New energy access power distribution network carbon response model power supply capability improvement method

Abstract

The invention relates to the field of power distribution network planning, in particular to a new energy access power distribution network carbon response model power supply capability improvement method, which takes a carbon target as consideration when a power distribution network planning model is established, realizes a better emission reduction effect while improving the power supply capability of a power distribution network, and obtains an optimized system operation scheme. According to the method, the change of the carbon intensity in the moving process of the mobile energy storage system is considered, the energy storage-based carbon emission model is established, and through cooperation with the demand side space-time carbon response model of the schedulable load, the carbon emission is further reduced, and the power supply capability of the power distribution network is improved; according to the method, the demand side space-time carbon response model of the schedulable load is established, the new energy utilization rate of the power distribution network is effectively improved, coal-fired power generation is limited through demand side load transfer, and system carbon emission is further reduced.

Description

Technical Field

[0001] The present invention relates to the field of distribution network planning, and in particular to a method for improving the power supply capacity of a distribution network with carbon response model for access to new energy. Background Art

[0002] The power industry is a major source of carbon emissions, accounting for approximately one-third of total emissions. These emissions primarily come from the combustion of fossil fuels during power generation. Existing research on transmission network emissions reduction focuses on decommissioning older coal-fired generators, coordinated gas network planning, transmission line expansion, and the installation of renewable power plants. Reducing the carbon intensity of the distribution system can also effectively contribute to emissions reductions within the main grid. Within the distribution network, a large number of highly flexible devices, such as distributed energy resources, energy storage systems, and electric vehicles, offer greater potential for system emissions reduction planning. Research on emissions reduction planning can effectively enhance the power supply capacity of the distribution network.

[0003] Setting emission targets as constraints in distribution network optimization models is a typical approach in emission control planning research. Most existing studies focus on demand-side carbon response on a temporal scale. For example, renewable energy and energy storage systems can be planned collaboratively to optimize distribution system operation and reduce system emissions. However, since energy storage systems are installed in fixed locations, they can only be dispatched locally on a temporal scale, resulting in insufficient utilization. Mobile energy storage systems can be dispatched not only on a temporal scale but also on a spatial scale, which can significantly improve the efficiency and flexibility of the system. In distribution systems, in addition to mobile energy storage systems, other types of geographically dispatchable loads include distributed data centers and electric vehicles. To date, only a few studies have mentioned the spatiotemporal carbon response function of dispatchable loads. Summary of the Invention

[0004] In order to overcome the deficiencies in the prior art, the present invention provides a method for improving the power supply capacity of a distribution network with new energy access based on a carbon response model. The model includes both clear emission reduction targets and the spatiotemporal carbon response of dispatchable loads.

[0005] To achieve the above objectives, the present invention discloses a method for improving the power supply capacity of a distribution network with a carbon response model for renewable energy access, comprising the following steps:

[0006] S1. Establish a distribution network planning model with the objective function of maximizing the total power generation of the distribution network, and incorporate node power balance constraints, power factor constraints of renewable energy devices, charging and discharging constraints of energy storage, power limit constraints when generators are in use, power output constraints of renewable energy equipment, maximum power constraints of substations, and upper bound constraints of each decision variable;

[0007] S2. Establish an improved carbon emission model based on energy storage to calculate the carbon emission of each node;

[0008] S3. Establish a demand-side spatiotemporal carbon response model for dispatchable loads, taking minimization of total carbon emissions as the objective function, incorporating distributed data center constraints and mobile energy storage constraints, and solving for the decision variables of the energy storage system and controllable loads.

[0009] S4. Use the gurobi solver to solve the model.

[0010] Furthermore, in step S1, the objective function is established by maximizing the total power generation of the distribution network;

[0011] The objective function includes the sum of the following four items: the first item is the sum of the active power output of the generators connected to each node, the second item is the sum of the active power output of all substations, the third item is the sum of the active power output of renewable energy sources of all nodes, and the fourth item is the sum of the energy storage capacity of all mobile energy storage systems.

[0012] Furthermore, a barrier function f(e) for the system carbon target is established. f(e) is the product of the active power output of the generator connected to each node and the carbon intensity of the generator in all time periods t in the yth year, plus the product of the active power flowing into the substation at each node and the carbon intensity of the substation, and finally minus the carbon emission target for the yth year. The carbon emission target is represented by ε. When f(e) ≤ 0, ε = 0, that is, the carbon emission target is achieved; otherwise, ε = [max{0,f(e)}] 2 .

