Power distribution network low-carbon optimization method for electric car cluster clustering and stepped carbon transaction

Abstract

The invention discloses a power distribution network low-carbon optimization method for electric vehicle cluster clustering and stepped carbon transaction, and relates to the technical field of low-carbon optimization, and the method comprises the following steps: carrying out the clustering analysis of an electric vehicle charging station based on a preset adaptive FCM algorithm; constructing an electric vehicle regional aggregator according to a clustering analysis result, and controlling an electric vehicle cluster; a stepped carbon transaction mechanism is introduced to guide and control the carbon emission of the intelligent power distribution network; and with the minimum running cost of the intelligent power distribution network and the minimum running cost of the electric vehicle aggregator as the target, constructing a master-slave game double-layer optimization model of the intelligent power distribution network operator and the electric vehicle aggregator considering the stepped carbon transaction, and regulating and controlling the charging power of the electric vehicle by controlling the electricity price. The method is used for solving the low-carbon optimization problem of disorderly access of large-scale electric vehicles to the intelligent power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon optimization, and more specifically, to a distribution network low-carbon optimization method of tram cluster clustering and step-by-step carbon trading. Background Art

[0002] With the development of modern industry, carbon dioxide emissions continue to rise, environmental problems are becoming more serious, and people are paying more and more attention to global warming. In the era of global low-carbon development, the disorderly charging of large-scale electric vehicles in the smart distribution network (SDN) causes sudden changes in the load in the network, and carbon emissions will surge in some periods. Adopting a reasonable low-carbon optimization operation strategy for SDN can effectively improve energy utilization efficiency and reduce carbon emissions. Domestic and foreign scholars have conducted in-depth research on the low-carbon optimization operation of power systems, and the development of low-carbon optimization scheduling has become a research hotspot in the international energy field.

[0003] Some existing research methods do not perform cluster analysis based on actual data of electric vehicle charging stations when constructing electric vehicle clusters, but use fuzzy classification and partitioning methods, which reduces the accuracy of electric vehicle cluster division. Some research methods are of great significance for the carbon quota research of thermal power units and electric vehicles, but they do not involve the impact of all units and a large number of electric vehicles in microgrids on the low-carbon characteristics of the system, and the traditional carbon trading mechanism is also considered for carbon trading. Some research methods have formulated emission reduction measures, which are of great significance for the economic dispatch of microgrids, but they do not consider the positive role of electric vehicles in demand response.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a low-carbon optimization method for distribution networks of tram cluster clustering and step-by-step carbon trading. By comprehensively considering the positive role of the step-by-step carbon trading mechanism and electric vehicles, a two-layer optimization scheduling model including electric vehicles, photovoltaics, wind power and thermal power units is constructed to solve the low-carbon optimization problem of disorderly access of large-scale electric vehicles to the smart distribution network.

[0006] To achieve the above object, the present invention provides the following technical solutions: The low-carbon optimization method of distribution network with electric vehicle cluster clustering and step-by-step carbon trading includes the following steps: clustering analysis of electric vehicle charging stations based on a preset adaptive FCM algorithm; constructing electric vehicle regional aggregators according to the clustering analysis results to control electric vehicle clusters; introducing a step-by-step carbon trading mechanism to guide and control the carbon emissions of smart distribution networks; with the goal of minimizing the operating costs of smart distribution networks and electric vehicle aggregators, constructing a master-slave game two-layer optimization model of smart distribution network operators and electric vehicle aggregators considering step-by-step carbon trading, and regulating the charging power of electric vehicles by controlling electricity prices.

[0007] In a preferred embodiment, the cluster analysis of electric vehicle charging stations based on the preset adaptive FCM algorithm is specifically as follows: taking the daily load curve, daily voltage curve, daily current curve and daily load rate of the electric vehicle charging station as characteristic indicators of the controllable load, constructing a sample data set in which each sample is an m-dimensional vector; performing data normalization on the sample data set; obtaining the covariance matrix, calculating the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to them; arranging the calculated eigenvectors into a matrix in positive order according to their corresponding eigenvalues, taking the first k rows to form the matrix P, and obtaining a sample data set reduced to k dimensions; determining the optimal clustering number c according to the preset adaptive function of the clustering number, and dividing the sample data set into c categories; and clustering analysis of electric vehicle charging stations based on the FCM algorithm.

