Carbon response model of power distribution network with new energy access for power supply capacity improvement method

Abstract

The present application relates to the field of power distribution network planning, in particular to a power distribution network carbon response model power supply capacity improvement method for new energy access, which takes the carbon target as a consideration when establishing a power distribution network planning model, improves the power supply capacity of the power distribution network, and realizes good emission reduction effect, and obtains an optimized system operation scheme; The present application considers the change of carbon intensity of the mobile energy storage system during movement, establishes a carbon emission model based on energy storage, cooperates with a demand side space-time carbon response model of the dispatchable load, further reduces carbon emission, and improves the power supply capacity of the power distribution network; The present application establishes a demand side space-time carbon response model of the dispatchable load, effectively improves the utilization rate of new energy of the power distribution network, limits coal-fired power generation through demand side load transfer, and further reduces system carbon emission.

Description

Technical Field

[0001] This invention relates to the field of distribution network planning, and specifically to a method for improving the power supply capacity of a distribution network carbon response model with new energy access. Background Technology

[0002] The power industry is a major source of carbon emissions, accounting for approximately one-third of total emissions. Its emissions primarily originate from the combustion of fossil fuels during power generation. Existing research on emission reduction in power transmission networks mainly focuses on the decommissioning of aging coal-fired power units, coordinated planning of gas-fired networks, transmission line expansion, and the installation of renewable energy power plants. Reducing the carbon intensity of the distribution system can also effectively promote emission reduction in the main power grid. Within the distribution network, numerous highly flexible devices such as distributed energy resources, energy storage systems, and electric vehicles offer greater possibilities for system emission reduction planning. Researching emission reduction plans can effectively improve 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 research focuses on demand-side carbon response on a time scale. For example, renewable energy and energy storage systems can be planned collaboratively to optimize distribution system operation and reduce system emissions. However, because energy storage systems are installed in fixed locations, they can only be locally dispatched on a time scale, resulting in insufficient utilization. Mobile energy storage systems, on the other hand, can be dispatched not only on a time scale but also on a spatial scale, which can significantly improve system efficiency and flexibility. In distribution systems, besides mobile energy storage systems, other types of geographically dispatchable loads include distributed data centers and electric vehicles. To date, only a few studies have addressed the spatiotemporal carbon response capabilities of dispatchable loads. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a method for improving the power supply capacity of a distribution network carbon response model with new energy access. This 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 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 and solve for the carbon emissions at each node;

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

[0009] S4. Solve the model using the gurobi solver.

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

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

[0012] Furthermore, a barrier function f(e) for the system's carbon target is established. f(e) is the product of the active power output of the generators connected to each node and the carbon intensity of the generators during all time periods t in year y, plus the product of the active power flowing into the substations at each node and the carbon intensity of the substations, and finally subtracted from the carbon emission target for year y. The carbon emission target is denoted by ε. When f(e) ≤ 0, ε = 0, meaning the carbon emission target has been achieved; otherwise, ε = [max{0, f(e)}]. 2 .

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

[0014] The first constraint is: during the t period of year y, the active power flowing into the substation at node i minus the active power flowing out, plus the total active power output of the connected generators, the total active power output of renewable energy, 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 be equal to the active power flowing into node i minus the active power flowing out.

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

[0016] Furthermore, the charging and discharging constraints for energy storage are as follows: First, the energy storage of the m-th mobile energy storage system at node i in year y is defined as equal to its energy storage in time period t+1, 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 within time period t, the first constraint is that the energy storage of the mobile energy storage system at node i in time 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 its charging and discharging power must be greater than or equal to 0 and less than or equal to its upper limit of 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 must be less than its upper limit of active power output during the time period t in year y.

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

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

[0020] Further, in step S2, the improved carbon emission model based on energy storage is established as follows: during time period t in year y, the carbon intensity at node i is equal to the sum of the products of the generator's output active power and its carbon intensity, the mobile energy storage system's active power output and its carbon intensity, and the active power flowing through the connecting branch and its carbon intensity, divided by the sum of the node's generator's output active power, renewable energy active power output, mobile energy storage system discharge power, and active power flowing through the branch; wherein 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 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.

[0022] Furthermore, the constraints of distributed data centers include four constraints.

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

[0024] The second constraint is: during time period t in year y, the sum of the ratios of the regulated power load at node i and the increase in power consumption when the distributed data center is handling the maximum workload at the basic load must be equal to 0.

