Electric vehicle low-carbon scheduling method, device, equipment, storage medium and program product

Abstract

The application relates to an electric vehicle low-carbon scheduling method, device, equipment, storage medium and program product. The method comprises the following steps: firstly, constructing a power and traffic double network and initializing relevant parameters, simulating user travel and charging behavior by relying on a travel chain simulation system, and generating a charging load matrix; secondly, combining the network parameters, solving the optimal power flow of the power grid through a second-order cone programming, and obtaining the power flow distribution and active power output state of the generator set in the whole network; then, determining the power transmission proportion of the power supply to the load based on the Kirchhoff current law, combining the unit output and the carbon emission intensity to weightedly calculate the equivalent carbon emission intensity of the power supply node, and further obtaining the carbon emission amount set of each load node; finally, based on the node carbon emission amount and the time-sharing charging power of the charging station, an electric-carbon price strategy is formulated to perform low-carbon scheduling. The method can realize the tracing and responsibility determination of the carbon emission of the charging load; and through a node-level electric-carbon price combination strategy, the charging behavior of the electric vehicle is guided, and the overall carbon emission is effectively reduced.

Description

Technical Field

[0001] This application relates to the field of power system and transportation system collaborative optimization technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for low-carbon dispatching of electric vehicles. Background Technology

[0002] With the development of technologies for coordinated optimization of power and transportation systems, the large-scale penetration of electric vehicles (EVs) has brought opportunities for the low-carbon development of the transportation industry. EVs possess the dual attributes of both road network transportation vehicles and mobile electrical loads on the power grid. This enables a two-way dynamic interaction mechanism between the transportation and power networks through the driving and charging behavior of EVs, forming a coordinated control relationship.

[0003] Current electric vehicle charging scheduling methods are mainly based on price signals and generally use regional average carbon emission factors to calculate the carbon emissions of electric vehicle charging. However, this method ignores the impact of complex power flow distribution within the distribution network on carbon emission sources, and lacks the problem of accurate spatiotemporal quantification of carbon emission responsibility. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for low-carbon scheduling of electric vehicles that can fully consider the randomness of electric vehicle users' travel behavior and the spatiotemporal differences in the distribution of carbon emissions from the power grid, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a low-carbon scheduling method for electric vehicles, including:

[0006] Construct power grids and transportation networks, and initialize the network parameters of the power grid, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users;

[0007] Based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated through the travel chain simulation system to obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node;

[0008] Based on the charging load matrix and network parameters, the optimal power flow of the power grid is solved by second-order cone programming to obtain the power flow distribution of the entire network and the active power output status of the generator units.

[0009] The upstream and downstream power distribution relationship is constructed according to Kirchhoff's current law to determine the power transmission ratio from each power source node to each load node; the equivalent carbon emission intensity of each power source node is calculated by weighting based on the active power output status of the generator set and the carbon emission intensity of each generator set; based on the power transmission ratio and the equivalent carbon emission intensity, the node carbon emission set composed of the carbon emission of each load node is obtained.

[0010] Based on the set of node carbon emissions and the cumulative charging power in the charging load matrix, an electricity-carbon pricing strategy combining node electricity price and node carbon price is generated, and scheduling is carried out based on the electricity-carbon pricing strategy.

[0011] In one embodiment, prior to scheduling based on an electricity carbon pricing strategy, the method further includes:

[0012] The carbon pricing strategy for electricity is fed back to the travel chain simulation system. Through closed-loop iteration using an improved particle swarm optimization algorithm, the optimal carbon pricing strategy for electricity is obtained until the rate of change of carbon emissions in the travel chain simulation system is less than a preset threshold.

[0013] In one embodiment, the network parameters of the power network include the set of nodes, the set of lines, the line conductance, the susceptance, the upper and lower limits of node voltage, and the carbon emission intensity of each generator unit; the network parameters of the transportation network include the set of traffic nodes, the set of roads, the road segment length, the free flow period, the road segment capacity, the service capacity of fast charging stations, the expected road travel time calculated based on the congestion effect formula, and the queuing time of fast charging stations calculated based on the queuing theory formula.

[0014] In one embodiment, based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated using a travel chain simulation system to obtain a charging load matrix of time-of-use cumulative charging power for each charging station node, including:

[0015] Based on the initialized parameters, Monte Carlo sampling and a preset probability distribution function are used to determine the user's initial departure time, and the migration destination is determined by the Markov probability transition matrix.

[0016] Search for the shortest path in the transportation network, calculate the migration time, distance, and post-migration charge; if the charge is below a first preset threshold, select a target fast charging station for fast charging based on a generalized cost model; if the post-migration charge is above a second preset threshold, charge according to the preset slow charging power and parking time.

[0017] The cumulative charging power of each charging station node in each time period is counted to form a charging load matrix.

[0018] In one embodiment, based on the charging load matrix and network parameters, the optimal power flow of the power grid is solved using second-order cone programming to obtain the power flow distribution of the entire network and the active power output state of the generator units, including:

[0019] By using second-order cone programming, an optimization model is constructed with the goal of minimizing the overall power generation cost. The network parameters and charging load matrix are input into the optimization model, and active power balance constraints, reactive power balance constraints, rotating second-order cone inequality constraints, unit output constraints, and node voltage boundary constraints are applied. The power flow distribution of the entire network and the active power output state of the generator units are obtained by solving the problem.

[0020] In one embodiment, based on the active power output status of the generator sets and the carbon emission intensity of each generator set, the equivalent carbon emission intensity of each power node is calculated by weighting, including:

[0021] Based on the active power output status of each generator set, the total active power output of the power supply node is calculated.

[0022] The carbon emission intensity of each power node is obtained by weighting the carbon emission intensity of each generator unit by using the proportion of the active power output of a single generator unit to the total active power output of the power node.

[0023] Secondly, this application also provides a low-carbon dispatching device for electric vehicles, comprising:

[0024] The module is used to build power networks and transportation networks, and initialize the network parameters of the power network, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users.

[0025] The matrix generation module is used to simulate the travel and charging behavior of electric vehicle users through the travel chain simulation system based on the initialized parameters, and obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node;

[0026] The solution module is used to solve the optimal power flow of the power grid based on the charging load matrix and network parameters through second-order cone programming, so as to obtain the power flow distribution of the entire network and the active power output status of the generator units.

[0027] The acquisition module is used to construct the upstream and downstream power distribution relationship according to Kirchhoff's current law, determine the power transmission ratio of each power source node to each load node; calculate the equivalent carbon emission intensity of each power source node by weighting according to the active power output status of the generator set and the carbon emission intensity of each generator set; and obtain the node carbon emission set composed of the carbon emission of each load node based on the power transmission ratio and the equivalent carbon emission intensity.

[0028] The scheduling module is used to generate an electricity-carbon pricing strategy that combines node electricity price and node carbon price based on the node carbon emission set and the cumulative charging power in the charging load matrix, and to perform scheduling based on the electricity-carbon pricing strategy.