[0008] In a preferred embodiment, the introduction of a stepped carbon trading mechanism to guide and control the carbon emissions of the smart distribution network is specifically as follows: a carbon emission rights quota model is constructed based on the carbon emission sources in the smart distribution network, where the carbon emission sources in the smart distribution network include upstream electricity purchases, electric vehicles, coal-fired units, and photovoltaic units; the carbon emissions paid by electric vehicle owners for charging are eliminated to construct an actual carbon emission model; the carbon emission rights trading amount is calculated based on the actual carbon emissions and the carbon emission rights quota, combined with a preset carbon emission rights trading amount calculation formula, to construct a stepped carbon trading model.

[0009] In a preferred embodiment, the goal is to minimize the operating cost of the smart distribution network and the operating cost of the electric vehicle aggregator, and to construct a master-slave game two-layer optimization model of the smart distribution network operator and the electric vehicle aggregator considering the step-by-step carbon trading, and to regulate the charging power of the electric vehicle by controlling the electricity price. Specifically, the master-slave game upper model is constructed according to the energy purchase cost of the distribution network, the carbon trading cost of the distribution network, the abandoned light cost, the operating cost of the thermal power unit, the operating cost of the thermal power unit and the income from the distribution network operator selling electricity to the electric vehicle aggregator; the minimum operating cost of the upper distribution network operator is obtained according to the master-slave game upper model; the master-slave game lower model is constructed according to the electricity purchase cost of the electric vehicle, the battery loss cost of the electric vehicle and the income from the electric vehicle aggregator selling electricity to the distribution network operator; the minimum operating cost of the lower electric vehicle aggregator is obtained according to the master-slave game lower model; according to the master-slave game relationship between the distribution network operator and the electric vehicle aggregator, combined with the preset first constraint, the charging power of the electric vehicle is regulated by controlling the electricity price.

[0010] The technical effects and advantages of the low-carbon optimization method for distribution network of tram cluster clustering and step-by-step carbon trading of the present invention are as follows: The present invention optimizes the adaptive FCM algorithm for cluster analysis of electric vehicle charging stations, and constructs a master-slave game two-layer optimization model of smart distribution network operators and electric vehicle aggregators that considers stepped carbon trading, thereby regulating and optimizing the carbon emissions of large-scale electric vehicles that are disorderly connected to the smart distribution network, and effectively solves the problems raised in the above background technology. After considering the electric vehicle aggregators, the electric vehicles in the system are charged in an orderly manner, which reduces the system operation cost and plays a role in peak shaving and valley filling. In addition, the present invention takes into account the mutual game between the distribution network operator and the electric vehicle aggregator, and the distribution network operator controls the electricity price and then affects the charging power of the electric vehicle, thereby realizing the economical and efficient operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic flow chart of the low-carbon optimization method for distribution network of tram cluster clustering and step-by-step carbon trading of the present invention.

[0012] Figure 2 Flow chart of the adaptive FCM algorithm preset in the present invention.

[0013] Figure 3 It is a three-dimensional schematic diagram of the cluster analysis results of the electric vehicle charging station according to an embodiment of the present invention.

[0014] Figure 4 This is a schematic diagram of cluster analysis results of the first type of electric vehicle charging station according to an embodiment of the present invention.

[0015] Figure 5This is a schematic diagram of cluster analysis results of the second type of electric vehicle charging station according to an embodiment of the present invention.

[0016] Figure 6 This is a schematic diagram of the cluster analysis results of the third type of electric vehicle charging station according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1, Figure 1 The invention provides a low-carbon optimization method for distribution network with tram cluster clustering and step-by-step carbon trading, which includes the following steps: S1, cluster analysis of electric vehicle charging stations based on the preset adaptive FCM algorithm; S2, constructing regional aggregators of electric vehicles based on cluster analysis results to control the electric vehicle clusters; S3, introduce a tiered carbon trading mechanism to guide and control carbon emissions from smart distribution networks; S4, with the goal of minimizing the operating cost of the smart distribution network and the total operating cost of the electric vehicle aggregator, constructs a master-slave game two-layer optimization model between the smart distribution network operator and the electric vehicle aggregator considering the stepped carbon trading, and regulates the charging power of electric vehicles by controlling the electricity price.

