Power distribution network optimization method and device, electronic equipment and medium

Abstract

The invention discloses a power distribution network optimization method and device, electronic equipment and a medium, and relates to the field of electric power systems.According to the scheme, the minimum operation cost of a power distribution network is determined according to line parameters and load data of the power distribution network through an optimal power flow calculation method, and parameters of all elements are updated to corresponding target parameters; according to the scheme, whether the first target parameter meets the charging and discharging power constraint, the energy capacity constraint and the energy storage constraint of the energy storage system or not is judged, that is, whether the loss and the operation cost of the energy storage system meet the minimum cost operation target or not is judged; and more transformers or lines do not need to be additionally arranged in the active power distribution network or large-capacity transformers and lines do not need to be replaced, so that the system loss and cost of the power distribution network are greatly reduced. And secondly, the energy storage system is arranged in the power distribution network in the scheme, so that the stability of the voltage in the loop of the power distribution network can be maintained, and the bidirectional tidal current problem and the overvoltage problem in the power distribution network are solved on the basis of ensuring the minimum-cost operation.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and particularly to a method, device, electronic equipment and medium for optimizing a distribution network. Background Art

[0002] The large-scale integration of Renewable Energy Sources (RESs) in Active Distribution Networks (ADNs) offers many advantages, but at the same time poses higher requirements for the reliable operation of ANDs. Due to the intermittency and uncertainty of RESs, if their penetration exceeds the carrying capacity of the distribution network, two-way power flow and overvoltage problems will occur. Currently, to meet the growing energy demand and reduce the adverse effects brought about by the large-scale access of RESs, generally more transformers or lines are installed in the active distribution network, or transformers and lines with larger capacities are replaced. Although this method can alleviate the two-way power flow problem to a certain extent, it will significantly increase the operating cost of the active distribution network. Summary of the Invention

[0003] The purpose of the present invention is to provide a method, device, electronic equipment and medium for optimizing a distribution network. This solution uses the optimal power flow calculation method to determine the target parameters of each component in the distribution network corresponding to the minimum operating cost of the distribution network, that is, the minimum system power consumption target, based on the line parameters and load data of the distribution network, and through regulation, the parameters of each component are updated to the corresponding target parameters, thereby reducing the system loss and cost of the distribution network as much as possible, and there is no need to install more transformers or lines in the active distribution network, or replace transformers and lines with larger capacities. Secondly, because an energy storage system is provided in the distribution network of this solution, the energy storage system will store excess electric energy when the photovoltaic power generation exceeds the load requirement, reduce the reverse power flow, and release electric energy when the photovoltaic power generation is insufficient to supplement the load demand of the distribution network to maintain voltage stability. Therefore, this solution solves the two-way power flow problem and overvoltage problem in the distribution network.

[0004] To solve the above technical problems, the present invention provides a method for optimizing a distribution network, including:

[0005] Obtain the line parameters and load data of the distribution network;

[0006] Based on the line parameters and the load data, perform optimal power flow calculation to determine the first target parameter of the energy storage system in the distribution network, the second target parameter of the photovoltaic module in the distribution network, and the third target parameter of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network;

[0007] Adjust the parameters of the energy storage system to the first target parameters, adjust the parameters of the photovoltaic module to the second target parameters, and adjust the parameters of the capacitor bank to the third target parameters;

[0008] Determine the charge-discharge power constraint, energy capacity constraint, and stored energy constraint within a unit period of the energy storage system;

[0009] Judge whether the first target parameters satisfy the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint;

[0010] If the first target parameters do not satisfy the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, issue corresponding alarms.

[0011] Optionally, the optimal power flow calculation based on the line parameters and the load data to determine the first target parameters of the energy storage system, the second target parameters of the photovoltaic module, and the third target parameters of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network includes:

[0012] Build a three-phase power flow branch flow model in MATLAB based on the line parameters and the load data;

[0013] Determine the injection power of several nodes in the distribution network according to the line parameters, and calculate the voltage of each node according to each injection power;

[0014] Solve the three-phase power flow branch flow model by linearization and relaxation methods, and determine the first target parameters, the second target parameters, and the third target parameters according to the solution results of the three-phase power flow branch flow model and each voltage.