[0013] Furthermore, the power balance constraints of each node in the distribution network include two constraints:

[0014] The first constraint is: the active power flowing into the substation at node i during period t in year y minus the active power flowing out, plus the total active power output of the connected generators, the total active power output of renewable energy sources, and the discharge power of the mobile energy storage system, minus the charging power of the mobile energy storage system, the active load of the node, and the load of the distributed data center, must equal the active power flowing into node i minus the active power flowing out.

[0015] The second constraint is that the difference between the reactive power flowing into and out of the substation at node i during period t in year y plus the reactive power output of renewable energy, minus the value of the reactive load, must equal the reactive power flowing into node i minus the reactive power flowing out.

[0016] Furthermore, the charging and discharging constraints of energy storage are as follows: first, it is defined that the energy storage of the mth mobile energy storage system at node i in year y in period t+1 is equal to its energy storage in period t plus the power charged by the mobile energy storage system within the time step minus the power released; it also includes two constraints. If a mobile energy storage system is installed at node i in period t, the first constraint is that the energy storage of the mobile energy storage system at node i in period t must be greater than or equal to 0 and less than or equal to the upper limit of its energy storage; the second constraint is that its charging and discharging power must be greater than or equal to 0 and less than or equal to the upper limit of its charging and discharging power.

[0017] Furthermore, the power limit constraint when the generator is in use is that the generator output power at node i in the t period of year y must be less than its output active power upper limit;

[0018] The power output constraint of the renewable energy device is that the output active power of the renewable energy at node i during period t in year y must be equal to the power output function of the renewable energy device;

[0019] The maximum power constraint of the substation is that the complex power input and output of the substation at node i in the t period of year y must be less than its complex power limit rate.

[0020] Furthermore, the improved carbon emission model based on energy storage established in step S2 is that the carbon intensity at node i in the t period of year y is equal to the sum of the product of the active power output of the generator and its carbon intensity, the product of the active power output of the mobile energy storage system and its carbon intensity, and the product of the active power flowing through the connecting branch and its carbon intensity, divided by the sum of the active power output of the generator at the node, the active power output of renewable energy, the discharge power of the mobile energy storage system, and the active power flowing through the branch; when the active power flowing through branch ij is greater than or equal to 0, its carbon intensity is equal to the carbon intensity of node i; otherwise, it is equal to the carbon intensity of node j.

[0021] Furthermore, in step S3, an objective function is established with the minimum total carbon emissions, and the objective function includes the carbon emissions of the distributed data center load at node i and the carbon emissions of the mobile energy storage system.

[0022] Furthermore, the distributed data center constraints include four constraints:

[0023] The first constraint is: the distributed data center load at node i during period t in year y is equal to the sum of the base load and the regulated power load;

[0024] The second constraint is that the sum of the ratio of the regulated power load at node i to the increased power consumption when the distributed data center processes the maximum workload at the base load during period t in year y must be equal to 0;

[0025] The third constraint is: the regulated power load at node i during period t in year y is less than or equal to the increased power consumption of the distributed data center when processing the benchmark workload at the base load;

[0026] The fourth constraint is: the ratio of the regulated power load at node i to the increased power consumption when the distributed data center processes unit workload at the basic load during the t period of year y is greater than or equal to the idle load of the distributed data center.

[0027] Furthermore, the mobile energy storage system constraint is that no more than one mobile energy storage system is added or removed at node i during period t in year y.

[0028] The method for improving the power supply capacity of a distribution network of the present invention has at least the following beneficial effects:

[0029] (1) The present invention takes carbon targets into consideration when establishing a distribution network planning model, thereby achieving excellent emission reduction effects while improving the power supply capacity of the distribution network and obtaining an optimized system operation plan.

[0030] (2) The present invention takes into account the changes in carbon intensity of mobile energy storage systems during movement, establishes a carbon emission model based on energy storage, and further reduces carbon emissions and improves the power supply capacity of the distribution network by coordinating with the demand-side spatiotemporal carbon response model of dispatchable loads.

