Carbon-electricity cooperative power distribution network dispatching control method, device and equipment

Abstract

The invention relates to the technical field of power distribution networks, and discloses a carbon-electricity cooperative power distribution network dispatching control method, device and equipment. The method comprises: acquiring historical power grid data of a power distribution network; constructing a Wassertein constraint, a power flow constraint, an energy storage operation constraint, an SOP constraint and a security constraint according to historical power grid data of the power distribution network; in combination with the generated constraints, constructing a carbon-electricity collaborative planning model considering the system cost; solving the carbon-electricity collaborative planning model by using a CCG algorithm to obtain a power distribution network planning scheme; and according to the power distribution network planning scheme, adjusting the number, the access position and the content of the DGs and the E-SOPs accessed to the power distribution network. According to the invention, the application potentials of the energy storage device and the intelligent soft switch are fully exerted, the active and reactive power real-time adjustment capability between different branches or nodes is improved, equipment in the power distribution network is reasonably adjusted and controlled, the system voltage is improved, the system loss is reduced, and the economical efficiency of the power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a carbon-electricity coordinated distribution network dispatching control method, device and equipment. Background Art

[0002] In the context of the construction of a new power system dominated by new energy, the distribution network has become an important access entity for distributed power sources. The access of distributed power sources will change the power flow distribution of the distribution network, and distributed power sources generally have strong uncertainty, which will bring huge challenges to the safe and economic operation of the distribution network, causing problems such as voltage over-limit and increased network losses. In the face of the challenges brought by intermittent distributed power sources, relying solely on traditional regulation methods can no longer meet the requirements of refined power flow control in the distribution network.

[0003] At present, the planning method for E-SOP only considers the effect of loss reduction after its access to the distribution network, but fails to fully consider the comprehensive benefits of carbon electricity brought by DG access. Therefore, based on the existing theoretical research, it is necessary to further consider the comprehensive benefits of carbon electricity and propose a DG and E-SOP collaborative planning method that considers the comprehensive benefits of carbon electricity. Summary of the invention

[0004] The present invention provides a carbon-electricity coordinated distribution network dispatching control method, device and equipment, which can fully analyze the application potential of energy storage devices and intelligent soft switches, reasonably adjust and control the access equipment in the distribution network, improve the system voltage, reduce system losses, and improve the economy of the distribution network.

[0005] In order to solve the above technical problems, the present invention provides a carbon-electricity coordinated distribution network dispatching control method, comprising:

[0006] Obtain historical grid data of the distribution network;

[0007] Constructing Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints based on historical power grid data of the distribution network;

[0008] Combining Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints, a carbon-electricity collaborative planning model that considers system costs is constructed;

[0009] The CCG algorithm is used to solve the carbon-electricity collaborative planning model and obtain a distribution network planning solution;

[0010] According to the distribution network planning scheme, access adjustments are made to the DG and E-SOP in the distribution network respectively; wherein the access adjustments include access number adjustment, access position adjustment and access content adjustment.

[0011] Furthermore, the CCG algorithm is used to solve the carbon-electricity collaborative planning model to obtain a distribution network planning scheme, which is specifically:

[0012] Converting the power flow constraint into a linearized power flow, and simplifying the carbon-electricity synergistic planning model according to the linearized power flow to form a carbon-electricity synergistic two-stage optimization model;

[0013] The CCG algorithm is used to iteratively solve the carbon-electricity synergy two-stage optimization model to obtain the probability distribution of uncertain variables;

[0014] According to the probability distribution of the uncertain variables, the carbon-electricity synergy planning model is converted into a carbon-electricity synergy mixed integer second-order cone model, and the distribution network planning scheme is obtained by solving the carbon-electricity synergy mixed integer second-order cone model.

[0015] Furthermore, the Wassertein constraint, power flow constraint, energy storage operation constraint, SOP constraint and safety constraint are constructed according to the historical power grid data of the distribution network, specifically:

[0016] The confidence of the Wasserstein metric and the radius of the Wasserstein sphere are obtained from the historical power grid data of the distribution network, and the Wasserstein constraint is constructed, specifically:

[0017]

[0018] In the formula, is the empirical distribution based on historical power grid data; is a dataset containing all historical power grid data; β1 and β ∞ is the confidence of Wasserstein metric; κ1 and κ ∞ is the radius of the Wasserstein sphere; R is the true distribution; is a dataset containing all historical data; R t (ξ) is the real distribution of historical power grid data;

[0019] Active power demand, reactive power demand, operating time, line resistance and line impedance are obtained from the historical grid data of the distribution network, and power flow constraints are constructed, specifically:

[0020]

[0021] Where P j,t Active power injected into the node; Q j,t Reactive power injected into the node; is the distributed power output power of node j at time t; is the active power demand of the load at node j at time t; The reactive power demand of the load at node j at time t; P ESDj,t and P ESRj,t are the charge and discharge power of E-SOP of node j at time t; r ij and x ij are the resistance and impedance of branch ij respectively; U i,t and U j,t is the node voltage; I ij,t is the line current; P ij,t , P jk,t , Q ij,t and Q jk,t are the active and reactive power of different lines respectively;

[0022] The capacity of the E-SOP, the charging efficiency of the E-SOP, and the discharging efficiency of the E-SOP are obtained from the historical grid data of the distribution network, and energy storage operation constraints are constructed, specifically:

[0023] 0≤|P ESDi,t |≤S ESPi

[0024] 0≤|P ESRi,t |≤S ESPi

[0025]

[0026] Where P ESDi,t is the charging power of E-SOP at time t; P ESRi,t is the discharge power of E-SOP at time t; S ESPi , S ESCi is the capacity of E-SOP; E ESi,t is the charge state of E-SOP at time t; η ESD is the charging efficiency of E-SOP; η ESR is the discharge efficiency of E-SOP; Δt is the time interval; E ESi,0 is the initial state of the energy storage system; E ESi,24 is the final state of the energy storage system;

