A method and apparatus for orderly charging control of electric vehicles considering carbon emissions from charging.

Abstract

This invention discloses a method and apparatus for orderly charging regulation of electric vehicles (EVs) that considers charging carbon emissions. The invention includes a carbon emission flow calculation module at the power system level, a user charging demand analysis module at the EV user level, and an EV orderly charging regulation optimization module based on an improved small-world particle swarm optimization algorithm, thereby achieving orderly charging regulation of EVs. This invention fully considers both EV charging carbon emissions and EV user charging response costs, aiming to promote the absorption of new energy sources in the power system, control the intensity of EV charging carbon emissions, and, as far as possible, consider the travel characteristics of EV users and reduce user charging response costs.

Description

Technical Field

[0001] This invention relates to the fields of smart grids and electric vehicle charging management, and in particular to a method and apparatus for orderly charging control of electric vehicles that takes into account carbon emissions from charging. Background Technology

[0002] Electric vehicles, as one of the core carriers of low-carbon travel in the transportation sector, have been widely used. Electric vehicle charging is a crucial link supporting their daily use. Influenced by various factors such as user travel choices, the planning and construction of transportation networks, charging infrastructure networks, and power distribution networks, as well as their dynamic characteristics and real-time scheduling, electric vehicle charging currently still largely relies on users' autonomous travel choices, exhibiting significant spatiotemporal dynamic characteristics. In some cases, it displays high randomness, strong concentration, and low cluster coordination efficiency. Large-scale, disorderly charging of electric vehicles can easily cause road network congestion, increase user charging response costs, and hinder regional economic development. It can also easily lead to overload of electric vehicle charging facilities, rapid increase in regional electricity load, widening the overall load peak-valley difference, which is not conducive to the safe and stable operation of the power system and restricts the coordinated planning and development of regional power and transportation infrastructure. In addition, under the dual-carbon strategy and the new mechanism of comprehensive transformation from dual control of energy consumption to dual control of carbon emissions, renewable energy power generation and flexible adjustable loads are continuously connected to the power system, which has a profound impact on the boundary conditions, operation mode and dispatch mode of the power system. It is facing multiple changes in system topology, policy market mechanism and business operation mode. The single charging mode with the lowest economic cost of conventional electric vehicle charging is difficult to adapt to promote the high proportion of new energy consumption. Summary of the Invention

[0003] To address the aforementioned technical challenges, this invention proposes an orderly charging control method for electric vehicles that considers charging carbon emissions. This method considers electric vehicle charging carbon emissions from the macro level of the power system and user charging response costs from the individual user level. Its optimization objective is to minimize both electric vehicle charging carbon emissions and user charging response costs, ultimately achieving the goal of promoting the absorption of new energy sources in the power system, controlling the intensity of electric vehicle charging carbon emissions, and minimizing user charging response costs while taking into account the travel characteristics of electric vehicle users.

[0004] The regulation method of this invention specifically includes three optimization modules: a carbon emission flow calculation module, a user charging demand analysis module, and an electric vehicle orderly charging regulation optimization module.

[0005] The core of the carbon emission flow calculation module lies in calculating the carbon emission flow on the electricity consumption side of the system, based on renewable energy output forecasting with opportunity constraints and power system flow calculations. Specifically, by introducing opportunity-constrained forecasting, based on historical renewable energy output data and power weather forecasts, it generates renewable energy active power output forecasts and dynamic confidence intervals for different scenarios and times, and extends them to a conventional deterministic power flow calculation model. Then, based on the power flow calculation that has reached equilibrium, it removes dangling nodes based on the adjacency characteristics of carbon emission flows, and performs matrix calculations layer by layer through a recursive algorithm to obtain key parameters such as branch carbon flow rate, node carbon potential, and load carbon flow rate. Solving for the system node carbon potential is the core of the carbon emission flow calculation, and the system node carbon potential matrix E is the result. N The solution principle is as follows:

[0006]

[0007] In the formula, P N P is the active flux matrix of the nodes; B P is the node branch path flow matrix; G For the active power injection matrix of the generator set; E G This is the carbon emission intensity matrix for generator sets.

