Power distribution network optimization method and device based on power flow constraint, equipment and storage medium

Abstract

The invention discloses a power distribution network optimization method and device based on power flow constraint, equipment and a storage medium. The method comprises the following steps: acquiring power distribution network data at each moment in a preset time period; constructing a soft switching operation optimization model and constraint conditions of the soft switching operation optimization model by taking the minimum network loss in a preset time period as a target according to the data of the power distribution network; then under each constraint condition, solving the soft switching operation optimization model to obtain a full-branch current, each node voltage and each branch power when the network loss in a preset time period is minimum; and finally, optimizing the power distribution network according to the full-branch current, each node voltage and each branch power when the network loss in the preset time period is minimum. By implementing the method and the device, the problem of reverse power transmission caused by use increase of the intermittent distributed power supply can be avoided, and meanwhile, the accuracy of an optimization result and the solving efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a power distribution network optimization method, device, equipment and storage medium based on power flow constraints. Background Art

[0002] With the increasing popularity of intermittent distributed generation (DG), the demand for energy decarbonization can be effectively met. However, due to the uncertainty and volatility of the output of distributed generation, the operation of the distribution network also faces challenges such as voltage over-limit and increased network loss. As a flexible regulation device at the "grid" level of the distribution system, the soft open point (SOP) can change the traditional open-loop operation mode of the system, realize the continuous regulation of the power flow between feeders, and effectively deal with a series of new problems brought by distributed generation.

[0003] In the prior art, distflow (distributed) power flow is usually used to solve the SOP optimization scheduling model in the distribution network. However, the distflow power flow equation can only be established under the premise that the power flow direction remains consistent. At present, with the high proportion of intermittent distributed power sources being used, the probability of generating undirectional power flow in the distribution network has greatly increased, and then power backflow has occurred, resulting in the distflow power flow being no longer applicable in solving the optimization scheduling problem of the distribution network. Summary of the invention

[0004] The present invention provides a power distribution network optimization method, device, equipment and storage medium based on power flow constraints, which can improve the efficiency and accuracy of solving the optimization results of the distribution network optimization scheduling.

[0005] An embodiment of the present invention provides a power flow constraint-based distribution network optimization method, comprising:

[0006] Obtain the distribution network data at each moment within a preset time period; wherein the distribution network data includes: the distributed power supply power of each node, the power demand of each node, the injected power of each node, the bus injected branch current matrix, the branch current to bus voltage matrix, the standard voltage, the power injected into the VSC by each node connected to the soft switch, the voltage amplitude of each branch, the voltage amplitude of each node, the power emitted by each node, and the voltage amplitude limit of each node;

[0007] According to the above distribution network data, with the goal of minimizing network loss during a preset period, a soft switch operation optimization model and constraint conditions of the above soft switch operation optimization model are constructed; wherein the above constraint conditions include: soft switch operation constraint, soft switch capacity constraint, forward and backward power flow constraint, power balance constraint and safety constraint;

[0008] Under various constraints, the above soft switch operation optimization model is solved to obtain the full branch current, each node voltage and each branch power when the network loss is minimum during the above preset period;

[0009] The distribution network is optimized based on the full branch current, each node voltage and each branch power when the network loss is minimized during the above preset period.

[0010] Furthermore, the objective function of the above soft switching operation optimization model is:

[0011]

[0012] In the formula, F1 represents the objective function, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, and r ij represents the resistance of the ijth branch, P ij,t represents the active power of the ijth branch at time t, Q ij,t represents the reactive power of the ijth branch at time t, U ij,t Represents the voltage amplitude of the ijth branch at time t.

[0013] Furthermore, the above soft switch operation constraints are:

[0014]

[0015] In the formula, represents the active power injected into the VSC at node i connected to the soft switch at time t, represents the active power injected into the VSC at node j connected to the soft switch at time t, represents the power loss of the port at node i connected to the soft switch at time t, represents the power loss of the port at node j connected to the soft switch at time t, represents the loss factor at node i connected to the soft switch, represents the reactive power injected into the VSC at node i connected to the soft switch at time t, represents the loss factor at node j connected to the soft switch, represents the reactive power injected into the VSC at node j connected to the soft switch at time t;

[0016] The above soft switch capacity constraint is:

[0017]

[0018] In the formula, represents the VSC capacity between nodes i and j connected to the soft switch;

[0019] The forward-backward power flow constraints are:

[0020]

[0021] In the formula, represents the branch current of the entire branch at the kth iteration at time t, represents the voltage of node i at the kth iteration at time t, θ represents the phase angle difference between each node voltage and each branch current, P i,t represents the active injected power at node i at time t, Q i,t represents the reactive injection power at node i at time t, represents the injected current at node i at the kth iteration at time t, BIBC represents the busbar injected branch current matrix, represents the full branch voltage drop vector of the k+1th iteration at time t, BCBV represents the branch current to bus voltage matrix, represents the full branch voltage vector of the k+1th iteration at time t, V 0 Indicates standard voltage;

[0022] The above power balance constraint is:

[0023]

[0024] In the formula, represents the active power injected by node i at time t, represents the active power emitted by node i at time t, represents the active power generated by distributed energy at node i at time t, represents the active power demand of node i at time t, represents the reactive power injected by node i at time t, represents the reactive power generated by node i at time t, represents the reactive power demand of node i at time t;

[0025] The above security constraints are:

[0026] U i,t,min i,t i,t,max

[0027] Where U i,t,min represents the lower limit of the voltage amplitude of node i at time t, U i,t,max represents the upper limit of the voltage amplitude of node i at time t, U i,t Represents the voltage amplitude of node i at time t.