[0027] Obtaining the SOP converter loss coefficient, the upper and lower limits of the SOP reactive power, and the capacity of the SOP from the historical grid data of the distribution network, and constructing the SOP constraint;

[0028] The SOP constraints include SOP active power constraints, SOP reactive power constraints and SOP capacity constraints;

[0029] The SOP active power constraint is:

[0030]

[0031] The SOP reactive power constraint is:

[0032]

[0033] The SOP capacity constraint is:

[0034]

[0035] Where i and j are the node numbers of the distribution network to which the SOP is connected; P SOP,i,t , P SOP,j,t , Q SOP,i,t and Q SOP,j,t are the active and reactive power injected by SOP at nodes i and j at time t respectively; P loss,i,t and P loss,j,t are the active power loss of SOP at node i and j at time t; A SOP,i and A SOP,J is the SOP converter loss coefficient; Q SOP,imax , Q SOP,imin , Q SOP,jmax and Q SOP,jmin are the upper and lower limits of the reactive power injected by SOP at nodes i and j respectively; S SOP,ij is the capacity of SOP; is the active power loss of SOP at node i; is the active power loss of SOP at node j;

[0036] The upper and lower limits of the distribution network voltage are obtained from the historical grid data of the distribution network, and safety constraints are constructed, specifically:

[0037]

[0038] Where U j,t is the voltage amplitude at node j; U min is the lower limit of voltage amplitude; U max is the upper limit of voltage amplitude; P 0.t To prevent reverse power flow from being injected into the main power grid; ij is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; χ is the load rate requirement of the network.

[0039] Furthermore, the Wassertein constraint, power flow constraint, energy storage operation constraint, SOP constraint and safety constraint are combined to construct a carbon-electricity collaborative planning model that considers system costs, specifically:

[0040] The annual operating cost coefficient, the capital cost of distributed energy and E-SOP, the main grid carbon trading price, the main grid node carbon density, the life of distributed power sources and the life of E-SOP are obtained from the historical grid data of the distribution network, and the objective function of the carbon-electricity collaborative planning model is constructed, which is specifically:

[0041]

[0042] In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T REG and T ES are the lifespan of distributed power supply and E-SOP respectively; T y and T d are the running time; REG , ESC and λ ESP are the capital costs of distributed energy and E-SOP respectively; CO2 The carbon trading price of the main network; The carbon density of the main network nodes; The marginal price of the location obtained for the main network; S REG,i , S ESC,i and S ESP,i are the capacities of distributed generation and E-SOP respectively; η ope is the annual operating cost coefficient; d is the annual discount rate.

[0043] Furthermore, the power flow constraint is converted into a linearized power flow, specifically:

[0044] The linearized power flow is:

[0045]

[0046] Where P ij is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; b,0 , γ b,1 and γ b,2 are the coefficients of each linear term.

[0047] Furthermore, the CCG algorithm is used to solve the carbon-electricity synergy two-stage optimization model to obtain the probability distribution of uncertain variables, specifically:

[0048] The carbon-electricity synergy two-stage optimization model includes a main problem and sub-problems;

[0049] The CCG algorithm is used to iteratively solve the carbon-electricity synergy two-stage optimization model, specifically:

[0050] Set the upper and lower limits of the target value respectively;

[0051] By solving the main problem, updating the upper limit value;

[0052] By solving the sub-problems, updating the lower limit value;

[0053] Calculate the difference between the updated upper and lower limits;

[0054] If the difference reaches a preset convergence threshold, the iteration is terminated and the solution result of the current model is determined as the probability distribution of the uncertain variable.

[0055] Furthermore, according to the probability distribution of the uncertain variables, the carbon-electricity synergy planning model is converted into a carbon-electricity synergy mixed integer second-order cone model, and the distribution network planning scheme is obtained by solving the carbon-electricity synergy mixed integer second-order cone model, specifically:

[0056] The carbon-electric synergistic mixed integer second-order cone model is:

[0057]

[0058] In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T y and T d are the running time; CO2 is the carbon trading price; P j,t Active power injected into the node; Q j,t Reactive power injected into the node; S ESC and S ESP is the capacity of E-SOP; P ESDi,t is the charging power of E-SOP at time t; P ESRi,t is the discharge power of E-SOP at time t; N is the number of data samples; The marginal price of the location obtained for the main network; is the active power demand of the load; is the renewable energy power generation output of node j at time t; Reactive power demand of the load.

[0059] The present invention provides a carbon-electricity coordinated distribution network dispatching and control method, obtains historical grid data of the distribution network; constructs Wassertein constraints, flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical grid data of the distribution network; constructs a carbon-electricity coordinated planning model considering system costs in combination with the generated constraints; solves the carbon-electricity coordinated planning model using the CCG algorithm to obtain a distribution network planning scheme; and adjusts the number, access position and content of the DG and E-SOP connected to the distribution network according to the distribution network planning scheme. Under the premise of ensuring the absorption of distributed power sources, the present invention can realize the complementary advantages of multiple resources of energy storage systems and intelligent soft switch devices, give full play to the application potential of energy storage devices and intelligent soft switches, enhance the real-time regulation capability of active and reactive power between different branches or nodes, reasonably adjust and control the equipment in the distribution network, improve the system voltage, reduce system losses, and improve the economy of the distribution network.

[0060] Accordingly, the present invention provides a carbon-electricity coordinated distribution network dispatching control device, comprising: a data acquisition module, a constraint construction module, a model construction module, a solution module and an adjustment module;

[0061] The data acquisition module is used to acquire historical power grid data of the distribution network;

[0062] The constraint building module is used to build Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical power grid data of the distribution network;

[0063] The model building module is used to combine Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints to build a carbon-electricity collaborative planning model that considers system costs;

[0064] The solution module is used to solve the carbon-electricity collaborative planning model using the CCG algorithm to obtain a distribution network planning solution;

[0065] The adjustment module is used to adjust the access of DG and E-SOP in the distribution network according to the distribution network planning scheme; wherein the access adjustment includes access number adjustment, access position adjustment and access content adjustment.