[0008] The core of the user charging demand analysis module lies in conducting user charging demand analysis based on user travel characteristic analysis and charging network transfer performance evaluation. Specifically, it treats user travel destinations and travel routes as nodes and edges, respectively, and considers edge weights to construct a complex network of user travel behavior. Nodes in the network include typical features such as node strength and node clustering coefficient. Then, focusing on the transfer behavior of electric vehicles in the traffic network, it uses traffic assignment theory and the Logit flow delay function to describe the traffic network road resistance model, and conducts an electric vehicle charging network transfer performance evaluation that includes road segment transit time and intersection transit time. Finally, based on the strength of user charging demand in the time dimension and the user's acceptability of charging transfer time in the spatial dimension, it constructs a user i charging demand set X. i .

[0009] X i =[SOC i H i E i ,Y i ,F i (2)

[0010] In the formula, SOC i The current battery level of user i's electric vehicle; H i H serves as a basis for determining charging demand. i =0 indicates no charging requirement, H i=1 indicates a charging requirement; E i Select the user's initial charging station; Y i In response to the orderly charging control signal, Y i =0 indicates no response, Y i =1 indicates a response; F i A collection of transfer charging stations acceptable to users.

[0011] The core of the electric vehicle orderly charging regulation and optimization module lies in optimizing the constructed objective function that considers charging carbon emissions and user response costs under the constraints of charging station access to the power system stability and user charging transfer acceptability. This is achieved through an improved small-world particle swarm optimization algorithm. Specifically, regulating the charging behavior of electric vehicles can effectively improve the level of renewable energy consumption and reduce the carbon emissions of electric vehicle charging. However, it may require users to pay additional transfer time and electricity costs. The optimization objective function for electric vehicle orderly charging regulation considering charging carbon emissions and user response costs is as follows:

[0012]

[0013] In the formula, α and β are the weighting coefficients for charging carbon emissions and user response costs, respectively; ΔR is the total carbon emissions from user charging; N is the number of electric vehicle users accepting charging regulation; C Ti C Oi These represent the unit time cost and unit electricity cost for user i's charging transfer, respectively; T Di-Wi L Di-Wi For user i from D i Charging stations moved to W i The transfer time and distance of the charging station.

[0014] The constraints for the orderly charging regulation and optimization of electric vehicles include the stability constraints of charging station access to the power system and the acceptability constraints of electric vehicle users charging transfer. The stability constraints of charging station access to the power system include node voltage constraints, node current constraints, node access capacity constraints, and system power balance constraints. The acceptability constraints of electric vehicle users charging transfer include user charging acceptable distance constraints and electric vehicle current power constraints.

[0015] Node voltage constraints:

[0016] U nmin ≤U n ≤U nmax (4)

[0017] Node current constraints:

[0018] I n ≤I nmax (5)

[0019] Node access capacity constraints:

[0020] ∑P n,l +ΣP n,ev +∑P n,r +∑P n,ess-c ≤S n,T cosθ (6) System power balance constraint:

[0021] ∑P g +ΣP w +∑P pv +∑P hy +∑P ess-d =∑P l +∑P ev +∑P r +∑P ess-c +∑P loss (7) In the formula, U n U nmin U nmax These represent the node voltage amplitude, the minimum and maximum allowable node voltage during normal operation, respectively; I n I nmax These represent the amplitude of the node branch current and the amplitude of the maximum current-carrying capacity of the node branch during normal operation, respectively; P n,l P n,ev P n,r P n,ess-c These represent the node's conventional load power, node's electric vehicle charging power, node's adjustable load power, and node's energy storage station charging power, respectively; S n,T P represents the total capacity of the node transformers; cosθ represents the node power factor; g P w P pv P hy These represent the power generation capacities of thermal power plants, gas-fired power plants, wind power plants, photovoltaic power plants, and hydropower plants in the system, respectively; P ess-d With P ess-c These represent the discharge power and charging power of the energy storage station in the system, respectively; P l P ev P r P loss These are the system's conventional load power, electric vehicle charging power, adjustable load power, and system transmission and transformation link losses, respectively.