[0028] Furthermore, under the above constraints, the above soft switch operation optimization model is solved to obtain the full branch current, each node voltage and each branch power when the network loss is minimum in the above preset time period, including:

[0029] ​​Repeat the model solving operation until all constraints in the soft switch operation optimization model are satisfied, and obtain the full branch current, each node voltage and each branch power when the network loss is minimum in the preset time period;

[0030] The above model solving operation includes:

[0031] Obtain the injection power of each node at each moment of the current cycle; wherein the injection power of each node at each moment of the initial cycle is the injection power of each node at each moment in the above-mentioned preset time period;

[0032] According to the injected power of each node at each moment of the current cycle, the full branch current, each node voltage and each branch power at each moment of the current cycle are calculated;

[0033] Substitute the target full branch current, node voltage and branch power at each moment of the current cycle into the constraints of the soft switching operation optimization model, and determine whether the constraints are met;

[0034] If not, the injection power of each node at each moment of the current cycle is adjusted, and the adjusted injection power of each node at each moment is used as the injection power of each node at each moment of the next cycle.

[0035] Furthermore, the above calculations based on the injected power of each node at each moment of the current cycle to obtain the target full branch current, each node voltage and each branch power at each moment of the current cycle include:

[0036] Obtaining the voltage vector of all branches at each initial moment, the upper limit of each branch current, the upper limit of the system voltage amplitude, and the lower limit of the system voltage amplitude;

[0037] According to the above-mentioned upper limits of the current of each branch, the upper limit of the system voltage amplitude and the lower limit of the system voltage amplitude, a current amplitude constraint and a linear voltage constraint are constructed;

[0038] Among them, the above current amplitude constraint is:

[0039]

[0040] In the formula, I ij,max Indicates the upper limit of the current of branch ij;

[0041] The above linear voltage constraint is:

[0042]

[0043] Where V min Indicates the lower limit of the system voltage amplitude, V max Indicates the upper limit of the system voltage amplitude;

[0044] Under the above current amplitude constraint and the above linear voltage constraint, repeatedly perform the power flow iteration operation until the minimum normal mode corresponding to the current iteration is less than a preset threshold;

[0045] The power of each branch at each moment of the current iteration is calculated according to the voltage of each node at each moment of the current iteration and the branch current of the entire branch at each moment of the current iteration;

[0046] The full branch current, each node voltage and each branch power at each moment corresponding to the current iteration are used as the above-mentioned target full branch current, each node voltage and each branch power at each moment;

[0047] The above power flow iteration operation includes:

[0048] Obtain the full branch voltage vector at each moment of the current iteration and the injected power of each node at each moment of the current cycle; wherein the full branch voltage vector at each moment of the initial iteration is the full branch voltage vector at each initial moment;

[0049] Taking each component of the full branch voltage vector at each moment of the current iteration as each node voltage at each moment of the current iteration;

[0050] The injected current at each node at each moment of the current iteration is calculated according to the voltage of each node at each moment of the current iteration and the injected power of each node at each moment of the current cycle;

[0051] The branch current of the entire branch at each moment of the current iteration is calculated based on the product of the injected current at each node at each moment of the current iteration and the above-mentioned busbar injected branch current matrix;

[0052] The voltage drop vector of the entire branch at each moment of the next iteration is calculated based on the product of the branch current of the entire branch at each moment of the current iteration and the above-mentioned branch current to bus voltage matrix;

[0053] The full branch voltage vector at each moment of the next iteration is calculated based on the product of the full branch voltage drop vector at each moment of the next iteration and the above standard voltage;

[0054] Calculate the minimum normal mode of the difference between the full branch voltage vector at each moment of the current iteration and the full branch voltage vector at each moment of the next iteration, and compare the minimum normal mode with a preset threshold;

[0055] If the minimum normal mode is not less than the preset threshold, the full branch voltage vector at each moment of the next iteration is used as the full branch voltage vector at each moment of the next iteration.

[0056] Furthermore, after obtaining the full branch current, each node voltage and each branch power when the network loss is the smallest during the preset period, the method further includes:

[0057] The minimum network loss value is calculated based on the power of each branch when the network loss is the smallest during the preset period and the voltage amplitude of each branch;

[0058] Among them, the above minimum network loss value is calculated according to the following formula:

[0059]

[0060] Where N min represents the minimum network loss value, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, r ij represents the resistance of the ijth branch, P′ ij,t It represents the active power of the ijth branch at time t when the network loss is minimum during the preset period, Q′ ij,t It represents the reactive power of the ijth branch at time t when the network loss is minimum during the preset period, U ij,t Represents the voltage amplitude of the ijth branch at time t.

