established.

[0192] Electric vehicle fast / slow charging scheduling strategy: Some private cars choose to slow charge in work areas during the day and in residential areas at night. Due to the long slow charging time, these electric vehicles are considered time-shiftable loads. Due to the shorter fast charging time, scheduling is more flexible. Private cars and taxis can fast charge at different charging stations within the charging period and be considered spatially shiftable loads. In addition, since the charging time and location of buses are relatively fixed, their charging mode is switched within relevant constraints, and the charging time can be flexibly arranged within a fixed period.

[0193] With the goal of maximizing user charging efficiency, a temporal and spatial distribution model of upper-level electric vehicle load is established, and the objective function is as follows:

[0194] The upper layer uses user satisfaction to characterize user charging efficiency. User satisfaction is the sum of travel satisfaction and charging cost satisfaction. The specific expression is:

[0195]

[0196]

[0197]

[0198] Where f is the objective function; are the travel satisfaction and charging fee satisfaction of electric vehicle j going to charging station i; D max and D min are the farthest and shortest distances for electric vehicles in the road network from charging demand nodes to charging stations; is the distance from electric vehicle j to charging station i at time t; and are the highest and lowest charging costs for the corresponding vehicle type at charging station i; is the charging electricity price of electric vehicle j at charging station i at time t; For the jth car The remaining battery percentage at the time of arrival at the charging station.

[0199] Determine the electric vehicle battery SOC, electricity price upper and lower limits, and charging time constraints:

[0200] (1) Battery SOC constraint

[0201] In order to prevent the electric vehicle from running out of power, a lower limit of SOC is set to ensure that the electric vehicle meets the energy consumption requirements of traveling from the charging demand node to the selected charging station; at the same time, in order to prevent overcharging from having a serious adverse effect on the battery life, an upper limit of SOC is set, namely:

[0202]

[0203] Where, and are the maximum and minimum values ​​of the battery SOC allowed respectively;

[0204] (2) Electricity price upper and lower limits

[0205] In order to protect the interests of electric vehicle users and microgrid operators, the charging price of electric vehicles must be kept within reasonable upper and lower limits, namely:

[0206]

[0207] Where, and are the minimum and maximum charging prices of charging station i, respectively;

[0208] (3) Charging time constraints

[0209] In order to prevent users from charging for too long, which would reduce their satisfaction with the charging time, an upper limit on the charging time is set, namely:

[0210] 0≤T i f,t ≤T i f,max

[0211] 0≤T i s,t ≤T i s,max

[0212] Where, T i f,max and T i s,maxare the minimum and maximum fast / slow charging times of charging station i, respectively; T i f,t and T i s,t are the fast charging / slow charging time of charging station i at time t respectively.

[0213] The microgrid load model construction module is used to take into account the fast-charging and slow-charging loads of electric vehicles, establish the net load model of each microgrid, and describe the risks faced by the microgrid when the electric vehicle charging load is concentrated based on the net load fluctuation amplitude and the climbing performance requirements;

[0214] Electric vehicle charging electricity price pricing strategy based on the operating status of the microgrid: When an electric vehicle generates a charging demand, it will go to the nearest charging station to charge. At this time, the electric vehicle does not participate in the optimized scheduling, and the charging electricity price is set to the peak electricity price. When the electric vehicle selects the charging location and charging start time based on the charging electricity price, the electric vehicle participates in the optimized scheduling. The electric vehicle charging electricity price pricing strategy based on the operating status of the microgrid sets a dynamic electricity price.

[0215] The peak and valley electricity prices are divided into two intervals:

[0216]

[0217] Where C 1,i and C 2,i They are electricity price range 1 and electricity price range 2, C v 、C n and C p They are the valley value, average value and peak value of electricity price respectively; is the net load size of each microgrid at time t;

[0218] First, determine whether the net load at each moment is negative. If it is negative, it means that there is wind / solar power that has not been absorbed. At this time, the electricity price is mapped in electricity price range 1 according to the proportion of wind / solar power that has not been absorbed:

[0219]

[0220] Where, is the net load valley value within each microgrid dispatch cycle;

[0221] If the net load is positive, it means that all wind / solar output has been absorbed. At this time, the electricity price is mapped to the electricity price range 2 according to the net load ramp rate:

[0222]

[0223]

[0224]

[0225] Where, and are the size of the net load ramp of each microgrid at time t and the maximum value of the net load ramp within the scheduling period, respectively.