[0015] Optionally, the solving of the three-phase power flow branch flow model by linearization and relaxation methods includes:

[0016] Assume that the phase difference between the three-phase voltage angles corresponding to the three-phase power flow branch flow model is 120° to simplify the calculation of the apparent power flow in the three-phase power flow branch flow model;

[0017] Assume that the line loss of the distribution network corresponding to the three-phase power flow branch flow model is less than the branch power flow of the distribution network to linearize the calculation of the total approximate power flow of the three-phase power flow branch flow model;

[0018] Solve the simplified and linearized three-phase power flow branch flow model.

[0019] Optionally, after determining whether the first target parameter meets the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, the method further includes:

[0020] If the first target parameter meets the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, calculate the system loss of the distribution network and the voltage deviation of the distribution network in the current scheduling mode of the energy storage controller in the energy storage system within a preset adjustment period;

[0021] Determine whether the system loss is less than a preset loss threshold and whether the voltage deviation is less than a preset voltage deviation threshold;

[0022] If the system loss is not less than the preset loss threshold and the voltage deviation is not less than the preset voltage deviation threshold, adjust the scheduling mode of the energy storage controller until the system loss is less than the preset loss threshold and the voltage deviation is less than the preset voltage deviation threshold.

[0023] Optionally, before determining whether the first target parameter meets the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, the method further includes:

[0024] Determine the irradiance constraint of the photovoltaic module and the voltage constraint of the capacitor bank;

[0025] Under the charge-discharge power constraint, the energy capacity constraint, the stored energy constraint, the irradiance constraint, and the voltage constraint, use OpenDSS to model the energy storage system, the photovoltaic module, and the capacitor bank to obtain an energy storage system model, a photovoltaic model, and a capacitor bank model.

[0026] Optionally, determining whether the first target parameter meets the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint includes:

[0027] Determine whether the first target parameter meets the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, whether the second target parameter meets the irradiance constraint, and whether the third target parameter meets the voltage constraint;

[0028] The calculating the system loss of the distribution network and the voltage deviation of the distribution network in the current scheduling mode of the energy storage controller in the energy storage system within a preset adjustment period includes:

[0029] Calculate the system loss and the voltage deviation according to the energy storage system model, the photovoltaic model, the capacitor bank model, the first target parameter, the second target parameter, and the third target parameter.

[0030] To solve the above technical problems, the present invention also provides a distribution network optimization device, including:

[0031] An acquisition module, configured to acquire line parameters and load data of the distribution network;

[0032] A calculation module, configured to perform optimal power flow calculation based on the line parameters and the load data to determine a first target parameter of an energy storage system in the distribution network, a second target parameter of a photovoltaic module in the distribution network, and a third target parameter of a capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network;

[0033] An adjustment module, configured to adjust the parameters of the energy storage system to the first target parameter, adjust the parameters of the photovoltaic module to the second target parameter, and adjust the parameters of the capacitor bank to the third target parameter;

[0034] A determination unit, configured to determine the charge and discharge power constraint, energy capacity constraint, and stored energy constraint within a unit period of the energy storage system;

[0035] A judgment unit, configured to judge whether the first target parameter satisfies the charge and discharge power constraint, the energy capacity constraint, and the stored energy constraint;

[0036] An alarm unit, configured to issue a corresponding alarm when the first target parameter does not satisfy the charge and discharge power constraint, the energy capacity constraint, and the stored energy constraint.

[0037] Optionally, the calculation module includes:

[0038] A three-phase power flow branch flow model building unit, configured to build a three-phase power flow branch flow model in MATLAB based on the line parameters and the load data;

[0039] A calculation subunit, configured to determine the injection power of several nodes in the distribution network according to the line parameters, and calculate the voltage of each node according to each injection power;

[0040] A determination subunit, configured to solve the three-phase power flow branch flow model by a linearization and relaxation method, and determine the first target parameter, the second target parameter, and the third target parameter according to the solution result of the three-phase power flow branch flow model and each voltage.

