A low-carbon regulation method and system for a cluster of electric vehicles to improve the carrying capacity of a distribution network

Abstract

The application discloses a kind of electric vehicle cluster low-carbon regulation methods and systems for improving distribution network carrying capacity, and relates to power distribution network technical field.The method includes: carbon flow tracing is carried out to the power distribution network containing charging station and electric vehicle;Compensation factor considering low-carbon preference and distribution network carrying capacity is formed;Electric vehicle charging and discharging model is constructed;Double-layer charging station cost model is constructed;Lower electric vehicle low-carbon charging method based on matching theory;Upper optimization scheduling model considering multi-charging station energy interaction;Upper and lower layer collaborative optimization, after multiple upper and lower layer interaction, gradually approach global optimal solution.The method can realize accurate matching of user demand and charging station resources, improve power grid carrying capacity and solve regional load imbalance and power grid stability risk.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a low-carbon control method and system for electric vehicle clusters to enhance the carrying capacity of power distribution networks. Background Technology

[0002] With the massive increase in electric vehicles, their large-scale and disorderly integration can lead to problems such as exacerbated peak-valley load differences in the power distribution network, deterioration of power quality, increased investment and maintenance costs for the power grid, and hindered renewable energy absorption, posing a significant challenge to the carrying capacity of the power distribution network. In response to these challenges, the concept of V2G (Vehicle-to-Grid) has been proposed.

[0003] Vehicle-to-grid (V2G) is a two-way energy exchange between electric vehicles and the power grid. Specifically, it uses electric vehicle batteries as distributed mobile energy storage units to participate in grid load regulation, renewable energy consumption, and emergency power supply. Its core is a smart dispatch system that dynamically adjusts the charging and discharging behavior of electric vehicles based on grid signals (such as electricity prices, load demand, and renewable energy output). By adjusting the charging and discharging behavior of electric vehicles, excess renewable energy (such as wind and solar power) can be stored in electric vehicles during periods of electricity surplus; during periods of renewable energy shortage or peak electricity demand, the stored energy can be released back into the grid, thereby optimizing energy distribution, reducing dependence on fossil fuels, and lowering carbon emissions.

[0004] In existing electric vehicle (EV) charging and discharging scheduling schemes, dynamic adjustments based on real-time or time-of-use (TOU) pricing are the mainstream approach. Leveraging the predictability of EV users' travel and the elasticity of their charging demand, guiding user charging behavior through guiding factors can be summarized as a closed-loop process of "information interaction - updating guiding factors - behavior adjustment." In the stage of updating guiding factors, the SOC status of EVs, travel plans, and charging demand are typically collected. Simultaneously, charging facility capacity constraints, distributed power generation output forecasts, and grid operating parameters are integrated to construct a mixed-integer programming model with the objectives of minimizing system carbon emissions, maximizing charging station operating revenue, or balancing distribution network load. Finally, the optimization results are transformed into TOU pricing signals, guiding users to spontaneously choose charging and discharging times and charging stations, actively participating in renewable energy consumption and carbon emission reduction.

[0005] The shortcomings of existing electric vehicle charging scheduling technologies include the following aspects:

[0006] 1) Existing EV scheduling typically assumes that electric vehicle users will fully respond to scheduling instructions, neglecting the various costs EV users face in choosing between charging and discharging. However, actual user decisions are the result of multiple trade-offs, taking into account not only electricity costs but also the distance to charging stations, queuing time, and nodal carbon intensity. Existing research has failed to incorporate nodal carbon intensity as an independent variable into scheduling models, and user behavior models are overly simplified, leading to optimization strategies that are disconnected from the actual user choice logic, resulting in low user response and poor carbon reduction effects.

[0007] 2) Existing EV charging and discharging guidance mechanisms are insufficient to leverage the effect of electric vehicles in enhancing the carrying capacity of the power distribution network. Current EV regulation technologies typically prioritize cost control when guiding EV charging and discharging, which can easily lead to concentrated large-scale charging by electric vehicle users, exacerbating local line pressure and causing power quality issues such as voltage exceeding limits at some nodes.

[0008] 3) Existing EV dispatching technologies severely lack consideration for the carrying capacity of the distribution network. Current research considering energy coordination among multiple charging stations completely ignores the physical operational constraints of the distribution network, such as line transmission capacity limitations and node voltage safety limits. The dispatching strategies generated by these studies are highly likely to cause local line overloads or voltage exceedances in the distribution network during actual implementation. This not only fails to improve the carrying capacity of the distribution network but also directly threatens the safe and stable operation of the power grid. Summary of the Invention

[0009] The technical problem to be solved by this invention is how to provide a low-carbon regulation method for electric vehicle clusters that can achieve precise matching between user needs and charging station resources, improve the grid carrying capacity, and solve the risks of regional load imbalance and grid stability.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for low-carbon regulation of electric vehicle clusters to improve the carrying capacity of power distribution networks, comprising the following steps:

[0011] Obtain carbon density: Perform carbon flow tracing on the power distribution network, including charging stations and electric vehicles, to obtain the carbon density of charging station nodes and the carbon density of each electric vehicle.