[0061] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0062] The present invention provides a power distribution network optimization device based on power flow constraints, comprising:

[0063] Distribution network data acquisition module, optimization model construction module, optimization model solution module and distribution network optimization module;

[0064] The distribution network data acquisition module is used to acquire the distribution network data at each moment within a preset time period; wherein the distribution network data includes: the power of distributed power sources at each node, the power demand of each node, the injected power of each node, the bus injected branch current matrix, the branch current to bus voltage matrix, the standard voltage, the power injected into the VSC by each node connected to the soft switch, the voltage amplitude of each branch, the voltage amplitude of each node, the power emitted by each node and the voltage amplitude limit of each node;

[0065] The optimization model building module is used to build a soft switch operation optimization model and constraints of the soft switch operation optimization model based on the distribution network data and with the goal of minimizing network loss during a preset period of time; wherein the constraints include: soft switch operation constraints, soft switch capacity constraints, forward and backward power flow constraints, power balance constraints, and safety constraints;

[0066] The optimization model solving module is used to solve the soft switch operation optimization model under various constraints to obtain the full branch current, each node voltage and each branch power when the network loss is minimum during the preset period;

[0067] The distribution network optimization module is used to optimize the distribution network according to the full branch current, each node voltage and each branch power when the network loss is minimized during the preset time period.

[0068] Furthermore, it also includes: a minimum network loss calculation module;

[0069] The minimum network loss calculation module is used to calculate the minimum network loss value according to the power of each branch when the network loss is the minimum in a preset period and the voltage amplitude of each branch;

[0070] Among them, the above minimum network loss value is calculated according to the following formula:

[0071]

[0072] Where N min represents the minimum network loss value, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, r ij represents the resistance of the ijth branch, P′ ij,t It represents the active power of the ijth branch at time t when the network loss is minimum during the preset period, Q′ ij,t It represents the reactive power of the ijth branch at time t when the network loss is minimum during the preset period, U ij,t Represents the voltage amplitude of the ijth branch at time t.

[0073] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0074] The present invention provides a terminal device, including 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, a distribution network optimization method based on flow constraints described in any embodiment of the present invention is implemented.

[0075] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0076] The present invention provides a storage medium, including 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, a distribution network optimization method based on flow constraints described in any embodiment of the present invention is implemented.

[0077] The embodiments of the present invention have the following beneficial effects:

[0078] The present invention provides a distribution network optimization method based on power flow constraints, the method comprising: firstly obtaining distribution network data at each moment in a preset time period; then, based on the distribution network data, taking the minimum network loss in the preset time period as the goal, constructing a soft switch operation optimization model and the constraint conditions of the soft switch operation optimization model; wherein the constraint conditions include: soft switch operation constraint, soft switch capacity constraint, forward-backward power flow constraint, power balance constraint and safety constraint; then, under each constraint condition, solving the soft switch operation optimization model to obtain the full branch current, each node voltage and each branch power when the network loss in the preset time period is the minimum; finally, optimizing the distribution network according to the full branch current, each node voltage and each branch power when the network loss in the preset time period is the minimum. Therefore, the present invention utilizes forward-backward flow constraints to replace the use of distflow flow in the prior art to iteratively solve the SOP power optimization model, thereby avoiding the need to consider the power backflow problem caused by the increased use of intermittent distributed power sources, and at the same time, the optimization problem of the distribution network is modeled as a MILP (mixed integer linear programming) problem, and finally the branch current, node voltage and branch power of each node in the distribution network are obtained when the network loss is minimized, thereby improving the accuracy of the optimization results and the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flow chart of a distribution network optimization method based on power flow constraints provided by one embodiment of the present invention.

[0080] Figure 2 It is the IEEE33 node topology diagram.

[0081] Figure 3 It is the IEEE33 node topology diagram after the access location is set.

[0082] Figure 4 It is a schematic diagram of the structure of a distribution network optimization device based on power flow constraints provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0084] like Figure 1 As shown, an embodiment of the present invention provides a distribution network optimization method based on power flow constraints, including:

[0085] Step S101: Obtain distribution network data at each time within a preset time period; wherein the distribution network data includes: distributed power supply power of each node, power demand of each node, injected power of each node, bus injection branch current matrix, branch current to bus voltage matrix, standard voltage, power injected into VSC by each node connected to soft switch, voltage amplitude of each branch, voltage amplitude of each node, power emitted by each node and voltage amplitude limit of each node;

[0086] Specifically, the above distribution network data can be obtained from relevant measuring equipment in the distribution network, and the above standard voltage is 1.

[0087] Step S102: Based on the distribution network data, with the goal of minimizing network loss during a preset period, a soft switch operation optimization model and constraints of the soft switch operation optimization model are constructed; wherein the constraints include: soft switch operation constraints, soft switch capacity constraints, forward and backward power flow constraints, power balance constraints, and safety constraints;

[0088] In a preferred embodiment, the objective function of the above soft switching operation optimization model is:

[0089]

[0090] In the formula, F1 represents the objective function, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, and r ij represents the resistance of the ijth branch, P ij,t represents the active power of the ijth branch at time t, Q ij,t represents the reactive power of the ijth branch at time t, U ij,t Represents the voltage amplitude of the ijth branch at time t.

[0091] In this preferred embodiment, the objective function of the soft switch operation optimization model is constructed with the goal of minimizing network loss.