[0029] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0030] Construct power grids and transportation networks, and initialize the network parameters of the power grid, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users;

[0031] Based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated through the travel chain simulation system to obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node;

[0032] Based on the charging load matrix and network parameters, the optimal power flow of the power grid is solved by second-order cone programming to obtain the power flow distribution of the entire network and the active power output status of the generator units.

[0033] The upstream and downstream power distribution relationship is constructed according to Kirchhoff's current law to determine the power transmission ratio from each power source node to each load node; the equivalent carbon emission intensity of each power source node is calculated by weighting based on the active power output status of the generator set and the carbon emission intensity of each generator set; based on the power transmission ratio and the equivalent carbon emission intensity, the node carbon emission set composed of the carbon emission of each load node is obtained.

[0034] Based on the set of node carbon emissions and the cumulative charging power in the charging load matrix, an electricity-carbon pricing strategy combining node electricity price and node carbon price is generated, and scheduling is carried out based on the electricity-carbon pricing strategy.

[0035] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0036] Construct power grids and transportation networks, and initialize the network parameters of the power grid, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users;

[0037] Based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated through the travel chain simulation system to obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node;

[0038] Based on the charging load matrix and network parameters, the optimal power flow of the power grid is solved by second-order cone programming to obtain the power flow distribution of the entire network and the active power output status of the generator units.

[0039] The upstream and downstream power distribution relationship is constructed according to Kirchhoff's current law to determine the power transmission ratio from each power source node to each load node; the equivalent carbon emission intensity of each power source node is calculated by weighting based on the active power output status of the generator set and the carbon emission intensity of each generator set; based on the power transmission ratio and the equivalent carbon emission intensity, the node carbon emission set composed of the carbon emission of each load node is obtained.

[0040] Based on the set of node carbon emissions and the cumulative charging power in the charging load matrix, an electricity-carbon pricing strategy combining node electricity price and node carbon price is generated, and scheduling is carried out based on the electricity-carbon pricing strategy.

[0041] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0042] Construct power grids and transportation networks, and initialize the network parameters of the power grid, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users;

[0043] Based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated through the travel chain simulation system to obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node;

[0044] Based on the charging load matrix and network parameters, the optimal power flow of the power grid is solved by second-order cone programming to obtain the power flow distribution of the entire network and the active power output status of the generator units.

[0045] The upstream and downstream power distribution relationship is constructed according to Kirchhoff's current law to determine the power transmission ratio from each power source node to each load node; the equivalent carbon emission intensity of each power source node is calculated by weighting based on the active power output status of the generator set and the carbon emission intensity of each generator set; based on the power transmission ratio and the equivalent carbon emission intensity, the node carbon emission set composed of the carbon emission of each load node is obtained.

[0046] Based on the set of node carbon emissions and the cumulative charging power in the charging load matrix, an electricity-carbon pricing strategy combining node electricity price and node carbon price is generated, and scheduling is carried out based on the electricity-carbon pricing strategy.

[0047] The aforementioned electric vehicle low-carbon dispatching method, device, computer equipment, computer-readable storage medium, and computer program product first construct and initialize parameters related to the power and transportation dual networks and electric vehicle user travel behavior. Relying on a travel chain simulation system, it simulates user travel and charging behavior, generating a time-sharing cumulative charging power load matrix for each charging station node. Then, combining network parameters, it solves for the optimal power flow of the power grid using second-order cone programming, obtaining the power flow distribution of the entire network and the active power output status of generator units. The travel chain simulation accurately depicts the spatiotemporal distribution characteristics of electric vehicle charging loads, and the second-order cone programming achieves efficient solution for the optimal power flow of the power grid, ensuring the accuracy and efficiency of the power flow calculation. Subsequently, based on Kirchhoff's current law, it determines the power transmission ratio of the power source to the load, and calculates the equivalent carbon emission intensity of the power source node by weighting the unit output and carbon emission intensity, thereby obtaining the carbon emission set of each load node. This enables source tracing and accountability for carbon emissions from charging loads. Finally, based on the node carbon emission amount and the time-sharing charging power of the charging station, it formulates an electricity-carbon pricing strategy combining node electricity price and carbon price, and uses this as the basis for carrying out low-carbon dispatching of electric vehicles. By guiding electric vehicle charging behavior through a combination strategy of nodal electricity carbon pricing, we can achieve low-carbon scheduling of electric vehicle charging load, effectively reduce overall carbon emissions, optimize power grid power flow distribution, improve the efficiency of power resource allocation, balance the safe and economical operation of the power system with the achievement of dual carbon objectives, and adapt to the actual travel and charging needs of electric vehicle users, thus achieving coordinated optimization of source, grid, load and carbon. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a diagram illustrating the application environment of a low-carbon scheduling method for electric vehicles in one embodiment.

[0050] Figure 2 This is a flowchart illustrating a low-carbon scheduling method for electric vehicles in one embodiment;

[0051] Figure 3 This is a structural block diagram of an electric vehicle low-carbon dispatching device in one embodiment;

[0052] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0055] The low-carbon scheduling method for electric vehicles provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on the cloud or other network servers. First, the power and transportation dual networks and parameters related to electric vehicle user travel behavior are constructed and initialized. Then, relying on a travel chain simulation system, user travel and charging behavior are simulated to generate a time-sharing cumulative charging power load matrix for each charging station node. Next, combined with network parameters, the optimal power flow of the power grid is solved using second-order cone programming to obtain the power flow distribution of the entire network and the active power output status of generator units. Subsequently, based on Kirchhoff's current law, the power transmission ratio of the power source to the load is determined. The equivalent carbon emission intensity of the power source node is calculated by weighting the unit output and carbon emission intensity, thereby obtaining the carbon emission set of each load node. Finally, based on the node carbon emission and the time-sharing charging power of the charging station, an electricity-carbon pricing strategy combining node electricity price and carbon price is formulated, and this is used as the basis for low-carbon scheduling of electric vehicles.

[0056] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0057] In one exemplary embodiment, such as Figure 2 As shown, a low-carbon scheduling method for electric vehicles is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 202 to 210. Wherein:

[0058] Step 202: Construct the power network and transportation network, and initialize the network parameters of the power network, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users;

[0059] Among them, the network parameters of the power network refer to the basic parameters describing the characteristics and operating status of each component of the power network, including generator parameters (upper and lower limits of active power output, carbon emission intensity), grid component parameters (line resistance / reactance, transformer capacity, upper and lower limits of node voltage), charging station node parameters (upper and lower limits of charging power, location of node connected to the grid), and conventional load parameters (time-of-use load values), etc. The network parameters of the transportation network refer to the basic parameters describing the spatial and traffic characteristics of each element of the transportation network, including road parameters (segment length, travel time), node parameters (spatial coordinates of each travel destination / charging station, node connection relationships), road network topology parameters (connection structure of road segments and intersections), and charging station spatial layout parameters (location of each charging station, number / type of charging piles), etc. The travel behavior parameters of electric vehicle users refer to the basic statistical / calibration parameters characterizing the travel and charging behavior patterns of electric vehicle users, including the user's initial travel location, initial charge, travel mode proportion, parking time distribution parameters at different locations, Markov location transition probability matrix, charging demand threshold, etc.