[0025] The third constraint is: during time period t in year y, the regulated power load at node i is less than or equal to the power consumption increase of the distributed data center when handling the baseline workload with the base load;

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

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

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

[0029] (1) This invention takes carbon targets into account when establishing a power distribution network planning model, and achieves excellent emission reduction effect while improving the power supply capacity of the power distribution network, thus obtaining an optimized system operation scheme.

[0030] (2) This invention takes into account the change in carbon intensity of mobile energy storage systems during the movement process, 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 cooperating with the demand-side spatiotemporal carbon response model of dispatchable loads.

[0031] (3) This invention establishes a demand-side spatiotemporal carbon response model for dispatchable loads, which effectively improves the utilization rate of new energy in the distribution network and limits coal-fired power generation by demand-side load transfer, thereby further reducing system carbon emissions. Attached Figure Description

[0032] Figure 1 This is a flowchart of the power distribution network power supply capacity improvement method of the present invention;

[0033] Figure 2 The diagram shows the operating curves of the mobile energy storage system and the carbon intensity of the corresponding nodes under the planning method of this invention. Detailed Implementation

[0034] The following is in conjunction with the appendix Figure 1 To be continued Figure 2 The principles and features of the present invention are described, and the examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0035] like Figure 1As shown, the method for improving the power supply capacity of a distribution network carbon response model for new energy access provided by this invention includes: First, establishing a distribution network planning model with the objective function of maximizing the total power generation of the distribution network, 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 for the carbon emissions of each node; establishing a demand-side spatiotemporal carbon response model for dispatchable loads with the objective function of minimizing total carbon emissions, considering constraints of distributed data centers and mobile energy storage; finally, solving the model using the gurobi solver. A specific embodiment of this invention is presented below, using a power distribution system with 33 nodes, including the following steps:

[0036] S0. System initialization: Input distribution network data for 33 nodes. The distribution network 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 embodiment, the maximum dispatchable capacity of the distributed data center is 0.3MW; the rated capacity of the mobile energy storage system is 2MWh; and the unit emission coefficient is 0.875 (tCO2 / MWh) for coal-fired generators and 0.385 (tCO2 / MWh) for gas-fired generators.

[0037] S1. A distribution network planning model is established with the objective function of maximizing the total power generation of the distribution network. A series of constraints are established to ensure that the system power solution obtained in the objective function is feasible in practice and within a safe range. The power of generators, transformers, lines, and other equipment is limited to the rated range to prevent overload or damage, and to comply with relevant regulations and standards. The various constraints interact and jointly define the feasible solution space for power. The obtained energy storage system decision variables are then used... Transmit to step four;

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

[0039]

[0040] In the formula: 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,busThese are the sets of substation nodes, renewable energy device nodes, mobile energy storage system nodes, generator type sets, and generator node sets, respectively. This refers to the active power output by the generator. The active power output by the substation; Active power output from renewable energy sources; The energy storage capacity of a mobile energy storage system; This is an integer variable that determines whether to expand the substation or install renewable energy devices at node i. An integer value of 1 indicates installation, and a value of 0 indicates no installation. Let m be the decision variable, determining whether to install the m-th mobile energy storage system.

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

[0042]

[0043] ε = [max{0, f(e)}] 2 ;

[0044] In the formula: the 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 These represent the carbon intensity of the generator and the substation, respectively. The carbon emission target for year y.

[0045] (2) The constraints considered include: node power balance constraints, power factor constraints of renewable energy equipment, charging and discharging constraints of energy storage, power limit constraints of generators during operation, power output constraints of renewable energy equipment, and upper bound constraints of each decision variable. Specifically:

[0046] ① The node power balance constraint includes two constraints.

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

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

[0049] The specific formula is as follows:

[0050]

[0051] In the formula: These represent the active power flowing into and out of the substation, respectively. The active power output of renewable energy; and These are the charging and discharging power of the mobile energy storage system, respectively. Active load; For distributed data center load, the result can be obtained from step four; P zi,t,y and P zi,t,y Ω represents the effective inflow and outflow power at node i; bus A set of nodes; and These represent reactive power output and load, respectively; Q zi,t,y and Q ij,t,y For no useful current; This refers to the reactive power flowing into and out of the substation.

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

[0053]

[0054] In the formula, tanθ is the ratio of reactive power to active power. When tanθ = 0.48, it indicates that the power factor (power factor = active power / apparent power) is stable around 0.9. Under normal operating conditions, the power factor of industrial equipment is usually between 0.8 and 0.9. In power systems, the normal range of tanθ is between 0.2 and 0.6.