[0012] Develop charging and discharging compensation factors: Develop charging and discharging compensation factors based on the carbon density of charging station nodes, the carbon density of each electric vehicle, and the carrying capacity of the power distribution network.

[0013] Constructing a two-layer charging station cost model: Based on user time, distance, and low-carbon costs, a two-layer charging station cost model is constructed, including an upper layer and a lower layer. The lower layer represents the cost of transactions with electric vehicles; the upper layer represents the cost of energy interaction with other charging stations and the power grid.

[0014] Lower-level matching: Charging stations select and match EVs based on the lower-level cost model, and directly provide charging services to EV users, thereby optimizing the low-carbon charging of electric vehicles at the lower level;

[0015] Upper-level optimized scheduling: Based on the load matched at the lower level, energy interaction is carried out with other charging stations and grid entities according to the upper-level cost model;

[0016] Optimal output and transaction scheme acquisition: The cost model of the two-layer charging station is optimized through the collaborative optimization of the upper and lower layers. After multiple interactions between the upper and lower layers, it gradually approaches the global optimal solution to obtain the optimized cost model of the two-layer charging station. The optimal scheduling scheme of electric vehicles and the optimal output and transaction scheme of the charging station are obtained by using the cost model of the two-layer charging station.

[0017] The present invention also discloses a computer system, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the electric vehicle cluster low-carbon control method.

[0018] The beneficial effects of adopting the above technical solution are as follows: The method described in this invention considers the costs for EV (electric vehicle) users from multiple dimensions. A user cost model is constructed that includes time, distance, low-carbon characteristics, and carrying capacity adjustment. By establishing a bilateral preference list for electric vehicles and charging stations, one-to-many optimal allocation is achieved using matching theory; and by dynamically updating the interaction data between users and charging stations through an iterative framework, the problem of the disconnect between actual scheduling and theoretical strategies caused by the simplicity of the user model in existing technologies is effectively solved, achieving precise matching between user needs and charging station resources.

[0019] The proposed dynamic compensation factor is deeply integrated with the distribution network's carrying capacity and low-carbon goals. Based on the theory of inter-node power transfer and carbon flow tracing, a carrying capacity factor is creatively introduced into the compensation factor. The carrying capacity factor is linked with the node's carbon potential, guiding users to areas with sufficient grid carrying capacity, thereby achieving peak shaving and valley filling and enhancing carrying capacity.

[0020] In the upper-level multi-charging-station collaborative optimization model, distribution network carrying capacity constraints, including branch power flow constraints and node voltage constraints, are explicitly considered. The ADMM (Alternating Directional Multiplier Method) is employed to decouple the coupling constraints between multiple charging stations, and the impact of node output changes on power flow is quantified through the transfer factor (SF), enabling dynamic correction of charging and discharging power. This overcomes the limitations of single-station optimization and the lack of network constraints, solves the risks of regional load imbalance and grid stability, and achieves optimized scheduling of electric vehicle clusters within the framework of improving distribution network carrying capacity. Attached Figure Description

[0021] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0022] Figure 1 This is the main flowchart of the method described in Embodiment 1 of the present invention;

[0023] Figure 2 This is a diagram illustrating the operational scenarios of the upper and lower layers in the method described in Embodiment 1 of the present invention.

[0024] Figure 3 This is a flowchart illustrating the iterative process of gradually approximating the global optimal solution in the method described in Embodiment 1 of the present invention.

[0025] Figure 4 This is a schematic diagram of the computer system described in Embodiment 2 of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0028] Example 1:

[0029] Overall, this invention discloses a low-carbon regulation method for electric vehicle clusters to improve the carrying capacity of power distribution networks, the method comprising the following steps:

[0030] First, based on carbon flow theory, the carbon emission intensity of charging station nodes and electric vehicles is calculated. Then, dynamic guidance factors are formulated according to the energy transfer between charging station nodes and the node carbon potential to guide users to actively improve the carrying capacity of the distribution network and participate in carbon emission reduction. Next, in the interaction between lower-level electric vehicles and charging stations, a charging and discharging model considering distance cost, time cost, low-carbon cost, and carrying capacity cost is constructed. Matching theory is used to achieve one-to-many optimal allocation between electric vehicles and charging stations. In the upper-level optimization, an optimal scheduling model considering the energy interaction of multiple charging stations is established. The ADMM (Alternating Direction Multiplier Method) is used to decouple the coupling constraints between multiple charging stations, strictly considering the carrying capacity constraints of the distribution network such as power flow and voltage, to achieve distributed collaborative optimization among multiple charging stations. Finally, the optimal scheduling scheme is obtained through multiple iterations at both the upper and lower levels. This method is divided into two-stage optimization at the upper and lower levels. In the lower-level CS (Power Controller) and EV users, energy interaction is conducted, guiding EVs to actively participate in improving the carrying capacity of the distribution network and realizing their low-carbon potential while meeting the needs of EV users. In the upper-level CS, energy interaction is conducted between CSs, optimizing the benefits of CSs based on the physical constraints of the distribution network.