[0060] Step 204: Based on the initialized parameters, simulate the travel and charging behavior of electric vehicle users through the travel chain simulation system to obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node;

[0061] The travel chain simulation system refers to a dedicated simulation platform that integrates user travel behavior modeling, traffic network traffic simulation, dynamic updating of electric vehicle charge status, and charging behavior decision-making. Based on initial parameters of the power / traffic network and user behavior, it can reconstruct the entire behavioral trajectory of an electric vehicle user from the start to the end of their trip, serving as a core tool for characterizing the spatiotemporal distribution of charging load. Travel behavior refers to the all-time and all-space movement of electric vehicle users within the traffic network, including user location migration, route selection, parking duration at different destinations, and travel mode switching. Charging behavior refers to the charging-related behaviors of electric vehicle users at charging stations under constraints such as meeting travel charge requirements and parking time windows, including charging triggering (e.g., initiating charging when the charge level is below a threshold), charging power selection, charging duration determination, and charging completion determination.

[0062] Step 206: Based on the charging load matrix and network parameters, solve the optimal power flow of the power grid through second-order cone programming to obtain the power flow distribution of the entire network and the active power output status of the generator units;

[0063] Second-order cone programming, a convex optimization method and an important branch of mathematical programming, transforms the nonlinear, non-convex constraints in power flow calculations into second-order cone convex constraints. It can efficiently and stably solve the optimal power flow problem in a power grid, guaranteeing a globally optimal solution and avoiding the problems of traditional optimization methods easily getting trapped in local optima and having low solution efficiency. The overall power flow distribution refers to the overall distribution of power (active and reactive) transmission states and voltage and phase angle states of all transmission / distribution lines, transformers, buses, and other components in the power network. It encompasses the power flow and electrical quantity states on the generation side, grid side, and load side, and is a core result reflecting the overall operating state of the power grid.

[0064] Step 208: Construct the upstream and downstream power distribution relationship according to Kirchhoff's current law, and determine the power transmission ratio from each power source node to each load node; calculate the equivalent carbon emission intensity of each power source node by weighting based on the active power output status of the generator set and the carbon emission intensity of each generator set; obtain the node carbon emission set composed of the carbon emission of each load node based on the power transmission ratio and the equivalent carbon emission intensity.

[0065] Kirchhoff's Current Law is a fundamental electrical law for power network analysis. Its core principle is that the sum of the inflow currents to any node in a power network equals the sum of the outflow currents. In terms of power, this can be expressed as the sum of the active power flowing into each node equals the sum of the active power flowing out. Carbon emission intensity refers to the amount of carbon emissions generated per unit of active power output by each generator unit, and it is a fundamental parameter characterizing the carbon emission characteristics of generator units.

[0066] Step 210: Based on the set of node carbon emissions and the cumulative charging power in the charging load matrix, generate an electricity-carbon pricing strategy that combines node electricity price and node carbon price, and perform scheduling based on the electricity-carbon pricing strategy.

[0067] Among them, the electricity carbon pricing strategy refers to a combined price control scheme that integrates nodal electricity prices and nodal carbon prices, combining the energy cost of electricity consumption with the environmental cost of carbon emissions, and ultimately reflecting the comprehensive electricity cost of each charging station node at different times.

[0068] The aforementioned low-carbon dispatching method for electric vehicles first constructs and initializes parameters related to the power and transportation networks and the travel behavior of electric vehicle users. It then uses a travel chain simulation system to simulate user travel and charging behavior, generating a time-of-use cumulative charging power load matrix for each charging station node. Next, combining network parameters, it solves for the optimal power flow of the grid using second-order cone programming, obtaining the power flow distribution across the entire network and the active power output status of generator units. The travel chain simulation accurately depicts the spatiotemporal distribution characteristics of electric vehicle charging loads, and the second-order cone programming enables efficient solution of the optimal power flow of the grid, ensuring the accuracy and efficiency of the power flow calculation. Subsequently, based on Kirchhoff's current law, it determines the power transmission ratio of the power source to the load. Combining the unit output and carbon emission intensity, it calculates the equivalent carbon emission intensity of the power source node, thereby obtaining the carbon emission set of each load node. This enables source tracing and accountability for carbon emissions from charging loads. Finally, based on the node carbon emission amount and the time-of-use charging power of the charging station, it formulates an electricity-carbon pricing strategy that combines node electricity price and carbon price, and uses this as the basis for low-carbon dispatching of electric vehicles. By guiding electric vehicle charging behavior through a combination strategy of nodal electricity carbon pricing, we can achieve low-carbon scheduling of electric vehicle charging load, effectively reduce overall carbon emissions, optimize power grid power flow distribution, improve the efficiency of power resource allocation, balance the safe and economical operation of the power system with the achievement of dual carbon objectives, and adapt to the actual travel and charging needs of electric vehicle users, thus achieving coordinated optimization of source, grid, load and carbon.

[0069] In one embodiment, prior to scheduling based on an electricity carbon pricing strategy, the method further includes:

[0070] The carbon pricing strategy for electricity is fed back to the travel chain simulation system. Through closed-loop iteration using an improved particle swarm optimization algorithm, the optimal carbon pricing strategy for electricity is obtained until the rate of change of carbon emissions in the travel chain simulation system is less than a preset threshold.

[0071] Specifically, after generating the electricity-carbon pricing strategy that combines node electricity prices and node carbon prices, and before formally implementing scheduling, a closed-loop iterative optimization step is added. The initially formulated electricity-carbon pricing strategy is fed back to the travel chain simulation system, driving the system to re-simulate the travel and charging behavior of electric vehicle users. At the same time, an improved particle swarm optimization algorithm is used to iteratively optimize and adjust the electricity-carbon pricing strategy. This feedback-simulation-optimization process is repeated until the carbon emission change rate output by the travel chain simulation system drops below a preset threshold. The electricity-carbon pricing strategy determined at this point is the optimal electricity-carbon pricing strategy that adapts to the low-carbon scheduling requirements.

[0072] In the above embodiments, by adding a closed-loop iterative link between the electricity carbon pricing strategy and the travel chain simulation system, and combining the efficient optimization characteristics of the improved particle swarm optimization algorithm, dynamic correction and precise optimization of the electricity carbon pricing strategy are achieved, effectively avoiding the problem of insufficient adaptability between the initial electricity carbon pricing strategy and users' actual charging behavior. By using the threshold constraint of the carbon emission change rate, the low-carbon regulation effect of the optimal electricity carbon pricing strategy is guaranteed, which can guide users' charging behavior towards a low-carbon direction to the greatest extent. At the same time, the application of the improved particle swarm optimization algorithm improves the efficiency and solution accuracy of iterative optimization, and can quickly converge to the optimal solution, making the final electricity carbon pricing strategy more in line with the actual needs of low-carbon grid operation and user travel charging, further enhancing the low-carbon effect of charging load scheduling and the practicality of the strategy.