[0041] To solve the above technical problems, the present invention also provides an electronic device, including:

[0042] A memory, configured to store a computer program;

[0043] A processor, which is configured to implement the steps of the distribution network optimization method as described above when executing the computer program.

[0044] To solve the above technical problems, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the distribution network optimization method as described above.

[0045] The objective of the present invention is to provide a distribution network optimization method, device, electronic device and medium. This solution uses the optimal power flow calculation method to determine the target parameters of each component in the distribution network corresponding to the minimum operating cost of the distribution network, that is, the minimum system power consumption target, based on the line parameters and load data of the distribution network. And through regulation, the parameters of each component are updated to the corresponding target parameters, thereby reducing the system loss and cost of the distribution network as much as possible, and there is no need to install more transformers or lines in the active distribution network or replace large-capacity transformers and lines. Secondly, because an energy storage system is provided in the distribution network of this solution, the energy storage system will store the excess electric energy when the photovoltaic power generation exceeds the load requirement, reducing the reverse power flow. When the photovoltaic power generation is insufficient, it releases electric energy to supplement the load demand of the distribution network to maintain voltage stability. Therefore, this solution solves the problems of two-way power flow and overvoltage in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0047] Figure 1 It is a process flow chart of a distribution network optimization method provided by the present invention;

[0048] Figure 2 It is a general schematic diagram of a distribution network optimization method provided by the present invention;

[0049] Figure 3 It is a specific schematic diagram of a distribution network optimization method provided by the present invention;

[0050] Figure 4 It is a structural schematic diagram of a distribution network optimization device provided by the present invention;

[0051] Figure 5 It is a structural schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The core of the present invention is to provide a distribution network optimization method, device, electronic device and medium. This solution uses the optimal power flow calculation method to determine the target parameters of each component in the distribution network corresponding to the minimum operating cost of the distribution network, that is, the minimum system power consumption target, based on the line parameters and load data of the distribution network. And through regulation, the parameters of each component are updated to the corresponding target parameters, reducing the system loss and cost of the distribution network as much as possible, and there is no need to install more transformers or lines or replace large-capacity transformers and lines in the active distribution network. Secondly, because an energy storage system is set in the distribution network of this solution, the energy storage system will store the excess electric energy when the photovoltaic power generation exceeds the load requirement, reducing the reverse power flow, and release electric energy when the photovoltaic power generation is insufficient to supplement the load demand of the distribution network to maintain voltage stability. Therefore, this solution solves the problems of two-way power flow and overvoltage in the distribution network.

[0053] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] Please refer to Figure 1 , Figure 1 which is a process flow chart of a distribution network optimization method provided by the present invention. The distribution network optimization method includes:

[0055] S11: Obtain the line parameters and load data of the distribution network;

[0056] S12: Perform optimal power flow calculation based on the line parameters and load data to determine the first target parameter of the energy storage system, the second target parameter of the photovoltaic module, and the third target parameter of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network;

[0057] S13: Adjust the parameters of the energy storage system to the first target parameter, adjust the parameters of the photovoltaic module to the second target parameter, and adjust the parameters of the capacitor bank to the third target parameter;

[0058] S14: Determine the charge and discharge power constraint, energy capacity constraint, and stored energy constraint within a unit cycle of the energy storage system;

[0059] S15: Determine whether the first target parameter satisfies the charge and discharge power constraint, energy capacity constraint, and stored energy constraint;

[0060] S16: If the first target parameter does not satisfy the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, then corresponding warnings are issued.