[0066] Furthermore, the solution module includes: a simplification unit, an iteration unit and a solution unit;

[0067] The simplification unit is used to convert the power flow constraint into a linearized power flow, and simplify the carbon-electricity synergy planning model according to the linearized power flow to form a carbon-electricity synergy two-stage optimization model;

[0068] The iteration unit is used to iteratively solve the carbon-electricity synergy two-stage optimization model using the CCG algorithm to obtain the probability distribution of uncertain variables;

[0069] The solving unit is used to convert the carbon-electricity synergy planning model into a carbon-electricity synergy mixed integer second-order cone model according to the probability distribution of the uncertain variables, and obtain a distribution network planning scheme by solving the carbon-electricity synergy mixed integer second-order cone model.

[0070] The present invention provides a carbon-electricity coordinated distribution network dispatching and control device, which obtains historical grid data of the distribution network based on the organic combination of modules; constructs Wassertein constraints, flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical grid data of the distribution network; constructs a carbon-electricity coordinated planning model considering system costs in combination with the generated constraints; solves the carbon-electricity coordinated planning model using the CCG algorithm to obtain a distribution network planning scheme; and adjusts the number, access position and content of the DG and E-SOP connected to the distribution network according to the distribution network planning scheme. Under the premise of ensuring the absorption of distributed power sources, the present invention can realize the complementary advantages of multiple resources of energy storage systems and intelligent soft switch devices, give full play to the application potential of energy storage devices and intelligent soft switches, improve the real-time regulation capability of active and reactive power between different branches or nodes, reasonably adjust and control the equipment in the distribution network, improve the system voltage, reduce system losses, and improve the economy of the distribution network.

[0071] The present invention also provides a carbon-electricity coordinated distribution network dispatching and control device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the carbon-electricity coordinated distribution network dispatching and control method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A flow chart of an embodiment of the carbon-electricity coordinated distribution network dispatching control method provided by the present invention;

[0073] Figure 2 A distribution network node topology diagram provided by the present invention;

[0074] Figure 3 A structural schematic diagram of an embodiment of a carbon-electricity coordinated distribution network dispatching control device provided by the present invention;

[0075] Figure 4 A structural schematic diagram of an embodiment of a carbon-electricity coordinated distribution network dispatching control device provided by the present invention. DETAILED DESCRIPTION

[0076] 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.

[0077] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0078] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0079] In the description of the present invention, it should be understood that the terms used in the present invention are as follows:

[0080] (1) Distributed Generation (DG): refers to a power source that is connected to a power grid at a voltage level of 35 kV or below, located near the user, and mainly consumed locally at a voltage level of 35 kV or below.

[0081] (2) E-SOP: refers to the E-SOP formed by connecting the energy storage system (ESS) to the DC link of the intelligent soft switch (SOP). As a new type of power electronic device integrating intelligent soft switch and energy storage device, E-SOP can not only perform peak load shaving and valley filling, but also realize continuous regulation of active / reactive power of distribution network, and can be flexibly adjusted according to the real-time operating status of the distribution network.

[0082] Example 1

[0083] See also Figure 1 , is a flow chart of an embodiment of the carbon-electricity coordinated distribution network dispatching control method provided by the present invention, the method includes steps 101 to 105, each step is specifically as follows:

[0084] Step 101: Obtain historical power grid data of the distribution network.

[0085] Step 102: constructing Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical power grid data of the distribution network.

[0086] Furthermore, in the first embodiment of the present invention, the confidence of the Wasserstein metric and the radius of the Wasserstein sphere are obtained from the historical power grid data of the distribution network, and the Wasserstein constraint is constructed, specifically:

[0087]

[0088] In the formula, is the empirical distribution based on historical power grid data; is a dataset containing all historical power grid data; β1 and β ∞ is the confidence of Wasserstein metric; κ1 and κ ∞ is the radius of the Wasserstein sphere; R is the true distribution; is a dataset containing all historical data; R t (ξ) is the real distribution of historical power grid data; at the same time, the Wasserstein constraint of the present invention also considers the Wasserstein metric with 1 norm and the Wasserstein metric with infinite norm. The former is related to the general difference between probability distributions, and the latter represents the local deviation between probability distributions. If κ1 or κ∞ approaches 0, this problem is equivalent to stochastic optimization.

[0089] Furthermore, in the first embodiment of the present invention, active power demand, reactive power demand, operating time, line resistance and line impedance are obtained from the historical power grid data of the distribution network, and power flow constraints are constructed, specifically:

[0090]

[0091] Where P j,t Active power injected into the node; Q j,t is the reactive power injected into the node, and the inequality is in the standard second-order cone form; is the distributed power output power of node j at time t; is the active power demand of the load at node j at time t; The reactive power demand of the load at node j at time t; P ESDj,t and P ESRj,t are the charge and discharge power of E-SOP of node j at time t; r ij and x ij are the resistance and impedance of branch ij respectively; U i,t and U j,t is the node voltage; I ij,t is the line current; P ij,t , P jk,t , Q ij,t and Q jk,tare the active and reactive power of different lines respectively; ij,t for.

[0092] Further, in the first embodiment of the present invention, the capacity of E-SOP, the charging efficiency of E-SOP and the discharging efficiency of E-SOP are obtained from the historical grid data of the distribution network, and energy storage operation constraints are constructed, specifically:

[0093] 0≤|P ESDi,t |≤S ESPi

[0094] 0≤|P ESRi,t |≤S ESPi

[0095]

[0096] Where P ESDi,t is the charging power of E-SOP at time t; P ESRi,t is the discharge power of E-SOP at time t; S ESPi , S ESCi is the capacity of E-SOP; E ESi,t is the charge state of E-SOP at time t; η ESD is the charging efficiency of E-SOP; η ESR is the discharge efficiency of E-SOP; Δt is the time interval; E ESi,0 is the initial state of the energy storage system; E ESi,24 The energy storage operation constraint of the present invention defines the charge and discharge constraint conditions of the energy storage system and the relationship between the charging power, the discharging power and the charging state, and also defines that the initial state and the final state of the energy storage system are the same.