[0092] In another preferred embodiment, the above soft switch operation constraints are:

[0093]

[0094] In the formula, represents the active power injected into the VSC at node i connected to the soft switch at time t, represents the active power injected into the VSC at node j connected to the soft switch at time t, represents the power loss of the port at node i connected to the soft switch at time t, represents the power loss of the port at node j connected to the soft switch at time t, represents the loss factor at node i connected to the soft switch, represents the reactive power injected into the VSC at node i connected to the soft switch at time t, represents the loss factor at node j connected to the soft switch, represents the reactive power injected into the VSC at node j connected to the soft switch at time t;

[0095] The above soft switch capacity constraint is:

[0096]

[0097] In the formula, represents the VSC capacity between nodes i and j connected to the soft switch;

[0098] The forward-backward power flow constraints are:

[0099]

[0100] In the formula, represents the branch current of the entire branch at the kth iteration at time t, represents the voltage of node i at the kth iteration at time t, θ represents the phase angle difference between each node voltage and each branch current, P i,t represents the active injected power at node i at time t, Q i,t represents the reactive injection power at node i at time t, represents the injected current at node i at the kth iteration at time t, BIBC represents the busbar injected branch current matrix, represents the full branch voltage drop vector of the k+1th iteration at time t, BCBV represents the branch current to bus voltage matrix, represents the full branch voltage vector of the k+1th iteration at time t, V 0 Indicates standard voltage;

[0101] The above power balance constraint is:

[0102]

[0103] In the formula, represents the active power injected by node i at time t, represents the active power emitted by node i at time t, represents the active power generated by distributed energy at node i at time t, represents the active power demand of node i at time t, represents the reactive power injected by node i at time t, represents the reactive power generated by node i at time t, represents the reactive power demand of node i at time t;

[0104] The above security constraints are:

[0105] U i,t,min i,t i,t,max

[0106] Where U i,t,min represents the lower limit of the voltage amplitude of node i at time t, U i,t,max represents the upper limit of the voltage amplitude of node i at time t, U i,t Represents the voltage amplitude of node i at time t.

[0107] Preferably, the power flow calculation within the forward and backward power flow constraint adopts the BFS power flow model, which solves the power flow by iterative "scanning" and updating the variables at each iteration. The structure of the calculation process consists of two matrices: the bus injection to 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 complex impedance of the line as an element.

[0108] In this preferred embodiment, soft switch operation constraints, soft switch capacity constraints, forward and backward power flow constraints, power balance constraints and safety constraints are constructed through the acquired distribution network data.

[0109] Step S103: Under various constraints, the soft switch operation optimization model is solved to obtain the full branch current, each node voltage and each branch power when the network loss is minimum during the preset period;

[0110] In a preferred embodiment, the above-mentioned soft switch operation optimization model is solved under various constraints to obtain the full branch current, each node voltage and each branch power when the network loss is minimum in the above-mentioned preset time period, including:

[0111] Repeat the model solving operation until all constraints in the soft switch operation optimization model are satisfied, and obtain the full branch current, each node voltage and each branch power when the network loss is minimum in the preset time period;

[0112] The above model solving operation includes:

[0113] Obtain the injection power of each node at each moment of the current cycle; wherein the injection power of each node at each moment of the initial cycle is the injection power of each node at each moment in the above-mentioned preset time period;

[0114] According to the injected power of each node at each moment of the current cycle, the full branch current, each node voltage and each branch power at each moment of the current cycle are calculated;

[0115] ​​Substitute the target full branch current, node voltage and branch power at each moment of the current cycle into the constraints of the soft switching operation optimization model, and determine whether the constraints are met;

[0116] If not, the injection power of each node at each moment of the current cycle is adjusted, and the adjusted injection power of each node at each moment is used as the injection power of each node at each moment of the next cycle.

[0117] Specifically, the power of each branch includes the active power and reactive power of each branch, and the injected power of each node at each moment includes the active injected power and reactive injected power of each node at each moment.

[0118] Indicatively, in the 7th cycle, the target full-branch current at each moment, the voltage at each node, and the power at each branch corresponding to the 7th cycle are substituted into the various constraints of the soft switch operation optimization model for calculation and judgment. If the target full-branch current at each moment, the voltage at each node, and the power at each branch corresponding to the 7th cycle do not meet the soft switch capacity constraint, the injected power of each node at each moment corresponding to the 7th cycle is adjusted, and the next cycle is entered to calculate the target full-branch current at each moment, the voltage at each node, and the power at each branch corresponding to the 8th cycle; if the target full-branch current at each moment, the voltage at each node, and the power at each branch corresponding to the 7th cycle meet all the constraints in the soft switch operation optimization model, the cycle is terminated, and the target full-branch current at each moment, the voltage at each node, and the power at each branch corresponding to the 7th cycle are used as the full-branch current, the voltage at each node, and the power at each branch when the network loss is minimized in the final preset time period.

[0119] In this preferred embodiment, by repeatedly executing the model solving operation, the full branch current, each node voltage and each branch power when the network loss is minimum during the preset time period are finally determined.

[0120] In another preferred embodiment, the above-mentioned calculation of the target full branch current, node voltage and branch power at each moment of the current cycle according to the injected power of each node at each moment of the current cycle includes:

[0121] Obtaining the voltage vector of all branches at each initial moment, the upper limit of each branch current, the upper limit of the system voltage amplitude, and the lower limit of the system voltage amplitude;

[0122] Specifically, the voltage vector value of all branches is 1 at each initial moment.

[0123] According to the above-mentioned upper limits of the current of each branch, the upper limit of the system voltage amplitude and the lower limit of the system voltage amplitude, a current amplitude constraint and a linear voltage constraint are constructed;

[0124] Among them, the above current amplitude constraint is:

[0125]

[0126] In the formula, I ij,max Indicates the upper limit of the current of branch ij;

[0127] The above linear voltage constraint is:

[0128]

[0129] Where V min Indicates the lower limit of the system voltage amplitude, V max Indicates the upper limit of the system voltage amplitude;

[0130] Specifically, after calculating the voltage values ​​of all nodes, only the real part of the voltage drop is considered, and the voltage of each node is approximated using the following formula:

[0131]

[0132] In the formula, Represents the voltage of each node at each moment during the kth iteration.

