Flexible interconnected distribution network resource scheduling method and system considering demand response

Abstract

The present application provides a method and system for dispatching resources of a flexible interconnected distribution network taking demand response into account, including: obtaining the actual response capability of users according to the deviation coefficient and distribution dispatching capability of users participating in demand response, and constructing a demand response model based on the actual response capability of users; obtaining the power flow distribution of the system through multiple power flow iterations based on the demand response model; and taking the carbon potential of the main grid as a reference benchmark, establishing an optimal planning model with the lowest comprehensive cost according to whether the carbon potential of the node connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model; converting the optimal planning model into a mixed integer second-order cone programming model by converting the constraint conditions into rotating cone constraints, solving the mixed integer second-order cone programming model based on the rotating cone constraints, and obtaining a scheduling plan, so that resources are scheduled according to the scheduling plan; the use of the present application can improve the accuracy of resource scheduling.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network resource scheduling, and in particular to a method and system for scheduling resources of a flexible interconnected distribution network taking demand response into account. Background Art

[0002] With the further advancement of energy transformation and upgrading, the proportion of distributed renewable energy resources (DERs) connected to the distribution network has increased year by year. However, due to the output fluctuations and dispersed distribution of DERs themselves, the distribution network has encountered problems such as line blocking and voltage over-limit, which has brought huge challenges to the safe and economical operation of the distribution network.

[0003] Based on the above problems, the introduction of flexible resources in the optimal dispatching of distribution networks has become one of the important ways to ensure the safe and economical operation of distribution networks. The existing related technologies use the SOP (Soft open point, soft switch) robust optimization dispatching model to verify that SOP can effectively reduce system losses and alleviate voltage deviations to a certain extent after it is connected to the distribution network; or use the timing optimization method of SOP and traditional tie switches to coordinate. The optimization results also verify that the optimization scheme of SOP and traditional distribution equipment has a significant role in reducing system losses, improving voltage levels, and promoting the consumption of new energy.

[0004] In summary, the existing related technologies have been studied for the optimal scheduling of SOP, but the research is mainly aimed at reducing system power loss and improving DG absorption, and most of them use Distflow power flow, which can only calculate one-way power flow and has the defect of low efficiency in solving nonlinear problems, resulting in low scheduling accuracy. Summary of the invention

[0005] The purpose of this application is to address the deficiencies of the above-mentioned existing related technologies and provide a flexible interconnected distribution network resource scheduling method and system taking demand response into account, which can improve the accuracy of the scheduling strategy of dispatchable resources.

[0006] In a first aspect, the present application provides a method for dispatching resources of a flexible interconnected distribution network taking into account demand response, comprising:

[0007] According to the deviation coefficient and distribution dispatching capacity of the users participating in the demand response, the actual response capacity of the users is obtained, and a demand response model is constructed based on the actual response capacity of the users;

[0008] Based on the demand response model, a power flow distribution of the system is obtained through multiple power flow iterations; wherein the power flow distribution includes: node voltage; the node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration when back-calculating the kth iteration each time; k is a positive integer;

[0009] Based on the node voltage, taking the main grid carbon potential as a reference benchmark, and according to whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established; the optimal planning model includes: SOP operation constraints;

[0010] The optimal planning model is converted into a mixed integer second-order cone programming model by converting the SOP running constraints into rotating cone constraints, and the mixed integer second-order cone programming model is solved based on the rotating cone constraints to obtain a scheduling scheme, so that resources are scheduled according to the scheduling scheme.

[0011] The present application obtains accurate power flow distribution through a demand response model, and approximates the true value of the voltage with the real part of the voltage drop when iteratively reversing the node voltage, which can improve the convergence of the optimal power flow distribution and reflect the consideration of the bidirectional power flow characteristics in the solution process, thereby improving the accuracy of the scheduling plan for dispatchable resources; and, by comparing the node carbon potential and the main grid carbon potential, to construct an optimal planning model with the lowest comprehensive cost, it can reduce the comprehensive cost of dispatchable resources, thereby improving the accuracy of resource scheduling while reducing costs and achieving cost reduction and efficiency improvement; in addition, by converting the SOP operation constraints, it can improve the model solution efficiency and solution accuracy of the scheduling plan, thereby further improving the accuracy of the scheduling plan for dispatchable resources.

[0012] Further, the optimal planning model with the lowest comprehensive cost is established according to whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model, including:

[0013] If the carbon potential of the node connected by the node load is higher than the reference benchmark, the node carbon emission cost to be paid by the distribution network is obtained according to the additional carbon emission penalty coefficient; if the node carbon potential is lower than the reference benchmark, the node carbon emission cost is obtained according to the compensation incentive coefficient; the sum of the additional carbon emission penalty coefficient and the compensation incentive coefficient is a preset value;

[0014] According to the node carbon emission cost and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established.

[0015] This application calculates the node carbon emission costs for situations exceeding the reference benchmark and below the reference benchmark respectively through additional carbon emission penalty coefficients and compensation incentive coefficients, so as to build a more accurate optimal planning model, thereby improving the accuracy of the scheduling plan for schedulable resources.

[0016] Further, the optimal planning model includes: an objective function and constraints; the constraints include: the SOP operation constraints; the optimal planning model with the lowest comprehensive cost is established according to the node carbon emission cost and the demand response cost corresponding to the demand response model, including:

[0017] According to the node carbon emission cost, the demand response cost corresponding to the demand response model, the loss cost of the system and the penalty cost of wind and solar power abandonment of the system, the objective function and the constraint conditions with the lowest comprehensive cost are established; the constraints also include: distribution network flow constraints, distribution network operation safety constraints, distribution network power balance constraints, SOP capacity configuration constraints and demand response constraints corresponding to the demand response model.

[0018] Furthermore, the node carbon emission cost is expressed as:

[0019] ;

[0020] in, is a 0-1 variable, indicating the node carbon potential of node i at time t Is it higher than the reference benchmark? , if higher than the reference When Take 1; is the carbon emission cost coefficient; To compensate for the incentive factor, it is used to encourage users to use energy with low carbon emission intensity; is the time difference, which is 1h; is the scheduling cycle length; is the set of distribution network nodes.