[0022] Acceptable charging distance constraints for users:

[0023] d i ≤d imax (8)

[0024] Current battery capacity constraints for electric vehicles:

[0025] SOC i ≥SOC ilim (9)

[0026] In the formula, d i d imax The acceptable transfer distance and the maximum acceptable transfer distance for user i are respectively; SOC i SOC ilim These represent the current remaining battery power of user i's electric vehicle and the battery power required to transfer to an acceptable target charging station, respectively.

[0027] The optimization model is optimized using an improved small-world particle swarm optimization (PSO) algorithm. The random reconnection mechanism for vertices in the small-world PSO algorithm is improved into a dynamic optimization reconnection mechanism closely related to the objective function value. After the improvement, the selection of reconnected vertices depends on the excellence of the particles. The higher the excellence of the particle, that is, the smaller the objective function value of the particle, the greater the probability of it being reconnected. The probability P(x) of vertex x being reconnected in the improved small-world network is defined as follows:

[0028]

[0029] In the formula, k is the reconnection coefficient of the small-world network vertex, and f(x) is the optimization objective function value of vertex x.

[0030] In the early stages of computation, the network vertices are widely and randomly connected. As the optimization process progresses, high-quality particles are given a greater probability of reconnection. Local short-range connections enable in-depth optimization, while global long-range connections are still retained to avoid getting stuck in local extreme points, maintain population diversity, and balance global exploration with high-quality local mining to adapt to the optimization of orderly charging regulation of electric vehicles under strong constraints and nonlinearity.

[0031] The electric vehicle orderly charging control device of the present invention, through a physical system integrating a data acquisition module, a data communication module, a data storage module, and a calculation and control module, executes the corresponding computer program configured in the device memory to realize the above-mentioned electric vehicle orderly charging control method considering charging carbon emissions, thereby minimizing electric vehicle charging carbon emissions and electric vehicle user charging response costs.

[0032] The data acquisition module of the electric vehicle orderly charging control device is used to collect various data of electric vehicle users in the area, such as the current power and driving range of electric vehicles and the set of charging needs of electric vehicle users.

[0033] The device's data communication module is used to transmit collected electric vehicle user data, traffic network data such as regional traffic network topology, real-time traffic flow of road segments and intersections provided by the traffic dispatch center, charging facility network data of regional charging station charging load provided by charging station operators, and power network data provided by the power dispatch center, including basic power data such as regional power network topology, node voltage, main transformer load rate, and real-time distribution of line power flow, as well as historical data of renewable energy output and power weather forecast data. It also transmits generated electric vehicle orderly charging control instructions and various data between and within the various modules of the device.

[0034] The device's data storage module is used to store various collected and transmitted data, corresponding computer programs for implementing an orderly charging control method for electric vehicles that takes into account charging carbon emissions, and data and programs for the intermediate processes of device operation.

[0035] The device's calculation and control module is used to execute the corresponding computer program for an orderly charging control method for electric vehicles that takes into account charging carbon emissions, as well as computer programs for data acquisition, data communication, and data storage. It performs relevant calculations for optimizing the orderly charging control of electric vehicles, generates and outputs a set of electric vehicle charging control instructions, and achieves optimal scheduling to control the carbon emissions of electric vehicle charging and reduce the user's charging response cost.

[0036] In summary, compared with existing technologies, this invention aims to minimize carbon emissions from electric vehicle charging and the charging response cost for electric vehicle users. It takes into account various influencing factors such as transportation networks, charging infrastructure networks, power networks, and carbon emission networks. While ensuring the economic viability of charging for electric vehicle users, it guides users to charge at charging stations with lower nodal carbon potential. This is beneficial to promoting the high proportion of renewable energy consumption, reducing carbon emissions from electric vehicle charging and the impact on the power system, improving the power system's regulation capacity and operational reliability, and deepening the integration of energy and transportation. Attached Figure Description

[0037] Figure 1 This is an optimized flowchart of an electric vehicle orderly charging control method that takes into account charging carbon emissions proposed in this invention.