[0133] Specifically, in the above formula used to express the voltage of each node, the imaginary part of all elements is 0, so the following formula can be further obtained:

[0134]

[0135] Furthermore, according to the above formula, the linear voltage constraint is finally obtained.

[0136] Preferably, through this approximation, the use of non-convex precise AC power flow can be avoided, and the optimal power flow is solved in one BFS iteration. After obtaining the optimal setting value of the optimal power flow problem, the precise BFS power flow is performed to update the working point and project it into the feasible domain of the precise power flow equation. This new working point will be used as the input of subsequent iterations of the optimal power flow problem of BFS, and the cycle will be repeated until the minimum normal mode of the difference between the full branch voltage vector at each moment of the current iteration and the full branch voltage vector at each moment of the next iteration is less than a preset threshold.

[0137] Preferably, this method not only performs one BFS iteration after solving the optimal power flow, but also can fully calculate the voltage and current after each optimal power flow solution. Compared with a single BFS iteration, the convergence of the optimal power flow of BFS can be improved, especially when the current operating point is far from the optimal or close to the stability limit, the corresponding approximate values ​​of the voltage and current can be obtained well.

[0138] Under the above current amplitude constraint and the above linear voltage constraint, repeatedly perform the power flow iteration operation until the minimum normal mode corresponding to the current iteration is less than a preset threshold;

[0139] The power of each branch at each moment of the current iteration is calculated according to the voltage of each node at each moment of the current iteration and the branch current of the entire branch at each moment of the current iteration;

[0140] Specifically, the branch power at each moment of the current iteration is calculated according to the following formula:

[0141]

[0142] The full branch current, each node voltage and each branch power at each moment corresponding to the current iteration are used as the above-mentioned target full branch current, each node voltage and each branch power at each moment;

[0143] The above power flow iteration operation includes:

[0144] Obtain the full branch voltage vector at each moment of the current iteration and the injected power of each node at each moment of the current cycle; wherein the full branch voltage vector at each moment of the initial iteration is the full branch voltage vector at each initial moment;

[0145] Taking each component of the full branch voltage vector at each moment of the current iteration as each node voltage at each moment of the current iteration;

[0146] The injected current at each node at each moment of the current iteration is calculated according to the voltage of each node at each moment of the current iteration and the injected power of each node at each moment of the current cycle;

[0147] Specifically, the injected current at each node at each moment of the current iteration is calculated according to the following formula:

[0148]

[0149] The branch current of the entire branch at each moment of the current iteration is calculated based on the product of the injected current at each node at each moment of the current iteration and the above-mentioned busbar injected branch current matrix;

[0150] Specifically, the branch current of all branches at each moment of the current iteration is calculated according to the following formula:

[0151]

[0152] The voltage drop vector of the entire branch at each moment of the next iteration is calculated based on the product of the branch current of the entire branch at each moment of the current iteration and the above-mentioned branch current to bus voltage matrix;

[0153] Specifically, the full branch voltage drop vector at each moment of the next iteration is calculated according to the following formula:

[0154]

[0155] The full branch voltage vector at each moment of the next iteration is calculated based on the product of the full branch voltage drop vector at each moment of the next iteration and the above standard voltage;

[0156] Specifically, the full branch voltage vector at each moment of the next iteration is calculated according to the following formula:

[0157]

[0158] Calculate the minimum normal mode of the difference between the full branch voltage vector at each moment of the current iteration and the full branch voltage vector at each moment of the next iteration, and compare the minimum normal mode with a preset threshold;

[0159] Specifically, the formula for comparing the above minimum norm with the preset threshold is as follows:

[0160]

[0161] In the formula, Indicates the above preset threshold.

[0162] If the minimum normal mode is not less than the preset threshold, the full branch voltage vector at each moment of the next iteration is used as the full branch voltage vector at each moment of the next iteration.

[0163] Indicatively, in the 5th iteration, the branch current of the whole branch at each moment corresponding to the 5th iteration, the whole branch voltage vector at each moment and the whole branch voltage vector at each moment corresponding to the 6th iteration are obtained by corresponding calculation. The minimum normal mode of the difference between the whole branch voltage vector at each moment corresponding to the 5th iteration and the whole branch voltage vector at each moment corresponding to the 6th iteration is compared with the preset threshold. If it is less than the preset threshold, the branch current of the whole branch at each moment corresponding to the 5th iteration and the whole branch voltage vector at each moment are used as the target whole branch current at each moment and each node voltage. Then, according to the branch current of the whole branch at each moment corresponding to the 5th iteration and the branch voltage vector at each moment, the target branch current at each moment and the node voltage at each node are calculated. The full-branch voltage vector at each moment is calculated to obtain the target branch power at each moment, and enter the model solving operation in the previous preferred embodiment; if it is not less than the preset threshold, enter the 6th iteration, and according to the full-branch voltage vector at each moment corresponding to the 6th iteration that has been calculated, under the current amplitude constraint and the linear voltage constraint, the corresponding iterative calculation is re-performed, wherein, since each component in the full-branch voltage vector at each moment of the current iteration is used as the node voltage at each moment of the current iteration, the node voltage in the entire iteration process will be updated due to the update of the full-branch voltage vector at each moment, and then the branch current of the full branch at each moment will also be updated.