[0021] Further, the demand response constraint is expressed as:

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] in, and are the active power and reactive power of load shifting of node i at time t respectively; and are the active power and reactive power of load reduction of node i at time t respectively; and are the active load and reactive load of node i at time t respectively; is the peak load of node i; is the proportion of the maximum reserved response capacity among demand-side resources; is the response capacity reserved by the user at the i-th load point; is the reduction or translation of node i at time t; It is a binary variable, which is 1 when the distribution network dispatches load; is the upper limit of load shedding time; and are the start and end times of the shiftable load in a day, is the interval between the initial time before the load is transferred and the new time after the load is transferred.

[0028] Furthermore, the node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration each time the kth iteration is reversed, including:

[0029] Each time for the kth iteration, the real value of the node voltage is approximated based on the initial voltage and the real part of the voltage drop after the k+1th iteration to obtain the node voltage; wherein the sum or difference between the initial voltage and the real part must meet a preset range.

[0030] The present application can improve the convergence of the optimal power flow distribution by iteratively back-calculating the node voltage and approximating the real value of the voltage with the real part of the voltage drop. At the same time, it can limit the sum or difference of the initial voltage and the real part of the voltage drop within a preset range, which can further limit the convergence state of the optimal power flow distribution and obtain a more accurate power flow distribution, thereby improving the accuracy of the scheduling plan for the schedulable resources.

[0031] Further, the sum or the difference satisfies a preset range and is represented by a linear voltage constraint; the node voltage and the linear voltage constraint are respectively represented as:

[0032] ;

[0033] ;

[0034] in, , and are the node voltage of the kth approximate voltage true value of node i at time t, the initial voltage, and the value of the voltage drop at the k+1th iteration; is the real part operation; and are the minimum and maximum node voltages, respectively.

[0035] Further, the SOP operation constraint is a nonlinear constraint limit on the active power of the voltage source converter injected into the node to which the SOP is connected and the total power loss of the node to which the SOP is connected; and the conversion of the SOP operation constraint into a rotating cone constraint includes:

[0036] The nonlinear constraints are converted into linear constraints through variable substitution, and the SOP operation constraints are converted into rotation cone constraints.

[0037] Further, the SOP operation constraint is expressed as:

[0038] ;

[0039] in, is the power loss of SOP at time t; i and j are the nodes connected to SOP; and The active power injected into the voltage source converter at nodes i and j respectively; is the loss factor of the voltage source converter;

[0040] The rotating cone constraint is expressed as:

[0041] ;

[0042] ;

[0043] in, and is the active power and reactive power injected from SOP terminal i at time t; and are the power loss at SOP terminals i and j respectively; and is the active power and reactive power injected from SOP terminal j at time t; and are the loss coefficients of SOP terminals i and j, respectively; and are the port capacities of SOP terminals i and j respectively.

[0044] In a second aspect, the present application provides a flexible interconnected distribution network resource scheduling system taking into account demand response, including: a demand response model construction module, a power flow distribution acquisition module, an optimal planning model construction module and a resource scheduling module; wherein,

[0045] The demand response model building module is used to obtain the actual response capability of the user according to the deviation coefficient and the distribution scheduling capability of the user participating in the demand response, and to build a demand response model based on the actual response capability of the user;

[0046] The power flow distribution acquisition module is used to obtain the power flow distribution of the system through multiple power flow iterations based on the demand response model; wherein the power flow distribution includes: node voltage; the node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration when back-calculating the kth iteration each time; k is a positive integer;

[0047] The optimal planning model building module is used to establish an optimal planning model with the lowest comprehensive cost based on the node voltage and the main grid carbon potential as a reference benchmark, according to whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model; the optimal planning model includes: SOP operation constraints;

[0048] The resource scheduling module is used to convert the SOP operation constraints into rotating cone constraints, convert the optimal planning model into a mixed integer second-order cone programming model, solve the mixed integer second-order cone programming model based on the rotating cone constraints, and obtain a scheduling plan so that resources can be scheduled according to the scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for resource scheduling of a flexible interconnected distribution network taking into account demand response provided by the present application;

[0050] Figure 2 It is the IEEE 33-node system line diagram provided by this application;

[0051] Figure 3 It is a schematic diagram of the IEEE 33-node system line connected to SOP provided by this application;

[0052] Figure 4 This is a structural diagram of a flexible interconnected distribution network resource scheduling system taking demand response into account provided in this application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] It is worth noting that the existing related technologies are mainly carried out with the goal of reducing system power loss and improving DG absorption, ignoring the impact of the system carbon potential distribution on distribution network scheduling, and unable to formulate scheduling strategies for dispatchable resources based on the distribution of carbon emissions in the distribution network, resulting in low scheduling accuracy; moreover, most of them use the Distflow flow of radial networks, but can only calculate unidirectional flow and cannot accurately calculate bidirectional flow, and there is a defect of low efficiency in solving nonlinear problems, which further reduces the low scheduling accuracy.

[0055] Based on this, the flexible interconnected distribution network resource scheduling method and system taking demand response into account provided in the present application establish a demand response model based on the deviation coefficient and distribution scheduling capability participating in the demand response; then, based on the demand response model, solve the power flow distribution, and based on the power flow distribution, establish an optimal planning model with the lowest comprehensive cost, and the optimal planning model includes: SOP operation constraints; then, by converting the SOP operation constraints, the optimal planning model is converted into a mixed integer second-order cone programming (Mixed-integer second-order cone programming, MISOCP) model, and the solution is performed to obtain the globally optimal scheduling plan, so that resources can be scheduled according to the scheduling plan.

[0056] In order to more clearly illustrate the flexible interconnected distribution network resource scheduling method and system taking into account demand response provided by the present application, a detailed description will be given through the following embodiments.

[0057] Example 1

[0058] See also Figure 1 , is a flow chart of a method for resource scheduling of a flexible interconnected distribution network taking into account demand response provided in the present application, including: steps S11 to S14, each step corresponds to a demand response model construction process, a flow distribution acquisition process, an optimal planning model construction process and a resource scheduling process, and each process will be introduced in detail below.