[0031] like Figure 1 As shown below, the steps described above will be explained in detail with reference to specific content:

[0032] Step S1: Perform carbon flow tracing on the power distribution network, including charging stations and electric vehicles, to obtain the carbon density of charging station nodes and the carbon density of each electric vehicle.

[0033] Carbon emission intensity is defined as the amount of carbon emissions contained in a unit of active power production or transmission, including node carbon emission intensity and branch carbon emission intensity. For node i, its node carbon emission intensity is defined as the node-weighted average carbon emission of all injected active power, including the power flowing into other nodes and its own power generation. The calculation formula is as follows:

[0034] (1)

[0035] Wherein, the numerator represents the total injected carbon emissions, and the denominator represents the total injected active power. and This represents the carbon intensity of the generator and energy storage device at node i. This represents the carbon intensity of the electric vehicle discharging at node i. , , , These are the fuel cell injection power, energy storage unit injection power, photovoltaic unit injection power, and electric vehicle discharge injection power at node i, respectively. This indicates that the direction of electrical energy flow is from node i through line l to the receiving node. The carbon emission intensity of the branch of line l is represented by the following formula:

[0036] (2)

[0037] Energy storage devices change their carbon intensity during node charging, and their carbon content decreases as electrical energy decreases during discharging, but their carbon density does not change. Therefore, after obtaining the carbon potential of each node through carbon flow tracing theory, the carbon density of each energy storage device, such as energy storage units and electric vehicles, can be further calculated at each moment.

[0038] For energy storage devices, the carbon density of the device at different times can be calculated using the following formula:

[0039] (3)

[0040] (4)

[0041] (5)

[0042] and It is the equivalent carbon emissions of the energy storage equipment equipped at charging station m during the charging and discharging process; , These are the charging and discharging power of the energy storage device. It refers to the discharge efficiency of energy storage devices. , These are the carbon densities of the charging station m and the energy storage device at time t, respectively. and These are the state of charge and rated capacity of the energy storage device at time t, respectively. The carbon density of the energy storage device does not change during the discharge process, so it is only necessary to calculate the carbon density during the charging process, as shown in formulas (3) and (5).

[0043] Carbon emissions from electric vehicles:

[0044] (6)

[0045] (7)

[0046] (8)

[0047] in, and It is the equivalent carbon emissions during the charging and discharging process of an electric vehicle. and It is the charging and discharging power. yes The discharge efficiency, and They are and The carbon density at time t, yes The state of charge at time t, for The rated capacity of the battery.

[0048] Step S2: Formulate additional charging and discharging compensation factors that take into account low-carbon preferences and the carrying capacity of the distribution network.

[0049] The carbon density of charging station nodes and the carbon density of each electric vehicle are obtained by carbon flow tracing. Based on the carbon density of the charging station and the electric vehicle itself, an additional compensation factor for charging and discharging that reflects the low-carbon energy performance of the charging station is formulated, as follows:

[0050] (9)

[0051] (10)

[0052] in, , The carbon price for buying and selling in the external carbon market. For time t, the additional cost that electric vehicle n needs to pay when charging at charging station m is consistent with the external carbon market price in the method; however, for electric vehicles that are discharging, users are concerned not only with the carbon density of the charging station but also with their own carbon density, and the additional carbon price for discharging is... As can be seen from the formula, the higher the carbon density of the charging station itself and the lower the carbon density of the electric vehicle, the more carbon compensation the electric vehicle user will receive. That is, when the charging station mainly relies on non-clean energy for power supply, the electric vehicle will gain higher carbon compensation benefits by choosing to discharge. At this time, the electric vehicle user is more likely to choose to discharge to reduce the use of high-carbon electricity.

[0053] Meanwhile, considering only carbon flow in the calculation might lead to electric vehicle users flocking to charging stations with low carbon potential, causing power imbalances between distribution network nodes. Therefore, a carrying capacity factor is introduced. The introduction of the carrying capacity factor allows the compensation factor to reflect the true spatiotemporal value of the power situation, guiding users to actively choose charging stations with sufficient power reserves, reducing the current carrying pressure on lines between charging stations. The updated charging and discharging compensation factor is as follows:

[0054] (11)

[0055] (12)

[0056] in, The bearing capacity coefficient is a given value, while This represents the energy exchange between charging station m and other charging stations at time t. This value reflects the pressure on the distribution network's carrying capacity caused by energy sharing between charging stations, and is provided by the upper-level optimization results. When The higher the value, the higher the charging compensation factor of charging station m, and the higher the cost for electric vehicle users to charge at charging station m, thereby guiding users to switch to other charging stations and reducing the pressure on the power distribution network capacity.