[0097] Further, in the first embodiment of the present invention, the SOP converter loss coefficient, the upper and lower limits of the SOP reactive power and the capacity of the SOP are obtained in the historical power grid data of the distribution network, and the SOP constraint is constructed;

[0098] The SOP constraints include SOP active power constraints, SOP reactive power constraints and SOP capacity constraints;

[0099] The SOP active power constraint is:

[0100]

[0101] The SOP reactive power constraint is:

[0102]

[0103] The SOP capacity constraint is:

[0104]

[0105] Where i and j are the node numbers of the distribution network to which the SOP is connected; P SOP,i,t , P SOP,j,t , Q SOP,i,t and Q SOP,j,t are the active and reactive power injected by SOP at nodes i and j at time t respectively; P loss,i,t and P loss,j,t are the active power loss of SOP at node i and j at time t; A SOP,i and A SOP,J is the SOP converter loss coefficient; Q SOP,imax , Q SOP,imin , Q SOP,jmax and Q SOP,jmin are the upper and lower limits of the reactive power injected by SOP at nodes i and j respectively; S SOP,ij is the capacity of SOP; is the active power loss of SOP at node i; is the active power loss of SOP at node j.

[0106] Furthermore, in the first embodiment of the present invention, the upper and lower limits of the distribution network voltage are obtained from the historical power grid data of the distribution network, and safety constraints are constructed, specifically:

[0107]

[0108] Where U j,t is the voltage amplitude at node j; U min is the lower limit of voltage amplitude; U max is the upper limit of voltage amplitude; P 0.t To prevent reverse power flow from being injected into the main power grid; ij is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; χ is the load rate requirement of the network.

[0109] Step 103: Combine Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints to build a carbon-electricity collaborative planning model that considers system costs.

[0110] Furthermore, in the first embodiment of the present invention, a carbon-electricity collaborative planning model considering system costs is constructed by combining Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints, specifically:

[0111] The annual operating cost coefficient, the capital cost of distributed energy and E-SOP, the main grid carbon trading price, the main grid node carbon density, the life of distributed power sources and the life of E-SOP are obtained from the historical grid data of the distribution network, and the objective function of the carbon-electricity collaborative planning model is constructed, which is specifically:

[0112]

[0113] C ope =η ope (C REG +C ES )

[0114]

[0115] In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T REG and T ES are the lifespan of distributed power supply and E-SOP respectively; T y and T d are the running time; REG , ESC and λ ESP are the capital costs of distributed energy and E-SOP respectively; CO2 The carbon trading price of the main network; The carbon density of the main network nodes; The marginal price of the location obtained for the main network; S REG,i , S ESC,i and S ESP,i are the capacities of distributed generation and E-SOP respectively; η ope is the annual operating cost coefficient; d is the annual discount rate.

[0116] Step 104: Use the CCG algorithm to solve the carbon-electricity collaborative planning model to obtain a distribution network planning solution.

[0117] Furthermore, in the first embodiment of the present invention, the CCG algorithm is used to solve the carbon-electricity collaborative planning model to obtain a distribution network planning scheme, which is specifically:

[0118] Converting the power flow constraint into a linearized power flow, and simplifying the carbon-electricity synergistic planning model according to the linearized power flow to form a carbon-electricity synergistic two-stage optimization model;

[0119] The CCG algorithm is used to iteratively solve the carbon-electricity synergy two-stage optimization model to obtain the probability distribution of uncertain variables;

[0120] According to the probability distribution of the uncertain variables, the carbon-electricity synergy planning model is converted into a carbon-electricity synergy mixed integer second-order cone model, and the distribution network planning scheme is obtained by solving the carbon-electricity synergy mixed integer second-order cone model.

[0121] Further, in the first embodiment of the present invention, the power flow constraint is converted into a linearized power flow, specifically:

[0122] The power flow constraint can be simplified as:

[0123]

[0124] The simplified power flow constraint is a quadratic constraint, which can be linearized by polygonal approximation method to form a linearized power flow:

[0125]

[0126] Where Pi j is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; b,0 , γ b,1 and γ b,2 are the coefficients of each linear term.

[0127] Therefore, the carbon-electricity collaborative planning model is transformed into a linear programming model with Wassertein constraints:

[0128]

[0129] The following constraints are included:

[0130] y≥0

[0131] y REG =F3

[0132] G1x ≥ G2-G3y

[0133]

[0134] When the convexity of the optimization problem is guaranteed, the linear programming model can be transformed into a two-stage optimization model for carbon-electricity synergy:

[0135]

[0136] y≥0

[0137] y REG =F3

[0138] G1x ≥ G2-G3y

[0139]

[0140] Furthermore, in the first embodiment of the present invention, the CCG algorithm is used to solve the carbon-electricity synergy two-stage optimization model to obtain the probability distribution of uncertain variables, specifically:

[0141] The carbon-electricity synergy two-stage optimization model includes a main problem and sub-problems;

[0142] The CCG algorithm is used to iteratively solve the carbon-electricity synergy two-stage optimization model, specifically:

[0143] Set the upper and lower limits of the target value respectively;

[0144] By solving the main problem, updating the upper limit value;

[0145] By solving the sub-problems, updating the lower limit value;

[0146] Calculate the difference between the updated upper and lower limits;

[0147] If the difference reaches a preset convergence threshold, the iteration is terminated and the solution result of the current model is determined as the probability distribution of the uncertain variable.

[0148] In the first embodiment of the present invention, the two-stage optimization model of carbon-electricity synergy is decomposed into a main problem and sub-problems according to different objectives, and the optimal value of the decision variable is obtained through iteration.

[0149] The main problem is:

[0150]

[0151] y≥0

[0152]

[0153] The main problem is related to the planning strategy of the first stage. The uncertain variables of the main problem are The value of is the input parameter, which is obtained according to the optimal solution of the subproblem.