[0019] In this embodiment, the cluster analysis of electric vehicle charging stations based on the preset adaptive FCM algorithm is specifically as follows: Taking the daily load curve, daily voltage curve, daily current curve and daily load rate of electric vehicle charging stations as characteristic indicators of controllable load, a sample data set is constructed in which each sample is an m-dimensional vector; Perform data normalization on the sample data set; Find the covariance matrix and calculate the eigenvalues ​​of the covariance matrix and its corresponding eigenvectors; Arrange the calculated eigenvectors into a matrix in the positive order of their corresponding eigenvalues, take the first k rows to form the matrix P, and obtain a sample data set reduced to k dimensions; Determine the optimal number of clusters c according to the preset adaptive function of the number of clusters, and divide the sample data set into c categories; Cluster analysis of electric vehicle charging stations is performed based on the FCM algorithm.

[0020] It should be noted that when the variance corresponding to a principal component accounts for a higher proportion of the total variance, it means that the principal component has a higher contribution rate to the characteristics of the original data set, that is, it can reflect more information of the original sample set. The contribution rate and importance of the principal components after dimensionality reduction are arranged in descending order, and are generally selected until the cumulative variance accounts for more than 80%.

[0021] The Fuzzy-C Means clustering algorithm is a partition-based clustering algorithm. The concept of membership is introduced in the algorithm. Different from the traditional "hard partitioning", it adopts the "soft partitioning" clustering method of fuzzy partitioning, relying on continuous iterative updates of the cluster center matrix and the membership matrix to finally achieve the optimal solution of the objective function.

[0022] In this embodiment, the adaptive function of the preset number of clusters is specifically formulated as follows:

[0023]

[0024] In the formula, is the center vector of the sample data, is the sample data, is the i-th cluster center, m is the fuzzy weight coefficient, c is the number of clusters, is an element in the membership matrix.

[0025] In this embodiment, for the sample data set , , that is, each sample in the sample data is an m-dimensional vector, Divided into c ( ) class, cluster center matrix Membership Matrix ,in satisfy:

[0026] Cluster centers of FCM algorithm , membership And the algorithm objective function for:

[0027]

[0028]

[0029] In the formula, For sample data With the i-th cluster center The Euclidean distance of , ; m is the fuzzy weighting coefficient. The size of m is proportional to the fuzziness of the classification. Generally, m is 2.

[0030] In this embodiment, the step-by-step carbon trading mechanism is introduced to guide and control the carbon emissions of the smart distribution network, specifically: Constructing a carbon emission quota model based on carbon emission sources in the smart distribution network, wherein the carbon emission sources in the smart distribution network include upstream power purchase, electric vehicles, coal-fired units and photovoltaic units; Excluding the carbon emissions paid by electric car owners for charging, the actual carbon emissions model is constructed; According to the actual carbon emissions and carbon emission rights quota, the carbon emission rights trading amount is calculated based on the preset carbon emission rights trading amount calculation formula, and a step-by-step carbon trading model is constructed.

[0031] In this embodiment, the specific calculation formula of the carbon emission quota model is:

[0032] In the formula, , , , and They are the carbon emission quotas of SDN, upstream power purchase, EVs, CHP and PV; , , They are the carbon emission quotas for coal-fired units within SDN, coal-fired units outside SDN, and PV unit electricity consumption; is the amount of electricity purchased by the superior at time t; is the unit power generation of CHP at time t; is the unit power generation of PV at time t; is the driving distance of the i-th EV at time t; and They are the carbon emission coefficient per unit mileage of fuel vehicles and the carbon emission coefficient per unit electricity generation; is the charging power of the i-th EV at time t.

[0033] In this embodiment, the specific calculation formula of the actual carbon emission model is:

[0034] In the formula, , , and They are the actual carbon emissions of SDN, upper-level power purchase, coal-fired units, and photovoltaic units; , , They are the carbon emission calculation parameters of the main grid coal-fired units and the coal-fired units in the SDN; is the carbon emission intensity of unit electricity of thermal power unit i; is the new energy reserve coefficient of the thermal power generating unit; The power output of photovoltaic power plant i.

[0035] In this embodiment, the preset carbon emission rights trading amount calculation formula is as follows:

[0036] The specific calculation formula of the step-by-step carbon trading model is:

[0037] In the formula, Carbon trading quota for smart distribution networks, is the carbon trading cost, As the basic carbon price, is the length of the carbon trading interval, is the carbon price growth rate.

