Charging station planning method, system and device based on cooperation of traffic network and low-carbon power grid, and storage medium

Abstract

The invention discloses a traffic network and low-carbon power grid coordinated charging station planning method, system and device and a storage medium, and relates to the technical field of traffic network and low-carbon power grid coupling planning, and the method comprises the steps: carrying out the modeling of the road resistance, the driving path, the electric quantity and the charging load based on the traffic condition of an urban road network and the electric quantity information of an electric vehicle, obtaining traffic network charging load space-time distribution and different regional time period charging requirements; calculating a load carbon flow rate and a node carbon potential according to the charging load energy consumption condition and a carbon emission flow proportion sharing principle to obtain charging carbon emission space-time distribution data of the electric vehicle; establishing a charging station planning double-layer model; solving to obtain an optimal charging station site selection and constant volume scheme; according to the invention, deep collaboration of the traffic network and the low-carbon power grid is realized, the power grid carbon emission is obviously reduced while the charging requirement of the electric vehicle is met, and the investment cost is effectively controlled.

Description

Technical Field

[0001] This invention relates to the field of coupled planning technology for transportation networks and low-carbon power grids, and in particular to a method, system, equipment, and storage medium for planning charging stations in coordination between transportation networks and low-carbon power grids. Background Technology

[0002] The large-scale deployment of electric vehicles has adversely affected the operation of the power distribution system. Inappropriate installation locations of electric vehicle charging stations can reduce the power quality of the distribution system and affect the safe and stable operation of the power grid. Therefore, the planning of electric vehicle charging stations is not merely a transportation network issue, but also involves multiple aspects such as the operational stability of the power grid, load distribution, and carbon emission control. As a power system primarily guided by carbon emission flows, the low-carbon power grid exhibits a complex coupling relationship between load fluctuations and the spatiotemporal distribution of electric vehicle charging demand.

[0003] Traditional methods often treat charging station site selection and capacity determination primarily as traffic network issues. For example, they consider only traffic factors such as traffic flow, vehicle routes, and distances between charging stations and traffic nodes to optimize charging station layout and make it easier for electric vehicles to reach charging stations. Site selection is based on factors like traffic hubs and densely populated areas; capacity determination may involve estimating potential charging demand based on surrounding traffic flow to determine the number and size of charging piles. These methods often neglect important factors such as grid load balancing and carbon emission control. When determining the location and capacity of charging stations, they fail to fully consider the impact of charging station integration on grid stability and load distribution, nor do they account for carbon emissions under different power supply structures. For instance, they don't consider the potential for local grid overload caused by charging station integration during peak electricity consumption periods, or the differences in carbon emissions from charging station electricity consumption when powered by different power sources (such as high-carbon and new energy units); some existing planning methods rely solely on the grid average carbon emission factor when considering carbon emissions. This method estimates the carbon emissions of charging stations by calculating the average carbon emission intensity of the entire power grid and then applying it to the electricity consumption calculation of charging stations. For example, assuming the average carbon emission factor of the power grid is a fixed value, the carbon emission estimate is obtained by multiplying the electricity consumption of charging stations by this factor. However, this method does not take into account the spatiotemporal distribution characteristics of different power sources in the power grid and the fluctuations in renewable energy output. In reality, the carbon emission intensity of the power grid varies with time and space, and the carbon emission situation of the power grid can vary greatly in different time periods and regions. For example, the carbon emission intensity of the power grid will be lower in periods and regions with sufficient renewable energy generation, while it will be higher in periods and regions with a higher proportion of high-carbon generating units. Some planning methods use single-layer models, planning only from a single perspective (such as transportation networks or power grids). For example, they only consider the charging load demand at the transportation network level, without considering factors such as the operating costs and carbon emissions of the power grid; or they only focus on the operation optimization of the power grid, ignoring the charging demand of electric vehicles and the layout of charging stations in the transportation network; some open-loop optimization methods lack feedback mechanisms in the planning process and cannot adjust the planning of charging stations in real time according to the operation of the power grid. For example, after determining the location and capacity of charging stations, the plan fails to take into account potential load changes and fluctuations in renewable energy output during grid operation, making it impossible to dynamically adjust the charging station plan to adapt to the actual operating needs of the grid. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to effectively combine the charging needs of electric vehicles with the operating characteristics of a low-carbon power grid, so as to ensure the availability of charging services, optimize the load distribution of the power grid, and reduce carbon emissions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for planning charging stations in coordination with transportation networks and low-carbon power grids, comprising:

[0008] Based on urban road network traffic conditions and electric vehicle battery information, we model urban road resistance, driving routes, electric vehicle battery power and charging load to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods.

[0009] Based on the actual energy consumption of the charging load and the principle of proportional sharing in the carbon emission flow, the load carbon flow rate and node carbon potential in the power grid are calculated to obtain the spatiotemporal distribution characteristics of carbon emissions during the charging process of electric vehicles.

[0010] Based on the spatiotemporal distribution of charging load in the transportation network, the spatiotemporal distribution characteristics of charging demand in different regions and time periods, and the carbon emission during electric vehicle charging, a two-layer model for charging station planning is established.

[0011] The optimal charging station site selection and capacity scheme is obtained by solving the two-layer model of charging station planning.