[0154] The sub-problems are:

[0155] The subproblem is a minimax optimization problem, which can be reformulated as follows using the strong duality theorem:

[0156]

[0157] The CCG algorithm is used to iteratively solve the main problem and sub-problems. During the iteration process, the two will pass variables to each other and then calculate the error until the error converges or reaches the preset number of times, and the iteration stops.

[0158] Specifically, first set the lower limit L B =0, set upper limit U B = +∞, initialize the number of loops k = 0; update the lower limit by solving the main problem Update the upper limit by solving the subproblem Check the convergence of the model based on the updated upper and lower limits. B -L B If γ is less than or equal to γ, (γ is a preset error threshold), the model is determined to have reached the convergence requirement; otherwise, the number of cycles is updated k=k+1, and new constraints are added to solve the model next time.

[0159] Furthermore, in the first embodiment of the present invention, according to the probability distribution of the uncertain variables, the carbon-electricity synergistic planning model is converted into a carbon-electricity synergistic mixed integer second-order cone model, and the distribution network planning scheme is obtained by solving the carbon-electricity synergistic mixed integer second-order cone model, specifically:

[0160] The carbon-electric synergistic mixed integer second-order cone model is:

[0161]

[0162] In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T y and T d are the running time; CO2 is the carbon trading price; P j,t Active power injected into the node; Q j,t Reactive power injected into the node; S ESC and S ESP is the capacity of E-SOP; P ESDi,t is the charging power of E-SOP at time t; P ESRi,t is the discharge power of E-SOP at time t; N is the number of data samples; The marginal price of the location obtained for the main network; is the active power demand of the load; is the renewable energy power generation output of node j at time t; Reactive power demand of the load.

[0163] In the first embodiment of the present invention, based on the probability distribution of uncertain variables obtained by solving the carbon-electricity synergy two-stage optimization model, the uncertain variables in the carbon-electricity synergy planning model are replaced with known discrete distributions to transform it into a carbon-electricity synergy mixed integer second-order cone model. The mixed second-order cone integer model is a linear optimization model that can be solved using an optimizer (such as IBM CPLEX).

[0164] Step 105: According to the distribution network planning scheme, access adjustment is performed on the DG and E-SOP in the distribution network respectively; wherein the access adjustment includes access number adjustment, access position adjustment and access content adjustment.

[0165] In the first embodiment of the present invention, a distribution network planning scheme can be obtained by solving a mixed second-order cone integer model, and the scheme includes the number of DG accesses, access positions and access content, and the number of E-SOP accesses, access positions and access content. Therefore, by accessing DG and E-SOP according to the distribution network planning scheme, and adjusting the number, access position and content during access, the distribution network can be optimized and the operating cost of the distribution network can be reduced.

[0166] As an example of the first embodiment of the present invention, see Figure 2 , is a kind of distribution network node topology diagram provided by the present invention, between node 8 and node 14 is a tie switch, between node 7 and node 20 is a tie switch, between node 24 and node 28 is a tie switch, between node 11 and node 21 is a tie switch, and between node 17 and node 32 is a tie switch. E-SOP device is accessed between node 11 and node 21, E-SOP device is accessed between node 17 and node 32, DG accesses node No. 20, node No. 4, node No. 32 respectively, and DG and E-SOP access content are adjusted according to the distribution network planning scheme. Compared to the situation where DG and E-SOP are not accessed, after the distribution network accesses DG and E-SOP, although the construction cost of DG and ESOP is increased, the comprehensive cost of distribution network is reduced, and the comprehensive investment income is improved.

[0167] In summary, the first embodiment of the present invention provides a carbon-electricity coordinated distribution network dispatching and control method, obtains historical grid data of the distribution network; constructs Wassertein constraints, flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical grid data of the distribution network; constructs a carbon-electricity coordinated planning model considering system costs in combination with the generated constraints; solves the carbon-electricity coordinated planning model using the CCG algorithm to obtain a distribution network planning scheme; and adjusts the number, access position and content of the DG and E-SOP connected to the distribution network according to the distribution network planning scheme. Under the premise of ensuring the absorption of distributed power sources, the present invention can realize the complementary advantages of multiple resources of energy storage systems and intelligent soft switch devices, give full play to the application potential of energy storage devices and intelligent soft switches, improve the real-time regulation capability of active and reactive power between different branches or nodes, reasonably adjust and control the equipment in the distribution network, improve the system voltage, reduce system losses, and improve the economy of the distribution network.

[0168] Example 2

[0169] See also Figure 3 , is a structural schematic diagram of an embodiment of a carbon-electricity coordinated distribution network dispatching control device provided by the present invention, the device includes a data acquisition module, a constraint construction module, a model construction module, a solution module and an adjustment module;

[0170] The data acquisition module 201 is used to acquire historical power grid data of the distribution network;

[0171] The constraint construction module 202 is used to construct Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical power grid data of the distribution network;

[0172] The model building module 203 is used to build a carbon-electricity collaborative planning model that considers system costs by combining Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints, and safety constraints;

[0173] The solution module 204 is used to solve the carbon-electricity collaborative planning model using the CCG algorithm to obtain a distribution network planning solution;

[0174] The adjustment module 205 is used to adjust the access of DG and E-SOP in the distribution network according to the distribution network planning scheme; wherein the access adjustment includes access number adjustment, access position adjustment and access content adjustment.

[0175] Further, in the second embodiment of the present invention, the solution module 204 includes: a simplification unit, an iteration unit and a solution unit;

[0176] The simplification unit is used to convert the power flow constraint into a linearized power flow, and simplify the carbon-electricity synergy planning model according to the linearized power flow to form a carbon-electricity synergy two-stage optimization model;

[0177] The iteration unit is used to iteratively solve the carbon-electricity synergy two-stage optimization model using the CCG algorithm to obtain the probability distribution of uncertain variables;

[0178] The solving unit is used to convert the carbon-electricity synergy planning model into a carbon-electricity synergy mixed integer second-order cone model according to the probability distribution of the uncertain variables, and obtain a distribution network planning scheme by solving the carbon-electricity synergy mixed integer second-order cone model.