[0226] Considering the microgrid operation risks caused by the concentrated charging of electric vehicles, taking into account factors such as net load fluctuation, ramping demand and new energy consumption, and aiming at the highest operating efficiency of each microgrid, a lower-level multi-microgrid fast / slow charging pile configuration model is established. The objective function is as follows:

[0227] (1) The net load fluctuation of the microgrid is minimal:

[0228]

[0229]

[0230]

[0231] Where, is the average net load of each microgrid, and are the charging power of electric vehicles, the power of basic loads, the power of energy storage equipment, and the output of wind / solar units at time t. and is the average value of the transmission power of each part in the scheduling period; T represents the scheduling period, and this paper takes 1 hour as the unit time period, that is, T = 24;

[0232] (2) The comprehensive operating cost of the microgrid is minimal:

[0233]

[0234] Where S2 is the comprehensive operation cost of the microgrid; S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of microgrid (yuan / day); S i,2 is the operating cost of the output unit in each region; S i,3 The ramping cost of the microgrid output unit in each region;

[0235]

[0236] Where S i,1 (i=1,2,3) is the construction cost of fast / slow charging piles in each region of the microgrid (yuan / day); and are the price and number of fast / slow charging piles, respectively; n is the operating life of the charging pile; d is the discount rate; and η is the percentage of operation and maintenance costs to investment costs;

[0237]

[0238]

[0239]

[0240]

[0241] Where, and They are the operating costs of diesel generator sets, energy storage equipment and distribution network tie lines in each region’s microgrid; and are respectively the operation and maintenance cost, fuel cost and environmental management cost of the diesel generator set; C de,om is the operation and maintenance coefficient of the diesel generator set, is the output power of the diesel generator set in area i at time t, a, b and c are the fuel coefficients, C k ,λ de,k are the cost of treating the kth type of pollutant and the emission of the kth type of pollutant generated by operation; C ES,om and are the energy storage equipment operating cost coefficient and the charging and discharging power of the energy storage equipment in area i at time t, respectively; and are the electricity transaction cost of the distribution network tie line and the environmental governance cost of the distribution network, is the electricity price of the distribution network at time t, is the power of the distribution network tie line. The positive and negative values ​​of the power represent the amount of electricity sold from the microgrid to the main grid. DN,k Emissions of type k pollutants generated by the operation of distribution network tie lines;

[0242]

[0243]

[0244]

[0245] Where, and are the ramping cost of diesel generator sets and tie line ramping cost of microgrids in each region; C de and C DN are the output ramp cost coefficients of diesel generator sets and distribution network tie lines, and are the transmission power of the diesel generator set and the distribution network tie line at time t in each regional microgrid; is the cost coefficient of spinning reserve for distribution network tie lines.

[0246] Determine the output and ramp constraints of the microgrid generation units, as well as the power balance constraints of each area:

[0247] (1) Power generation unit output constraints

[0248]

[0249] Where, and and and are the minimum and maximum outputs of the diesel generator set, energy storage equipment, and distribution network tie line, respectively;

[0250] (2) Power generation unit climbing constraints

[0251]

[0252] Where, are the maximum values ​​of output ramp of diesel generator set and distribution network tie line respectively;

[0253] (3) Power balance constraints

[0254]

[0255] The electricity price adjustment module sets the electricity price for electric vehicle charging based on the temporal and spatial distribution characteristics of electric vehicle loads, taking into account the microgrid operation risks caused by the concentrated charging of electric vehicles, and taking into account factors such as net load fluctuations, ramping requirements, and new energy consumption, with the goal of improving the operating efficiency of the microgrid.