[0012] As a preferred approach for planning charging stations that coordinates transportation networks and low-carbon power grids, the following is provided:

[0013] The model, based on urban road network traffic conditions and electric vehicle battery information, models urban road congestion, driving routes, electric vehicle battery levels, and charging load to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods, including:

[0014] Taking into account the influence of road conditions and traffic lights at intersections during vehicle travel, a road segment impedance model and a node impedance model are constructed respectively. Based on the road segment impedance model and the node impedance model, an urban road resistance model is established, in which the traffic saturation of a node is determined by the road segment with the highest saturation among its adjacent road segments.

[0015] The beneficial effects of this preferred technical solution are as follows: by comprehensively considering factors such as road conditions and traffic lights at intersections to construct a road resistance model, it can more accurately reflect the actual traffic conditions of urban roads, providing a more reliable basis for subsequent route selection and charging load calculation, thereby improving the accuracy and rationality of charging station planning.

[0016] As a preferred approach for planning charging stations that coordinates transportation networks and low-carbon power grids, the following is provided:

[0017] The modeling of urban road congestion, driving routes, electric vehicle battery levels, and charging load based on urban road network traffic conditions and electric vehicle battery information, to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods, also includes:

[0018] Based on the urban road resistance model, the driving path is selected with the goal of minimizing the road impedance. After determining the driving path, a model of the remaining electric vehicle charge is established to calculate the change in the electric vehicle charge during driving. A model of the electric vehicle charge waiting amount is established, and the charge waiting amount is calculated by combining the target state of charge, charging efficiency and minimum rated charge.

[0019] As a preferred approach for planning charging stations that coordinates transportation networks and low-carbon power grids, the following is provided:

[0020] The modeling of urban road congestion, driving routes, electric vehicle battery levels, and charging load based on urban road network traffic conditions and electric vehicle battery information, to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods, also includes:

[0021] Based on the driving path and battery level changes of electric vehicles, a model for the distribution of electric vehicle charging load on road segments of the transportation network is established to calculate the charging load distribution at the transportation network level. Furthermore, a model is established to represent the total electric vehicle charging load generated at the transportation network level in each time period.

[0022] As a preferred approach for planning charging stations that coordinates transportation networks and low-carbon power grids, the following is provided:

[0023] The process of calculating the load carbon flow rate and node carbon potential in the power grid based on the actual energy consumption of the charging load and the proportional sharing principle in the carbon emission flow, to obtain the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, includes:

[0024] Considering the power system with new energy output and high-carbon units, calculate the carbon potential of a certain node based on the relevant connection relationships and the active power output of each power source.

[0025] The carbon potential vector of the distribution network is calculated based on the node active power flux matrix, branch power flow distribution matrix, unit injection distribution matrix, and unit equivalent carbon emission intensity vector.

[0026] The beneficial effects of this preferred technical solution are as follows: it takes into account the impact of new energy output and high-carbon units, and can accurately calculate the carbon potential of nodes in the power grid and the carbon potential vector of the distribution network, reflecting the carbon emission situation at different locations in the power grid. This provides an important basis for subsequent analysis of the spatiotemporal distribution characteristics of carbon emissions during the electric vehicle charging process, and helps to achieve the planning goal of a low-carbon power grid.

[0027] As a preferred approach for planning charging stations that coordinates transportation networks and low-carbon power grids, the following is provided:

[0028] The process of calculating the load carbon flow rate and node carbon potential in the power grid based on the actual energy consumption of the charging load and the proportional sharing principle in the carbon emission flow to obtain the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging also includes:

[0029] By introducing the coupled node charging load matrix and the node actual load matrix, and applying the proportional sharing principle in the power flow tracing algorithm, the specific allocation ratio of grid node carbon emissions to electric vehicle charging power is calculated. Based on the allocation ratio, the carbon emission allocation coefficient is determined, and the carbon flow rate of each node is allocated to the carbon flow rate from the electric vehicle charging load and the carbon flow rate from other load sources, thus obtaining the spatiotemporal distribution characteristics data of carbon emissions during the electric vehicle charging process.

[0030] The beneficial effects of this preferred technical solution are as follows: by introducing a correlation matrix and applying the proportional sharing principle, the node carbon flow rate can be accurately allocated to electric vehicle charging load and other load sources, accurately quantifying the impact of electric vehicle charging on carbon emissions, realizing precise coupling between electric vehicles and grid carbon emissions, and providing more refined carbon emission data support for the planning of low-carbon charging stations.

[0031] As a preferred approach for planning charging stations that coordinates transportation networks and low-carbon power grids, the following is provided:

[0032] The two-layer model for charging station planning, based on the spatiotemporal distribution of charging load in the transportation network, the charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, includes:

[0033] Based on the spatiotemporal distribution of charging load in the transportation network, charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, a new two-layer model for the site selection and capacity determination of electric vehicle charging stations under the coupling of transportation network and low-carbon power grid is constructed, with investment cost, charging load coverage and power grid carbon emissions as constraints.

[0034] Secondly, the present invention provides a charging station planning system that coordinates transportation networks and low-carbon power grids, comprising:

[0035] The traffic network information modeling and demand analysis module is used to model urban road congestion, driving routes, electric vehicle power and charging load based on urban road network traffic conditions and electric vehicle power information, so as to obtain the spatiotemporal distribution of traffic network charging load and charging demand in different areas and time periods.