[0055] ③ The charging and discharging constraints of mobile energy storage systems are defined as follows: First, the energy storage of the m-th mobile energy storage system at node i in year y during time period t+1 is equal to its energy storage during time 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 within time period t, the first constraint is that the energy storage of the mobile energy storage system at node i during time 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 its charging and discharging power must be greater than or equal to 0 and less than or equal to its upper limit of charging and discharging power. The specific formula is as follows:

[0056]

[0057] In the formula: Energy storage for mobile energy storage systems; Δt is the time step; and For the charging and discharging efficiency of mobile energy storage systems; ζ m,i,t,y The result of step four indicates whether the m-th mobile energy storage system is at node i at time t. and These are the upper limits of the charging and discharging power of mobile energy storage systems.

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

[0059]

[0060] In the formula: This represents the upper limit of the generator's output active power. Let g be the decision variable, determining whether the g-th generator needs to be unloaded.

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

[0062]

[0063] In the formula: This is the power output function for renewable energy devices.

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

[0065]

[0066] In the formula: This refers to the complex power limitation rate of the substation.

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

[0068]

[0069] S2. Establish an improved carbon emission model based on energy storage, solve for the carbon emissions of each node, and transmit the results to step four.

[0070] The carbon emission model for energy storage is established as follows: During time period t in year y, the carbon intensity at node i is equal to the sum of the products of the generator's active power output and its carbon intensity, the mobile energy storage system's active power output and its carbon intensity, and the active power flowing through the connecting branch and its carbon intensity, divided by the sum of the generator's active power output, renewable energy active power output, mobile energy storage system discharge power, and active power flowing through the branch at that node. Specifically, when the active power flowing through branch ij is greater than or equal to 0, its carbon intensity is equal to the carbon intensity at node i; otherwise, it is equal to the carbon intensity at node j. The specific formula is as follows:

[0071]

[0072]

[0073] In the formula: The active power output of the g-type generator and its carbon intensity are given. The active power discharge power of the mobile energy storage system; P represents the internal carbon intensity of the mobile energy storage system at time t. + The set of nodes receiving the incoming power; Indicates the carbon intensity of the branch between nodes i and j; P represents the carbon intensity at the corresponding node. ij,t,y It provides active power output for renewable energy.

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

[0075] (1) An objective function is established to minimize 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] In the formula: ω kΩ represents the probability weight of the k-th scene appearing in the set of all scenes K; D,bus It is a set of nodes for a distributed data center.

[0078] (2) Considering the constraints of distributed data centers and mobile energy storage, the following are the constraints:

[0079] ① The constraints of a distributed data center include four constraints. The first constraint is: during time period t in year y, the distributed data center load at node i is equal to the sum of the base load and the regulated power load. The second constraint is: during time period t in year y, the sum of the ratios of the regulated power load at node i to the increase in power consumption when the distributed data center handles the maximum workload at the base load must be equal to 0. The third constraint is: during time period t in year y, the regulated power load at node i is less than or equal to the increase in power consumption when the distributed data center handles the baseline workload at the base load. The fourth constraint is: during time period t in year y, the ratio of the regulated power load at node i to the increase in power consumption when the distributed data center handles a unit workload at the base load is greater than or equal to the idle load of the distributed data center. The formula is as follows:

[0080]

[0081] In the formula: the load of a distributed data center consists of the basic load. and regulated power load composition; These represent the power consumption increase when a distributed data center processes a unit workload at a basic load, and the power consumption increase when a distributed data center processes the maximum workload at a basic load, respectively. This refers to the increased power consumption of a distributed data center when handling a baseline workload at a base load; Γ 1i,t,y This refers to the idle load in a distributed data center.

[0082] ② The constraint for mobile energy storage systems is that no more than one mobile energy storage system can be added or removed at node i during time period t in year y, as shown in the following formula:

[0083]

[0084] In the formula: Indicates whether the m-th 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 Let i represent whether the m-th mobile energy storage system has arrived at and left node i.

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

[0086] Appendix Figure 2This figure shows the operating curves of the mobile energy storage system and the carbon intensity results at corresponding nodes under the planning method of this 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 changes of the bars indicate that the mobile energy storage system moves to the corresponding node for charging and discharging, and the horizontal axis represents time. As can be seen from the figure, at times 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 node 20 has the lowest carbon intensity at this time. At times 18 / 19 / 20 / 21, the power is negative, indicating that the mobile energy storage system is discharging at node 28, and the carbon intensity at node 28, corresponding to the red broken line, is relatively high. This process can utilize low-carbon electricity, reducing carbon emissions during charging, and discharging when the power generation ratio is high can reduce the demand for high-carbon electricity, thus lowering the overall carbon emissions of the system.