[0179] Further, in the second embodiment of the present invention, Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints are constructed according to the historical power grid data of the distribution network, specifically:

[0180] The confidence of the Wasserstein metric and the radius of the Wasserstein sphere are obtained from the historical power grid data of the distribution network, and the Wasserstein constraint is constructed, specifically:

[0181]

[0182] In the formula, is the empirical distribution based on historical power grid data; is a dataset containing all historical power grid data; β1 and β ∞ is the confidence of Wasserstein metric; κ1 and κ ∞ is the radius of the Wasserstein sphere; R is the true distribution; is a dataset containing all historical data; R t (ξ) is the real distribution of historical power grid data;

[0183] Active power demand, reactive power demand, operating time, line resistance and line impedance are obtained from the historical grid data of the distribution network, and power flow constraints are constructed, specifically:

[0184]

[0185] Where P j,t Active power injected into the node; Q j,t Reactive power injected into the node; is the distributed power output power of node j at time t; is the active power demand of the load at node j at time t; The reactive power demand of the load at node j at time t; P ESDj,tand P ESRj,t are the charge and discharge power of E-SOP of node j at time t; r ij and x ij are the resistance and impedance of branch ij respectively; U i,t and U j,t is the node voltage; I ij,t is the line current; P ij,t , P jk,t , Q ij,t and Q jk,t are the active and reactive power of different lines respectively;

[0186] The capacity of the E-SOP, the charging efficiency of the E-SOP, and the discharging efficiency of the E-SOP are obtained from the historical grid data of the distribution network, and energy storage operation constraints are constructed, specifically:

[0187] 0≤|P ESDi,t |≤S ESPi

[0188] 0≤|P ESRi,t |≤S ESPi

[0189]

[0190] Where P ESDi,t is the charging power of E-SOP at time t; P ESRi,t is the discharge power of E-SOP at time t; S ESPi , S ESCi is the capacity of E-SOP; E ESi,t is the charge state of E-SOP at time t; η ESD is the charging efficiency of E-SOP; η ESR is the discharge efficiency of E-SOP; Δt is the time interval; E ESi,0 is the initial state of the energy storage system; E ESi,24 is the final state of the energy storage system;

[0191] Obtaining the SOP converter loss coefficient, the upper and lower limits of the SOP reactive power, and the capacity of the SOP from the historical grid data of the distribution network, and constructing the SOP constraint;

[0192] The SOP constraints include SOP active power constraints, SOP reactive power constraints and SOP capacity constraints;

[0193] The SOP active power constraint is:

[0194]

[0195]

[0196] The SOP reactive power constraint is:

[0197]

[0198] The SOP capacity constraint is:

[0199]

[0200] Where i and j are the node numbers of the distribution network to which the SOP is connected; P SOP,i,t , P SOP,j,t , Q SOP,i,t and Q SOP,j,t are the active and reactive power injected by SOP at nodes i and j at time t respectively; P loss,i,t and P loss,j,t are the active power loss of SOP at node i and j at time t; A SOP,i and A SOP,J is the SOP converter loss coefficient; Q SOP,imax , Q SOP,imin , Q SOP,jmax and Q SOP,jmin are the upper and lower limits of the reactive power injected by SOP at nodes i and j respectively; S SOP,ij is the capacity of SOP; is the active power loss of SOP at node i; is the active power loss of SOP at node j;

[0201] The upper and lower limits of the distribution network voltage are obtained from the historical grid data of the distribution network, and safety constraints are constructed, specifically:

[0202]

[0203] Where U j,t is the voltage amplitude at node j; U min is the lower limit of voltage amplitude; U max is the upper limit of voltage amplitude; P 0.t To prevent reverse power flow from being injected into the main power grid; ij is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; χ is the load rate requirement of the network.

[0204] Furthermore, in the second embodiment of the present invention, a carbon-electricity collaborative planning model considering system costs is constructed by combining Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints, specifically:

[0205] The annual operating cost coefficient, the capital cost of distributed energy and E-SOP, the main grid carbon trading price, the main grid node carbon density, the life of distributed power sources and the life of E-SOP are obtained from the historical grid data of the distribution network, and the objective function of the carbon-electricity collaborative planning model is constructed, which is specifically:

[0206]

[0207] C ope =η ope (C REG +C ES )

[0208]

[0209] In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T REG and T ES are the lifespan of distributed power supply and E-SOP respectively; T y and T d are the running time; REG , ESC and λ ESP are the capital costs of distributed energy and E-SOP respectively; CO2 The carbon trading price of the main network; The carbon density of the main network nodes; The marginal price of the location obtained for the main network; S REG,i , S ESC,i and S ESP,i are the capacities of distributed generation and E-SOP respectively; η ope is the annual operating cost coefficient; d is the annual discount rate.

[0210] Further, in the second embodiment of the present invention, the power flow constraint is converted into a linearized power flow, specifically:

[0211] The linearized power flow is:

[0212]

[0213] Where P ij is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; b,0 , γ b,1 and γ b,2are the coefficients of each linear term.

[0214] Furthermore, in the second embodiment of the present invention, the CCG algorithm is used to solve the carbon-electricity synergy two-stage optimization model to obtain the probability distribution of uncertain variables, specifically:

[0215] The carbon-electricity synergy two-stage optimization model includes a main problem and sub-problems;

[0216] The CCG algorithm is used to iteratively solve the carbon-electricity synergy two-stage optimization model, specifically:

[0217] Set the upper and lower limits of the target value respectively;

[0218] By solving the main problem, updating the upper limit value;

[0219] By solving the sub-problems, updating the lower limit value;

[0220] Calculate the difference between the updated upper and lower limits;

[0221] If the difference reaches a preset convergence threshold, the iteration is terminated and the solution result of the current model is determined as the probability distribution of the uncertain variable.