[0036] The module for calculating the spatiotemporal characteristics of carbon emissions during charging is used to calculate the load carbon flow rate and node carbon potential in the power grid based on the principle of proportional sharing in the carbon emission flow, according to the actual energy consumption of the charging load. This yields spatiotemporal distribution characteristic data of carbon emissions during the charging process of electric vehicles.

[0037] The charging station planning two-layer model construction module is used to establish a charging station planning two-layer model based on the spatiotemporal distribution of charging load in the transportation network, the charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging.

[0038] The charging station planning solution module is used to solve the two-layer model of charging station planning to obtain the optimal charging station site selection and capacity scheme.

[0039] Thirdly, the present invention provides an electronic device, comprising:

[0040] Memory and processor;

[0041] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the charging station planning method for the coordination of transportation networks and low-carbon power grids as described in this invention.

[0042] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned charging station planning method for the coordination of transportation networks and low-carbon power grids.

[0043] The beneficial effects of this invention are as follows: This invention deeply integrates the transportation network impedance model with the power grid carbon emission flow theory. Through the bidirectional coupling of charging load spatiotemporal distribution simulation and carbon emission source tracing, it solves the problem of independent transportation demand and power grid carbon emissions in traditional planning, achieving coordinated optimization of transportation and power grid, and significantly improving the globality and systematic nature of the planning. By using carbon emission flow based on the proportional sharing principle, the spatiotemporal carbon emission intensity of charging station electricity consumption can be accurately quantified, breaking through the limitation of traditional methods that rely solely on the average carbon emission factor of the power grid, making the planning results more consistent with the dynamic operating characteristics of a low-carbon power grid. It innovatively adopts a two-layer model of upper layer (charging station site selection and capacity determination) - lower layer (power grid multi-objective operation), combined with the genetic algorithm and the NSGA-III collaborative optimization mechanism to form a closed-loop feedback. Compared with single-layer planning or open-loop optimization, this method ensures investment economy while taking into account the multi-objective balance of power grid operating costs, network losses, and carbon emissions. The spatiotemporal distribution data of charging load generated by the road network impedance model can accurately reflect the dynamic impact of traffic congestion on charging demand, avoid capacity configuration deviations caused by static load assumptions in existing technologies, and reduce the risk of traffic node overload. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is an overall flowchart of the charging station planning method that coordinates transportation networks and low-carbon power grids provided by the present invention. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0047] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a charging station planning method that coordinates transportation networks and low-carbon power grids, including:

[0048] S1: Based on urban road network traffic conditions and electric vehicle battery information, model urban road resistance, driving routes, electric vehicle battery power and charging load to obtain the spatiotemporal distribution of traffic network charging load and charging demand in different areas and time periods.

[0049] S2: Based on the actual energy consumption of the charging load and the principle of proportional sharing in the carbon emission flow, calculate the load carbon flow rate and node carbon potential in the power grid to obtain the spatiotemporal distribution characteristics of carbon emissions during the charging process of electric vehicles.

[0050] S3: Based on the spatiotemporal distribution of charging load in the transportation network, the spatiotemporal distribution characteristics of charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, a two-layer model for charging station planning is established.

[0051] S4: Solve the two-layer model of charging station planning to obtain the optimal charging station site selection and capacity scheme.

[0052] It should be noted that steps S1-S4 integrate information from multiple aspects, including the spatiotemporal distribution of charging load in the transportation network, charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging. By utilizing the established two-layer charging station planning model and effectively solving it, the optimal charging station site selection and capacity allocation scheme are obtained. This achieves deep synergy between the transportation network and the low-carbon power grid, significantly reducing grid carbon emissions while meeting electric vehicle charging needs and effectively controlling investment costs. This provides strong technical support and theoretical basis for the scientific planning and sustainable development of urban electric vehicle charging infrastructure.

[0053] Example 2, refer to Figure 1 As one embodiment of the present invention, based on the previous embodiment, a charging station planning method that coordinates transportation networks and low-carbon power grids is provided, including:

[0054] In this embodiment, step S1 above models urban road traffic conditions, driving routes, electric vehicle battery levels, and charging load based on urban road congestion, driving routes, electric vehicle battery levels, and charging load, obtaining the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods, including:

[0055] A time-flow model is used to model urban road congestion, taking into account factors such as road conditions and traffic lights at intersections during vehicle travel. This model is represented as follows:

[0056] Establish the road segment impedance model:

[0057]

[0058] In the formula: R ij,d Let d be the road segment impedance from node i to node j in the traffic network; t0 be the road segment travel time when the traffic flow is zero; and d be the road segment impedance from node i to node j in the traffic network. ij denoted as , where is the distance between node i and node j in km; v0 is the vehicle speed; k is the urban road segment traffic saturation evaluation index, k = Q / C (Q is the road segment traffic flow in pcu / h; C is the traffic capacity in pcu / h); α and β are impedance influence factors, with α = 0.15 and β = 4 being preferred.

[0059] Establish the node impedance model:

[0060]

[0061] In the formula: R i,t Let be the node impedance of node i; c be the signal period, preferably 0.5 min; λ be the proportion of green light time in the entire signal period, preferably 0.7; and q be the vehicle arrival rate of the road segment, preferably 0.8.