[0059] Demand response model building process

[0060] Step S11: According to the deviation coefficient and distribution dispatching capacity of the users participating in the demand response, the actual response capacity of the users is obtained, and a demand response model is constructed based on the actual response capacity of the users.

[0061] In some embodiments, the management of demand-side resources of the demand response model is mainly achieved through demand response (DR), and the characteristics of DR are closely related to the specific model. Incentive DR participates in scheduling under the incentive mechanism of the distribution system operator (DSO), and can change the original electricity consumption mode by reducing or transferring its own load. However, due to the uncertainty of the resource willingness of users participating in demand response, the demand response scheduling results may deviate from the DSO's scheduling plan. This application assumes that the response capacity of users participating in demand response is based on the scheduling capacity of the distribution company and fluctuates within a certain scale.

[0062] In some embodiments, the actual response capability of the user is obtained based on the deviation coefficient of the demand response and the distribution scheduling capability, including: fluctuating the distribution scheduling capability according to a preset variation range of the deviation coefficient of the demand response to obtain the actual response capability of the user.

[0063] In some embodiments, the user's actual responsiveness may be expressed as:

[0064] ;

[0065] in, is the actual response capability of users participating in demand response at time t; is the deviation coefficient reflected by users participating in demand response, For distribution dispatching capability.

[0066] It is worth noting that the response characteristics of users participating in demand response are very different. Industrial loads are mainly assembly line operations with fixed processes, and their main response mode is load transfer. Commercial loads are mainly composed of lighting and temperature control loads, and the resulting response is load reduction. Residential loads mainly include household appliances, and their response mode is load reduction. On this basis, shiftable and reducible response models are established respectively.

[0067] In some embodiments, the translatable load may be modeled as:

[0068] ;

[0069] ;

[0070] in, is the dispatched active power at the initial time t of the zth day; is the active power after shifting at time t+t0 on the zth day; and are the start and end time of the shiftable load in a day; is the interval between the initial time before the load is transferred and the new time after the load is transferred; Unit electricity price subsidy for load shifting; Total electricity subsidy for load shifting in dispatch; is the time difference, take 1h; It is a series of days in a year.

[0071] In some embodiments, the curtailable load may be modeled as:

[0072] ;

[0073] ;

[0074] in, is the load reduction active power at time t; Scheduling time for load reduction within a day; is the total power of load reduction during the dispatch time; Reducing electricity subsidies for unit load; is the total electricity subsidy for dispatching load reduction.

[0075] It is worth noting that the demand response model is one of the models in the distribution network operation model, which also includes: SOP model and carbon emission and carbon potential model. When constructing the demand response model, it is also necessary to construct the SOP model and carbon emission and carbon potential model. Among them, SOP is a power electronic device based on a fully controlled device with load transfer and voltage support functions. The steady-state model of SOP is usually developed into a power injection model, which involves power injection at the SOP terminal, and its controllable variables are the transmission of active power and the compensation of reactive power.

[0076] In some embodiments, the SOP model can be represented by the active exchange, reactive upper and lower limits, and power loss of the SOP, which can be written as:

[0077] ;

[0078] ;

[0079] ;

[0080] in, is the total number of terminals of SOP terminal number i; and is the active power and reactive power injected from SOP terminal i at time t; and are the minimum and maximum reactive power that can be output at the i-th end of the SOP respectively; is the power loss of converter i of SOP; is the loss coefficient.

[0081] It is worth noting that the carbon emissions generated in the distribution network are closely related to the power flow conditions of the system. The analysis of the sources of carbon emissions is crucial to reducing the total carbon emissions of the distribution network. By analyzing the distribution and characteristics of carbon emission flows in the distribution network, the factors affecting the carbon emissions of the distribution network can be identified, thereby more accurately optimizing the system scheduling strategy and reducing the system's carbon emissions.

[0082] In some embodiments, the carbon emission and carbon potential model can be represented by the carbon emission intensity of the generator set, the node carbon potential vector, the branch carbon flow rate distribution matrix and the system node carbon potential.

[0083] In some embodiments, the carbon emission intensity of a generator set can be expressed as follows: If the carbon emission intensity of the kth generator set is defined as When , the carbon emission intensity vector of the system generator set is .

[0084] In some embodiments, the node carbon potential vector can be expressed as: If the carbon potential of the i-th node is defined as When , the system node carbon potential vector is .

[0085] In some embodiments, the branch carbon flow rate distribution matrix can be expressed as: is the branch carbon flow rate distribution matrix, is the number of nodes in the power grid. For any branch ij in the power grid, the positive direction of the flow on the branch is specified as , the element in the i-th row and j-th column of the branch carbon flow rate distribution matrix is ​​R Bij , if the forward carbon flow rate on this branch is r, then On the contrary, if the reverse carbon flow rate on the branch is r, then ; If there is no direct branch connection between the node pair (m, n), then ; All diagonal elements of the branch carbon flow rate distribution matrix are 0.

[0086] In some embodiments, the nodal carbon potential of the system can be expressed as:

[0087] ;

[0088] in, is the carbon potential of the ith node, R Bsis the carbon flow rate of the upstream branch s of node i. The physical meaning of the node carbon potential is that the carbon potential of node i is determined by the carbon emission flow generated by the power injected into the upstream branch of node i and the carbon emission flow generated by the power injected into node i by the generator set; is the set of upstream branches of any node; The power injected into the node for the upstream branch; The power injected into the node by the generator set; is the carbon emission intensity of the i-th generator set.

[0089] In some embodiments, the node carbon potential calculation formula can be rewritten into a matrix form, which can be expressed as:

[0090] ;

[0091] in, is the system node carbon potential vector; is the carbon emission intensity vector of the system’s generator sets; is the matrix of the injected power at node i (all off-diagonal elements are 0); is the matrix of the power injected by the generator set at node i (all off-diagonal elements are 0); is the matrix consisting of the injection power of the upstream branch of node i (all non-diagonal elements are 0).