[0073] In one embodiment, the network parameters of the power network include the set of nodes, the set of lines, the line conductance, the susceptance, the upper and lower limits of node voltage, and the carbon emission intensity of each generator unit; the network parameters of the transportation network include the set of traffic nodes, the set of roads, the road segment length, the free flow period, the road segment capacity, the service capacity of fast charging stations, the expected road travel time calculated based on the congestion effect formula, and the queuing time of fast charging stations calculated based on the queuing theory formula.

[0074] Specifically, power network data construction:

[0075] The power distribution system of the target area is abstracted as a directed graph. ,in, For the set of nodes in the distribution network, For any node, the set of lines is defined. ,definition For connection at node A set of indices for all power generation units. By monitoring or consulting the typical carbon emission factors of the corresponding generator sets in real time, the carbon emission intensity of each generator is obtained. Represents a node The The carbon emission intensity of each generator. Read network parameters to obtain the connection between any two nodes. and nodes Line conductance susceptance Upper and lower limits of node voltage .

[0076] Traffic network data construction:

[0077] Abstract the traffic network of the target area into a graph. ,in As a traffic node, The actual roads connecting traffic nodes, including road segment length. Free-flow time Current traffic flow on this road section Total capacity of road section Based on the congestion effect, on roads The estimated travel time can be expressed as:

[0078]

[0079] Traffic map edge weight Defined as estimated travel time .use This indicates the nodes of each electric vehicle fast charging station, at the [number]th [time / location]. The queuing time at a fast charging station can be expressed using Davidson's formula from queuing theory, which is:

[0080]

[0081] in, Average charging time For the current station load, For the service capacity of the station, This represents the congestion coefficient, which is determined by the congestion situation in a specific area.

[0082] In the above embodiments, by defining the network parameters of the power and transportation networks in a specific and detailed manner, the parameter input of the scheduling method becomes more standardized and practical, avoiding simulation and calculation deviations caused by parameter ambiguity. The network parameters directly cover the core indicators of power grid topology, operational constraints, and carbon emission accounting, laying a precise data foundation for optimal power flow solutions and node carbon emission quantification. The transportation network not only incorporates basic parameters of road networks and fast charging stations, but also integrates dynamic parameters such as road travel time considering congestion effects and fast charging station queuing time based on queuing theory. This significantly improves the fit between the travel chain simulation and actual traffic conditions and charging scenarios, enabling a more accurate depiction of the travel and charging behavior of electric vehicle users. Consequently, the generated charging load matrix becomes more realistic, ultimately improving the computational accuracy and practical application value of the entire low-carbon scheduling method, and ensuring the scientific nature and feasibility of the scheduling strategy.

[0083] In one embodiment, based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated using a travel chain simulation system to obtain a charging load matrix of time-of-use cumulative charging power for each charging station node, including:

[0084] Based on the initialized parameters, Monte Carlo sampling and a preset probability distribution function are used to determine the user's initial departure time, and the migration destination is determined by the Markov probability transition matrix.

[0085] Search for the shortest path in the transportation network, calculate the migration time, distance, and post-migration charge; if the charge is below a first preset threshold, select a target fast charging station for fast charging based on a generalized cost model; if the post-migration charge is above a second preset threshold, charge according to the preset slow charging power and parking time.

[0086] The cumulative charging power of each charging station node in each time period is counted to form a charging load matrix.

[0087] Specifically, assuming the current number of electric vehicles in the target area is... .initialization Electric vehicle users, each user They all have the following attributes: Brand type Battery capacity Fast charging rated power Energy consumption per unit distance Time value coefficient , No. Node location during the second migration Charge .in , , Electric vehicle brand type related, It follows a normal distribution, reflecting the user's sensitivity to time. Assume each user... initial charge Follows a normal distribution The value is in Within the interval. Among them, The average initial charge. Let represent the variance. In the simulation, three travel modes are defined as HWH, HOH, and HWOH, where H represents home, W represents workplace, and O represents other locations. Based on actual data, to obtain the proportion of each travel mode, a travel mode is randomly assigned to each electric vehicle user.

[0088] The Monte Carlo sampling method is used to transform macroscopic statistical patterns into specific microscopic individual journeys. The time step is set to 1 hour. For the... For each user, their travel patterns are first randomly selected. , , , and Secondly, the initial departure time is obtained by sampling the Burr Type XII probability distribution function. Its probability density function is expressed as:

[0089]

[0090] in, For shape parameters, The scaling parameter was obtained by fitting resident travel survey data for the city. Subsequently, initial locations were randomly selected based on the vehicle number distribution in each area. .

[0091] After determining the initial state of the electric vehicles, the simulation of 24-hour migration of electric vehicle users began. For the first... The first user's The next migration, based on the current moment The lower position Using Markov probability transition matrix Decide on the destination for the next migration For each mode of transportation Each has a corresponding transition probability matrix, which can be used... Indicates at time The following are the travel modes: Electric vehicle users from location Migrate to location The probability of [the path to the destination]. After determining the starting and ending positions, Dijkstra's algorithm is used to search for the shortest path. Given a set of paths, the resulting migration time It can be represented as:

[0092]

[0093] achievable The migration route can be represented as:

[0094]

[0095] Electric vehicle users' charge after migration It can be represented as:

[0096]

[0097] Users exhibit two charging behaviors: one is that when their battery level drops below a certain threshold, they immediately head to a fast charging station; the other is that upon arriving at their home or workplace, if their battery level falls below a certain threshold, they will engage in slow charging. Firstly, for fast charging behavior, monitoring is implemented for each user. At various times Charge status ,when At that time, the vehicle is put into "search for fast charging stations" mode to evaluate all available fast charging stations. And select the site with the lowest generalized cost. The decision model is expressed as:

[0098]

[0099] in, The nodal electricity price is issued by the higher-level decision-making body. The node carbon price is issued by the higher-level decision-making body. See S3 for the specific calculation steps. The current power requirement can be expressed as:

[0100]

[0101] Assuming a user needs to fully charge their phone at a fast charging station before leaving, what is the charging time for a full charge? It can be represented as:

[0102]

[0103] Simultaneously record the first Individual users During this period at the station Real-time charging power .

[0104] For slow charging, the parking time at different destinations follows a different distribution depending on the nature of the location. When the destination is home, the parking time... It follows a Burr Type XII distribution, with the following probability density function:

[0105]

[0106] in, These are the fitting parameters used to simulate the characteristics of users staying at night for extended periods.

[0107] Parking time at the user's workplace It follows a stable distribution, and its probability density function can be expressed as:

[0108]

[0109] in, It was obtained by fitting actual data.

[0110] Parking time of users in other locations It follows a generalized extreme value distribution, and its probability density function can be expressed as:

[0111]

[0112]

[0113] in, It was obtained by fitting actual data.