[0222] Further, in the second embodiment of the present invention, according to the probability distribution of the uncertain variables, the carbon-electricity synergistic planning model is converted into a carbon-electricity synergistic mixed integer second-order cone model, and the distribution network planning scheme is obtained by solving the carbon-electricity synergistic mixed integer second-order cone model, specifically:

[0223] The carbon-electric synergistic mixed integer second-order cone model is:

[0224]

[0225] In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T y and T d are the running time; CO2 is the carbon trading price; P j,t Active power injected into the node; Q j,t Reactive power injected into the node; S ESC and S ESP is the capacity of E-SOP; P ESDi,t is the charging power of E-SOP at time t; P ESRi,tis the discharge power of E-SOP at time t; N is the number of data samples; The marginal price of the location obtained for the main network; is the active power demand of the load; is the renewable energy power generation output of node j at time t; Reactive power demand of the load.

[0226] In summary, the second embodiment of the present invention provides a carbon-electricity coordinated distribution network dispatching and control device, which obtains historical power grid data of the distribution network based on the organic combination of modules; constructs Wassertein constraints, flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical power grid data of the distribution network; constructs a carbon-electricity coordinated planning model considering system costs in combination with the generated constraints; solves the carbon-electricity coordinated planning model using the CCG algorithm to obtain a distribution network planning scheme; and adjusts the number, access position and content of the DG and E-SOP connected to the distribution network according to the distribution network planning scheme. Under the premise of ensuring the absorption of distributed power sources, the present invention can realize the complementary advantages of multiple resources of energy storage systems and intelligent soft switch devices, give full play to the application potential of energy storage devices and intelligent soft switches, improve the real-time regulation capability of active and reactive power between different branches or nodes, reasonably adjust and control the equipment in the distribution network, improve the system voltage, reduce system losses, and improve the economy of the distribution network.

[0227] See also Figure 4 , is a structural diagram of an embodiment of a carbon-electricity coordinated distribution network dispatching and control device provided by the present invention. The device can be a server, including a processor, a memory and a network interface connected through a system bus, wherein the memory can include a non-volatile storage medium and an internal memory.

[0228] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0229] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any carbon-electricity coordinated distribution network dispatching control method.

[0230] The internal memory provides an environment for the operation of computer programs in non-volatile storage media. When the computer program is executed by the processor, the processor can execute any carbon-electricity coordinated distribution network dispatching control method.

[0231] The network interface is used for network communication, such as sending assigned tasks.

[0232] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0233] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0234] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A carbon-electricity coordinated distribution network dispatching and control method, characterized in that: include: Obtain historical grid data of the distribution network; Constructing Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints based on historical power grid data of the distribution network; Combining Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints, a carbon-electricity collaborative planning model that considers system costs is constructed; The CCG algorithm is used to solve the carbon-electricity collaborative planning model and obtain a distribution network planning solution; According to the distribution network planning scheme, access adjustments are made to the DG and E-SOP in the distribution network respectively; wherein the access adjustments include access number adjustment, access position adjustment and access content adjustment.

2. The carbon-electricity coordinated distribution network dispatching and control method according to claim 1 is characterized in that: The CCG algorithm is used to solve the carbon-electricity collaborative planning model to obtain a distribution network planning solution, specifically: Converting the power flow constraint into a linearized power flow, and simplifying the carbon-electricity synergistic planning model according to the linearized power flow to form a carbon-electricity synergistic two-stage optimization model; The CCG algorithm is used to iteratively solve the carbon-electricity synergy two-stage optimization model to obtain the probability distribution of uncertain variables; According to the probability distribution of the uncertain variables, the carbon-electricity synergy planning model is converted into a carbon-electricity synergy mixed integer second-order cone model, and the distribution network planning scheme is obtained by solving the carbon-electricity synergy mixed integer second-order cone model.

3. The carbon-electricity coordinated distribution network dispatching and control method according to claim 1 is characterized in that: The Wassertein constraint, power flow constraint, energy storage operation constraint, SOP constraint and safety constraint are constructed according to the historical power grid data of the distribution network, specifically: The confidence of the Wasserstein metric and the radius of the Wasserstein sphere are obtained from the historical power grid data of the distribution network, and the Wasserstein constraint is constructed, specifically: In the formula, is the empirical distribution based on historical power grid data; is a dataset containing all historical power grid data; β1 and β ∞ is the confidence of Wasserstein metric; κ1 and κ ∞ is the radius of the Wasserstein sphere; R is the true distribution; is a dataset containing all historical data; R t (ξ) is the real distribution of historical power grid data; Active power demand, reactive power demand, operating time, line resistance and line impedance are obtained from the historical grid data of the distribution network, and power flow constraints are constructed, specifically: Where P j,t Active power injected into the node; Q j,t Reactive power injected into the node; is the distributed power output power of node j at time t; is the active power demand of the load at node j at time t; The reactive power demand of the load at node j at time t; P ESDj,t and P ESRj,t are the charge and discharge power of E-SOP of node j at time t; r ij and x ij are the resistance and impedance of branch ij respectively; U i,t and U j,t is the node voltage; I ij,t is the line current; P ij,t , P jk,t , Q ij,t and Q jk,t are the active and reactive power of different lines respectively; The capacity of the E-SOP, the charging efficiency of the E-SOP, and the discharging efficiency of the E-SOP are obtained from the historical grid data of the distribution network, and energy storage operation constraints are constructed, specifically: 0≤|P ESDi,t |≤S ESPi 0≤|P ESRi,t |≤S ESPi Where P ESDi,t is the charging power of E-SOP at time t; P ESRi,t is the discharge power of E-SOP at time t; S ESPi , S ESCi is the capacity of E-SOP; E ESi,t is the charge state of E-SOP at time t; η ESD is the charging efficiency of E-SOP; η ESR is the discharge efficiency of E-SOP; Δt is the time interval; E ESi,0 is the initial state of the energy storage system; E ESi,24 is the final state of the energy storage system; Obtaining the SOP converter loss coefficient, the upper and lower limits of the SOP reactive power, and the capacity of the SOP from the historical grid data of the distribution network, and constructing the SOP constraint; The SOP constraints include SOP active power constraints, SOP reactive power constraints and SOP capacity constraints; The SOP active power constraint is: The SOP reactive power constraint is: The SOP capacity constraint is: Where i and j are the node numbers of the distribution network to which the SOP is connected; P SOP,i,t , P SOP,j,t , Q SOP,i,t and Q SOP,j,t are the active and reactive power injected by SOP at nodes i and j at time t respectively; P loss,i,t and P loss,j,t are the active power loss of SOP at node i and j at time t; A SOP,i and A SOP,J is the SOP converter loss coefficient; Q SOP,imax , Q SOP,imin , Q SOP,jmax and Q SOP,jmin are the upper and lower limits of the reactive power injected by SOP at nodes i and j respectively; S SOP,ij is the capacity of SOP; is the active power loss of SOP at node i; is the active power loss of SOP at node j; The upper and lower limits of the distribution network voltage are obtained from the historical grid data of the distribution network, and safety constraints are constructed, specifically: Where U j,t is the voltage amplitude at node j; U min is the lower limit of voltage amplitude; U max is the upper limit of voltage amplitude; P 0.t To prevent reverse power flow from being injected into the main grid; P ij is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; χ is the load rate requirement of the network.