[0092] Flow distribution acquisition process

[0093] Step S12, based on the demand response model, through multiple power flow iterations, the power flow distribution of the system is obtained; wherein the power flow distribution includes: node voltage; the node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration each time the kth iteration is reversed; k is a positive integer.

[0094] It is worth noting that after a large number of distributed power sources are connected, the distribution network will show the characteristics of bidirectional power flow. This application embodies the characteristics of solving the bidirectional power flow through the calculation process of power flow distribution.

[0095] In some embodiments, the node voltage is obtained by approximating the true value of the voltage based on the real part of the voltage drop after the k+1th iteration each time the kth iteration is reversed, including: each time the kth iteration, approximating the true value of the node voltage based on the initial voltage and the real part of the voltage drop after the k+1th iteration to obtain the node voltage; wherein the sum or difference of the initial voltage and the real part must satisfy a preset range.

[0096] The present application can improve the convergence of the optimal power flow distribution by iteratively back-calculating the node voltage and approximating the real value of the voltage with the real part of the voltage drop. At the same time, it can limit the sum or difference of the initial voltage and the real part of the voltage drop within a preset range, which can further limit the convergence state of the optimal power flow distribution and obtain a more accurate power flow distribution, thereby improving the accuracy of the scheduling plan for the schedulable resources.

[0097] In some embodiments, a BFS (Backward Forward Sweep) optimal power flow model may be used to solve the power flow distribution.

[0098] It is worth noting that this application adopts the BFS power flow model, which can provide accurate power flow results, thereby improving the accuracy and efficiency of the results. The solution to the power flow problem is achieved by iterative "scanning" and updating the variables at each iteration. The structure of the calculation process consists of two matrices: the bus injection branch current (BIBC) matrix and the branch current to bus voltage (BCBV) matrix. BIBC is a real element matrix consisting of 1 and 0, reflecting the topology of a given network, while BCBV is a matrix with the three-phase complex impedance of the line as elements.

[0099] In some embodiments, solving the power flow distribution may include two scans: a back-substitution process and a forward-substitution process.

[0100] In some embodiments, the power flow distribution includes: node voltage and branch current.

[0101] In some embodiments, the back-substitution process can be expressed as:

[0102] In the descendant step of the kth BFS iteration, the current injections at all nodes are calculated and the corresponding branch currents are computed using the BIBC matrix.

[0103] At this time, the node power injection constraint that needs to be satisfied can be expressed as:

[0104] ;

[0105] ;

[0106] in, , Active and reactive power demand of node i at time t; DG active power at node i at time t; and are the active power and reactive power injected from SOP terminal i at time t.

[0107] After the node power injection constraint, the branch current can be expressed as:

[0108] ;

[0109] ;

[0110] in, is the injected current of node i at time t; , is the active and reactive injected power of node i at time t; is an imaginary unit; is the voltage of node i at time t; is the branch current of node i at time t.

[0111] In some embodiments, the push-forward process can be expressed as:

[0112] In the forward scan step, the currents are used to calculate the voltage drops of all branches using BCBV, and the newly derived node voltages are used for the next iteration.

[0113] At this time, the calculation process of the new node voltage can be expressed as:

[0114] ;

[0115] ;

[0116] in, is the voltage drop vector of the entire branch at time t (i.e., the value of the voltage drop of the entire branch at time t); is the branch current of node i at time t; is the voltage vector of all branches at time t; and are the initial voltage of node i at time t and the voltage drop value at the k+1th iteration.

[0117] Finally, the BFS procedure defined by the above iterative process (including the back-substitution process and the forward process) is repeated until the normal mode of the voltage difference calculated in two consecutive iterations is less than a given threshold.

[0118] In some embodiments, the process of solving the power flow distribution can refer to Table 1, which is the flow of the power flow distribution solution algorithm provided in this application.

[0119] Table 1 Flow chart of the algorithm for solving power flow distribution

[0120]

[0121] It should be noted that It is the voltage threshold, which can be customized according to requirements.

[0122] In some embodiments, in order to incorporate the BFS-based power balance equation into the optimal power flow framework, a single scan iteration of the BFS method replaces the power balance constraint. Therefore, using back substitution, at each iteration k and time step t, the current amplitude constraints of all branches i can be expressed as:

[0123] ;

[0124] in, and are the maximum current of branch i and the k-th current of branch i at time t.

[0125] In some embodiments, the sum or difference satisfies a preset range represented by a linear voltage constraint.

[0126] In some embodiments, the node voltage and linear voltage constraints are expressed as:

[0127] ;

[0128] ;

[0129] in, , and are the node voltage of the kth approximate voltage true value of node i at time t, the initial voltage, and the value of the voltage drop at the k+1th iteration; is the real part operation; and are the minimum and maximum node voltages, respectively.

[0130] In some embodiments, the node voltage amplitude can be expressed as:

[0131] ;

[0132] With this approximation, the use of non-convex exact AC power flow is avoided and the optimal power flow is solved in one iteration. After obtaining the optimal setpoints for the optimal power flow problem, an exact BFS power flow is performed to update the operating point and project it into the feasible domain of the exact power flow equation. This new operating point will be used as input for subsequent iterations of the optimal power flow problem and the cycle will be repeated until convergence in terms of voltage amplitude mismatch.

[0133] This method not only performs one iteration after the optimal power flow solution to fully calculate the voltage and current after each optimal power flow solution, but also improves the convergence of the optimal power flow, especially when the current operating point is far from the optimum or close to the stability limit, and a single iteration cannot approximate these values ​​well.

[0134] Optimal planning model construction process

[0135] Step S13: Based on the node voltage, taking the main grid carbon potential as a reference benchmark, and according to whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model, establish an optimal planning model with the lowest comprehensive cost; the optimal planning model includes: SOP operation constraints.

[0136] In some embodiments, an optimal planning model with the lowest comprehensive cost is established based on whether the carbon potential of the node to which the node load is connected is higher than the reference benchmark and the demand response cost corresponding to the demand response model, including: if the carbon potential of the node to which the node load is connected is higher than the reference benchmark, then the node carbon emission cost to be paid by the distribution network is obtained based on the additional carbon emission penalty coefficient; if the node carbon potential is lower than the reference benchmark, then the node carbon emission cost is obtained based on the compensation incentive coefficient; the sum of the additional carbon emission penalty coefficient and the compensation incentive coefficient is a preset value; based on the node carbon emission cost and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established.