[0114] If there is a slow-charging station at your destination And when the electric vehicle user arrives at the destination Charge If the user chooses to charge, the time required for the battery to fully charge can be expressed as:

[0115]

[0116] in, The power output for the slow charging station is set to 7kW. If... This means the user can fully charge the battery and leave. .like When the user leaves It can be represented as:

[0117]

[0118] Simultaneously record the first Individual users During this period at the station Real-time charging power .

[0119] Obtain the charging load matrix for the entire time period:

[0120] After the simulation is completed, the system outputs the charging load matrix for the entire period. Statistics on each charging station connected to the power grid. In each time period cumulative charging power for:

[0121]

[0122] in, For all in Always A user group for charging at the charging station.

[0123] In the above embodiments, the combination of Monte Carlo sampling, Markov probability transition matrix, and shortest path search makes the simulation of electric vehicle users' initial departure time, migration destination, and driving process more closely resemble the randomness and regularity of actual travel. The dynamic update of the vehicle's battery status and travel behavior are deeply linked. Based on the differentiated charging behavior decision based on dual thresholds of the vehicle's battery status, the system accurately distinguishes between users' urgent fast charging needs and regular slow charging needs. The method of selecting fast charging stations and implementing slow charging according to preset parameters, combined with the generalized cost model, is more in line with the actual charging selection logic of users. Furthermore, the method of statistically analyzing the cumulative charging power of charging station nodes in different time periods allows the generated charging load matrix to accurately depict the spatiotemporal distribution characteristics of the charging load, providing high-fitness and high-precision load data support for subsequent grid optimal power flow calculation and carbon flow tracking, thus improving the realism and reliability of the travel chain simulation as a whole.

[0124] In one embodiment, based on the charging load matrix and network parameters, the optimal power flow of the power grid is solved using second-order cone programming to obtain the power flow distribution of the entire network and the active power output state of the generator units, including:

[0125] By using second-order cone programming, an optimization model is constructed with the goal of minimizing the overall power generation cost. The network parameters and charging load matrix are input into the optimization model, and active power balance constraints, reactive power balance constraints, rotating second-order cone inequality constraints, unit output constraints, and node voltage boundary constraints are applied. The power flow distribution of the entire network and the active power output state of the generator units are obtained by solving the problem.

[0126] Specifically, the process of solving the optimal power flow of the power grid through second-order cone programming has been refined and implemented. With the minimum power generation cost of the entire network as the core optimization objective, an optimal power flow optimization model of the power grid is constructed based on second-order cone programming. The network parameters and charging load matrix are used as the core input data of the model. At the same time, multi-dimensional hard constraints of power grid operation, such as active power balance, reactive power balance, rotating second-order cone inequality, unit output and node voltage boundary, are applied to the model. By solving the constraint optimization model, the power flow distribution of the entire network and the active power output status of the generator units can be accurately obtained.

[0127] In the above embodiments, an optimization model is constructed with the goal of minimizing the overall power generation cost, taking into account the economic efficiency of power grid operation. The application of various power system operation constraints strictly follows the power grid safety operation criteria, ensuring the physical rationality and engineering feasibility of the optimal power flow solution from the source. The rotating second-order cone inequality constraint transforms the nonlinear and non-convex problem in power grid power flow calculation into a convex optimization problem. Combined with the solution characteristics of second-order cone programming, it can not only efficiently converge to the global optimum, but also avoid the problems of traditional methods being prone to getting trapped in local optima and having poor solution stability, thus improving the efficiency and accuracy of power flow calculation. Using the charging load matrix as the core input, the obtained power flow distribution and active power output status of the entire network can accurately reflect the actual operating status of the power grid after the charging load is connected, providing accurate and reliable basic data for subsequent carbon flow tracking and node carbon emission accounting.

[0128] In one embodiment, based on the active power output status of the generator sets and the carbon emission intensity of each generator set, the equivalent carbon emission intensity of each power node is calculated by weighting, including:

[0129] Based on the active power output status of each generator set, the total active power output of the power supply node is calculated.

[0130] The carbon emission intensity of each power node is obtained by weighting the carbon emission intensity of each generator unit by using the proportion of the active power output of a single generator unit to the total active power output of the power node.

[0131] Specifically, first, establish the source-load mapping relationship:

[0132]

[0133] in, Each element in Represents a node in the power grid The active power injected by the generator set is called the node. Active power injected by the power source . Each element in Represents a node in the power grid The total power consumed by the load can be expressed as , Represents a node The active power of the base's conventional load. Represents a node The total active charging load of the electric vehicle charging station connected at the location. The upstream distribution matrix is ​​a coefficient matrix constructed based on Kirchhoff's current law, describing how the power of each node is channeled in. The downstream distribution matrix is ​​a sparse matrix that describes how power flows out of each node. Therefore, a power flow tracing matrix can be defined. Each of its elements Indicates the first Of the power injected into the first node, what percentage is actually delivered to the second node? Each node.

[0134] Secondly, calculate the carbon emission vector allocated to each load node. Each element Represents a node The mathematical expression for the total carbon dioxide emissions corresponding to the electrical energy consumed at the current moment is:

[0135]

[0136] in, It is a diagonal matrix, with diagonal elements Represents a node The equivalent carbon emission intensity of the injected power at that node, which is obtained by the weighted average of all generator units at that node, can be expressed as:

[0137]

[0138] in, Indicates the first The active power of the generator set. Indicates the first The set of all generator sets contained in a node. Represents a node Inner Carbon emission intensity per unit active power of a generator set. If node If no motor is injected, then .

[0139] In the above embodiments, by using the proportion of active power output of generator units as the core weighting coefficient, the calculation of the equivalent carbon emission intensity of the power node is deeply linked to the actual output status of the units, which accurately reflects the impact of the output contribution of units with different carbon emission intensities within the power node on the overall carbon emission characteristics, and ensures the calculation accuracy and physical rationality of the equivalent carbon emission intensity.

[0140] In one embodiment, a low-carbon scheduling method for electric vehicles is presented. This method is implemented by constructing a two-layer closed-loop iterative system: the upper layer is a grid optimization and carbon flow tracking pricing layer based on second-order cone programming (SOCP), and the lower layer is a simulation layer of electric vehicle user micro-behavior based on trip chains. The two layers interact and iteratively converge through an improved particle swarm optimization (PSO) algorithm. Specific steps include:

[0141] S1: System Initialization and Data Modeling

[0142] Before performing scheduling calculations, it is necessary to first construct a refined transportation-power grid coupled network topology model and complete the initialization of basic parameters.

[0143] S11: Power Network Data Construction

[0144] The power distribution system of the target area is abstracted as a directed graph. ,in, For the set of nodes in the distribution network, For any node, the set of lines is defined. ,definition For connection at node A set of indices for all power generation units. By monitoring or consulting the typical carbon emission factors of the corresponding generator sets in real time, the carbon emission intensity of each generator is obtained. Represents a node The The carbon emission intensity of each generator. Read network parameters to obtain the connection between any two nodes. and nodes Line conductance susceptance Upper and lower limits of node voltage .