[0031] (3) The present invention establishes a demand-side spatiotemporal carbon response model for dispatchable loads, effectively improving the utilization rate of new energy in the distribution network, limiting coal-fired power generation through demand-side load transfer, and further reducing system carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of the method for improving the power supply capacity of the distribution network of the present invention;

[0033] Figure 2 It is the operation curve of the mobile energy storage system and the carbon intensity of the corresponding nodes under the planning method of the present invention. DETAILED DESCRIPTION

[0034] The following is combined with Figure 1 To the attached Figure 2 The principles and features of the present invention are described, and the examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0035] like Figure 1As shown, the method for improving the power supply capacity of the carbon response model of the distribution network with new energy access provided by the present invention includes: first, establishing a distribution network planning model with the maximization of the total power generation power of the distribution network as the objective function, and considering constraints such as node power balance constraints, power factor constraints of renewable energy devices, and charging and discharging constraints of energy storage; then establishing an improved carbon emission model based on energy storage to solve the carbon emissions of each node; establishing a demand-side spatiotemporal carbon response model of dispatchable loads, with the minimization of total carbon emissions as the objective function, considering distributed data center constraints and mobile energy storage constraints; finally, using the gurobi solver to solve the model. A power distribution system containing 33 nodes is now used as a specific embodiment to introduce the specific content of the present invention, including the following steps:

[0036] S0. The system initializes and inputs 33-node distribution network data. This data includes the maximum dispatchable capacity of the distributed data center, the rated capacity of the mobile energy storage system, and the carbon emissions of coal-fired and gas-fired generators. In this example, the maximum dispatchable capacity of the distributed data center is 0.3MW; the rated capacity of the mobile energy storage system is 2MWh; the unit emission coefficient is 0.875 (tCO2 / MWh) for the coal-fired generator set and 0.385 (tCO2 / MWh) for the gas-fired generator set.

[0037] S1. Establish a distribution network planning model with the objective function of maximizing the total power generation of the distribution network, and establish a series of constraints to ensure that the system power solution solved in the objective function is feasible in practice and within the safe range, limiting the power of generators, transformers, lines and other equipment within the rated range to prevent overload or damage, and comply with relevant regulations and standards. Each constraint condition affects each other and jointly defines the feasible solution space of power. The obtained energy storage system decision variables Transfer to step 4;

[0038] (1) The objective function is established by maximizing the total power generation of the distribution network. The objective function includes the sum of the following four items: the first item is the sum of the output active power of the generators connected to each node, the second item is the sum of the output active power of all substations, the third item is the sum of the output active power of renewable energy sources at all nodes, and the fourth item is the sum of the energy storage capacity of all mobile energy storage systems. The specific formula is as follows:

[0039]

[0040] Where: g represents the generator; i represents the node; m represents the mobile energy storage system; Ω ST,bus ,Ω R,bus ,Ω MESS ,Ω G ,Ω G,busThey are substation node set, renewable energy device node set, mobile energy storage system node set, generator type set, and generator node set; is the active power output by the generator; Active power output of the substation; Active power exported from renewable energy sources; is the energy storage capacity of the mobile energy storage system; is an integer variable that determines whether to expand the substation or install renewable energy devices at node i. When the integer variable is 1, it means installation, and when it is 0, it means no installation; is the decision variable that determines whether to install the mth mobile energy storage system.

[0041] Establish f(e) as the barrier function for the system's carbon target to ensure that the system maximizes power generation while keeping carbon emissions within the specified range. f(e) is the product of the active power output of the generator connected to each node and the carbon intensity of the generator, plus the product of the active power flowing into the substation at each node and the carbon intensity of the substation, minus the carbon emission target for year y, for all time periods t in year y. f(e) is as follows:

[0042]

[0043] ε=[max{0,f(e)}] 2 ;

[0044] Where: subscript g represents the generator, i represents the node, t represents the time, and y represents the year; ε represents the carbon emission target. When f(e)≤0, ε=0, that is, the carbon emission target is achieved; otherwise, ε=[max{0,f(e)}] 2 ; Active power flows into the substation; and represent the carbon intensity of generators and substations, respectively; is the carbon emission target for year y.

[0045] (2) The constraints to be considered include: node power balance constraints, power factor constraints of renewable energy equipment, charging and discharging constraints of energy storage, power limit constraints when generators are in use, power output constraints of renewable energy equipment, and upper bound constraints of each decision variable. The details are as follows:

[0046] ①Node power balance constraints include two constraints.