[0164] In this preferred embodiment, by repeatedly executing the power flow iteration operation under the current amplitude constraint and the linear voltage constraint until the minimum normal mode corresponding to the current iteration is less than the preset threshold, the power of each branch at each moment of the current iteration is calculated according to the voltage of each node at each moment of the current iteration and the branch current of the entire branch at each moment of the current iteration, and finally the full branch current, each node voltage and each branch power at each moment corresponding to the current iteration are used as the target full branch current, each node voltage and each branch power at each moment in the above-mentioned model solving operation.

[0165] Step S104: Optimizing the distribution network according to the full branch current, each node voltage and each branch power when the network loss is minimum during the preset period.

[0166] In a preferred embodiment, after obtaining the full branch current, each node voltage and each branch power with the minimum network loss in the preset time period, the following is further included:

[0167] The minimum network loss value is calculated based on the power of each branch when the network loss is the smallest during the preset period and the voltage amplitude of each branch;

[0168] Among them, the above minimum network loss value is calculated according to the following formula:

[0169]

[0170] Where N minrepresents the minimum network loss value, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, r ij represents the resistance of the ijth branch, P′ ij,t It represents the active power of the ijth branch at time t when the network loss is minimum during the preset period, Q′ ij,t It represents the reactive power of the ijth branch at time t when the network loss is minimum during the preset period, U ij,t Represents the voltage amplitude of the ijth branch at time t.

[0171] In this preferred embodiment, the minimum network loss value is calculated based on the power of each branch and the voltage amplitude of each branch when the network loss is the smallest during the preset time period.

[0172] In another preferred embodiment, schematically, a typical IEEE 33-node system of a medium voltage 10 kV line is used as an optimization object for illustration. The IEEE 33-node topology diagram is as follows: Figure 2 As shown in the figure, the line includes 33 nodes in total. The "Tieswitch" in the figure represents the tie switch. Then the SOP access position is set. The access position is the line tie switch. The IEEE33 node topology after setting the access position is as follows Figure 3 As shown, Figure 3 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, and a tie switch between node 17 and node 32. "Load" indicates the load. Then, Scheme 1: Optimizing the original system, and Scheme 2: Considering SOP for optimization scheduling, are respectively carried out. After executing the above two schemes, the network loss and loss reduction ratio of the two schemes are calculated and compared. The results are shown in the following table:

[0173] Results comparison table

[0174] Original system Access SOP Grid loss (kWh) 2430.06 1370.07 Loss reduction ratio - 43.62％

[0175] Specifically, in the above result comparison table, the network loss value of the original system is 2430.06kWh, and the loss reduction ratio is no loss reduction compared with itself; after connecting to the SOP, the network loss is reduced to 1370.07kWh, and the loss reduction ratio reaches 43.62% compared with the original system.

[0176] Indicatively, it can be seen from the above result comparison table that the above method of the present invention can well reduce network loss, because the flexible control of SOP balances the loads of the two feeders, so that both feeders are fully utilized. At the same time, the access of SOP can realize the functions of optimizing the operation of the distribution network, smoothing the fluctuations caused by the access of new energy, shaving peaks and filling valleys, and improving the absorption capacity, so that the converters on both sides of SOP can be effectively reused, and the utilization rate of the equipment is improved.

[0177] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0178] like Figure 4 As shown, an embodiment of the present invention provides a power flow constraint-based distribution network optimization device, comprising: a distribution network data acquisition module, an optimization model construction module, an optimization model solving module, and a distribution network optimization module;

[0179] The distribution network data acquisition module is used to acquire the distribution network data at each moment within a preset time period; wherein the distribution network data includes: the power of distributed power sources at each node, the power demand of each node, the injected power of each node, the bus injected branch current matrix, the branch current to bus voltage matrix, the standard voltage, the power injected into the VSC by each node connected to the soft switch, the voltage amplitude of each branch, the voltage amplitude of each node, the power emitted by each node and the voltage amplitude limit of each node;

[0180] The optimization model building module is used to build a soft switch operation optimization model and constraints of the soft switch operation optimization model based on the distribution network data and with the goal of minimizing network loss during a preset period of time; wherein the constraints include: soft switch operation constraints, soft switch capacity constraints, forward and backward power flow constraints, power balance constraints, and safety constraints;

[0181] The optimization model solving module is used to solve the soft switch operation optimization model under various constraints to obtain the full branch current, each node voltage and each branch power when the network loss is minimum during the preset period;

[0182] The distribution network optimization module is used to optimize the distribution network according to the full branch current, each node voltage and each branch power when the network loss is minimized during the preset time period.

[0183] Furthermore, it also includes: a minimum network loss calculation module;

[0184] The minimum network loss calculation module is used to calculate the minimum network loss value according to the power of each branch when the network loss is the minimum in a preset period and the voltage amplitude of each branch;

[0185] Among them, the above minimum network loss value is calculated according to the following formula:

[0186]

[0187] Where N min represents the minimum network loss value, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, r ij represents the resistance of the ijth branch, P′ ij,t It represents the active power of the ijth branch at time t when the network loss is minimum during the preset period, Q′ ij,t It represents the reactive power of the ijth branch at time t when the network loss is minimum during the preset period, U ij,t Represents the voltage amplitude of the ijth branch at time t.