[0256] A solution integration module is used to establish a multi-microgrid fast / slow charging pile capacity optimization configuration model. The model is solved using a multi-objective particle swarm algorithm to obtain an optimized charging pile configuration solution, improving user charging efficiency and microgrid operation efficiency.

[0257] like Figure 3 As shown in the figure, the model is solved using Monte Carlo simulation and a multi-objective particle swarm algorithm. The upper layer uses Monte Carlo simulation to generate charging demand nodes for each type of user, the initial charging demand moment, and the state of charge at that moment. The load curves of all users are superimposed to obtain the fast / slow charging load curves for each region, which are passed to the lower layer. The electricity price is determined based on the operating status of each regional microgrid. Different charging stations are selected for fast charging mode, and charging time points for slow charging mode are selected as variables. The multi-objective particle swarm algorithm is used to solve the two-layer model, obtaining the fast / slow charging loads that maximize user charging efficiency and the optimal configuration of fast / slow charging piles for each region with the highest operating efficiency.

[0258] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computers containing computer-usable program code, or on available storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.).

[0259] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0260] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0261] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0262] Compiling the above-mentioned method steps into a program and then storing it on a hard disk or other non-transitory storage medium constitutes an embodiment of "a non-transitory readable recording medium" of the present invention; and electrically connecting the storage medium to a computer processor and configuring an electric vehicle charging pile through data processing constitutes an embodiment of "a data processing system" of the present invention.

[0263] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for configuring an electric vehicle charging pile, characterized in that The following steps are involved: S1. Analyze multiple factors that affect the charging method, charging location, or charging time of electric vehicles, simulate and generate fast / slow charging load functions for electric vehicles in the study area, and describe the initial spatiotemporal distribution characteristics of electric vehicle loads; S2. Develop a temporal and spatial distribution model for electric vehicle loads based on the electricity price and the initial temporal and spatial distribution characteristics, with the goal of maximizing user charging efficiency. S3. Determine constraints in the spatiotemporal distribution model of the electric vehicle load, wherein the constraints include the state of charge of the electric vehicle battery, the upper and lower limits of the electricity price, and the charging time; S4. Calculate the fast-charging and slow-charging loads of electric vehicles in each operator's microgrid and establish a net load model for each microgrid. The net load model describes the risk faced by the microgrid when the electric vehicle charging load is concentrated based on the net load fluctuation amplitude and the required ramping performance. S5. Determine the output and ramp constraints of the microgrid power generation unit and the power balance constraints of each region in the net load model; S6. Integrate the characteristics of the EV load temporal and spatial distribution model and the net load model to improve microgrid operating efficiency. Iteratively adjust the fast / slow charging electricity price of EVs to shift the temporal and spatial distribution of EV load demand. S7. Substitute the adjusted electricity price into the net load model, where the electric vehicle charging load is represented by the capacity and number of fast / slow charging piles, and solve it through a multi-objective particle swarm algorithm to obtain the optimal configuration plan for electric vehicle charging piles; The temporal and spatial distribution law model of electric vehicle load is characterized by user satisfaction in terms of user charging efficiency. User satisfaction is the sum of travel satisfaction and charging cost satisfaction. The specific expression is: Where f is the objective function; are the travel satisfaction and charging fee satisfaction of electric vehicle j going to charging station i; D max and D min are the farthest and shortest distances for electric vehicles in the road network from charging demand nodes to charging stations; is the distance from electric vehicle j to charging station i at time t; and are the highest and lowest charging costs for the corresponding vehicle type at charging station i; is the charging electricity price of electric vehicle j at charging station i at time t; For the jth car The remaining battery percentage at the time of arrival at the charging station.