[0062] Based on the road segment impedance model and the node impedance model, an urban road resistance model is established, which is expressed as:

[0063]

[0064] In the formula: Z represents road impedance, i.e., vehicle travel time, in minutes; Z is the number of adjacent road segments to traffic node i. Node traffic saturation k i Based on the saturation of its adjacent road sections The decision was made regarding the largest section of the road.

[0065] In another possible implementation, road congestion modeling can employ a fuzzy logic model: considering the inherent uncertainty and fuzziness of urban road network traffic conditions, such as the impact of weather and traffic accidents on road traffic, a fuzzy logic model can be used to model urban road congestion.

[0066] Specifically, determine the input variables, such as traffic flow, road congestion level (which can be obtained through camera image analysis, etc.), and weather conditions (sunny, rainy, snowy, etc.).

[0067] Define fuzzy sets and membership functions. For example, divide traffic flow into three fuzzy sets: "low", "medium", and "high", and define a corresponding membership function for each set.

[0068] Establish fuzzy rules, such as "if traffic flow is high, road congestion is severe, and the weather is rainy, then road resistance is high."

[0069] The specific values ​​of road resistance are obtained through fuzzy reasoning and defuzzification processes.

[0070] In another possible implementation, road resistance modeling can employ dynamic modeling based on real-time traffic data: using IoT technology, traffic data on urban roads, such as vehicle speed and density, is collected in real time. This data is then processed and predicted using a Kalman filter algorithm to achieve dynamic modeling of road resistance.

[0071] Establish state equations and observation equations to describe the dynamic changes in road resistance and the relationship between observable data.

[0072] Based on real-time observation data, the estimated road resistance is updated using the Kalman filter algorithm.

[0073] Based on an urban road resistance model, a real-time Dijkstra algorithm is used to select a travel path with the objective of minimizing road impedance. This algorithm explores and determines the route with the minimum total impedance from the starting position within the graph structure of the traffic network. The process of selecting the path is called travel path modeling, and is represented as follows:

[0074]

[0075] In the formula: This represents the minimum travel time for the g-th route, in minutes; if route segment ij belongs to the minimum travel time path... A path in, v ij =1, otherwise v ij =0.

[0076] After determining the driving route, it is necessary to calculate the change in the electric vehicle's battery level during the driving process, which is directly related to charging demand.

[0077] Establish a model for the remaining battery power of an EV (Electric Vehicle):

[0078]

[0079] In the formula: η1 is the battery conversion efficiency; E c The vehicle's energy efficiency coefficient, expressed in kWh / km; Let ΔS be the remaining battery power of the k-th vehicle at time t+1; g Let represent the distance traveled by the kth vehicle from time t to time t+1, in km.

[0080] Establish an EV charging capacity model:

[0081]

[0082] In the formula: η represents the target state of charge when the g-th electric vehicle leaves the charging station; ev The charging efficiency of electric vehicle power batteries; This refers to the minimum rated power of an electric vehicle.

[0083] Calculate the charging load distribution and total amount at the transportation network level based on the electric vehicle's driving path and battery level changes;

[0084] Specifically, an EV charging load distribution model is established on the traffic network segment, represented as follows:

[0085]

[0086] In the formula: The charging load distribution of road segment ij at time t; The hydrogen loading is triggered at node i at time t.

[0087] The total EV charging load generated per time period at the transportation network level is further expressed as:

[0088]

[0089] In the formula: The EV charging load at the construction nodes of public charging stations in urban areas; The EV charging load generated at private charging pile nodes in urban areas; For the charging load in suburban areas; N1 represents the number of public charging stations built in urban areas and the additional charging distance in suburban areas; N2 represents the number of private charging station nodes in urban areas; N3 represents the number of nodes in suburban areas; and m represents the number of charging load nodes repeatedly captured in urban areas.

[0090] In another possible implementation, the A* algorithm can be used during the path modeling process: the A* algorithm is a heuristic search algorithm that is highly efficient in finding the shortest path. It combines the breadth-first search property of Dijkstra's algorithm with the heuristic search property of the greedy best-first search.

[0091] Specifically, define a heuristic function, such as using Euclidean distance or Manhattan distance, to estimate the distance from the current node to the target node.

[0092] During the search process, based on the actual cost of the node and the cost estimated by the heuristic function, the node with the lowest cost is selected for expansion until the target node is found.

[0093] In this embodiment, step S2 above calculates the load carbon flow rate and node carbon potential in the power grid based on the actual energy consumption of the charging load and the proportional sharing principle in the carbon emission flow, obtaining the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, including:

[0094] The carbon potential at a node in a power system that considers both renewable energy output and high-carbon generating units is calculated as follows:

[0095]

[0096] In the formula: The set of branches that inject active power into node i; The set of high-carbon generator sets connected to node i; The set of new energy generator sets connected to node i; P l Inject active power into node i into branch l; G g For the active power output of the high-carbon generator set g to node i; W z For the active power output of the new energy generator unit z to node i; ρ l,t The carbon flux density of branch l; e g,t The carbon emission intensity is 0 for high-carbon units; the carbon emission intensity of new energy units is 0.