[0038] In this embodiment, the goal is to minimize the operating cost of the smart distribution network and the operating cost of the electric vehicle aggregator, and to build a master-slave game two-layer optimization model of the smart distribution network operator and the electric vehicle aggregator considering the step-by-step carbon trading, and to control the charging power of the electric vehicle by controlling the electricity price, specifically: The upper-level model of the master-slave game is constructed based on the energy purchase cost of the distribution network, the carbon trading cost of the distribution network, the cost of abandoned solar power, the operating cost of thermal power units, the operating cost of thermal power units, and the revenue from the distribution network operator selling electricity to electric vehicle aggregators. The minimum operating cost of the upper distribution network operator is obtained according to the upper model of the master-slave game; The lower-level model of the master-slave game is constructed based on the electricity purchase cost of electric vehicles, the battery loss cost of electric vehicles, and the electricity sales revenue of electric vehicle aggregators to distribution network operators; Obtain the minimum operating cost of the lower-level electric vehicle aggregators based on the master-slave game lower-level model; According to the master-slave game relationship between distribution network operators and electric vehicle aggregators, combined with the preset first constraint, the charging power of electric vehicles is regulated by controlling electricity prices.

[0039] In this embodiment, the master-slave game upper model has a specific calculation formula as follows:

[0040]

[0041]

[0042]

[0043]

[0044] In the formula, is the minimum operating cost of the upper distribution network operator, is the energy purchase cost, is the electricity purchase price at time t, is the tiered carbon trading cost, is the cost of abandoned light, is the unit abandonment penalty cost, is the abandoned optical power at time t, is the operating cost of thermal power units, and Normal operation cost and startup and shutdown cost of thermal power units, is the operation status of the thermal power unit at time t, N is the number of units, T is the scheduling cycle, is the quadratic cost coefficient, is the first-order cost coefficient, is the constant cost coefficient, is the start-up and shutdown cost of thermal power units, The electricity sales revenue from distribution network operators to lower-level electric vehicle aggregators, is the electricity price at time t, is the electricity sales power of the distribution network operator at time t.

[0045] In this embodiment, the master-slave game lower layer model has a specific calculation formula as follows:

[0046]

[0047]

[0048]

[0049] In the formula, is the minimum operating cost of the lower-tier EV aggregator, For the cost of electric vehicles, The battery loss cost of electric vehicles, and are the electric vehicle power loss coefficients, The revenue from electricity sales by EV aggregators to distribution network operators.

[0050] In this embodiment, the specific calculation formula of the preset first constraint condition is:

[0051]

[0052]

[0053]

[0054]

[0055] In the formula, is the photovoltaic output power at time t, is the upper limit of photovoltaic output power, and are the lower and upper limits of thermal power unit output, and are the maximum downward and upward climbing rates of thermal power units, and are the state of charge and power consumption of the electric vehicle at time t, and are the charging efficiency and discharging efficiency of electric vehicles, and are the charging and discharging power of the electric vehicle at time t, and are the maximum and minimum state of charge of the electric vehicle, is the SOC value at the end of charging, is the mileage of the electric vehicle at time t, is the power consumption per unit mileage of electric vehicles, and are the maximum value of charging power and the maximum value of discharging power, respectively. is the electric load power at time t, is the charging power of the electric vehicle cluster at time t.

[0056] Embodiment 2 comprises the following steps: Cluster analysis of electric vehicle charging stations is performed based on the preset adaptive FCM algorithm; Based on the cluster analysis results, regional aggregators of electric vehicles are constructed to control the electric vehicle clusters; Introduce a tiered carbon trading mechanism to guide and control carbon emissions from smart distribution networks; With the goal of minimizing the operating costs of smart distribution networks and electric vehicle aggregators, a master-slave game two-layer optimization model between smart distribution network operators and electric vehicle aggregators considering stepped carbon trading was constructed, and the charging power of electric vehicles was regulated by controlling electricity prices.

[0057] In this embodiment, a cluster analysis is performed on 34 groups of electric vehicle charging stations in a certain area based on a preset adaptive FCM algorithm.

[0058] In this embodiment, the charging loads of different categories of electric vehicle charging stations are formed according to the voltage, current, and load rate, and then an electric vehicle aggregator is constructed to control the electric vehicle cluster.