[0137] This application calculates the node carbon emission costs for situations exceeding the reference benchmark and below the reference benchmark respectively through additional carbon emission penalty coefficients and compensation incentive coefficients, so as to build a more accurate optimal planning model, thereby improving the accuracy of the scheduling plan for schedulable resources.

[0138] In some embodiments, the node carbon emission cost is expressed as:

[0139] ;

[0140] in, is a 0-1 variable, indicating the node carbon potential of node i at time t Is it higher than the reference benchmark? , if higher than the reference When Take 1; is the carbon emission cost coefficient; To compensate for the incentive coefficient, it is used to encourage users to use energy with low carbon emission intensity; 1- is the additional carbon emission penalty coefficient; is the time difference, which is 1h; is the scheduling cycle length; is the set of distribution network nodes.

[0141] In some embodiments, the optimal planning model includes: an objective function and constraints; the constraints include: SOP operation constraints; based on the node carbon emission cost and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established, including: based on the node carbon emission cost, the demand response cost corresponding to the demand response model, the system's loss cost and the system's penalty cost for wind and solar power abandonment, an objective function and constraints with the lowest comprehensive cost are established; the constraints also include: distribution network flow constraints, distribution network operation safety constraints, distribution network power balance constraints, SOP capacity configuration constraints and demand response constraints corresponding to the demand response model.

[0142] In some embodiments, the objective function can be expressed as:

[0143] ;

[0144] in, for demand response costs; is the loss cost of the system; The cost of purchasing electricity for the system; Penalty costs for system curtailment of wind and solar power; is the node carbon emission cost.

[0145] In some embodiments, the demand response cost corresponding to the demand response model includes: demand response investment cost, and the demand response investment cost can be expressed as:

[0146] ;

[0147] ;

[0148] in, is a 0-1 variable, indicating whether the DSO selects demand-side resources in the distribution network node i; S is the total number of distribution network nodes; and are the capacity cost and electricity cost of the ith load point in a year, respectively. The load response mechanisms of each load point are different; is the unit capacity cost of demand-side resources; is the response capacity reserved by the user at the i-th load point.

[0149] In some embodiments, the distribution network loss cost can be expressed as:

[0150] ;

[0151] ;

[0152] ;

[0153] in, is the energy loss of the qth SOP at time t; is the current flowing through branch ij at time t; is the resistance of branch ij; is the set of branches ij; is the carbon emission cost corresponding to the system loss part; is the power loss of the distribution network at time t; is the unit loss cost of the distribution network; is the carbon potential of branch ij in period t; is a set of distribution network nodes; is the loss cost of the system; is the carbon price in period t.

[0154] In some embodiments, the penalty cost of wind and solar power abandonment in the distribution network can be expressed as:

[0155] ;

[0156] in, is the penalty cost of PV per unit reduced; is the active power of abandoned light; is the number of PV scenes; is the penalty cost of PV per unit reduced; is the active power abandoned by wind; is the number of wind power scenarios.

[0157] In some embodiments, the power flow constraints of the distribution network are:

[0158] ;

[0159] in, , is the voltage amplitude of node i, j at time t, is the phase angle difference at time t; , , , are the mutual conductance, mutual admittance, self-conductance and self-susceptance in the node admittance matrix respectively; is the set of nodes connected to node i.

[0160] In some embodiments, the distribution network operation security constraint can be expressed as:

[0161] ;

[0162] in, , are the upper and lower limits of the node voltage respectively; is the upper limit of the branch current.

[0163] In some embodiments, the power balance constraint of the distribution network can be expressed as:

[0164] ;

[0165] ;

[0166] in, and are the active power and reactive power injected into node i at time t, respectively; and are the active and reactive loads of node i at time t respectively; is the active power of node i DG at time t; is the reactive power reduced by node i at time t; and are the active power and reactive power of load shifting of node i at time t respectively; and It is the original active power and reactive power shifted from time t-t0 to time t.

[0167] In some embodiments, the SOP capacity configuration constraint may be expressed as:

[0168] ;

[0169] ;

[0170] ;

[0171] ;

[0172] in, and are the reactive power injected into the VSC respectively; and are the capacities of the VSCs connected to node i and node j, respectively.

[0173] In some embodiments, the SOP operation constraints may be expressed as:

[0174] ;

[0175] in, is the power loss of SOP at time t; i and j are the nodes connected to SOP; and The active power injected into the voltage source converter at nodes i and j respectively; is the loss factor of the voltage source converter.

[0176] In some embodiments, the demand response constraint may be expressed as:

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] in, and are the active power and reactive power of load shifting of node i at time t respectively; and are the active power and reactive power of load reduction of node i at time t respectively; and are the active load and reactive load of node i at time t respectively; is the peak load of node i; is the proportion of the maximum reserved response capacity among demand-side resources; is the response capacity reserved by the user at the i-th load point; is the reduction or translation of node i at time t; It is a binary variable, which is 1 when the distribution network dispatches load; is the upper limit of load shedding time; and are the start and end times of the shiftable load in a day, is the interval between the initial time before the load is transferred and the new time after the load is transferred.

[0183] Resource Scheduling Process

[0184] Step S14, converting the SOP running constraints into rotating cone constraints, converting the optimal planning model into a mixed integer second-order cone programming model, solving the mixed integer second-order cone programming model based on the rotating cone constraints, and obtaining a scheduling plan so that resources can be scheduled according to the scheduling plan.

[0185] In some embodiments, a mixed integer second-order cone programming model is solved based on a rotating cone constraint, and the resulting scheduling scheme includes: active and reactive power transmitted across the SOP.

[0186] It is worth mentioning that the initial mixed-integer nonlinear programming is converted into a mixed-integer second-order cone programming model in order to simplify the problem and improve the solution efficiency. It deals with some nonlinear constraints.