[0057] Step S3: Construct an electric vehicle charging and discharging model:

[0058] Construct a cost minimization model for electric vehicles that takes into account distance, time, and carbon emissions.

[0059] electric vehicles The cost model is as follows:

[0060] (13)

[0061] in, The cost of an electric vehicle consists of four main parts, namely, charging cost. Carbon cost of charging Discharge carbon compensation and time cost The specific expressions for each part are as follows:

[0062] (14)

[0063] (15)

[0064] (16)

[0065] (17)

[0066] (18)

[0067] (19)

[0068] in, and It is the charge / discharge coefficient with a value of 0 / 1, which reflects the state of the electric vehicle at time t. The relationship is expressed by the constraint equations (21)-(23). The charging price for the charging station is consistent with the external market electricity price. and This refers to the additional carbon price of an electric vehicle (n) charging and discharging at charging station (m). Regarding time cost, it refers to the time it takes for an electric vehicle to complete charging from the selection of a charging station. Time, This includes the travel time of electric vehicles to charging stations and the waiting time for charging. and express, yes arrive The distance between them It is the average speed of the electric vehicle. This invention divides the charging time into 15-minute time slots, and electric vehicle users can choose to purchase K time slots for charging.

[0069] (20)

[0070] in, for arrive The required power It is the energy consumption per kilometer. yes The rated capacity of the battery.

[0071] Electric vehicles must meet the following constraints:

[0072] (twenty one)

[0073] (twenty two)

[0074] (twenty three)

[0075] (twenty four)

[0076] (25)

[0077] (26)

[0078] (27)

[0079] (28)

[0080] (29)

[0081] (30)

[0082] Formulas (24)-(25) represent the energy constraints for electric vehicles during driving and charging / discharging phases. , This refers to the upper and lower limits of the State of Charge (SOC) for electric vehicles; the battery level of an electric vehicle when selecting a charging station must be sufficient to reach the selected station, and the battery level when the electric vehicle leaves after charging and discharging must be greater than the user's desired battery level. Equations (29) and (30) describe the power constraints of electric vehicles, where and yes Maximum charging and discharging power.

[0083] The electric vehicle (EV) user cost model described above comprehensively considers user time, distance, and low-carbon costs. Based on this model, EV users can balance all factors to obtain better charging services. For example, time-sensitive EV users typically choose charging stations that don't require queuing and are willing to pay higher charging fees. Therefore, EV users will choose their ideal charging station based on location, charging price, and nodal carbon intensity; charging stations, within their limited interface capacity, also need to select which EVs' charging reservations to accept.

[0084] Step S4: Construct a cost model for a two-layer charging station:

[0085] Charging stations at the lower level directly provide charging services to EV users and facilitate electricity and carbon trading; at the upper level, they interact with other charging stations and the power grid, such as... Figure 2 As shown, this can increase the individual revenue of charging stations and reduce carbon emissions while meeting user needs.

[0086] The lower-level model of the charging station can be represented as:

[0087] (31)

[0088] (32)

[0089] in, The revenue generated from electricity transactions between charging stations and EV users. The cost of carbon trading between charging stations and EV users; For charging stations A group of EV users who are charging.

[0090] The upper-level model of the charging station can be represented as:

[0091] (33)

[0092] (34)

[0093] (35)

[0094] (36)

[0095] in, The cost of using distributed generator sets for charging station m, The operating cost of the energy storage units equipped for charging stations for The cost of trading electricity with other charging stations and the main power grid provider. for The cost of trading carbon with the grid operator. , , This represents the cost coefficient for distributed units. The charging and discharging cost coefficient for energy storage devices. For the output power of traditional units, and It refers to the charging and discharging power of energy storage devices. and The charging and discharging efficiency of energy storage devices. It is by Transmit to power, and It refers to the electrical energy traded between charging stations and the main power grid. and The electricity price is the transaction price.

[0096] The following constraints should be met when the charging station is in operation:

[0097] (37)

[0098] (38)

[0099] (39)

[0100] (40)

[0101] (41)

[0102] (42)

[0103] (43)

[0104] (44)

[0105] (45)

[0106] (46)

[0107] Formula (37) represents the power balance constraint, formula (38) represents the power trading constraint between upper-level charging stations, and formula (39) represents the surface... At most at the same time Each EV user is provided with charging and discharging services. Formulas (40)-(45) are the operating constraints of the energy storage device, and formula (46) is the maximum output power constraint of the traditional generator set.

[0108] Step S5: Low-carbon charging method for lower-level electric vehicles based on matching theory

[0109] It includes a collection of electric vehicles and a collection of charging stations: and The matching objective is to maximize the utility of charging stations and electric vehicles, as stated below:

[0110] (47)

[0111] The matching process must meet the following constraints:

[0112] (48)

[0113] (49)

[0114] (50)

[0115] (51)

[0116] The optimization variable is the matching matrix. Each element is a binary variable, either 0 or 1. ,when represent choose Equation (49) shows that an electric vehicle can only be allocated to one CS, while At most, it can be used for Each electric vehicle provides charging and discharging opportunities. Equation (50) indicates that the total charging demand cannot exceed the maximum capacity, while Equation (51) indicates that the charging power of each CS cannot exceed the maximum tolerable charging power.