[0145] S12: Traffic Network Data Construction:

[0146] Abstract the traffic network of the target area into a graph. ,in As a traffic node, The actual roads connecting traffic nodes, including road segment length. Free-flow time Current traffic flow on this road section Total capacity of road section Based on the congestion effect, on roads The estimated travel time can be expressed as:

[0147] (1)

[0148] Traffic map edge weight Defined as estimated travel time .use This indicates the nodes of each electric vehicle fast charging station, at the [number]th [time / location]. The queuing time at a fast charging station can be expressed using Davidson's formula from queuing theory, which is:

[0149] (2)

[0150] in, Average charging time For the current station load, For the service capacity of the station, This represents the congestion coefficient, which is determined by the congestion situation in a specific area.

[0151] S13: User travel behavior modeling:

[0152] Assuming the current number of electric vehicles in the target area is .initialization Electric vehicle users, each user They all have the following attributes: Brand type Battery capacity Fast charging rated power Energy consumption per unit distance Time value coefficient , No. Node location during the second migration Charge .in , , Electric vehicle brand type related, It follows a normal distribution, reflecting the user's sensitivity to time. Assume each user... initial charge Follows a normal distribution The value is in Within the interval. Among them, The average initial charge. Let represent the variance. In the simulation, three travel modes are defined as HWH, HOH, and HWOH, where H represents home, W represents workplace, and O represents other locations. Based on actual data, to obtain the proportion of each travel mode, a travel mode is randomly assigned to each electric vehicle user.

[0153] S2: Lower Layer – Electric Vehicle User Behavior Simulation Based on the Travel Chain

[0154] Simulate the travel and charging behavior of each electric vehicle over a 24-hour period in a simulation environment.

[0155] S21: Generate travel chains:

[0156] The Monte Carlo sampling method is used to transform macroscopic statistical patterns into specific microscopic individual journeys. The time step is set to 1 hour. For the... For each user, their travel patterns are first randomly selected. , , , and Secondly, the initial departure time is obtained by sampling the Burr Type XII probability distribution function. Its probability density function is expressed as:

[0157] (3)

[0158] in, For shape parameters, The scaling parameter was obtained by fitting resident travel survey data for the city. Subsequently, initial locations were randomly selected based on the vehicle number distribution in each area. .

[0159] After determining the initial state of the electric vehicles, the simulation of 24-hour migration of electric vehicle users began. For the first... The first user's The next migration, based on the current moment The lower position Using Markov probability transition matrix Decide on the destination for the next migration For each mode of transportation Each has a corresponding transition probability matrix, which can be used... Indicates at time The following are the travel modes: Electric vehicle users from location Migrate to location The probability of [the path to the destination]. After determining the starting and ending positions, Dijkstra's algorithm is used to search for the shortest path. Given a set of paths, the resulting migration time It can be represented as:

[0160] (4)

[0161] achievable The migration route can be represented as:

[0162] (5)

[0163] Electric vehicle users' charge after migration It can be represented as:

[0164] (6)

[0165] Users exhibit two charging behaviors: one is that when their battery level drops below a certain threshold, they immediately head to a fast charging station; the other is that upon arriving at their home or workplace, if their battery level falls below a certain threshold, they will engage in slow charging. Firstly, for fast charging behavior, monitoring is implemented for each user. At various times Charge status When the charge level is lower than a first preset threshold, i.e. when At that time, the vehicle is put into "search for fast charging stations" mode to evaluate all available fast charging stations. And select the site with the lowest generalized cost. The decision model is expressed as:

[0166] (7)

[0167] in, The nodal electricity price is issued by the higher-level decision-making body. The node carbon price is issued by the higher-level decision-making body. See S3 for the specific calculation steps. The current power requirement can be expressed as:

[0168] (8)

[0169] Assuming a user needs to fully charge their phone at a fast charging station before leaving, what is the charging time for a full charge? It can be represented as:

[0170] (9)

[0171] Simultaneously record the first Individual users During this period at the station Real-time charging power .

[0172] For slow charging, the parking time at different destinations follows a different distribution depending on the nature of the location. When the destination is home, the parking time... It follows a Burr Type XII distribution, with the following probability density function:

[0173] (10)

[0174] in, These are the fitting parameters used to simulate the characteristics of users staying at night for extended periods.

[0175] Parking time at the user's workplace It follows a stable distribution, and its probability density function can be expressed as:

[0176] (11)

[0177] in, It was obtained by fitting actual data.

[0178] Parking time of users in other locations It follows a generalized extreme value distribution, and its probability density function can be expressed as:

[0179] (12)

[0180] (13)

[0181] in, It was obtained by fitting actual data.

[0182] If there is a slow-charging station at your destination And when the electric vehicle user arrives at the destination When the charge is higher than the second preset threshold, that is If the user chooses to charge, the time required for the battery to fully charge can be expressed as:

[0183] (14)

[0184] in, The power output for the slow charging station is set to 7kW. If... This means the user can fully charge the battery and leave. .like When the user leaves It can be represented as:

[0185] (15)

[0186] Simultaneously record the first Individual users During this period at the station Real-time charging power .

[0187] S22: Obtain the charging load matrix for the entire time period:

[0188] After the simulation is completed, the system outputs the charging load matrix for the entire period. Statistics on each charging station connected to the power grid. In each time period cumulative charging power for:

[0189] (16)

[0190] in, For all in Always A user group for charging at the charging station.

[0191] The charging load matrix It will be passed as a key parameter to the upper-level optimization model.

[0192] S3: Upper Layer – Optimal Power Flow and Carbon Price Calculation

[0193] This step receives load data from the lower layer for each time period. Mathematical optimization methods are used to calculate the operating status of the power grid, and carbon emissions are tracked based on power flow results to generate new price signals.

[0194] S31: Optimal power flow calculation based on second-order cone programming (SOCP):

[0195] Traditional AC Optimal Power Flow (ACOPF) is a non-convex problem, making it difficult to find the global optimum. This invention introduces intermediate variables to relax the voltage phasors, transforming it into a convex second-order cone programming problem.

[0196] First, to linearize the product of voltage magnitude and phase angle, an intermediate variable is defined. , , ,in, For node indexing in the power grid, They are nodes and nodes voltage amplitude, Represents a node and nodes The voltage phase angle between them.

[0197] Secondly, an upper-level optimization model is established. The optimization objective of the upper-level model is to minimize the total power generation cost of the entire network, and its mathematical expression is:

[0198] (17)

[0199] in, Indicates the first The active power of the generator set. Indicates the first The reactive power of the generator set. Indicates the first The set of all generator sets contained in a node. and Let be the generation cost functions for active power and reactive power, respectively. To accurately reflect the operating economy of the generator set and satisfy the convexity requirement of the second-order cone programming, they are expressed in the form of quadratic polynomials:

[0200] (18)

[0201] in, The first The active power cost factor of a generator set reflects the nonlinear relationship between fuel consumption and output power. This is the reactive power cost coefficient.