[0187] In some embodiments, the SOP operating constraints are nonlinear constraints on the active power of the voltage source converter injected into the node to which the SOP is connected and the total power loss of the node to which the SOP is connected; the SOP operating constraints are converted into rotating cone constraints, including: converting the nonlinear constraints into linear constraints through variable substitution, and converting the SOP operating constraints into rotating cone constraints.

[0188] In some embodiments, the rotation cone constraint is expressed as:

[0189] ;

[0190] ;

[0191] in, and is the active power and reactive power injected from SOP terminal i at time t; and are the power loss at SOP terminals i and j respectively; and is the active power and reactive power injected from SOP terminal j at time t; and are the loss coefficients of SOP terminals i and j, respectively; and are the port capacities of SOP terminals i and j respectively.

[0192] It is worth noting that the optimal planning model provided by this application is a non-convex mixed-integer nonlinear programming (MINLP) problem, which needs to be converted into a MISOCP model to solve the problem in an easy-to-handle way and achieve the global optimal solution. In the second-order cone programming (SOCP) model method, the problem achieves the minimization of the linear objective function under a set of linear equality constraints and convex cone constraints. Therefore, this application adopts the expression conversion of the power defined in the SOP operation constraint, so that the non-convex MINLP problem is converted into a MISOCP model to obtain a scheduling scheme, and resource scheduling is performed according to the scheduling scheme.

[0193] In some embodiments, a distribution network operation model can also be established based on the carbon emission flow theory, and the distribution network operation model includes: an SOP model, a demand response model, and a carbon emission and carbon potential model; then, based on the distribution network operation model, an optimal planning model with the lowest comprehensive cost is established, and the optimal planning model includes: an objective function and set constraints, and the constraints include: distribution network flow constraints, distribution network operation safety constraints, distribution network power balance constraints, SOP operation constraints, SOP capacity configuration constraints, and demand response constraints; then, based on the distribution network operation model, the flow distribution is solved, and based on the flow distribution, the optimal planning model is converted into a mixed integer second-order cone programming model to obtain the global optimal solution of the optimization problem, so that resources can be scheduled according to the global optimal solution.

[0194] The present application obtains accurate power flow distribution through a demand response model, and approximates the true value of the voltage with the real part of the voltage drop when iteratively reversing the node voltage, which can improve the convergence of the optimal power flow distribution, thereby improving the accuracy of the scheduling plan for dispatchable resources; and, by comparing the node carbon potential and the main grid carbon potential, to construct an optimal planning model with the lowest comprehensive cost, it can reduce the comprehensive cost of dispatchable resources, thereby reducing costs while improving the accuracy of resource scheduling, and achieving cost reduction and efficiency improvement; in addition, by converting the SOP operating constraints, it can improve the model solution efficiency and solution accuracy of the scheduling plan, thereby further improving the accuracy of the scheduling plan for dispatchable resources.

[0195] Example 2

[0196] This is a specific application provided by the present application, which applies the flexible interconnected distribution network resource scheduling taking into account demand response to a typical IEEE 33-node system to obtain a scheduling solution, including steps S201 to S203, specifically:

[0197] Step S201: A typical IEEE 33-bus system of a medium voltage 10 kV line is used as an optimization object for explanation. Figure 2 , is a schematic diagram of the IEEE 33-node system circuit provided by this application. Figure 2 The CPC consists of 33 nodes.

[0198] Step S202: Set the SOP access location. Two SOPs with a capacity of 300kW are connected to the distribution network. The access location is the line tie switch. Figure 3 , is a schematic diagram of the IEEE 33-node system line connected to SOP provided by this application. Among them, there is a tie switch between node 8 and node 14, a tie switch between node 7 and node 20, a tie switch between node 24 and node 28, a tie switch between node 11 and node 21, a tie switch between node 17 and node 32, and nodes 4, 14, and 29 are nodes containing demand response loads.

[0199] Step S203: setting three comparison schemes, including three schemes, specifically:

[0200] Solution 1: Only consider SOP to participate in the optimization scheduling;

[0201] Solution 2: Only consider demand response to participate in optimal scheduling;

[0202] Solution 3: Consider the collaborative participation of SOP and demand response in optimizing scheduling.

[0203] Step S204, calculate and compare the solution costs of the planning models of the three solutions. The solution costs include: demand response cost, system loss cost, system wind and solar power abandonment penalty cost and node carbon emission cost. The results are shown in Table 2, which is a comparison table of solution costs of the planning model provided in this application.

[0204] Table 2 Comparison of program costs

[0205]

[0206] Step S205: Select the optimal scheduling scheme for resource scheduling. According to Table 2, Scheme 3 should be selected as the optimal scheme with the lowest comprehensive cost for resource scheduling.

[0207] This application obtains accurate power flow distribution through a demand response model, and approximates the true value of voltage with the real part of voltage drop when iteratively reversing node voltage, which can improve the convergence of the optimal power flow distribution, thereby improving the accuracy of the scheduling plan for dispatchable resources; and, by comparing the node carbon potential and the main grid carbon potential, to construct an optimal planning model with the lowest comprehensive cost, it can reduce the comprehensive cost of dispatchable resources, thereby improving the accuracy of resource scheduling while reducing costs and achieving cost reduction and efficiency improvement; in addition, by converting the SOP operating constraints, it can improve the model solution efficiency and solution accuracy of the scheduling plan, thereby further improving the accuracy of the scheduling plan for dispatchable resources, which has obvious advantages in actual scenarios.

[0208] Example 3

[0209] See also Figure 4 , is a structural diagram of a flexible interconnected distribution network resource scheduling system taking into account demand response provided in the present application, including: a demand response model construction module 41, a flow distribution acquisition module 42, an optimal planning model construction module 43 and a resource scheduling module 44.

[0210] The demand response model building module 41 is used to obtain the actual response capability of the user according to the deviation coefficient and the distribution scheduling capability of the user participating in the demand response, and to build a demand response model based on the actual response capability of the user.

[0211] The power flow distribution acquisition module 42 is used to obtain the power flow distribution of the system through multiple power flow iterations based on the demand response model; wherein the power flow distribution includes: node voltage; the node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration each time the kth iteration is reversed; k is a positive integer.