[0117] Based on the established model, multiple electric vehicles are allocated to charging stations, and matching theory is used to solve the problem of electric vehicle allocation to charging stations in the reservation charging service scenario. Considering two aspects: 1) Each electric vehicle decides which service provider (CS) will charge it; 2) Each CS decides which vehicle it will provide charging service to, and the two sides have different preferences for each other. The following is the electric vehicle allocation procedure based on many-to-one matching.

[0118] (1) First, electric vehicle users send charging information:

[0119] Charging stations also need to send their own information, such as the carbon density of the charging station node and the additional carbon price.

[0120] (2) Establish an electric vehicle preference list: And a list of preferred charging stations: ,as follows:

[0121] (52)

[0122] (53)

[0123] (3) Selection is made based on the formed preference list. For each electric vehicle user, requests are sent sequentially from the first to the last charging station in the preference list, until a user is selected. make This means that all EV users have selected the charging stations they wish to serve. For each charging station, the system sequentially selects whether to accept or reject charging requests from EV users based on the preference list, until its charging ports are fully utilized. Or reach the maximum charging power of the charging station.

[0124] (4) Based on the above matching mechanism, query sequentially until either of the following two conditions is met:

[0125] (54)

[0126] (55)

[0127] Formula (54) indicates that the electric vehicle user's preference list is an empty set, meaning that the charging demand has been fully met. Formula (55) indicates that all charging interfaces of the charging station have been fully utilized. If neither condition is met, it proves that this round of matching is incomplete and transaction matching needs to continue, and another round of iteration needs to be executed. If either condition is met, it proves that the matching is over, and the matching result at this time can be output and transmitted to the upper layer for further optimization.

[0128] Step S6: Optimize scheduling using a higher-level model considering energy interaction among multiple charging stations:

[0129] For a multi-charging-station interconnected system, the upper-level optimization goal is to maximize the benefits of the charging station group while minimizing the sum of the charging station operating costs.

[0130] (56)

[0131] Charging stations must meet power constraints (37)-(39) to conduct electricity trading. The energy storage equipment and distributed units equipped by the charging station itself should also meet equipment constraints (40)-(46).

[0132] In addition, each charging station should meet network constraints:

[0133] (57)

[0134] (58)

[0135] (59)

[0136] (60)

[0137] (61)

[0138] The above constraints can be transformed into formulas using the Lagrange multiplier method:

[0139] (62)

[0140] (63)

[0141] in Let m be the set of independent variables for the charging station, including the output of the energy storage unit. and the output of fuel-fired units . The introduced Lagrange multipliers are used to decouple the power balance constraints between each pair of charging stations. To the side road Lagrange multipliers related to power flow constraints. If the power flow calculation results show branch... If the current exceeds the limit, the energy hub node needs to be changed based on the shift factor (SF). The electrical output is used to control the tidal current. Reduce to the upper limit This can be achieved by updating the Lagrange multipliers corresponding to the branch flow constraints:

[0142] (64)

[0143] In the formula, the transfer factor SF is used to represent the energy hub Changes in electrical energy output Shizhi Road Current changes The electrical output of each energy hub node needs to be varied based on the transfer factor SF in order to control the power flow. Reduce to the upper limit The following. (Used) replace In equation (60) It can be decomposed and remodeled for each charging station. .

[0144] (65)

[0145] in This is the set of all branches.

[0146] After decoupling, for each charging station:

[0147] (66)

[0148] (67)

[0149] in The gain parameter is adjusted based on the number of iterations and the deviation in electricity trading to improve the convergence speed. These are parameters that are set manually. This is an adjustment factor. ST is the threshold for trading volume mismatch. This represents the maximum number of iterations.

[0150] Each charging station updates its trading strategy locally and exchanges decision information such as electricity prices and electricity trading volumes with other charging stations. By solving the decoupled objective function, we can obtain the output of each charging station's energy storage units, the output of its fossil fuel units, the electricity prices between upper-level stations, and the electricity trading volumes between upper-level charging stations.

[0151] Step S7: Upper and lower layer collaborative optimization. Through multiple interactions between upper and lower layers, the global optimal solution is gradually approached.

[0152] like Figure 3 As shown, after decoupling and optimization, the upper layer yields the first round of optimization results, namely the output of the charging station's energy storage unit. Fuel unit output Electricity transaction price between upper-level charging stations and the transaction volume between upper-level charging stations The results obtained at this point are then evaluated for convergence. If the results converge, the optimization results of the upper and lower layers in this round are output; if the results do not converge, the optimization results of the upper layer in this round are matched with the matching results of the lower layer in this round (electric vehicle charging and discharging power). , Returning to step one, perform carbon flow tracing again, calculate the node carbon potential at this point, and combine it with the optimization results from the previous layer. The compensation factor is re-formed, and lower-level matching and upper-level optimization are performed. The results of the new round are judged. This process is repeated multiple times until the results converge, which will yield the optimal scheduling scheme for electric vehicles and the optimal output and trading scheme for charging stations.