[0097] The carbon potential vector of the distribution network is calculated as follows:

[0098]

[0099] In the formula: P N,t P is the active flux matrix of the nodes; B,t Branch power flow distribution matrix; P G,t Inject the distribution matrix into the unit; E N,t E represents the carbon potential vector of the distribution network during time period t. G,t For e g,t The equivalent carbon emission intensity vector of the unit.

[0100] By introducing the coupling node charging load matrix P h,t and the actual load matrix P of the nodes k,t By utilizing the proportional sharing principle in power flow tracing algorithms, the specific allocation ratio of grid node carbon emissions to EV charging power can be calculated. Based on this allocation ratio, a carbon emission allocation coefficient is further determined, distributing the carbon flow rate of each node into the carbon flow rate from EV charging load and the carbon flow rate from other load sources. This allows for accurate quantification of the impact of EV charging load on carbon emissions and achieves precise coupling between electric vehicles and grid carbon emissions.

[0101] Specifically, it is expressed as follows:

[0102] R N,t =P k,t ·E N,t

[0103] R h,t =R N.t ·C h,t

[0104] R l,t =R N,t -R h,t

[0105]

[0106] In the formula: R N,t R h,t R l,t These are the carbon flow rates at time t node, EV charging load, and other loads, respectively; C h,t Carbon emission allocation factor for EV charging load at nodes; C h,t,i C h,t The i-th element; The charging load power of EVs at node i during time period t; The power of other loads at node i during time period i.

[0107] In this embodiment, the step S3 above, which establishes a two-layer model for charging station planning based on the spatiotemporal distribution of charging load in the transportation network, the charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, includes:

[0108] Based on the spatiotemporal distribution of charging load in the transportation network, the spatiotemporal distribution characteristics of charging demand in different regions and time periods, and the carbon emission characteristics during electric vehicle charging, a new two-layer model for the site selection and capacity determination of electric vehicle charging stations under the coupling of transportation network and low-carbon power grid is planned, taking into account investment cost, charging load coverage, and power grid carbon emission constraints.

[0109] Specifically, the goals at the transportation network level are:

[0110] The coverage rate of EV charging load captured by the charging station service provider in the urban area and the construction cost of the charging station are expressed as follows:

[0111]

[0112] F1=β1[1-f1]+β2f2

[0113] In the formula: Let V be the charging load of node v in the transportation network; V be the total number of nodes in the transportation network; N be the number of charging stations; λ be the average depreciation rate of charging stations; μ be the maximum service life of charging stations; V i Let v be the annual construction cost of the i-th charging station. g The unit price of a high-power charging station; N g v represents the number of high-power charging piles in the i-th charging station; l The unit price of a low-power charging station; N l Let β1 be the number of low-power charging piles in the i-th charging station. β2 is a weighting coefficient reflecting the importance attached to charging load coverage.

[0114] It should be noted that EVs select charging stations based on proximity to minimize charging routes. Charging station service providers capture charging loads at various nodes within the city, but in cases where charging station service areas overlap, duplicate charging loads will only be captured once.

[0115] The traffic network constraints are expressed as follows:

[0116] f 1,min <f1≤1

[0117] L ev,min <L ev ≤L lb,min

[0118] m≤m max

[0119]

[0120] G min ≤G≤G max

[0121] In the formula: f 1,min The coverage rate of the minimum charging load of the charging station is generally taken as 60%; ev The distance between two charging stations, in km; L ev,min The minimum distance between two charging stations is typically taken as 5km; L lb,min This represents the driving range in low-battery mode for EVs, typically taken as 20km. `ceil(.)` is the floor function. N hp,z P represents the number of high-power charging piles in z charging stations; hp The power rating of the high-power charging station; N lp,z P represents the number of low-power charging piles in z charging stations; lp This represents the power output of the low-power charging station. T1 represents the average daily operating time of the charging station. G max The maximum capacity of a single charging station; G min This represents the minimum capacity of a single charging station.

[0122] Grid layer objectives:

[0123] minF2=[f3,f4,f5]

[0124]

[0125] In the formula: V t These represent the unit electricity purchase price for time period t; e c,t Let t be the unit carbon cost price that users need to pay during time period t; ξ is an n-row unit column vector. x is the labor cost ratio coefficient; y is the grid connection cost ratio coefficient.

[0126] Power grid constraints:

[0127]

[0128] Y l,t ≤Y l,max

[0129]