[0212] In some embodiments, the node voltage is obtained by approximating the true value of the voltage based on the real part of the voltage drop after the k+1th iteration each time the kth iteration is reversed, including: each time the kth iteration, approximating the true value of the node voltage based on the initial voltage and the real part of the voltage drop after the k+1th iteration to obtain the node voltage; wherein the sum or difference of the initial voltage and the real part must satisfy a preset range.

[0213] In some embodiments, the sum or difference satisfies a preset range represented by a linear voltage constraint.

[0214] In some embodiments, the node voltage and linear voltage constraints are expressed as:

[0215] ;

[0216] ;

[0217] in, , and are the node voltage of the kth approximate voltage true value of node i at time t, the initial voltage, and the value of the voltage drop at the k+1th iteration; is the real part operation; and are the minimum and maximum node voltages, respectively.

[0218] The optimal planning model building module 43 is used to establish an optimal planning model with the lowest comprehensive cost based on the node voltage and the main grid carbon potential as a reference benchmark, according to whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model; the optimal planning model includes: SOP operation constraints.

[0219] In some embodiments, an optimal planning model with the lowest comprehensive cost is established based on whether the carbon potential of the node to which the node load is connected is higher than the reference benchmark and the demand response cost corresponding to the demand response model, including: if the carbon potential of the node to which the node load is connected is higher than the reference benchmark, then the node carbon emission cost to be paid by the distribution network is obtained based on the additional carbon emission penalty coefficient; if the node carbon potential is lower than the reference benchmark, then the node carbon emission cost is obtained based on the compensation incentive coefficient; the sum of the additional carbon emission penalty coefficient and the compensation incentive coefficient is a preset value; based on the node carbon emission cost and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established.

[0220] In some embodiments, the node carbon emission cost is expressed as:

[0221] ;

[0222] in, is a 0-1 variable, indicating the node carbon potential of node i at time t Is it higher than the reference benchmark? , if higher than the reference When Take 1; is the carbon emission cost coefficient; To compensate for the incentive coefficient, it is used to encourage users to use energy with low carbon emission intensity; 1- is the additional carbon emission penalty coefficient; is the time difference, which is 1h; is the scheduling cycle length; is the set of distribution network nodes.

[0223] In some embodiments, the optimal planning model includes: an objective function and constraints; the constraints include: SOP operation constraints; based on the node carbon emission cost and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established, including: based on the node carbon emission cost, the demand response cost corresponding to the demand response model, the system's loss cost and the system's penalty cost for wind and solar power abandonment, an objective function and constraints with the lowest comprehensive cost are established; the constraints also include: distribution network flow constraints, distribution network operation safety constraints, distribution network power balance constraints, SOP capacity configuration constraints and demand response constraints corresponding to the demand response model.

[0224] In some embodiments, the SOP operation constraints are expressed as:

[0225] ;

[0226] in, is the power loss of SOP at time t; i and j are the nodes connected to SOP; and The active power injected into the voltage source converter at nodes i and j respectively; is the loss factor of the voltage source converter.

[0227] In some embodiments, the demand response constraint is expressed as:

[0228] ;

[0229] ;

[0230] ;

[0231] ;

[0232] ;

[0233] in, and are the active power and reactive power of load shifting of node i at time t respectively; and are the active power and reactive power of load reduction of node i at time t respectively; and are the active load and reactive load of node i at time t respectively; is the peak load of node i; is the proportion of the maximum reserved response capacity among demand-side resources; is the response capacity reserved by the user at the i-th load point; is the reduction or translation of node i at time t; It is a binary variable, which is 1 when the distribution network dispatches load; is the upper limit of load shedding time; and are the start and end times of the shiftable load in a day, is the interval between the initial time before the load is transferred and the new time after the load is transferred.

[0234] The resource scheduling module 44 is used to convert the SOP operation constraints into rotating cone constraints, convert the optimal planning model into a mixed integer second-order cone programming model, solve the mixed integer second-order cone programming model based on the rotating cone constraints, and obtain a scheduling plan so that resources can be scheduled according to the scheduling plan.

[0235] In some embodiments, the SOP operating constraints are nonlinear constraints on the active power of the voltage source converter injected into the node to which the SOP is connected and the total power loss of the node to which the SOP is connected; the SOP operating constraints are converted into rotating cone constraints, including: converting the nonlinear constraints into linear constraints through variable substitution, and converting the SOP operating constraints into rotating cone constraints.

[0236] In some embodiments, the rotation cone constraint is expressed as:

[0237] ;

[0238] ;

[0239] in, and is the active power and reactive power injected from SOP terminal i at time t; and are the power loss at SOP terminals i and j respectively; and is the active power and reactive power injected from SOP terminal j at time t; and are the loss coefficients of SOP terminals i and j, respectively; and are the port capacities of SOP terminals i and j respectively.

[0240] The present application constructs a demand response model through a demand response model construction module 41, and obtains accurate power flow distribution through a power flow distribution acquisition module 42, and when iteratively reversing the node voltage, the real part of the voltage drop is used to approximate the true value of the voltage, which can improve the convergence of the optimal power flow distribution, thereby improving the accuracy of the scheduling plan for schedulable resources; and, through the optimal planning model construction module 43, the node carbon potential and the main grid carbon potential are compared to construct an optimal planning model with the lowest comprehensive cost, which can reduce the comprehensive cost of schedulable resources, thereby improving the accuracy of resource scheduling while reducing costs and achieving cost reduction and efficiency improvement; in addition, through the resource scheduling module 44, the SOP operation constraints are converted, which can improve the model solution efficiency and solution accuracy of the scheduling plan, thereby further improving the accuracy of the scheduling plan for schedulable resources.

[0241] Those skilled in the art will appreciate that the embodiments of the present application may also provide computer program products. Therefore, the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application may adopt the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes.