[0188] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Ordinary technicians in this field can understand and implement it without creative work. The above schematic diagram is only an example of a distribution network optimization device based on flow constraints, and does not constitute a limitation on a distribution network optimization device based on flow constraints, and may include more or fewer components than shown in the figure, or combine certain components, or different components.

[0189] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0190] Another embodiment of the present invention provides a terminal device, including 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 a distribution network optimization method based on flow constraints described in any embodiment of the present invention.

[0191] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the device;

[0192] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor, a memory;

[0193] The processor referred to herein may be a central processing unit (CPU), 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. The general-purpose processor may be a microprocessor or any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect various parts of the entire device;

[0194] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0195] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0196] Another embodiment of the present invention provides a storage medium, the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the distribution network optimization method based on power flow constraints described in any embodiment of the present invention.

[0197] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0198] Compared with the prior art, by implementing the above-mentioned embodiments of the present invention, it is possible to avoid the need to consider the power backflow problem caused by the increased use of intermittent distributed power sources, while improving the accuracy of the optimization results and the solution efficiency.

[0199] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distribution network optimization method based on power flow constraints, characterized in that: include: Obtain the distribution network data at each moment within a preset time period; wherein the distribution network data includes: the distributed power supply power of each node, the power demand of each node, the injected power of each node, the bus injection branch current matrix, the branch current to bus voltage matrix, the standard voltage, the power injected into the VSC by each node connected to the soft switch, the voltage amplitude of each branch, the voltage amplitude of each node, the power emitted by each node, and the voltage amplitude limit of each node; According to the distribution network data, with the goal of minimizing network loss during a preset period, a soft switch operation optimization model and constraint conditions of the soft switch operation optimization model are constructed; wherein the constraint conditions include: soft switch operation constraint, soft switch capacity constraint, forward and backward power flow constraint, power balance constraint and safety constraint; Under various constraint conditions, the soft switch operation optimization model is solved to obtain the full branch current, each node voltage and each branch power when the network loss is minimum during the preset period; The distribution network is optimized according to the full branch current, each node voltage and each branch power when the network loss is minimum during the preset time period.

2. A power distribution network optimization method based on power flow constraints according to claim 1, characterized in that: The objective function of the soft switching operation optimization model is: In the formula, F1 represents the objective function, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, and r ij represents the resistance of the ijth branch, P ij,t represents the active power of the ijth branch at time t, Q ij,t represents the reactive power of the ijth branch at time t, U ij,t Represents the voltage amplitude of the ijth branch at time t.

3. A distribution network optimization method based on power flow constraints according to claim 2, characterized in that: The soft switching operation constraints are: In the formula, represents the active power injected into the VSC at node i connected to the soft switch at time t, represents the active power injected into the VSC at node j connected to the soft switch at time t, represents the power loss of the port at node i connected to the soft switch at time t, represents the power loss of the port at node j connected to the soft switch at time t, represents the loss factor at node i connected to the soft switch, represents the reactive power injected into the VSC at node i connected to the soft switch at time t, represents the loss factor at node j connected to the soft switch, represents the reactive power injected into the VSC at node j connected to the soft switch at time t; The soft switch capacity constraint is: In the formula, represents the VSC capacity between nodes i and j connected to the soft switch; The forward-backward power flow constraint is: In the formula, I t k represents the branch current of the entire branch at the kth iteration at time t, represents the voltage of node i at the kth iteration at time t, θ represents the phase angle difference between each node voltage and each branch current, P i,t represents the active injected power at node i at time t, Q i,t represents the reactive injection power at node i at time t, represents the injected current at node i at the kth iteration at time t, BIBC represents the busbar injected branch current matrix, represents the full branch voltage drop vector of the k+1th iteration at time t, BCBV represents the branch current to bus voltage matrix, represents the full branch voltage vector of the k+1th iteration at time t, V 0 Indicates standard voltage; The power balance constraint is: In the formula, represents the active power injected by node i at time t, represents the active power emitted by node i at time t, represents the active power generated by distributed energy at node i at time t, represents the active power demand of node i at time t, represents the reactive power injected by node i at time t, represents the reactive power generated by node i at time t, represents the reactive power demand of node i at time t; The safety constraints are: IN i,t,min i,t i,t,max ​​ Where U i,t,min Indicates the lower limit of the voltage amplitude at node i at time t, U i,t,max represents the upper limit of the voltage amplitude of node i at time t, U i,t Represents the voltage amplitude of node i at time t.

4. A power distribution network optimization method based on power flow constraints according to claim 3, characterized in that: Under the constraints, the soft switch operation optimization model is solved to obtain the full branch current, each node voltage and each branch power when the network loss is minimum during the preset period, including: Repeat the model solving operation until all constraint conditions in the soft switch operation optimization model are satisfied, and obtain the full branch current, each node voltage and each branch power when the network loss is minimum in the preset time period; The model solving operation includes: Obtaining the injection power of each node at each moment of the current cycle; wherein the injection power of each node at each moment of the initial cycle is the injection power of each node at each moment in the preset time period; According to the injected power of each node at each moment of the current cycle, the full branch current, each node voltage and each branch power at each target moment of the current cycle are calculated; Substituting the full branch current, each node voltage and each branch power at each target moment of the current cycle into the constraint conditions of the soft switch operation optimization model, and determining whether the constraint conditions are met; If not, the injection power of each node at each moment of the current cycle is adjusted, and the adjusted injection power of each node at each moment is used as the injection power of each node at each moment of the next cycle.