[0038] Figure 2 This is a schematic diagram illustrating the interaction relationships among various influencing factors of orderly charging of electric vehicles proposed in this invention.

[0039] Figure 3 This is a schematic diagram of the optimized constraints for orderly charging regulation of electric vehicles proposed in this invention;

[0040] Figure 4 This is a schematic diagram of the structure of the electric vehicle orderly charging control device proposed in this invention. Detailed Implementation

[0041] The following is a detailed description of the orderly charging control method and apparatus for electric vehicles that takes into account carbon emissions from charging, as proposed in this invention, with reference to the accompanying drawings.

[0042] This invention's control method considers carbon emissions from electric vehicle (EV) charging from a macro-level power system perspective and user charging response costs from an individual EV user perspective. Its optimization objective is to minimize both EV charging carbon emissions and user charging response costs, ultimately promoting the absorption of renewable energy in the power system, controlling the intensity of EV charging carbon emissions, and minimizing user charging response costs while considering EV user travel characteristics. Specifically, it includes three optimization modules: a carbon emission flow calculation module at the power system level, a user charging demand analysis module at the EV user level, and an EV orderly charging control optimization module based on an improved small-world particle swarm optimization algorithm. The overall optimization process is as follows: Figure 1 As shown.

[0043] The orderly charging regulation of electric vehicles, considering both charging carbon emissions and user response costs, is influenced by multiple factors including transportation networks, charging infrastructure networks, power grids, and carbon emission networks. Electric vehicles possess the dual attributes of being both a means of transportation and a mobile, adjustable load; their charging behavior is not only affected by user travel characteristics but also by the spatial layout of charging infrastructure networks and the dynamic characteristics of transportation networks. Furthermore, the spatial layout of charging infrastructure networks and the power flow distribution of power grids interact, further influencing the carbon potential distribution at nodes in the carbon emission network. The interactive relationships among these influencing factors are as follows: Figure 2 As shown.

[0044] The core of the carbon emission flow calculation module lies in calculating the carbon emission flow on the electricity consumption side of the system based on the renewable energy output forecast and power flow calculation that take into account opportunity constraints.

[0045] Due to the randomness and uncertainty of renewable energy output, the electricity load curve after deducting renewable energy output becomes more volatile, widening the peak-to-valley difference and threatening the safe operation of the power grid. Electric vehicles, as a mobile and adjustable load, can promote the consumption of new energy by guiding their orderly charging. By introducing opportunity-constrained prediction, based on historical renewable energy output data and power weather forecasts, renewable energy output is quantified, generating active power output predictions for different scenarios and times. These predictions are then extended to conventional deterministic power flow calculation models. Dynamic confidence intervals based on renewable energy active power output predictions are embedded in the power balance equations of power system nodes, constructing a power flow calculation framework that better reflects actual operation. By adjusting the output of other conventional units and adjustable loads, dynamic system balance under different boundary conditions and operating modes can be achieved, and the distribution of system node voltages and line power flows can be extracted.

[0046] The calculation of carbon emission flows on the electricity consumption side relies on power system flow calculations. Based on the principle of fair allocation, it fully reflects the carbon emissions from electricity consumption as emissions generated to meet the energy needs of electricity users from the perspective of the essential operation of the power system. The carbon flow density and nodal carbon potential are derived from the grid average carbon emission factor. While power flow calculations focus on solving the grid operation mode, carbon emission flow calculations focus on tracing the source of electricity carbon footprint. Solving for the carbon potential of system nodes is the core of carbon emission flow calculations. The system node carbon potential matrix E N The solution principle is as follows:

[0047]

[0048] In the formula, P N P is the active flux matrix of the nodes; B P is the node branch path flow matrix; G For the active power injection matrix of the generator set; E G This is the carbon emission intensity matrix for generator sets.