[0153] In summary, the method described in this invention involves two-stage optimization at the upper and lower levels. At the lower level, energy interaction occurs between the CS (charging station) and EV (electric vehicle) users. While meeting the needs of EV users, this guides EVs to actively participate in improving the distribution network's carrying capacity and unleashing their low-carbon potential. At the upper level, energy interaction occurs between CSs, optimizing the benefits of each CS within the physical constraints of the distribution network. Through this two-layer interactive iteration, electric vehicles are transformed from a burden on the grid into a control resource, improving the distribution network's capacity to accommodate a high proportion of electric vehicles and renewable energy while meeting user needs.

[0154] For the charging optimization model between EVs and CSs, considering the distance cost, time cost, low-carbon cost, and regulation capacity cost of EV users, a compensation factor based on the power transfer between nodes and carbon emission density is established to incentivize users to actively participate in improving the distribution network's carrying capacity and actively participate in the consumption of new energy. Furthermore, based on matching theory, a one-to-many optimization matching between EVs and CSs is achieved, reducing the deviation between strategy and actual response caused by ignoring the diverse costs of users, and improving the user responsiveness and actual effect of the scheduling strategy.

[0155] For the multi-CS optimization model considering energy interaction, while taking into account the energy interaction between multiple charging stations, strict constraints such as power flow and voltage capacity of the distribution network are also considered. The alternating direction multiplier method is used to solve the energy interaction model between multiple charging stations in a distributed manner, and the output is dynamically adjusted by the transfer factor (SF) to ensure that the scheduling scheme is not only economically optimal but also physically feasible, thereby realizing the positive role of electric vehicle clusters in improving the capacity of the distribution network.

[0156] Example 2

[0157] In one exemplary embodiment, the present invention also provides a computer system, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, the computer system includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the low-carbon control method for electric vehicle clusters described in Embodiment 1.

[0158] Those skilled in the art will understand that Figure 4 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer system to which the present application is applied. A specific computer system may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer system is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0159] In one exemplary embodiment, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0160] In one exemplary embodiment, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0161] 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.

[0162] Those skilled in the art will understand that all or part of the processes in 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. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile 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).

[0163] 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 units, data processing logic units, etc., and are not limited to these.

[0164] 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 specification.