[0202] Based on the intermediate variables defined above, the originally nonlinear nodal power balance equations are transformed into linear constraints. For any node in the distribution network... The following active and reactive power balance constraints must be met:

[0203] (19)

[0204] in, , They are nodes The active and reactive power injected by the power source at the node is 0 if there is no power source at that node. Representing nodes respectively The active and reactive power of the base conventional load. Represents a node The total active power load of the electric vehicle charging station connected at the location is the value obtained from the lower-level simulation at the corresponding time. of . For nodes The set of all directly connected adjacent nodes.

[0205] In addition to active and reactive power balance constraints, the optimization model also needs to satisfy non-convex constraints. The constraint is relaxed and transformed into a second-order cone inequality of rotation, thus converting the constraint into a convex constraint. This is:

[0206] (20)

[0207] Therefore, the upper-level optimization model can be expressed as:

[0208] (twenty one)

[0209] (twenty two)

[0210] (twenty three)

[0211] (twenty four)

[0212] (25)

[0213] (26)

[0214] in, These are the upper and lower limits for active power and reactive power, respectively. These are the upper and lower limits of the node voltage.

[0215] The solver can be used to obtain the power flow distribution of the entire network and the optimal set of active power from generator units. :

[0216] (27)

[0217] S32: Full-Network Carbon Emission Flow Tracking:

[0218] Based on the power flow distribution calculated by SOCP, carbon emissions from the source are allocated to the load nodes using the "proportional sharing principle".

[0219] First, establish the source-load mapping relationship:

[0220] (28)

[0221] in, Each element in Represents a node in the power grid The active power injected by the generator set, i.e., the power calculated in S31. . Each element in Represents a node in the power grid The total power consumed by the load can be expressed as . The upstream distribution matrix is ​​a coefficient matrix constructed based on Kirchhoff's current law, describing how the power of each node is channeled in. The downstream distribution matrix is ​​a sparse matrix that describes how power flows out of each node. Therefore, a power flow tracing matrix can be defined. Each of its elements Indicates the first Of the power injected into the first node, what percentage is actually delivered to the second node? Each node.

[0222] Secondly, calculate the carbon emission vector allocated to each load node. Each element Represents a node The mathematical expression for the total carbon dioxide emissions corresponding to the electrical energy consumed at the current moment is:

[0223] (29)

[0224] in, It is a diagonal matrix, with diagonal elements Represents a node The equivalent carbon emission intensity of the injected power at that node, which is obtained by the weighted average of all generator units at that node, can be expressed as:

[0225] (30)

[0226] in, Represents a node Inner Carbon emission intensity per unit active power of a generator set. If node If no motor is injected, then .

[0227] S33: Generate node carbon valence signal:

[0228] The calculated carbon emissions are converted into economic signals (prices) for use in lower-level simulations to guide electric vehicle charging.

[0229] At the node The carbon price signal is represented as:

[0230] (31)

[0231] in, The benchmark carbon tax is an externally set policy parameter that represents the penalty for emitting 1 kilogram of carbon dioxide. For the corresponding time obtained from the simulation of .

[0232] S4: Closed-loop iterative solution algorithm:

[0233] Since the lower-level microscopic simulation model is a complex nonlinear system that cannot be differentiated, an improved particle swarm optimization algorithm is used to connect the upper and lower levels and find the system equilibrium point.

[0234] S41: Particle Definition and Initialization:

[0235] First, define a population containing Each particle. Represents a set of potential pricing strategy vectors Price sequence for each fast charging station over 24 hours:

[0236] (32)

[0237] Within the allowed price range Inside, the positions of the particles are randomly initialized. and speed .

[0238] S42: Optimization Solution

[0239] First, a certain pricing strategy The input is fed into the lower-level simulation environment to simulate the responses of all electric vehicle users and obtain the load distribution. Subsequently, Substitute this into the upper-level SOCP model. If SOCP has a solution, it indicates that the power grid is safe. Then, calculate the objective function value. This refers to the total operating cost of the system; if SOCP has no solution, it indicates a voltage overrun, in which case a large penalty value is imposed. Its definition is the fitness function. The expression is:

[0240] (33)

[0241] Based on the historical optimal solution of the particle and the global optimal solution Update the next generation of pricing strategies:

[0242] (34)

[0243] (35)

[0244] in, Indicates the first During the nth iteration The first particle One element, This indicates the velocity corresponding to the element. For inertial weights, As a learning factor, It is a random number between [0,1].

[0245] Repeat the above process until the maximum number of iterations is reached. Or, the rate of change in the system's carbon emissions is less than a preset threshold after multiple consecutive iterations. The final output is the globally optimal solution. The corresponding carbon electricity price is the optimal time-of-use and zoned electricity-carbon pricing strategy, and the simulation results of the lower layer are the optimal operating state of the system.

[0246] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0247] Based on the same inventive concept, this application also provides an electric vehicle low-carbon scheduling device for implementing the electric vehicle low-carbon scheduling method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the electric vehicle low-carbon scheduling device provided below can be found in the limitations of the electric vehicle low-carbon scheduling method described above, and will not be repeated here.

[0248] In one exemplary embodiment, such as Figure 3 As shown, a low-carbon scheduling device for electric vehicles is provided, comprising: a construction module 302, a matrix generation module 304, a solution module 306, an acquisition module 308, and a scheduling module 310, wherein:

[0249] Module 302 is used to construct the power network and the transportation network, and initialize the network parameters of the power network, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users.

[0250] The matrix generation module 304 is used to simulate the travel and charging behavior of electric vehicle users through the travel chain simulation system based on the initialized parameters, and obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node;

[0251] Solver module 306 is used to solve the optimal power flow of the power grid based on the charging load matrix and network parameters through second-order cone programming, so as to obtain the power flow distribution of the entire network and the active power output status of the generator units;

[0252] The acquisition module 308 is used to construct the upstream and downstream power distribution relationship according to Kirchhoff's current law, determine the power transmission ratio of each power source node to each load node; calculate the equivalent carbon emission intensity of each power source node by weighting according to the active power output status of the generator set and the carbon emission intensity of each generator set; and obtain the node carbon emission set composed of the carbon emission of each load node based on the power transmission ratio and the equivalent carbon emission intensity.

[0253] The scheduling module 310 is used to generate an electricity-carbon price strategy that combines node electricity price and node carbon price based on the node carbon emission set and the cumulative charging power in the charging load matrix, and to perform scheduling based on the electricity-carbon price strategy.

[0254] In one embodiment, before scheduling based on the electricity carbon price strategy, the scheduling module 310 is further configured to:

[0255] The carbon pricing strategy for electricity is fed back to the travel chain simulation system. Through closed-loop iteration using an improved particle swarm optimization algorithm, the optimal carbon pricing strategy for electricity is obtained until the rate of change of carbon emissions in the travel chain simulation system is less than a preset threshold.