[0049] Carbon emission flow calculation is based on the power flow calculation that has reached equilibrium, taking into account the small-world network characteristics of the power system. It eliminates dangling nodes through which no power flow passes, starts from the nodes directly connected to the generator units, and performs matrix calculations in a hierarchical manner through a recursive algorithm based on the adjacency characteristics of carbon emission flow, to obtain key parameters such as branch carbon flow rate, node carbon potential, and load carbon flow rate.

[0050] The core of the user charging demand analysis module lies in conducting user charging demand analysis based on user travel characteristic analysis and charging network transfer performance evaluation.

[0051] For massive and nonlinear electric vehicle travel data, analyzing user travel characteristics is of great significance for revealing the spatial distribution of electric vehicles and mining user travel behavior patterns. To facilitate the analysis of user travel characteristics, a complex network of user travel behavior is constructed, treating user travel destinations and travel routes as nodes and edges, respectively, and considering the weight of the edges. The higher the travel frequency and the greater the traffic of an edge, the greater its weight. The nodes in the network include typical characteristics such as node strength and node clustering coefficient. The greater the node strength, the higher the frequency of users moving from that node to other nodes, and the node often plays the role of a transportation hub. The greater the node clustering coefficient, the closer the connection between the surrounding nodes, the stronger the regional clustering, and the greater the probability of population agglomeration.

[0052] Electric vehicle charging demand can be viewed as a mobile adjustable load, influenced by factors such as battery capacity, driving range, traffic network topology, and road traffic saturation. To more accurately characterize the transfer behavior of electric vehicles in the traffic network, a traffic network road resistance model is described using traffic assignment theory and the Logit flow delay function. The transfer performance of the electric vehicle charging network is defined as the time it takes for a user to transfer from one charging station to another in the network. This includes road segment transit time and intersection transit time. Road segment transit time is determined by the travel time of the charging transfer road segment, which is mainly affected by road grade, road segment capacity, and traffic flow. Intersection transit time is determined by the waiting time at the charging transfer intersection, which is mainly affected by the traffic light status, intersection capacity, and traffic flow.

[0053] User charging demand is strongly correlated with the current battery level of electric vehicles, but the relationship is not simple linear. The temporal scheduling of user charging demand depends on the gradient of the intensity of battery demand. Further fine-grained temporal division of user charging demand intensity is performed: remaining battery level below 20% is considered urgent charging demand; remaining battery level between 20% and 60% is considered supplementary charging demand; and remaining battery level above 60% is considered full-charge charging demand. The spatial scheduling of user charging demand depends on the user's acceptable charging transfer time, and is influenced by the location and load of electric vehicle charging stations, as well as the spatial elasticity of user charging. The two dimensions of user charging demand scheduling are relatively independent yet interconnected. To more accurately describe user charging demand, a user i charging demand set X is constructed. i .

[0054] X i =[SOC i H i E i ,Y i ,F i (2)

[0055] In the formula, SOC i The current battery level of user i's electric vehicle; H i H serves as a basis for determining charging demand. i =0 indicates no charging requirement, H i =1 indicates a charging requirement; E i Select the user's initial charging station; Y i In response to the orderly charging control signal, Y i =0 indicates no response, Y i =1 indicates a response; F i A collection of transfer charging stations acceptable to users.

[0056] The core of the electric vehicle orderly charging regulation and optimization module lies in optimizing the constructed objective function that considers charging carbon emissions and user response costs under the constraints of charging station access to the power system stability and user charging transfer acceptability, thereby realizing orderly charging regulation of electric vehicles.