5. A power distribution network optimization method based on power flow constraints according to claim 4, characterized in that: The method of calculating the target full branch current, node voltage and branch power at each moment of the current cycle according to the injected power of each node at each moment of the current cycle includes: Obtaining the voltage vector of all branches at each initial moment, the upper limit of each branch current, the upper limit of the system voltage amplitude, and the lower limit of the system voltage amplitude; According to the upper limits of the currents of each branch, the upper limit of the system voltage amplitude and the lower limit of the system voltage amplitude, a current amplitude constraint and a linear voltage constraint are constructed; Wherein, the current amplitude constraint is: In the formula, I ij,max Indicates the upper limit of the current of branch ij; The linear voltage constraint is: Where V min Indicates the lower limit of the system voltage amplitude, V max Indicates the upper limit of the system voltage amplitude; Under the current amplitude constraint and the linear voltage constraint, repeatedly performing the power flow iteration operation until the minimum normal mode corresponding to the current iteration is less than a preset threshold; The power of each branch at each moment of the current iteration is calculated according to the voltage of each node at each moment of the current iteration and the branch current of the entire branch at each moment of the current iteration; Taking the full branch current, each node voltage and each branch power at each moment corresponding to the current iteration as the target full branch current, each node voltage and each branch power at each moment; The power flow iteration operation includes: Obtaining the full branch voltage vector at each moment of the current iteration and the injected power of each node at each moment of the current cycle; wherein the full branch voltage vector at each moment of the initial iteration is the full branch voltage vector at each initial moment; Taking each component of the full branch voltage vector at each moment of the current iteration as each node voltage at each moment of the current iteration; The injected current at each node at each moment of the current iteration is calculated according to the voltage of each node at each moment of the current iteration and the injected power of each node at each moment of the current cycle; The branch current of the entire branch at each moment of the current iteration is calculated according to the product of the injected current at each node at each moment of the current iteration and the busbar injected branch current matrix; The voltage drop vector of the entire branch at each moment of the next iteration is calculated according to the product of the branch current of the entire branch at each moment of the current iteration and the branch current to bus voltage matrix; The full-branch voltage vector at each moment of the next iteration is calculated according to the product of the full-branch voltage drop vector at each moment of the next iteration and the standard voltage; Calculate the minimum normal mode of the difference between the full branch voltage vector at each moment of the current iteration and the full branch voltage vector at each moment of the next iteration, and compare the minimum normal mode with a preset threshold; If the minimum normal mode is not less than the preset threshold, the full branch voltage vector at each moment of the next iteration is used as the full branch voltage vector at each moment of the next iteration.

6. A power flow constraint-based distribution network optimization method according to claim 5, characterized in that: After obtaining the full branch current, each node voltage and each branch power when the network loss is minimum during the preset time period, the method further includes: The minimum network loss value is calculated based on the power of each branch when the network loss is the smallest during the preset period and the voltage amplitude of each branch; The minimum network loss value is calculated according to the following formula: Where N min represents the minimum network loss value, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, r ij represents the resistance of the ijth branch, P′ ij,t It represents the active power of the ijth branch at time t when the network loss is minimum during the preset period, Q′ ij,t It represents the reactive power of the ijth branch at time t when the network loss is minimum during the preset period, U ij,t Represents the voltage amplitude of the ijth branch at time t.

7. A distribution network optimization device based on power flow constraints, characterized in that: include: Distribution network data acquisition module, optimization model construction module, optimization model solution module and distribution network optimization module; The distribution network data acquisition module is used to acquire the distribution network data at each moment in a preset time period; wherein the distribution network data includes: the power of distributed power sources at each node, the power demand of each node, the injected power of each node, the bus injection branch current matrix, the branch current to bus voltage matrix, the standard voltage, the power injected into the VSC by each node connected to the soft switch, the voltage amplitude of each branch, the voltage amplitude of each node, the power emitted by each node, and the voltage amplitude limit of each node; The optimization model building module is used to build a soft switch operation optimization model and constraint conditions of the soft switch operation optimization model according to the distribution network data and with the goal of minimizing network loss in a preset time period; wherein the constraint conditions include: soft switch operation constraint, soft switch capacity constraint, forward and backward power flow constraint, power balance constraint and safety constraint; The optimization model solving module is used to solve the soft switch operation optimization model under various constraint conditions to obtain the full branch current, each node voltage and each branch power when the network loss is minimum during the preset time period; The distribution network optimization module is used to optimize the distribution network according to the full branch current, each node voltage and each branch power when the network loss is minimized during the preset time period.

8. A power distribution network optimization device based on power flow constraints according to claim 7, characterized in that: It also includes: minimum network loss calculation module; The minimum network loss calculation module is used to calculate the minimum network loss value according to the power of each branch when the network loss is the minimum during a preset period and the voltage amplitude of each branch; The minimum network loss value is calculated according to the following formula: Where N min represents the minimum network loss value, T represents the preset time period, n represents the total number of branches, ij represents the ijth branch, r ij represents the resistance of the ijth branch, P i ′ j,t It represents the active power of the ijth branch at time t when the network loss is minimum during the preset period, Q′ ij,t It represents the reactive power of the ijth branch at time t when the network loss is minimum during the preset period, U ij,t Represents the voltage amplitude of the ijth branch at time t.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a distribution network optimization method based on power flow constraints as described in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute a distribution network optimization method based on power flow constraints as described in any one of claims 1 to 6.

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