2. The electric vehicle charging pile configuration method according to claim 1, characterized in that: The net load model includes: Microgrid net load fluctuation minimum sub-model: in, Where, is the average net load of each microgrid, and are the charging power of electric vehicles, the power of basic loads, the power of energy storage equipment, and the output of wind / solar units at time t. and is the average value of the transmission power of each part in the scheduling period; T represents the scheduling period, and this paper takes 1 hour as the unit time period, that is, T = 24; Microgrid comprehensive operation cost minimum sub-model: Where, S2 is the comprehensive operation cost of the microgrid; S i,1 , is the construction cost of fast / slow charging piles in microgrids in each region, in yuan / day; S i,2 is the operating cost of the output unit in each region; S i,3 The ramping cost of the microgrid output unit in each region; Where S i,1 , is the construction cost of fast / slow charging piles for microgrids in each region, in yuan / day; and are the price and number of fast / slow charging piles, respectively; n is the operating life of the charging pile; d is the discount rate; and η is the percentage of operation and maintenance costs to investment costs; Where, and They are the operating costs of diesel generator sets, energy storage equipment and distribution network tie lines in each region’s microgrid; and are respectively the operation and maintenance cost, fuel cost and environmental management cost of the diesel generator set; C de,om is the operation and maintenance coefficient of the diesel generator set, is the output power of the diesel generator set in area i at time t, a, b and c are the fuel coefficients, C k ,λ de,k are the cost of treating the kth type of pollutant and the emission of the kth type of pollutant generated by operation; C ES,om and are the energy storage equipment operating cost coefficient and the charging and discharging power of the energy storage equipment in area i at time t, respectively; and are the electricity transaction cost of the distribution network tie line and the environmental governance cost of the distribution network, is the electricity price of the distribution network at time t, is the power of the distribution network tie line. The positive and negative values ​​of the power represent the amount of electricity sold from the microgrid to the main grid. DN,k Emissions of type k pollutants generated by the operation of distribution network tie lines; Where, and are the ramping cost of diesel generator sets and tie line ramping cost of microgrids in each region; C de and C DN are the output ramp cost coefficients of diesel generator sets and distribution network tie lines, and are the transmission power of the diesel generator set and the distribution network tie line at time t in each regional microgrid; is the cost coefficient of spinning reserve for distribution network tie lines.

3. The method for configuring an electric vehicle charging pile according to claim 2, characterized in that: The method for shifting the temporal and spatial distribution of electric vehicle load demand includes shifting the temporal distribution of the load by delaying slow charging; and shifting the spatial distribution of the load by switching the points of fast charging.

4. An electric vehicle charging pile configuration system, characterized in that: Includes the following functional modules: A charging demand analysis module is used to analyze multiple factors that affect the charging method, charging location or charging time of electric vehicles, simulate and generate fast / slow charging load functions of electric vehicles in the study area, and describe the initial spatiotemporal distribution characteristics of electric vehicle loads; a charging demand model construction module, configured to establish a temporal and spatial distribution law model of electric vehicle loads based on the goal of maximizing user charging efficiency and combining electricity prices and the initial temporal and spatial distribution characteristics; and to determine constraints in the temporal and spatial distribution law model of electric vehicle loads, wherein the constraints include the state of charge of the electric vehicle battery, upper and lower limits of electricity prices, and charging time; A microgrid load model construction module is used to calculate the fast-charging and slow-charging loads of electric vehicles in each operator's microgrid and establish a net load model for each microgrid. The net load model describes the risks faced by the microgrid when the electric vehicle charging load is concentrated based on the net load fluctuation amplitude and the climbing performance requirement. The net load model determines the output and climbing constraints of the microgrid's power generation units and the power balance constraints of each area. The electricity price adjustment module is used to integrate the characteristics of the electric vehicle load spatiotemporal distribution model and the net load model to iteratively adjust the electricity price of electric vehicles for fast / slow charging with the goal of improving the operating efficiency of the microgrid and shift the spatiotemporal distribution of electric vehicle load demand; A solution integration module is used to substitute the adjusted electricity price into the net load model, where the electric vehicle charging load is represented by the capacity and number of fast / slow charging piles, and solve it through a multi-objective particle swarm algorithm to obtain the optimal configuration plan of the electric vehicle charging piles; The charging demand model construction module establishes a temporal and spatial distribution law model of electric vehicle load; the temporal and spatial distribution law model of electric vehicle load uses user satisfaction to characterize user charging efficiency. User satisfaction is the sum of travel satisfaction and charging cost satisfaction. The specific expression is: Where f is the objective function; are the travel satisfaction and charging fee satisfaction of electric vehicle j going to charging station i; D max and D min are the farthest and shortest distances for electric vehicles in the road network from charging demand nodes to charging stations; is the distance from electric vehicle j to charging station i at time t; and are the highest and lowest charging costs for the corresponding vehicle type at charging station i; is the charging electricity price of electric vehicle j at charging station i at time t; For the jth car The remaining battery percentage at the time of arrival at the charging station.