[0057] Regulating the charging behavior of electric vehicles can effectively improve the overall distribution of electricity load, enhance the absorption of new energy sources, reduce the curtailment rate of wind and solar power, improve system stability, guide users to charge at charging stations with lower nodal carbon potential, and reduce carbon emissions from electric vehicle charging. However, this charging strategy may require electric vehicle users to pay additional transfer time and transfer electricity costs. The objective function for optimizing the orderly charging regulation of electric vehicles, considering charging carbon emissions and user response costs, is as follows:

[0058]

[0059] In the formula, α and β are the weighting coefficients for charging carbon emissions and user response costs, respectively; ΔR is the total carbon emissions from user charging; N is the number of electric vehicle users accepting charging regulation; C Ti C Oi These represent the unit time cost and unit electricity cost for user i's charging transfer, respectively; T Di-Wi L Di-Wi For user i from D i Charging stations moved to W i The transfer time and distance of the charging station.

[0060] The optimization constraints for orderly charging regulation of electric vehicles include the stability constraints of charging station access to the power system and the acceptability constraints of charging user switching. A schematic diagram of the optimization constraints is shown below. Figure 3 As shown, the stability constraints for charging stations connected to the power system include node voltage constraints, node current constraints, node access capacity constraints, and system power balance constraints. The acceptable charging transfer constraints for electric vehicle users include user charging acceptable distance constraints and electric vehicle current power constraints.

[0061] Node voltage constraints:

[0062] U nmin ≤U n ≤U nmax (4)

[0063] Node current constraints:

[0064] I n ≤I nmax (5)

[0065] Node access capacity constraints:

[0066] ∑Pn,l +∑P n,ev +∑P n,r +∑P n,ess-c ≤S n,T cosθ (6) System power balance constraint:

[0067] ∑P g +∑P w +∑P pv +∑P hy +∑P ess-d =∑P l +∑P ev +∑P r +∑P ess-c +∑P loss (7) In the formula, U n U nmin U nmax These represent the node voltage amplitude, the minimum and maximum allowable node voltage during normal operation, respectively; I n I nmax These represent the amplitude of the node branch current and the amplitude of the maximum current-carrying capacity of the node branch during normal operation, respectively; P n,l P n,ev P n,r P n,ess-c These represent the node's conventional load power, node's electric vehicle charging power, node's adjustable load power, and node's energy storage station charging power, respectively; S n,T P represents the total capacity of the node transformers; cosθ represents the node power factor; g P w P pv P hy These represent the power generation capacities of thermal power plants, gas-fired power plants, wind power plants, photovoltaic power plants, and hydropower plants in the system, respectively; P ess-d With P ess-c These represent the discharge power and charging power of the energy storage station in the system, respectively; P l P ev P r P loss These are the system's conventional load power, electric vehicle charging power, adjustable load power, and system transmission and transformation link losses, respectively.

[0068] Acceptable charging distance constraints for users:

[0069] d i ≤d imax (8)

[0070] Current battery capacity constraints for electric vehicles:

[0071] SOC i ≥SOC ilim (9)

[0072] In the formula, d i d imax The acceptable transfer distance and the maximum acceptable transfer distance for user i are respectively; SOC i SOC ilim These represent the current remaining battery power of user i's electric vehicle and the battery power required to transfer to an acceptable target charging station, respectively.

[0073] Under the above constraints, an improved small-world particle swarm optimization (SSO) algorithm is used to optimize the model. The SSO algorithm is based on a small-world network topology and facilitates particle interaction. The network begins as a regular network, and edges are perturbed with a certain probability, causing them to reconnect to a randomly selected vertex, thus enabling information exchange and transmission between particles. Improving the vertex selection in the small-world network effectively enhances the efficiency of information exchange between particles. The original random reconnection mechanism is transformed into a dynamic, optimal reconnection mechanism closely related to the objective function value. The selection of the reconnected vertex depends on the particle's excellence; the higher the particle's excellence (i.e., the smaller its objective function value), the greater its probability of being reconnected. The probability P(x) of vertex x being reconnected in the improved small-world network is defined as follows:

[0074]

[0075] In the formula, k is the reconnection coefficient of the small-world network vertex, and f(x) is the optimization objective function value of vertex x.