[0165] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A low-carbon regulation method for a cluster of electric vehicles to improve the carrying capacity of a distribution network, characterized in that The method comprises the following steps: Obtaining carbon density: carbon flow tracing is performed on a power distribution network comprising charging stations and electric vehicles to obtain carbon density of the charging station nodes and carbon density of each electric vehicle; Formulating a charging and discharging compensation factor: the charging and discharging compensation factor is formulated based on the carbon density of the charging station nodes, the carbon density of each electric vehicle and the power distribution network carrying capacity; Building a double-layer charging station cost model: a double-layer charging station cost model is built according to user time, distance and low-carbon cost, comprising an upper layer and a lower layer, the lower layer being a cost of transaction with an electric vehicle, and the upper layer being a cost of energy interaction with other charging stations and a power grid main body; Lower layer matching: the charging station selects and matches with an EV according to the lower layer cost model, directly provides charging service to the EV user and performs lower layer electric vehicle low-carbon charging optimization; Upper layer optimization scheduling: on the basis of the load after lower layer matching, energy interaction is performed with other charging stations and a power grid main body according to the upper layer cost model; Optimal output and transaction scheme acquisition: the double-layer charging station cost model is cooperatively optimized in the upper and lower layers, and after multiple upper and lower layer interactions, the global optimal solution is gradually approached, an optimized double-layer charging station cost model is obtained, and the electric vehicle optimal scheduling scheme and the charging station optimal output and transaction scheme are obtained by using the double-layer charging station cost model; The method for obtaining the charging and discharging compensation factor comprises: According to the carbon density of the charging station and the electric vehicle, a charging and discharging additional compensation factor reflecting the low-carbon property of the charging station energy is formulated, and the formula is as follows: （9） （10） wherein, , is the carbon price bought and sold by the external carbon market, is the cost that the electric vehicle n needs to pay additionally when charging at the charging station m at time t; On this basis, a carrying capacity factor is introduced, the carrying capacity factor is used to make the compensation factor reflect the real-time and space value of the power situation, guide the user to actively select the charging station with sufficient power surplus for charging, reduce the line current carrying pressure among the charging stations, and update the charging and discharging compensation factor as follows: （11） （12） wherein, is a given value, is the power exchange between charging station m and other nodes at time t, which is used to reflect the pressure on the power distribution network carrying capacity brought by energy sharing between charging stations, provided by the upper layer optimization result; when The higher the value, the higher the charging compensation factor of charging station m, and the higher the cost of electric vehicle users charging at charging station m, thereby guiding users to charge at other charging stations, reducing the pressure on the carrying capacity of the power distribution network. 2.The low-carbon regulation method for an electric vehicle cluster to improve the power distribution network carrying capacity, according to claim 1, wherein The method for obtaining the carbon density of the charging station nodes and the carbon density of each electric vehicle comprises the following steps: The carbon emission intensity is defined as the carbon emission amount contained in the unit active power production or transmission process, including node carbon emission intensity and branch carbon emission intensity; for a node i, the node carbon emission intensity of the node i is defined as the node weighted average carbon emission amount of all injected active power of the node i, including the inflow power of other nodes and the power generation amount of itself, and the calculation formula is as follows: （1） wherein the numerator represents the total injected carbon emissions and the denominator represents the total injected active power, and represents the carbon intensity of the generator and energy storage device of node i, represents the carbon intensity of the discharging electric vehicle of node i; are the fuel unit injection power, energy storage unit injection power, photovoltaic unit injection power and electric vehicle discharging injection power of the i-th node, respectively; represents that the power flow is from node i to the receiving node through line l, represents the branch carbon emission intensity of line l, and its calculation formula is:​​​ （2） For the energy storage equipment, the carbon density of each time point of the equipment is calculated by the following formula: （3） （4） （5） wherein, and is the equivalent carbon emission of the energy storage device equipped in the charging station m in the charging and discharging process; , are the charging and discharging power of the energy storage device, respectively, is the discharging efficiency of the energy storage device, , are the carbon densities of the charging station m and the energy storage device at time t, respectively, and are the state of charge and the rated capacity of the energy storage device at time t, respectively; Electric vehicle carbon emission: （6） （7） （8） wherein, and are equivalent carbon emissions in the charging and discharging process of the electric vehicle n; and are charging and discharging power, is discharging efficiency of and are and carbon density at time t, is state of charge at time t, is battery rated capacity of 3.The low-carbon regulation method for an electric vehicle cluster to improve the power distribution network carrying capacity according to claim 1, wherein The method for building an electric vehicle charging and discharging model comprises the following steps: Electric vehicle The cost model for the electric vehicle is as follows: （13） represents the cost of the electric vehicle, including four parts, namely the charging cost , the charging carbon cost , the discharging carbon compensation and the time cost ; the specific expressions of each part are as follows: （14） （15） （16） （17） （18） （19） wherein and are charge-discharge coefficients with values of 0 or 1, reflecting the state of the electric vehicle at time t, which is expressed by constraints (21) - (23); is the charging price of the charging station, and is the charge-discharge additional carbon price of the electric vehicle n at the charging station m; for the time cost, the electric vehicle experiences a time of from the selection of the charging station to the completion of charging, including the route time of the electric vehicle to the charging station and the waiting time for charging, which is expressed by and is the distance between and , is the average speed of the electric vehicle, and for , the charging time is divided into 15-minute periods, and the electric vehicle user can choose to purchase K periods for charging:​ （20） wherein, is to the amount of electricity required, is the amount of electrical energy consumed per kilometer, is the rated capacity of the battery; The electric vehicle satisfies the following constraint conditions: （21） （22） （23） （24） （25） （26） （27） （28） （29） （30） Formulas (24)-(25) represent the energy constraints for electric vehicles during driving and charging / discharging phases. , This refers to the upper and lower limits of the State of Charge (SOC) for electric vehicles; the battery level of an electric vehicle when selecting a charging station must be sufficient to reach the selected station, and the battery level when the electric vehicle leaves after charging and discharging must be greater than the user's desired battery level. Formulas (29) and (30) are used to describe the power constraints of electric vehicles, where and yes Maximum charging and discharging power. 4.The low-carbon regulation method for an electric vehicle cluster to improve the power distribution network carrying capacity according to claim 1, wherein, The method for building a double-layer charging station cost model comprises the following steps: The lower layer model of the charging station is represented as: （31） （32） wherein: a revenue for the charging station to conduct electricity transaction with EV users, a cost for the charging station to conduct carbon transaction with EV users; a charging station, a set of EV users charged at the charging station; The upper layer model of the charging station is represented as: （33） （34） （35） （36） Cost of using distributed generating units for charging station m, Operating cost of energy storage units equipped for charging station, For Cost of electricity trading with other charging stations and grid entities, For Cost of carbon trading with grid entities; wherein , , Cost coefficient of distributed generating units, Charging and discharging cost coefficient of energy storage devices, Output power of traditional generating units, And Charging and discharging power of energy storage devices, And Charging and discharging efficiency of energy storage devices; Is the power transmitted to , , And Electricity traded by charging station with grid entities, And Transaction electricity price; The charging station satisfies the following constraints during operation: （37） （38） （39） （40） （41） （42） （43） （44） （45） （46） where equation (37) is the power balance constraint, equation (38) is the power trading constraint between upper charging stations, and equation (39) represents at most EV users at the same time, and equations (40)-(45) are the energy storage device operation constraints, and equation (46) is the maximum output power constraint of the traditional generator set.