[0130] f4≤f 4,c,max

[0131] In the formula: and Let i be the active and reactive power injected into node i at time t, in kWh and var. For the active and reactive power consumption of node i; Ui and U j Let θ be the voltage at node i and node j; ij G represents the voltage phase angle difference between node i and node j. ij and B ij U represents the conductance and susceptance of the line between node i and node j; i,min U i,max I represents the minimum and maximum values ​​of the voltage amplitude at node i; ij,min and I ij,max Y represents the minimum and maximum amplitudes of the current flowing through branches i and j. l,t For the complex power flowing through the l-th branch in time period t, Y l,max This represents the maximum power that can be transmitted by the l-th branch. Minimum output of high-carbon generator units; Maximum output of high-carbon unit. Minimum output for new energy generating units; To maximize the output of new energy generating units; P h,j,t P represents the electric vehicle charging load (i.e., the charging pile load) at node j at time t; l,j,t Let P be the active power load (total electricity demand) of node j at time, including electricity demand from residential, commercial, and other sectors; g,j,t For the active power output of the high-carbon generator unit at node j at time t; P z,j,t The active power output of the new energy generating unit at node j at time t; P grid,j,t P represents the active power received by node j from the main grid at time t. net,t Q represents the net active power demand of node j at time t, which is the power supplied by the external grid or from other scheduling arrangements; h,j,t Q represents the reactive power demand of the electric vehicle charging load at node j at time t. l,j,t Q represents the reactive load of node j at time t. g,j,t For the reactive power output of the high-carbon generator unit at node j at time t; Q z,j,t The active power output of the new energy generating unit at node j at time t; Q grid,j,t The reactive power received by node j from the grid at time t; Q net,t Let f be the net reactive power requirement of node j at time t. 4,c,max This is the maximum set carbon emission cost.

[0132] In this embodiment, the solution to the two-layer model for charging station planning in step S4 above, to obtain the optimal charging station site selection and capacity allocation scheme, includes:

[0133] To address the issues of inconsistent decision variables and varying optimization types in two-level models, a nested optimization solution method involving iterative interaction between upper and lower level optimization algorithms is adopted.

[0134] The two-layer model for charging station planning is divided into an upper layer (transportation network) and a lower layer (low-carbon power grid). The upper layer model is a multi-objective model that takes the location and capacity of charging stations as decision variables and considers the maximum charging load coverage and the total cost of charging pile construction. It is transformed into a single-objective model through a weighted method. The lower layer is a multi-objective model that takes carbon emissions as decision variables and considers the minimum power grid operation cost, carbon emission cost and network loss cost.

[0135] In the upper-level model, due to the discreteness and combinatorial optimization characteristics of the charging pile location and capacity construction scheme, a genetic algorithm is used for global optimization. Through operations such as selection, crossover, and mutation, the genetic algorithm can effectively explore a large solution space and find the optimal configuration of charging pile location and capacity to maximize charging load coverage and minimize charging pile construction costs.

[0136] In the lower-level model, the power grid optimization problem is characterized by continuity and multiple objectives, and the objective function involves power grid operating costs, carbon emission costs, and grid loss costs. Therefore, NSGA-III is used for optimization. NSGA-III is a multi-objective evolutionary algorithm that can handle non-dominated ranking among multiple objectives and utilizes hyperplane ranking to effectively handle optimization problems with more objectives, ensuring that the power grid achieves cost minimization and carbon emission minimization during operation.

[0137] Information exchange between the two layers is conducted through iterative nesting. Specifically, in each iteration, the location and capacity configuration of charging piles are first determined by the upper-level genetic algorithm, and this is used as the input for the lower-level grid optimization. Then, in the lower layer, NSGA-III is used to optimize grid operation, minimizing grid operating costs, carbon emission costs, and grid loss costs to obtain an optimized grid operation scheme. Finally, the optimization results of the lower layer are fed back to the upper layer, and the planning of charging piles is adjusted according to the grid operation, forming a closed-loop optimization process that gradually approaches the global optimum.

[0138] It should be noted that this interactive iterative process of optimization between upper and lower levels can not only effectively combine the coupling relationship between the transportation network and the low-carbon power grid, but also take into account both short-term and long-term goals in the planning stage, so as to achieve coordinated optimization between the construction of charging piles and the operation of the power grid.

[0139] Example 3: The above is an illustrative scheme of the charging station planning method that coordinates transportation networks and low-carbon power grids in this embodiment. It should be noted that the technical solution of the charging station planning system that coordinates transportation networks and low-carbon power grids is based on the same concept as the technical solution of the charging station planning method that coordinates transportation networks and low-carbon power grids described above. Details not described in detail in the technical solution of the charging station planning system that coordinates transportation networks and low-carbon power grids in this embodiment can be found in the description of the technical solution of the charging station planning method that coordinates transportation networks and low-carbon power grids described above.

[0140] This embodiment also provides a charging station planning system that coordinates transportation networks and low-carbon power grids, including:

[0141] The traffic network information modeling and demand analysis module is used to model urban road congestion, driving routes, electric vehicle power and charging load based on urban road network traffic conditions and electric vehicle power information, so as to obtain the spatiotemporal distribution of traffic network charging load and charging demand in different areas and time periods.

[0142] The module for calculating the spatiotemporal characteristics of carbon emissions during charging is used to calculate the load carbon flow rate and node carbon potential in the power grid based on the principle of proportional sharing in the carbon emission flow, according to the actual energy consumption of the charging load. This yields spatiotemporal distribution characteristic data of carbon emissions during the charging process of electric vehicles.

[0143] The charging station planning two-layer model construction module is used to establish a charging station planning two-layer model based on the spatiotemporal distribution of charging load in the transportation network, the charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging.

[0144] The charging station planning solution module is used to solve the two-layer model of charging station planning to obtain the optimal charging station site selection and capacity scheme.

[0145] This embodiment also provides an electronic device applicable to the charging station planning method that coordinates transportation networks and low-carbon power grids, including:

[0146] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the charging station planning method that coordinates transportation networks and low-carbon power grids as proposed in the above embodiments.