[0076] In the initial stage of the improved small-world particle swarm optimization algorithm, the network vertex connections are extensive and random. As the optimization process deepens, high-quality particles are given a greater probability of reconnection. Local short-range connections can achieve in-depth optimization. At the same time, global long-range connections are still retained to avoid getting trapped in local extreme points, maintain population diversity, and take into account both global exploration and high-quality local mining, so as to adapt to the optimization of orderly charging regulation of electric vehicles with strong constraints and nonlinearity.

[0077] This invention relates to an electric vehicle orderly charging control device. Through a physical system integrating a data acquisition module, a data communication module, a data storage module, and a calculation and control module, it executes a corresponding computer program configured in the device's memory to implement the aforementioned electric vehicle orderly charging control method that considers charging carbon emissions. This achieves the goal of minimizing both electric vehicle charging carbon emissions and the charging response cost for electric vehicle users. A schematic diagram of the device structure is shown below. Figure 4 As shown.

[0078] The data acquisition module of the electric vehicle orderly charging control device is used to collect various data of electric vehicle users in the area, such as the current power and driving range of electric vehicles and the set of charging needs of electric vehicle users, through various terminal devices, including but not limited to charging station equipment and authorized electric vehicle user on-board equipment.

[0079] The device's data communication module transmits collected electric vehicle user data, traffic network data (including regional traffic network topology, real-time traffic flow at road segments and intersections) provided by the traffic dispatch center, charging facility network data (including regional charging station charging load provided by charging station operators), and power network data (including regional power network topology, node voltage, main transformer load rate, real-time power flow distribution, renewable energy output history data, and power weather forecast data) provided by the power dispatch center. It also transmits electric vehicle orderly charging control instructions generated by the device's dispatch control module and transmits various data between and within the device's modules through various communication devices (including switches and buses) on the internal side.

[0080] The device's data storage module, through non-volatile and volatile memory including but not limited to read-only memory and random access memory, is used to store various acquired and transmitted data, corresponding computer programs for implementing an orderly charging control method for electric vehicles that takes into account charging carbon emissions, and data and programs for the intermediate processes of device operation.

[0081] The device's computing and control module, through various processor devices including but not limited to servers and central processing units, executes corresponding computer programs for an orderly charging control method for electric vehicles that considers charging carbon emissions, as well as computer programs for data acquisition, data communication, and data storage. It performs relevant calculations for optimizing the orderly charging control of electric vehicles, generates and outputs a set of electric vehicle charging control instructions, and achieves optimal scheduling to control the carbon emissions of electric vehicle charging and reduce the user's charging response costs.

[0082] In summary, compared with existing technologies, this invention aims to minimize carbon emissions from electric vehicle charging and the charging response cost for electric vehicle users. It takes into account various influencing factors such as transportation networks, charging infrastructure networks, power networks, and carbon emission networks. While ensuring the economic viability of charging for electric vehicle users, it guides users to charge at charging stations with lower nodal carbon potential. This is beneficial to promoting the high proportion of renewable energy consumption, reducing carbon emissions from electric vehicle charging and the impact on the power system, improving the power system's regulation capacity and operational reliability, and deepening the integration of energy and transportation.