[0256] In one embodiment, the network parameters of the power network include the set of nodes, the set of lines, the line conductance, the susceptance, the upper and lower limits of node voltage, and the carbon emission intensity of each generator unit; the network parameters of the transportation network include the set of traffic nodes, the set of roads, the road segment length, the free flow period, the road segment capacity, the service capacity of fast charging stations, the expected road travel time calculated based on the congestion effect formula, and the queuing time of fast charging stations calculated based on the queuing theory formula.

[0257] In one embodiment, the matrix generation module 304 is further configured to:

[0258] Based on the initialized parameters, Monte Carlo sampling and a preset probability distribution function are used to determine the user's initial departure time, and the migration destination is determined by the Markov probability transition matrix.

[0259] Search for the shortest path in the transportation network, calculate the migration time, distance, and post-migration charge; if the charge is below a first preset threshold, select a target fast charging station for fast charging based on a generalized cost model; if the post-migration charge is above a second preset threshold, charge according to the preset slow charging power and parking time.

[0260] The cumulative charging power of each charging station node in each time period is counted to form a charging load matrix.

[0261] In one embodiment, the solving module 306 is further configured to:

[0262] By using second-order cone programming, an optimization model is constructed with the goal of minimizing the overall power generation cost. The network parameters and charging load matrix are input into the optimization model, and active power balance constraints, reactive power balance constraints, rotating second-order cone inequality constraints, unit output constraints, and node voltage boundary constraints are applied. The power flow distribution of the entire network and the active power output state of the generator units are obtained by solving the problem.

[0263] In one embodiment, the acquisition module 308 is further configured to:

[0264] Based on the active power output status of each generator set, the total active power output of the power supply node is calculated.

[0265] The carbon emission intensity of each power node is obtained by weighting the carbon emission intensity of each generator unit by using the proportion of the active power output of a single generator unit to the total active power output of the power node.

[0266] The modules in the aforementioned low-carbon dispatching device for electric vehicles can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0267] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a low-carbon scheduling method for electric vehicles. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0268] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0269] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0270] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0271] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0272] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0273] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0274] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0275] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A low-carbon scheduling method for electric vehicles, characterized in that, The method includes: Construct a power network and a transportation network, and initialize the network parameters of the power network, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users; Based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated through the travel chain simulation system to obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node; Based on the charging load matrix and the network parameters, the optimal power flow of the power grid is solved by second-order cone programming to obtain the power flow distribution of the entire network and the active power output status of the generator units. The upstream and downstream power distribution relationship is constructed according to Kirchhoff's current law to determine the power transmission ratio from each power source node to each load node; the equivalent carbon emission intensity of each power source node is calculated by weighting based on the active power output status of the generator set and the carbon emission intensity of each generator set; based on the power transmission ratio and the equivalent carbon emission intensity, a set of node carbon emissions composed of the carbon emissions of each load node is obtained. Based on the set of node carbon emissions and the cumulative charging power in the charging load matrix, an electricity-carbon pricing strategy combining node electricity price and node carbon price is generated, and scheduling is performed based on the electricity-carbon pricing strategy.

2. The method according to claim 1, characterized in that, Before the scheduling based on the electricity carbon price strategy, the following is also included: The electric carbon pricing strategy is fed back to the travel chain simulation system, and the optimal electric carbon pricing strategy is obtained by iterating in a closed loop using an improved particle swarm optimization algorithm until the carbon emission change rate of the travel chain simulation system is less than a preset threshold.

3. The method according to claim 1, characterized in that, The network parameters of the power network include the set of nodes, the set of lines, the line conductance, the susceptance, the upper and lower limits of node voltage, and the carbon emission intensity of each generator unit; the network parameters of the transportation network include the set of traffic nodes, the set of roads, the road segment length, the free flow period, the road segment capacity, the service capacity of fast charging stations, the estimated road travel time calculated based on the congestion effect formula, and the queuing time of fast charging stations calculated based on the queuing theory formula.

4. The method according to claim 1, characterized in that, Based on the initialized parameters, the travel and charging behaviors of electric vehicle users are simulated through a travel chain simulation system to obtain a charging load matrix of time-of-use cumulative charging power for each charging station node, including: Based on the initialized parameters, Monte Carlo sampling and a preset probability distribution function are used to determine the user's initial departure time, and the migration destination is determined by the Markov probability transition matrix. Search for the shortest path in the transportation network, calculate the migration time, distance, and charge after migration; if the charge is lower than a first preset threshold, select a target fast charging station for fast charging based on a generalized cost model; if the charge is higher than a second preset threshold, charge according to a preset slow charging power and parking time. The cumulative charging power of each charging station node in each time period is counted to form a charging load matrix.

5. The method according to claim 1, characterized in that, The process of solving the optimal power flow of the power grid using second-order cone programming based on the charging load matrix and the network parameters to obtain the power flow distribution of the entire network and the active power output state of the generator units includes: An optimization model is constructed using second-order cone programming with the goal of minimizing the overall power generation cost. The network parameters and the charging load matrix are input into the optimization model, and active power balance constraints, reactive power balance constraints, rotating second-order cone inequality constraints, unit output constraints, and node voltage boundary constraints are applied to solve for the overall power flow distribution and the active power output state of the generator units.

6. The method according to claim 1, characterized in that, The process of calculating the equivalent carbon emission intensity of each power node by weighting the active power output status of the generator sets and the carbon emission intensity of each generator set includes: Based on the active power output status of each generator set, the total active power output of the power supply node is calculated. The carbon emission intensity of each power node is obtained by weighting the carbon emission intensity of each generator set by using the proportion of the active power output of a single generator set to the total active power output of the power node.

7. A low-carbon dispatching device for electric vehicles, characterized in that, The device includes: The module is used to construct power networks and transportation networks, and initialize the network parameters of the power network, the network parameters of the transportation network, and the travel behavior parameters of electric vehicle users. The matrix generation module is used to simulate the travel and charging behavior of electric vehicle users through the travel chain simulation system based on the initialized parameters, and obtain the charging load matrix of the time-sharing cumulative charging power of each charging station node; The solution module is used to solve the optimal power flow of the power grid based on the charging load matrix and the network parameters, and to obtain the power flow distribution of the entire network and the active power output status of the generator units. The acquisition module is used to construct the upstream and downstream power distribution relationship according to Kirchhoff's current law, determine the power transmission ratio of each power source node to each load node; calculate the equivalent carbon emission intensity of each power source node by weighting according to the active power output status of the generator set and the carbon emission intensity of each generator set; and obtain the node carbon emission set composed of the carbon emission of each load node based on the power transmission ratio and the equivalent carbon emission intensity. The scheduling module is used to generate an electricity-carbon price strategy that combines the node electricity price and the node carbon price based on the set of node carbon emissions and the cumulative charging power in the charging load matrix, and to perform scheduling based on the electricity-carbon price strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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