5. The electric vehicle charging pile configuration system according to claim 4, characterized in that: The microgrid load model building module establishes a net load model of each microgrid, and the net load model includes: Microgrid net load fluctuation minimum sub-model: in, Where, is the average net load of each microgrid, are the charging power of electric vehicles, the power of basic loads, the power of energy storage equipment, and the output of wind / solar units at time t. and is the average value of the transmission power of each part in the scheduling period; T represents the scheduling period, and this paper takes 1 hour as the unit time period, that is, T = 24; Microgrid comprehensive operation cost minimum sub-model: Where, S2 is the comprehensive operation cost of the microgrid; S i,1 , is the construction cost of fast / slow charging piles in microgrids in each region, in yuan / day; S i,2 is the operating cost of the output unit in each region; S i,3 The ramping cost of the microgrid output unit in each region; Where S i,1 , is the construction cost of fast / slow charging piles for microgrids in each region, in yuan / day; and are the price and number of fast / slow charging piles, respectively; n is the operating life of the charging pile; d is the discount rate; and η is the percentage of operation and maintenance costs to investment costs; Where, and They are the operating costs of diesel generator sets, energy storage equipment and distribution network tie lines in each region’s microgrid; and are respectively the operation and maintenance cost, fuel cost and environmental management cost of the diesel generator set; C de,om is the operation and maintenance coefficient of the diesel generator set, is the output power of the diesel generator set in area i at time t, a, b and c are the fuel coefficients, C k ,λ de,k are the cost of treating the kth type of pollutant and the emission of the kth type of pollutant generated by operation; C ES,om and are the energy storage equipment operating cost coefficient and the charging and discharging power of the energy storage equipment in area i at time t, respectively; and are the electricity transaction cost of the distribution network tie line and the environmental governance cost of the distribution network, is the electricity price of the distribution network at time t, is the power of the distribution network tie line. The positive and negative values ​​of the power represent the amount of electricity sold from the microgrid to the main grid. DN,k Emissions of type k pollutants generated by the operation of distribution network tie lines; Where, and are the ramping cost of diesel generator sets and tie line ramping cost of microgrids in each region; C de and C DN are the output ramp cost coefficients of diesel generator sets and distribution network tie lines, and are the transmission power of the diesel generator set and the distribution network tie line at time t in each regional microgrid; is the cost coefficient of spinning reserve for distribution network tie lines.

6. The electric vehicle charging pile configuration system according to claim 5, characterized in that: The electricity price adjustment module involves a method for shifting the temporal and spatial distribution of electric vehicle load demand, which includes shifting the temporal distribution of the load through a slow charging delay; and shifting the spatial distribution of the load through a fast charging point switch.

7. A non-transitory readable recording medium for storing one or more programs comprising a plurality of instructions, characterized in that: The program includes the steps included in the electric vehicle charging pile configuration method according to any one of claims 1 to 3.

8. A data processing system comprising a processing circuit and a memory electrically coupled thereto, characterized in that: The memory configuration stores at least one program, the program includes multiple instructions, and the processing circuit runs the program to execute the steps included in the electric vehicle charging pile configuration method according to any one of claims 1-3.

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