4. The carbon-electricity coordinated distribution network dispatching and control method according to claim 1 is characterized in that: The Wassertein constraint, power flow constraint, energy storage operation constraint, SOP constraint and safety constraint are combined to construct a carbon-electricity collaborative planning model that considers system costs. Specifically, The annual operating cost coefficient, the capital cost of distributed energy and E-SOP, the main grid carbon trading price, the main grid node carbon density, the life of distributed power sources and the life of E-SOP are obtained from the historical grid data of the distribution network, and the objective function of the carbon-electricity collaborative planning model is constructed, which is specifically: C ope =η ope (C REG +C ES ) In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T REG and T ES are the lifespan of distributed power supply and E-SOP respectively; T y and T d are the running time; REG , ESC and λ ESP are the capital costs of distributed energy and E-SOP respectively; CO2 The carbon trading price of the main network; The carbon density of the main network nodes; The marginal price of the location obtained for the main network; S REG,i , S ESC,i and S ESP,i are the capacities of distributed generation and E-SOP respectively; η ope is the annual operating cost coefficient; d is the annual discount rate.

5. The carbon-electricity coordinated distribution network dispatching and control method according to claim 1 is characterized in that: The step of converting the power flow constraint into a linearized power flow is specifically as follows: The linearized power flow is: Where P ij is the active power of branch ij; Q ij is the reactive power of branch ij; S L is the load capacity; b,0 , γ b,1 and γ b,2 are the coefficients of each linear term.

6. The carbon-electricity coordinated distribution network dispatching and control method according to claim 2 is characterized in that: The CCG algorithm is used to solve the carbon-electricity synergy two-stage optimization model to obtain the probability distribution of uncertain variables, specifically: The carbon-electricity synergy two-stage optimization model includes a main problem and sub-problems; The CCG algorithm is used to iteratively solve the carbon-electricity synergy two-stage optimization model, specifically: Set the upper and lower limits of the target value respectively; By solving the main problem, updating the upper limit value; By solving the sub-problems, the lower limit value is updated; Calculate the difference between the updated upper and lower limits; If the difference reaches a preset convergence threshold, the iteration is terminated and the solution result of the current model is determined as the probability distribution of the uncertain variable.

7. The carbon-electricity coordinated distribution network dispatching and control method according to claim 2 is characterized in that: According to the probability distribution of the uncertain variables, the carbon-electricity synergy planning model is converted into a carbon-electricity synergy mixed integer second-order cone model, and the distribution network planning scheme is obtained by solving the carbon-electricity synergy mixed integer second-order cone model, specifically: The carbon-electric synergistic mixed integer second-order cone model is: In the formula, C REG is the annual investment cost of distributed power generation; C ES is the annual investment cost of E-SOP; C ope is the operation and maintenance cost of E-SOP; C E,t and C CO2,t is the energy transaction cost; T y and T d are the running time; CO2 is the carbon trading price; P j,t Active power injected into the node; Q j,t Reactive power injected into the node; S ESC and S ESP is the capacity of E-SOP; P ESDi,t is the charging power of E-SOP at time t; P ESRi,t is the discharge power of E-SOP at time t; N is the number of data samples; The marginal price of the location obtained for the main network; is the active power demand of the load; is the renewable energy power generation output of node j at time t; Reactive power demand of the load.

8. A carbon-electricity coordinated distribution network dispatching control device, characterized in that: include: Data acquisition module, constraint building module, model building module, solution module and adjustment module; The data acquisition module is used to acquire historical power grid data of the distribution network; The constraint building module is used to build Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints according to the historical power grid data of the distribution network; The model building module is used to combine Wassertein constraints, power flow constraints, energy storage operation constraints, SOP constraints and safety constraints to build a carbon-electricity collaborative planning model that considers system costs; The solution module is used to solve the carbon-electricity collaborative planning model using the CCG algorithm to obtain a distribution network planning solution; The adjustment module is used to adjust the access of DG and E-SOP in the distribution network according to the distribution network planning scheme; wherein the access adjustment includes access number adjustment, access position adjustment and access content adjustment.

9. The carbon-electricity coordinated distribution network dispatching control device according to claim 8 is characterized in that: The solution module includes: a simplification unit, an iteration unit and a solution unit; The simplification unit is used to convert the power flow constraint into a linearized power flow, and simplify the carbon-electricity synergy planning model according to the linearized power flow to form a carbon-electricity synergy two-stage optimization model; The iteration unit is used to iteratively solve the carbon-electricity synergy two-stage optimization model using the CCG algorithm to obtain the probability distribution of uncertain variables; The solving unit is used to convert the carbon-electricity synergy planning model into a carbon-electricity synergy mixed integer second-order cone model according to the probability distribution of the uncertain variables, and obtain a distribution network planning scheme by solving the carbon-electricity synergy mixed integer second-order cone model.

10. A carbon-electricity coordinated distribution network dispatching and control device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the carbon-electricity coordinated distribution network dispatching control method as described in any one of claims 1 to 7.

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