[0147] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the charging station planning method for the coordination of transportation networks and low-carbon power grids as proposed in the above embodiments.

[0148] The storage medium proposed in this embodiment and the charging station planning method for the coordination of transportation network and low-carbon power grid proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0149] Example 4, referring to Tables 1-2, is an embodiment of the present invention, providing a charging station planning method that coordinates transportation networks and low-carbon power grids. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0150] This embodiment demonstrates the feasibility of four planning schemes.

[0151] Option 1: Traditional planning method: The traditional single-level planning model is adopted, and the solution is only aimed at minimizing investment costs.

[0152] Option 2: Existing low-carbon planning methods: Introduce carbon emission constraints on the basis of traditional methods, usually based on fixed carbon emission coefficients for load allocation and site planning.

[0153] Option 3: Transportation Network-Power Grid Coordinated Planning (No Carbon Constraints): Without setting carbon emission constraints, this option considers the synergistic effect between the transportation network and the power grid to optimize the location and capacity configuration of charging stations.

[0154] Option 4: The proposed method for coordinated planning of transportation networks and low-carbon power grids: Based on the simulation of the spatiotemporal distribution of charging load and the source analysis of carbon emission flow, a two-way coupling mechanism is established to optimize the site selection and capacity of electric vehicle charging stations by comprehensively considering investment costs, load coverage and carbon emission levels.

[0155] The total new installed capacity of conventional thermal power in Schemes 1 to 4 is 535MW, and the new installed capacity of new energy units is 1595MW respectively. In the transportation network, nodes 8, 10, 14, 22, and 30 are designed as charging stations, corresponding to nodes 19, 10, 24, 8, and 15 in the IEEE 33-node standard distribution network. Nodes 2 and 9 in the power grid are photovoltaic power generation, and nodes 16 and 20 are thermal power generation. The power of each EV charging station is set at 100MW and 2800 vehicles / h, the maximum route capacity is 1200 vehicles / h, and the EV charging demand of the entire transportation network is set at 10000 vehicles / h.

[0156] To analyze the impact of the grid-low-carbon power grid collaborative planning model submitted in this embodiment on the system's economy, Table 1 compares the cost results of the four planning schemes.

[0157] Table 1 Total Planning Costs of Four Planning Schemes

[0158]

[0159] As shown in Table 1, compared with Scheme 1, Scheme 2 has increased investment costs for charging stations and grid operation costs, while carbon emission costs have decreased by 23%, resulting in an overall increase in total cost. This is mainly because adjusting the site layout to meet carbon constraints increases investment costs. Compared with Scheme 1, Scheme 3 has decreased investment costs for charging stations by 3.7%, but grid operation costs and carbon emission costs have both increased, resulting in a slightly higher total cost. This is because Scheme 3 improves the rationality of charging station layout and reduces initial investment by considering the coordination between the transportation network and the grid, but the lack of carbon emission constraints leads to increased carbon emissions, further pushing up carbon emission-related expenditures. Compared with Schemes 2 and 3, Scheme 4 shows a downward trend in charging station investment costs, grid operation costs, and carbon emission costs, with the most significant decrease in total cost. The reason for this is that the method of the present invention not only achieves more precise spatiotemporal matching between the transportation network and the power grid load, but also realizes detailed source tracing and control of carbon emissions based on the carbon emission flow theory, thereby ensuring the coverage of the transportation load while taking into account the overall system operating efficiency and carbon emission reduction benefits.

[0160] In summary, compared with existing methods, the collaborative planning method for transportation networks and low-carbon power grids proposed in this invention can achieve comprehensive performance improvement in terms of reducing investment expenditure, optimizing power grid operation efficiency, and reducing carbon emission costs. It significantly improves the problems of investment, operation, and high carbon emissions existing in traditional planning methods, and has high engineering application value and promotion prospects.

[0161] To analyze the synergistic effect of the model proposed in this embodiment on low carbon emissions, Table 2 compares the carbon emissions of the four planning schemes.

[0162] Table 2. Carbon Emissions of Four Planning Schemes

[0163]

[0164] Table 2 shows that Scheme 4 has the lowest carbon emissions. In this embodiment, Scheme 4, at the transportation level, uses refined modeling of traffic impedance to reasonably predict the spatiotemporal distribution of electric vehicle travel and charging demand, making the charging load layout more consistent with actual traffic behavior, thereby reducing unnecessary long-distance transmission losses and ineffective traffic load concentration. On the other hand, at the power grid level, Scheme 4 tracks the carbon flow rate and carbon potential distribution of nodes in real time based on the proportional sharing principle, and quantitatively assesses the carbon emission intensity of different nodes according to carbon emission flow theory. By introducing node carbon emission indicators as decision variables in the site selection and capacity configuration optimization process, priority is given to grid areas with lower carbon emission levels and cleaner energy structures for charging load access, effectively reducing the overall carbon emission level of the system.