Claims

1. A method for orderly charging control of electric vehicles considering carbon emissions from charging, characterized in that, It includes a carbon emission flow calculation module, a user charging demand analysis module, and an electric vehicle orderly charging regulation and optimization module; The carbon emission flow calculation module, by introducing opportunity-constrained prediction, generates renewable energy active power output predictions and dynamic confidence intervals for different scenarios and times based on historical renewable energy output data and power weather forecasts. It then extends these predictions to a conventional deterministic power flow calculation model. Based on the power flow calculation that has reached equilibrium, it eliminates dangling nodes based on the adjacency characteristics of carbon emission flows and performs matrix calculations in a hierarchical manner using a recursive algorithm to obtain key parameters such as branch carbon flow rate, node carbon potential, and load carbon flow rate. The user charging demand analysis module treats user travel destinations and travel routes as nodes and edges, respectively, and considers edge weights to construct a complex network of user travel behavior. It then uses traffic assignment theory and the Logit flow delay function to describe the traffic network road resistance model, and conducts an electric vehicle charging network transfer performance evaluation that includes road segment transit time and intersection transit time. Finally, based on the strength of user charging demand in the time dimension and the user's acceptability of charging transfer time in the spatial dimension, it constructs a user... i Charging demand set X i ; (1) In the formula, SOC i For users i The electric vehicle's current battery level; H i As a basis for judging charging demand, H i =0 indicates no charging requirement. H i =1 indicates that there is a need for charging; E i Select the user's initial charging station; Y i Respond to the orderly charging control indicator for users. Y i =0 indicates no response. Y i =1 indicates a response; F i A collection of transfer charging stations acceptable to users; The electric vehicle orderly charging regulation and optimization module, under the constraints of charging station access to the power system stability and user charging transfer acceptability, optimizes the constructed objective function considering charging carbon emissions and user response costs using an improved small-world particle swarm optimization algorithm to achieve orderly charging regulation of electric vehicles. The orderly charging regulation and optimization objective function is as follows: (2) In the formula, α , β These are the weighting coefficients for charging carbon emissions and user response costs, respectively. ΔR Total carbon emissions from charging users; N The number of electric vehicle users subject to charging regulation; C Ti , C Oi users respectively i The unit time cost and unit electricity cost of charging transfer; T Di-Wi , L Di-Wi users respectively i from D i Charging stations moved to W i The transfer time and distance of the charging station; The stability constraints for charging station access to the power system include node voltage constraints, node current constraints, node access capacity constraints, and system power balance constraints. The acceptability constraints for electric vehicle user charging transfer include user charging acceptable distance constraints and electric vehicle current battery level constraints. Furthermore, the random reconnection mechanism for vertex selection in the small-world particle swarm optimization algorithm is improved into a dynamic, optimal reconnection mechanism closely related to the objective function value. After the improvement, the selection of reconnected vertices depends on the particle's excellence; the higher the particle's excellence (i.e., the smaller the particle's objective function value), the greater its probability of being reconnected. In the improved small-world network, the vertex selection... x Probability of being reconnected P ( x The definition is as follows: (3) In the formula, k Let be the reconnection coefficient of vertices in a small-world network. f ( x ) is the vertex x The objective function value is optimized.

2. An apparatus for implementing the orderly charging control method for electric vehicles considering charging carbon emissions as described in claim 1, characterized in that, The control method described in claim 1 is realized by a physical system that integrates a data acquisition module, a data communication module, a data storage module, and a calculation and control module. The integrated data acquisition module is used to collect various data of electric vehicles in the region, including the current battery level and driving range of electric vehicles, the charging needs of electric vehicle users, and other data of electric vehicle users. The data communication module is used to transmit collected electric vehicle user data, traffic network data such as regional traffic network topology, real-time traffic flow of road segments and intersections provided by the traffic dispatch center, charging facility network data of regional charging station charging load provided by the charging station operator, and power network data provided by the power dispatch center, including regional power network topology, node voltage, main transformer load rate, real-time distribution of line power flow, historical data of renewable energy output, and power meteorological forecast data. It also transmits generated electric vehicle orderly charging control instructions and various data between and within the modules of the transmission device. The data storage module is used to store various collected and transmitted data, corresponding computer programs for implementing the control method described in claim 1, and data and programs for the intermediate process of device operation; The calculation and control module is used to execute the corresponding computer program of the control method described in claim 1, as well as the computer program for data acquisition, data communication, and data storage, to perform relevant calculations for the orderly charging control optimization of electric vehicles, generate and output a set of electric vehicle charging control instructions, and achieve optimal scheduling to control carbon emissions from electric vehicle charging and reduce user charging response costs.

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