[0059] Figure 3 A three-dimensional schematic diagram obtained by clustering analysis based on a preset adaptive FCM algorithm in this embodiment is given, where the three coordinate axes are voltage, current and load rate, respectively. Different types of electric vehicle charging stations are marked with points of different grayscales.

[0060] Figure 4 , Figure 5 and Figure 6 The cluster analysis results of three different categories of electric vehicle charging stations are shown below. Figure 4 It can be seen that electric vehicles are at their peak power consumption at 8 pm and 2 am, and the rest of the time is at their low point. Figure 5 This is the second type of cluster curve. It can be seen that this type of curve is in a state of large fluctuation within a day, and the load curve has no obvious regularity; Figure 6 This is the third type of clustering curve. It can be seen that there are extremely steep load fluctuations at night and at noon, and the changes in other periods are not large, basically in a smooth straight line state.

[0061] In this embodiment, a coal-fired unit with typical characteristics is selected in the distribution network for simulation analysis.

[0062] The benchmark price of carbon emissions is set at 180 yuan / t, 24 hours is set as a cycle, and 1 hour is set as a scheduling time interval.

[0063] Based on the master-slave game two-layer optimization model, this embodiment sets four comparison scenarios, as shown in Table 1. Scenario 1, traditional carbon trading mechanism, electric vehicles participate in optimal scheduling with a fixed time-of-use electricity price; Scenario 2, traditional carbon trading mechanism, electric vehicles participate in optimal scheduling with an optimized electricity price; Scenario 3, step-by-step carbon trading mechanism, electric vehicles participate in optimal scheduling with a fixed time-of-use electricity price; Scenario 4, step-by-step carbon trading mechanism, electric vehicles participate in optimal scheduling with an optimized electricity price.

[0064] According to the above scenarios, the typical daily carbon emissions and comprehensive operating costs of the new distribution network under the four scenarios are shown in Table 2. In Table 2, the upper and lower operating costs and total operating costs of Scenario 1 and Scenario 2 are reduced, which shows the superiority of this embodiment for electric vehicles to adopt optimized electricity prices. Compared with Scenario 1, the total daily operating cost of Scenario 2 is reduced by 7.85%, indicating that the use of optimized electricity prices has a great effect on electric vehicle aggregators to adjust the charging time period of electric vehicles, and also reduces the operating costs of the system. After considering the use of tiered carbon prices, Scenario 3 has significantly reduced the upper and lower operating costs and the total carbon emissions of the system compared with Scenario 1, with carbon emissions reduced by 229kg and the total operating costs of the system reduced by 19,005,409 yuan. It can be seen from Scenario 4 that after adopting tiered carbon trading and optimized electricity prices, the carbon emissions and operating costs of the system have been more significantly reduced.

[0065] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0066] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0067] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0068] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0069] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0070] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0071]

[0072] Table 1

[0073] Table 2

Claims

1. A low-carbon optimization method for distribution network based on tram clustering and step-by-step carbon trading, characterized in that: The steps include: Cluster analysis of electric vehicle charging stations is performed based on the preset adaptive FCM algorithm; Based on the cluster analysis results, regional aggregators of electric vehicles are constructed to control the electric vehicle clusters; Introduce a tiered carbon trading mechanism to guide and control carbon emissions from smart distribution networks; With the goal of minimizing the operating costs of smart distribution networks and electric vehicle aggregators, a master-slave game two-layer optimization model between smart distribution network operators and electric vehicle aggregators considering stepped carbon trading was constructed, and the charging power of electric vehicles was regulated by controlling electricity prices.

2. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 1 is characterized in that: The cluster analysis of electric vehicle charging stations based on the preset adaptive FCM algorithm is specifically as follows: Taking the daily load curve, daily voltage curve, daily current curve and daily load rate of electric vehicle charging stations as characteristic indicators of controllable load, a sample data set is constructed in which each sample is an m-dimensional vector; Perform data normalization on the sample data set; Find the covariance matrix and calculate the eigenvalues ​​of the covariance matrix and its corresponding eigenvectors; Arrange the calculated eigenvectors into a matrix in the positive order of their corresponding eigenvalues, take the first k rows to form the matrix P, and obtain a sample data set reduced to k dimensions; Determine the optimal number of clusters c according to the preset adaptive function of the number of clusters, and divide the sample data set into c categories; Cluster analysis of electric vehicle charging stations is performed based on the FCM algorithm.

3. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 2 is characterized in that: The introduction of a tiered carbon trading mechanism to guide and control carbon emissions of smart distribution networks is as follows: Constructing a carbon emission quota model based on carbon emission sources in the smart distribution network, wherein the carbon emission sources in the smart distribution network include upstream power purchase, electric vehicles, coal-fired units and photovoltaic units; Excluding the carbon emissions paid by electric car owners for charging, the actual carbon emissions model is constructed; According to the actual carbon emissions and carbon emission rights quota, the carbon emission rights trading amount is calculated based on the preset carbon emission rights trading amount calculation formula, and a step-by-step carbon trading model is constructed.

4. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 3 is characterized in that: The goal is to minimize the operating costs of the smart distribution network and the electric vehicle aggregator, and to build a master-slave game two-layer optimization model of the smart distribution network operator and the electric vehicle aggregator considering the step-by-step carbon trading, and to regulate the charging power of electric vehicles by controlling the electricity price, specifically: The upper-level model of the master-slave game is constructed based on the energy purchase cost of the distribution network, the carbon trading cost of the distribution network, the cost of abandoned solar power, the operating cost of thermal power units, the operating cost of thermal power units, and the revenue from the distribution network operator selling electricity to electric vehicle aggregators. The minimum operating cost of the upper distribution network operator is obtained according to the upper model of the master-slave game; The lower-level model of the master-slave game is constructed based on the electricity purchase cost of electric vehicles, the battery loss cost of electric vehicles, and the electricity sales revenue of electric vehicle aggregators to distribution network operators; Obtain the minimum operating cost of the lower-level electric vehicle aggregators based on the master-slave game lower-level model; According to the master-slave game relationship between distribution network operators and electric vehicle aggregators, combined with the preset first constraint, the charging power of electric vehicles is regulated by controlling electricity prices.

5. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 4 is characterized in that: The specific formula of the adaptive function of the preset number of clusters is: In the formula, is the center vector of the sample data, is the sample data, is the i-th cluster center, m is the fuzzy weight coefficient, c is the number of clusters, is the element in the membership matrix.

6. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 5 is characterized in that: The preset carbon emission rights trading amount calculation formula is as follows: The specific calculation formula of the step-by-step carbon trading model is: In the formula, Carbon trading quota for smart distribution networks, is the carbon trading cost, As the basic carbon price, is the length of the carbon trading interval, is the carbon price growth rate.

7. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 6 is characterized in that: The specific calculation formula of the master-slave game upper model is: In the formula, is the minimum operating cost of the upper distribution network operator, is the energy purchase cost, is the electricity purchase price at time t, is the tiered carbon trading cost, is the cost of abandoned light, is the unit abandonment penalty cost, is the abandoned optical power at time t, is the operating cost of thermal power units, and Normal operation cost and startup and shutdown cost of thermal power units, is the operation status of the thermal power unit at time t, N is the number of units, T is the scheduling cycle, is the quadratic cost coefficient, is the first-order cost coefficient, is the constant cost coefficient, is the start-up and shutdown cost of thermal power units, The electricity sales revenue from distribution network operators to lower-level electric vehicle aggregators, is the electricity price at time t, is the electricity sales power of the distribution network operator at time t.

8. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 7 is characterized in that: The specific calculation formula of the master-slave game lower model is: In the formula, is the minimum operating cost of the lower-tier EV aggregator, For the cost of electric vehicles, is the battery loss cost of electric vehicles, and are the electric vehicle power loss coefficients, The revenue from electricity sales by EV aggregators to distribution network operators.

9. The method for low-carbon optimization of distribution network based on tram cluster clustering and step-by-step carbon trading according to claim 8 is characterized in that: The specific calculation formula of the preset first constraint condition is: In the formula, is the photovoltaic output power at time t, is the upper limit of photovoltaic output power, and are the lower and upper limits of thermal power unit output, and are the maximum downward and upward climbing rates of thermal power units, and are the state of charge and power consumption of the electric vehicle at time t, and are the charging efficiency and discharging efficiency of electric vehicles, and are the charging and discharging power of the electric vehicle at time t, and are the maximum and minimum state of charge of the electric vehicle, is the SOC value at the end of charging, is the mileage of the electric vehicle at time t, is the power consumption per unit mileage of electric vehicles, and are the maximum value of charging power and the maximum value of discharging power, respectively. is the electric load power at time t, is the charging power of the electric vehicle cluster at time t.

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