[0242] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0243] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0244] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0245] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A resource scheduling method for a flexible interconnected distribution network taking into account demand response, characterized in that: include: According to the deviation coefficient and distribution dispatching capacity of the users participating in the demand response, the actual response capacity of the users is obtained, and a demand response model is constructed based on the actual response capacity of the users; Based on the demand response model, a power flow distribution of the system is obtained through multiple power flow iterations; wherein the power flow distribution includes: node voltage; the node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration when back-calculating the kth iteration each time; k is a positive integer; Based on the node voltage, taking the main grid carbon potential as a reference benchmark, and according to whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established; the optimal planning model includes: SOP operation constraints; The optimal planning model is converted into a mixed integer second-order cone programming model by converting the SOP running constraints into rotating cone constraints, and the mixed integer second-order cone programming model is solved based on the rotating cone constraints to obtain a scheduling scheme, so that resources are scheduled according to the scheduling scheme.

2. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 1, characterized in that: The determining whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model, establishing an optimal planning model with the lowest comprehensive cost, includes: If the carbon potential of the node connected by the node load is higher than the reference benchmark, the node carbon emission cost to be paid by the distribution network is obtained according to the additional carbon emission penalty coefficient; if the node carbon potential is lower than the reference benchmark, the node carbon emission cost is obtained according to the compensation incentive coefficient; the sum of the additional carbon emission penalty coefficient and the compensation incentive coefficient is a preset value; According to the node carbon emission cost and the demand response cost corresponding to the demand response model, an optimal planning model with the lowest comprehensive cost is established.

3. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 2, characterized in that: The optimal planning model includes: an objective function and constraints; the constraints include: the SOP operation constraints; the optimal planning model with the lowest comprehensive cost is established according to the node carbon emission cost and the demand response cost corresponding to the demand response model, including: According to the node carbon emission cost, the demand response cost corresponding to the demand response model, the loss cost of the system and the penalty cost of wind and solar power abandonment of the system, the objective function and the constraint conditions with the lowest comprehensive cost are established; the constraints also include: distribution network flow constraints, distribution network operation safety constraints, distribution network power balance constraints, SOP capacity configuration constraints and demand response constraints corresponding to the demand response model.

4. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 3, characterized in that: The node carbon emission cost is expressed as: ； in, is a 0-1 variable, indicating the node carbon potential of node i at time t Is it higher than the reference benchmark? , if higher than the reference When Take 1; is the carbon emission cost coefficient; To compensate for the incentive factor, it is used to encourage users to use energy with low carbon emission intensity; is the time difference, which is 1h; is the scheduling cycle length; is the set of distribution network nodes.

5. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 3, characterized in that: The demand response constraint is expressed as: ； ； ； ； ； in, and are the active power and reactive power of load shifting of node i at time t respectively; and are the active power and reactive power of load reduction of node i at time t respectively; and are the active load and reactive load of node i at time t respectively; is the peak load of node i; is the proportion of the maximum reserved response capacity among demand-side resources; is the response capacity reserved by the user at the i-th load point; is the reduction or translation of node i at time t; It is a binary variable, which is 1 when the distribution network dispatches load; is the upper limit of load shedding time; and are the start and end times of the shiftable load in a day, is the interval between the initial time before the load is transferred and the new time after the load is transferred.

6. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 1, characterized in that: The node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration each time the kth iteration is reversed, including: Each time for the kth iteration, the real value of the node voltage is approximated based on the initial voltage and the real part of the voltage drop after the k+1th iteration to obtain the node voltage; wherein the sum or difference between the initial voltage and the real part must meet a preset range.

7. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 6, characterized in that: The sum or the difference satisfies the preset range and is represented by a linear voltage constraint; the node voltage and the linear voltage constraint are respectively represented as: ； ； in, , and are the node voltage of the kth approximate voltage true value of node i at time t, the initial voltage, and the value of the voltage drop at the k+1th iteration; is the real part operation; and are the minimum and maximum node voltages, respectively.

8. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 1, characterized in that: The SOP operation constraint is a nonlinear constraint limit on the active power of the voltage source converter injected into the node connected to the SOP and the total power loss of the node connected to the SOP; The constraint is converted to a rotation cone constraint by running it on the SOP, including: The nonlinear constraints are converted into linear constraints through variable substitution, and the SOP operation constraints are converted into rotation cone constraints.

9. The method for dispatching resources of a flexible interconnected distribution network taking into account demand response according to claim 8, characterized in that: The SOP operation constraints are expressed as: ； in, is the power loss of SOP at time t; i, j are the nodes connected to SOP; and The active power injected into the voltage source converter at nodes i and j respectively; is the loss factor of the voltage source converter; The rotating cone constraint is expressed as: ； ； in, and is the active power and reactive power injected from SOP terminal i at time t; and are the power loss at SOP terminals i and j respectively; and is the active power and reactive power injected from SOP terminal j at time t; and are the loss coefficients of SOP terminals i and j, respectively; and are the port capacities of SOP terminals i and j respectively.

10. A flexible interconnected distribution network resource dispatching system taking into account demand response, characterized in that: include: Demand response model building module, power flow distribution acquisition module, optimal planning model building module and resource scheduling module; among them, The demand response model building module is used to obtain the actual response capability of the user according to the deviation coefficient and the distribution scheduling capability of the user participating in the demand response, and to build a demand response model based on the actual response capability of the user; The power flow distribution acquisition module is used to obtain the power flow distribution of the system through multiple power flow iterations based on the demand response model; wherein the power flow distribution includes: node voltage; the node voltage is obtained by approximating the real value of the voltage based on the real part of the voltage drop after the k+1th iteration when back-calculating the kth iteration each time; k is a positive integer; The optimal planning model building module is used to establish an optimal planning model with the lowest comprehensive cost based on the node voltage and the main grid carbon potential as a reference benchmark, according to whether the node carbon potential connected to the node load is higher than the reference benchmark and the demand response cost corresponding to the demand response model; the optimal planning model includes: SOP operation constraints; The resource scheduling module is used to convert the SOP operation constraints into rotating cone constraints, convert the optimal planning model into a mixed integer second-order cone programming model, solve the mixed integer second-order cone programming model based on the rotating cone constraints, and obtain a scheduling plan so that resources can be scheduled according to the scheduling plan.

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