[0047] The first constraint is: the active power flowing into the substation at node i during period t in year y minus the active power flowing out, plus the total active power output of the connected generators, the total active power output of renewable energy sources, and the discharge power of the mobile energy storage system, and finally minus the charging power of the mobile energy storage system, the active load of the node, and the load of the distributed data center, must equal the active power flowing into node i minus the active power flowing out.

[0048] The second constraint is that the difference between the reactive power flowing into and out of the substation at node i during period t in year y plus the reactive power output of renewable energy, minus the value of the reactive load, must equal the reactive power flowing into node i minus the reactive power flowing out.

[0049] The specific formula is as follows:

[0050]

[0051] Where: are the active power flowing into and out of the substation respectively; is the output active power of renewable energy; and are the charging and discharging power of the mobile energy storage system respectively; is the active load; is the distributed data center load, which can be obtained from the result of step 4; P zi,t,y and P zi,t,y represents the effective inflow and outflow power of node i; Ω bus is a collection of nodes; and are reactive output and load respectively; Q zi,t,y and Q ij,t,y is the reactive current; is the reactive power flowing into and out of the substation.

[0052] ②Power factor constraints of renewable energy devices:

[0053]

[0054] Where tanθ is the ratio of reactive power to active power. When tanθ = 0.48, the power factor (power factor = active power / apparent power) is stable near 0.9. Under normal operating conditions, the power factor of industrial equipment is typically between 0.8 and 0.9. The normal range of tanθ in power systems is between 0.2 and 0.6.

[0055] ③ The charging and discharging constraints of the mobile energy storage system are first defined as follows: the energy storage of the mth mobile energy storage system at node i in year y in period t+1 is equal to its energy storage in period t plus the power charged by the mobile energy storage system in the time step minus the power released. It also includes two constraints. If a mobile energy storage system is installed at node i in period t, the first constraint is that the energy storage of the mobile energy storage system at node i in period t must be greater than or equal to 0 and less than or equal to the upper limit of its energy storage; the second constraint is that its charging and discharging power must be greater than or equal to 0 and less than or equal to the upper limit of its charging and discharging power. The specific formula is as follows:

[0056]

[0057] Where: is the energy storage of the mobile energy storage system; Δt is the time step; and is the charging and discharging efficiency of the mobile energy storage system; m,i,t,y Indicates whether the mth mobile energy storage system is at node i at time t, which can be derived from the result of step 4; and They are the upper limits of charging and discharging power of mobile energy storage systems respectively.

[0058] ④ The power limit constraint when the generator is in use is that the generator output power at node i in the t period of year y must be less than its output active power upper limit. The specific formula is as follows:

[0059]

[0060] Where: The upper limit of the active power output of the generator; is the decision variable that determines whether the g-th generator needs to be unloaded.

[0061] ⑤ The power output constraint of renewable energy equipment is that the output active power of renewable energy at node i in period t of year y must be equal to the power output function of renewable energy equipment. The specific formula is as follows:

[0062]

[0063] Where: is the power output function of the renewable energy device.

[0064] ⑥ The maximum power constraint of the substation is that the complex power input and output of the substation at node i in the t period of year y must be less than its complex power limit rate. The specific formula is as follows:

[0065]

[0066] Where: is the complex power limit rate of the substation.

[0067] ⑦ Upper bound constraints of each decision variable:

[0068]

[0069] S2. Establish an improved carbon emission model based on energy storage, calculate the carbon emission of each node and transmit it to the fourth step;

[0070] The carbon emission model for energy storage is established as follows: the carbon intensity at node i in period t of year y is equal to the sum of the product of the generator output active power and its carbon intensity, the product of the mobile energy storage system active power output and its carbon intensity, and the product of the active power flowing through the connected branch and its carbon intensity, divided by the sum of the generator output active power, renewable energy active output, mobile energy storage system discharge power, and active power flowing through the branch at that node; when the active power flowing through branch ij is greater than or equal to 0, its carbon intensity is equal to the carbon intensity of node i; otherwise, it is equal to the carbon intensity of node j. The specific formula is as follows:

[0071]

[0072]

[0073] Where: is the active output power of the g-type generator and its power generation carbon intensity; is the active power discharge power of the mobile energy storage system; is the internal carbon intensity of the mobile energy storage system at time t; P + is the set of nodes into which power flows; represents the carbon intensity of the branch between nodes i and j; is the carbon intensity of the corresponding node; P ij,t,y It is the active power output of renewable energy.