[0087] The charge-discharge curves show that the model can not only achieve carbon response on traditional time scales, but also achieve flexible spatial carbon response based on carbon intensity at different nodes, which can significantly improve 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 within the protection scope of the present invention.

Claims

1. A method for improving the power supply capacity of a distribution network with a carbon response model for renewable energy integration, characterized in that, Includes the following steps: 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. Establish the barrier function for the carbon target of the system , In the first y All time periods of the year t Within the node, the product of the generator's output active power and the generator's carbon intensity, plus the product of the active power flowing into the substation at each node and the substation's carbon intensity, is subtracted from the first... y The annual carbon emission target, using Indicate carbon emission targets; when hour, That is, to achieve the carbon emission target, otherwise ; S2. Establish an improved carbon emission model based on energy storage and solve for the carbon emissions at each node; To establish an improved carbon emission model based on energy storage, in the first... y years t Nodes within the time period i The carbon intensity at a given node is equal to the sum of the products of the generator's output active power and its carbon intensity, the mobile energy storage system's active power output and its carbon intensity, and the active power flowing through the connecting branch and its carbon intensity, divided by the sum of the generator's output active power, renewable energy active power output, mobile energy storage system discharge power, and the active power flowing through the branch at that node; where when the branch... ij When the active power flowing through is greater than or equal to 0, its carbon intensity is equal to that at the node. i The carbon strength; otherwise, equal to the node. j carbon strength; S3. Establish a demand-side spatiotemporal carbon response model for dispatchable loads, with the minimization of total carbon emissions as the objective function, incorporating constraints of distributed data centers and mobile energy storage, and solving for the decision variables of energy storage systems and controllable loads. 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 constraint of the mobile energy storage system is: in the first... y years t Nodes within the time period i No more than one mobile energy storage system may be added or removed at any location; S4. Solve the model using the gurobi solver.

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

3. The method for improving the power supply capacity of a distribution network with a carbon response model for new energy access according to claim 2, characterized in that, The power balance constraints at each node in a distribution network include two constraints. The first constraint is: during the t period of year y, the active power flowing into the substation at node i minus the active power flowing out, plus the total active power output of the connected generators, the total active power output of renewable energy, 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 be equal to the active power flowing into node i minus the active power flowing out. The second constraint is: the difference between the reactive power flowing into and out of the substation at node i during time period t in year y, plus the reactive power output from renewable energy sources, minus the reactive load value, must be equal to the reactive power flowing into node i minus the reactive power flowing out.

4. The method for improving the power supply capacity of a distribution network with a carbon response model for new energy access according to claim 3, characterized in that, The charging and discharging constraints for energy storage are as follows: First, the energy storage of the m-th mobile energy storage system at node i in year y is defined as equal to its energy storage in time period t+1, 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 time period t, the first constraint is that the energy storage of the mobile energy storage system at node i in time 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 its 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.

5. The method for improving the power supply capacity of a distribution network with a carbon response model for new energy access according to claim 4, characterized in that, The power limit constraint when the generator is in use is that the generator output power at node i must be less than its upper limit of active power output during the time period t in year y. The power output constraint of renewable energy equipment is that the active power output of renewable energy at node i during time period t in year y must be equal to the power output function of renewable energy equipment. The maximum power constraint of a substation is that the complex power input and output of the substation at node i must be less than its complex power limitation rate during time period t in year y.

6. The method for improving the power supply capacity of a distribution network with a carbon response model for new energy access according to claim 1, characterized in that, Distributed data center constraints include four constraints. The first constraint is: in the... y years t Nodes within the time period i The load of a distributed data center is equal to the sum of the basic load and the regulated power load; The second constraint is: in the... y years t Nodes within the time period i The sum of the ratios of the regulated power load at the location and the increase in power consumption when the distributed data center is handling the maximum workload at the basic load must be equal to 0. The third constraint is: in the... y years t Nodes within the time period i The regulated power load at the location is less than or equal to the increased power consumption of the distributed data center when handling the baseline workload at the base load. The fourth constraint is: in the first... y years t Nodes within the time period i The ratio of the regulated power load at the location to the increase in power consumption when the distributed data center processes a unit workload at its basic load is greater than or equal to the idle load of the distributed data center.

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