5. The low-carbon regulation method for the power-assisted vehicle cluster according to claim 1, wherein, The method for lower layer electric vehicle low-carbon charging optimization comprises the following steps: There is a set of electric vehicles and a set of charging stations: and The matching goal is to maximize the utility of charging stations and electric vehicles, which is expressed as follows: （47） The matching process needs to satisfy the following constraint conditions: （48） （49） （50） （51） where the optimization variables are the matching matrix , where each element is a binary variable 0 or 1, , when represents selection ; equation (49) indicates that one electric vehicle can only be assigned to one CS, while up to electric vehicles can be provided with charging and discharging opportunities at the same time; equation (50) indicates that the total amount of charging demand cannot exceed the maximum capacity, while equation (51) indicates that the charging power of each CS cannot exceed the tolerable maximum charging power; Based on the established model, a plurality of electric vehicles are distributed to charging stations, and the matching theory is used to solve the problem of distributing electric vehicles to charging stations in the pre-booking charging service scenario: (1) First, the electric vehicle user sends charging information: the charging station sends its own information; (2) Establish a list of preferences for the electric vehicle: and a list of preferences for the charging station: as follows: （52） （53） (3) For each electric vehicle user, send the request from the first to the last charging station in the preference list until make , for each charging station, accept or reject the charging request sent by the electric vehicle user in turn according to the preference list until the charging interface is fully used at this time , or the maximum charging power of the charging station is reached; (4) Based on the above matching mechanism, the following two conditions are sequentially inquired until any one of the two conditions is met: （54） （55） Formula (54) represents that the preference list of the electric vehicle user is an empty set, that is, the charging demand has been completely satisfied, formula (55) represents that all charging interfaces of the charging station are completely used; if both conditions are not met, it is proved that this round of matching is not complete, and the transaction matching is continued, and a round of iteration is re-executed; if any one of the conditions is met, it is proved that the matching is ended, and the matching result at this time is output, and is transmitted to the upper model to continue optimization.

6. The low-carbon regulation method for the power-assisted vehicle cluster according to claim 1, wherein, The method for the upper-layer optimization scheduling of the double-layer charging station cost model through multi-charging station energy interaction includes the following steps: The upper-layer optimization target is to minimize the sum of charging station operation costs: （56） Each charging station should satisfy network constraints: （57） （58） （59） （60） （61） The above constraints can be converted into formulas by using the Lagrange multiplier method: （62） （63） where is the set of independent variables for charging station m, including the energy storage unit output and the fuel unit output ; is the introduced Lagrange multiplier to decouple the power balance constraints between two charging stations, is the Lagrange multiplier associated with the branch power flow constraint; if the result of the power flow calculation shows that the power flow of branch is out of limit, the electrical energy output of the energy hub node is changed based on the transfer factor to reduce the power flow to the upper limit value Below, this is achieved by updating the Lagrange multiplier corresponding to the branch power flow constraint: （64） In the formula, the transfer factor SF is used to represent the energy hub Changes in electrical energy output Shizhi Road Current changes The electrical output of each energy hub node needs to be varied based on the transfer factor SF in order to control the power flow. Reduce to the upper limit The following; replace In equation (60) It can be decomposed and remodeled for each charging station. ; （65） wherein is the set of all branches; After decoupling, for each charging station: （66） （67） wherein is a gain parameter, for improving the convergence speed, the gain parameter is adjusted according to the iteration number and the electricity trading deviation; is a man-made parameter, is an adjustment coefficient, ST is a threshold value of the trading volume mismatch, is the maximum iteration number.

7. The low-carbon regulation method for the power-assisted vehicle cluster according to claim 1, wherein, The method for the optimal scheduling scheme of the electric vehicle and the optimal output and transaction scheme of the charging station includes the following steps: The upper layer of the double-layer charging station cost model is decoupled and optimized to obtain a round of optimization results: charging station energy storage unit output , fuel unit output , transaction electricity price between upper layer charging stations , and transaction volume between upper layer charging stations , the results obtained at this time are judged for convergence, and if the results converge, the upper and lower layer optimization results of this round are output; If the result does not converge, the upper layer optimization result of this round and the lower layer matching result of this round are returned to the first step to re-perform carbon flow tracing, calculate the node carbon potential at this time, and combine the upper layer optimization result The compensation factor is reformed, the lower layer matching and the upper layer optimization are performed, the new round of results is judged, and the iteration is performed for multiple times until the result converges, and the optimal scheduling scheme of the electric vehicle and the optimal output and transaction scheme of the charging station are obtained.

8. A computer system comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the low-carbon regulation method of the electric vehicle cluster for improving the power grid carrying capacity according to any one of claims 1-7.

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