[0074] S3. Establish a demand-side spatiotemporal carbon response model for dispatchable loads. Taking the minimization of total carbon emissions as the objective function, considering the constraints of distributed data centers and mobile energy storage, solve the decision variables ζ for the energy storage system and controllable loads. m,i,t,y and Transfer to the second step;

[0075] (1) The objective function is established to minimize the total carbon emissions. The objective function includes the carbon emissions of the distributed data center load at node i and the carbon emissions of the mobile energy storage system. The formula is as follows:

[0076]

[0077] Where: ω kis the probability weight of the kth scene in the set of all scenes K; Ω D,bus A collection of nodes in a distributed data center.

[0078] (2) Considering the distributed data center constraints and mobile energy storage constraints as follows:

[0079] ① Distributed data center constraints include four constraints. The first constraint is: the distributed data center load at node i during the t period of year y is equal to the sum of the base load and the regulated power load; the second constraint is: the sum of the ratio of the regulated power load at node i to the increased power consumption when the distributed data center processes the maximum workload at the base load during the t period of year y is equal to 0; the third constraint is: the regulated power load at node i during the t period of year y is less than or equal to the increased power consumption of the distributed data center when the base load processes the benchmark workload; the fourth constraint is: the ratio of the regulated power load at node i to the increased power consumption when the distributed data center processes the unit workload at the base load during the t period of year y is greater than or equal to the idle load of the distributed data center. The formula is as follows:

[0080]

[0081] Where: The distributed data center load is composed of the basic load and regulated power loads composition; They are the increased power consumption when the distributed data center processes unit workload at the base load, and the increased power consumption when the distributed data center processes maximum workload at the base load; is the increased power consumption of the distributed data center when processing the benchmark workload at the base load; 1i,t,y The amount of idle load in the distributed data center.

[0082] ② The mobile energy storage system constraint is that no more than one mobile energy storage system is added or removed at node i during period t in year y. The formula is as follows:

[0083]

[0084] Where: Indicates whether the mth mobile energy storage system moves from node i to node j at time t; α m,i,t,y and δ m,i,t,y are whether the m-th mobile energy storage system arrives at and leaves node i, respectively.

[0085] S4. Solve the model using the gurobi solver.

[0086] Attachment Figure 2It is the operating curve of the mobile energy storage system and the carbon intensity result diagram of the corresponding nodes under the planning method of the present invention. The bars in the figure represent the charging and discharging power of the mobile energy storage system, and the broken lines represent the real-time carbon intensity of different standby nodes. The color change of the bars in the figure indicates that the mobile energy storage system moves to the corresponding node for charging and discharging, and the horizontal axis represents time. It can be seen from the figure that at 11 / 12 / 13 / 14, the power is positive, indicating that the mobile energy storage system is charging at node 20, and the green broken line indicates that the carbon intensity of node 20 is the lowest at this time. At 18 / 19 / 20 / 21, the power is negative, indicating that the mobile energy storage system is discharging at node 28. At this time, the carbon intensity of node 28 corresponding to the red broken line is higher. This process can utilize low-carbon electricity to reduce carbon emissions during the charging process. Discharging when the power generation ratio is high can reduce the demand for high-carbon electricity and reduce the overall carbon emissions of the system.

[0087] The charging and discharging curves show that the model can not only achieve carbon response on the traditional time scale, but also achieve flexible spatial carbon response based on the carbon intensity of different nodes, and can achieve a significant improvement in the power supply capacity of the distribution network.

[0088] 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 scope of protection of the present invention.

Claims

1. A method for improving the power supply capacity of a distribution network with carbon response model for new energy access, characterized in that: The steps include: S1. Establish a distribution network planning model with the objective function of maximizing the total power generation of the distribution network, and incorporate node power balance constraints, power factor constraints of renewable energy devices, charging and discharging constraints of energy storage, power limit constraints when generators are in use, power output constraints of renewable energy equipment, maximum power constraints of substations, and upper bound constraints of each decision variable; S2. Establish an improved carbon emission model based on energy storage to calculate the carbon emission of each node; S3. Establish a demand-side spatiotemporal carbon response model for dispatchable loads, taking minimization of total carbon emissions as the objective function, incorporating distributed data center constraints and mobile energy storage constraints, and solving for the decision variables of the energy storage system and controllable loads. S4. Use the gurobi solver to solve the model.

2. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 1 is characterized in that: In step S1, the objective function is established by maximizing the total power generation of the distribution network; The objective function includes the sum of the following four items: the first item is the sum of the active power output of the generators connected to each node, the second item is the sum of the active power output of all substations, the third item is the sum of the active power output of renewable energy sources of all nodes, and the fourth item is the sum of the energy storage capacity of all mobile energy storage systems.

3. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 2 is characterized in that: Establish a barrier function f(e) for the system carbon target. f(e) is the product of the active power output of the generator connected to each node and the carbon intensity of the generator in all time periods t in the yth year, plus the product of the active power flowing into the substation at each node and the carbon intensity of the substation, and finally subtract the carbon emission target for the yth year. The carbon emission target is represented by ε. When f(e) ≤ 0, ε = 0, that is, the carbon emission target is achieved; otherwise, ε = [max{0,f(e)}] 2 .

4. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 3 is characterized in that: The power balance constraints of each node in the distribution network include two constraints: The first constraint is: the active power flowing into the substation at node i during period t in year y minus the active power flowing out, plus the total active power output of the connected generators, the total active power output of renewable energy sources, and the discharge power of the mobile energy storage system, minus the charging power of the mobile energy storage system, the active load of the node, and the load of the distributed data center, must equal the active power flowing into node i minus the active power flowing out. The second constraint is that the difference between the reactive power flowing into and out of the substation at node i during period t in year y plus the reactive power output of renewable energy, minus the value of the reactive load, must equal the reactive power flowing into node i minus the reactive power flowing out.

5. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 4 is characterized in that: The energy storage charging and discharging constraints are as follows: First, the energy storage of the mth mobile energy storage system at node i in year y during period t+1 is defined as equal to its energy storage during period t plus the power charged by the mobile energy storage system during the time step minus the power discharged. Two constraints are also included. If a mobile energy storage system is installed at node i during period t, the first constraint is that the energy storage of the mobile energy storage system at node i during period t must be greater than or equal to 0 and less than or equal to its upper limit of energy storage. The second constraint is that the charging and discharging power must be greater than or equal to 0 and less than or equal to the upper limit of the charging and discharging power.

6. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 5 is characterized in that: The power limit constraint when the generator is in use is that the generator output power at node i in the t period of year y must be less than its output active power upper limit; The power output constraint of the renewable energy device is that the output active power of the renewable energy at node i during period t in year y must be equal to the power output function of the renewable energy device; The maximum power constraint of the substation is that the complex power input and output of the substation at node i in the t period of year y must be less than its complex power limit rate.

7. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 6 is characterized in that: The improved carbon emission model based on energy storage established in step S2 is that the carbon intensity at node i in time period t of year y is equal to the sum of the product of the generator output active power and its carbon intensity, the product of the mobile energy storage system active power output and its carbon intensity, and the product of the active power flowing through the connecting branch and its carbon intensity, divided by the sum of the generator output active power, renewable energy active output, mobile energy storage system discharge power, and active power flowing through the branch at the node; when the active power flowing through branch ij is greater than or equal to 0, its carbon intensity is equal to the carbon intensity of node i; otherwise, it is equal to the carbon intensity of node j.

8. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 7 is characterized in that: In step S3, an objective function is established with the minimum total carbon emissions, and the objective function includes the carbon emissions of the distributed data center load at node i and the carbon emissions of the mobile energy storage system.

9. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 8 is characterized in that: Distributed data center constraints include four constraints: The first constraint is: the distributed data center load at node i during period t in year y is equal to the sum of the base load and the regulated power load; The second constraint is that the sum of the ratio of the regulated power load at node i to the increased power consumption when the distributed data center processes the maximum workload at the base load during period t in year y must be equal to 0; The third constraint is: the regulated power load at node i during period t in year y is less than or equal to the increased power consumption of the distributed data center when processing the benchmark workload at the base load; The fourth constraint is: the ratio of the regulated power load at node i to the increased power consumption when the distributed data center processes unit workload at the basic load during the t period of year y is greater than or equal to the idle load of the distributed data center.

10. The method for improving power supply capacity of a distribution network with carbon response model for new energy access according to claim 9 is characterized in that: The constraint of the mobile energy storage system is that no more than one mobile energy storage system is added or removed at node i during period t in year y.

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