[0165] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for planning charging stations that coordinates transportation networks and low-carbon power grids, characterized in that, include: Based on urban road network traffic conditions and electric vehicle battery information, we model urban road resistance, driving routes, electric vehicle battery power and charging load to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods. Based on the actual energy consumption of the charging load and the principle of proportional sharing in the carbon emission flow, the load carbon flow rate and node carbon potential in the power grid are calculated to obtain the spatiotemporal distribution characteristics of carbon emissions during the charging process of electric vehicles. Based on the spatiotemporal distribution of charging load in the transportation network, the spatiotemporal distribution characteristics of charging demand in different regions and time periods, and the carbon emission during electric vehicle charging, a two-layer model for charging station planning is established. The optimal charging station site selection and capacity scheme is obtained by solving the two-layer model of charging station planning.

2. The charging station planning method for the coordinated development of transportation networks and low-carbon power grids as described in claim 1, characterized in that, The model, based on urban road network traffic conditions and electric vehicle battery information, models urban road congestion, driving routes, electric vehicle battery levels, and charging load to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods, including: Taking into account the influence of road conditions and traffic lights at intersections during vehicle travel, a road segment impedance model and a node impedance model are constructed respectively. Based on the road segment impedance model and the node impedance model, an urban road resistance model is established, in which the traffic saturation of a node is determined by the road segment with the highest saturation among its adjacent road segments.

3. The charging station planning method for coordinated transportation network and low-carbon power grid as described in claim 2, characterized in that, The modeling of urban road congestion, driving routes, electric vehicle battery levels, and charging load based on urban road network traffic conditions and electric vehicle battery information, to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods, also includes: Based on the urban road resistance model, the driving path is selected with the goal of minimizing the road impedance. After determining the driving path, a model of the remaining electric vehicle charge is established to calculate the change in the electric vehicle charge during driving. A model of the electric vehicle charge waiting amount is established, and the charge waiting amount is calculated by combining the target state of charge, charging efficiency and minimum rated charge.

4. The charging station planning method for coordinated transportation network and low-carbon power grid as described in claim 3, characterized in that, The modeling of urban road congestion, driving routes, electric vehicle battery levels, and charging load based on urban road network traffic conditions and electric vehicle battery information, to obtain the spatiotemporal distribution of charging load in the traffic network and charging demand in different areas and time periods, also includes: Based on the driving path and battery level changes of electric vehicles, a model for the distribution of electric vehicle charging load on road segments of the transportation network is established to calculate the charging load distribution at the transportation network level. Furthermore, a model is established to represent the total electric vehicle charging load generated at the transportation network level in each time period.

5. The charging station planning method for coordinated transportation network and low-carbon power grid as described in claim 4, characterized in that, The process of calculating the load carbon flow rate and node carbon potential in the power grid based on the actual energy consumption of the charging load and the proportional sharing principle in the carbon emission flow, to obtain the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, includes: Considering the power system with new energy output and high-carbon units, calculate the carbon potential of a certain node based on the relevant connection relationships and the active power output of each power source. The carbon potential vector of the distribution network is calculated based on the node active power flux matrix, branch power flow distribution matrix, unit injection distribution matrix, and unit equivalent carbon emission intensity vector.

6. The charging station planning method for coordinated transportation network and low-carbon power grid as described in claim 5, characterized in that, The process of calculating the load carbon flow rate and node carbon potential in the power grid based on the actual energy consumption of the charging load and the proportional sharing principle in the carbon emission flow to obtain the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging also includes: By introducing the coupled node charging load matrix and the node actual load matrix, and applying the proportional sharing principle in the power flow tracing algorithm, the specific allocation ratio of grid node carbon emissions to electric vehicle charging power is calculated. Based on the allocation ratio, the carbon emission allocation coefficient is determined, and the carbon flow rate of each node is allocated to the carbon flow rate from the electric vehicle charging load and the carbon flow rate from other load sources, thus obtaining the spatiotemporal distribution characteristics data of carbon emissions during the electric vehicle charging process.

7. The charging station planning method for coordinated transportation network and low-carbon power grid as described in claim 6, characterized in that, The two-layer model for charging station planning, based on the spatiotemporal distribution of charging load in the transportation network, the charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, includes: Based on the spatiotemporal distribution of charging load in the transportation network, charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging, a new two-layer model for the site selection and capacity determination of electric vehicle charging stations under the coupling of transportation network and low-carbon power grid is constructed, with investment cost, charging load coverage and power grid carbon emissions as constraints.

8. A charging station planning system that coordinates transportation networks and low-carbon power grids, using the method described in any one of claims 1 to 7, characterized in that, include: The traffic network information modeling and demand analysis module is used to model urban road congestion, driving routes, electric vehicle power and charging load based on urban road network traffic conditions and electric vehicle power information, so as to obtain the spatiotemporal distribution of traffic network charging load and charging demand in different areas and time periods. The module for calculating the spatiotemporal characteristics of carbon emissions during charging is used to calculate the load carbon flow rate and node carbon potential in the power grid based on the principle of proportional sharing in the carbon emission flow, according to the actual energy consumption of the charging load. This yields spatiotemporal distribution characteristic data of carbon emissions during the charging process of electric vehicles. The charging station planning two-layer model construction module is used to establish a charging station planning two-layer model based on the spatiotemporal distribution of charging load in the transportation network, the charging demand in different regions and time periods, and the spatiotemporal distribution characteristics of carbon emissions during electric vehicle charging. The charging station planning solution module is used to solve the two-layer model of charging station planning to obtain the optimal charging station site selection and capacity scheme.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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