[0061] In the present invention, considering that in the prior art, to solve the problems of bidirectional power flow and overvoltage, generally more transformers or lines are installed in the active distribution network, or transformers and lines with larger capacity are replaced. This will increase the operating cost of the distribution network. Therefore, in this application, the optimal power flow calculation method is used to determine the target parameters of each component in the distribution network corresponding to the minimum operating cost of the distribution network, that is, the minimum system power consumption target, according to the line parameters and load data of the distribution network. And through regulation, the parameters of each component are updated to the corresponding target parameters, thereby reducing the system loss and cost of the distribution network as much as possible. In addition, considering that a photovoltaic module is generally installed in the distribution network to introduce photovoltaic power generation, but since the electric energy generated by the photovoltaic module depends on the irradiance, that is, the generated electric energy is unstable. Therefore, in this solution, an energy storage system is installed in the distribution network. The energy storage system will store the excess electric energy when the photovoltaic power generation exceeds the load requirement, reducing the reverse power flow. When the photovoltaic power generation is insufficient, it will release electric energy to supplement the load demand of the distribution network to maintain voltage stability. Therefore, this solution solves the problems of bidirectional power flow and overvoltage in the distribution network. In addition, considering that there are charge-discharge power constraints, energy capacity constraints, and stored energy constraints in the energy storage system in the distribution network, in order to reduce the loss and operating cost of the energy storage system, after adjusting the parameters of the energy storage system to the first target parameter, the parameters of the photovoltaic module to the second target parameter, and the parameters of the capacitor bank to the third target parameter, this solution will also determine the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint within a unit cycle of the energy storage system, and judge whether the first target parameter satisfies the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint. If the first target parameter does not satisfy the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, then corresponding warnings are issued, that is, by judging the first target parameter of the energy storage system, it is determined whether the loss and operating cost of the energy storage system meet the minimum cost operation target. If not, a warning is issued to timely remind the user or operator, improving the reliability of the solution.

[0062] This embodiment provides a method for optimizing a distribution network. This solution uses the optimal power flow calculation method to determine the target parameters of each component in the distribution network corresponding to the minimum operating cost of the distribution network, that is, the minimum system power consumption target, based on the line parameters and load data of the distribution network. And through regulation, the parameters of each component are updated to the corresponding target parameters, which reduces the system loss and cost of the distribution network as much as possible, and there is no need to install more transformers or lines in the active distribution network or replace transformers and lines with large capacities. Secondly, because an energy storage system is provided in the distribution network in this solution, the energy storage system will store excess electric energy when the photovoltaic power generation exceeds the load requirement, reduce the reverse power flow, and release electric energy when the photovoltaic power generation is insufficient to supplement the load demand of the distribution network to maintain voltage stability. Therefore, this solution solves the problems of two-way power flow and overvoltage in the distribution network.

[0063] Based on the above embodiment:

[0064] As an optional embodiment, based on the line parameters and load data, perform optimal power flow calculation to determine the first target parameter of the energy storage system in the distribution network, the second target parameter of the photovoltaic module in the distribution network, and the third target parameter of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network, including:

[0065] Build a three-phase power flow branch flow model in MATLAB based on the line parameters and load data;

[0066] Determine the injection power of several nodes in the distribution network according to the line parameters, and calculate the voltage of each node according to each injection power;

[0067] Solve the three-phase power flow branch flow model by linearization and relaxation methods, and determine the first target parameter, the second target parameter, and the third target parameter according to the solution results of the three-phase power flow branch flow model and each voltage.

[0068] In the present invention, the process of optimal power flow calculation is to input the line parameters and load data into MATLAB (Matrix Laboratory, a commercial mathematical software), and build a three-phase power flow branch flow model in MATLAB based on the line parameters and load data. Because there are multiple nodes on the distribution network, and the voltage of each node is related to the input cost and power consumption of the distribution network, it is also necessary to determine the injection power of several nodes in the distribution network according to the line parameters, and calculate the voltage of each node according to each injection power. Secondly, the three-phase power flow branch flow model built by MATLAB is a non-convex model, so it is also necessary to solve the three-phase power flow branch flow model by linearization and relaxation methods, and determine the first target parameter, the second target parameter, and the third target parameter according to the solution results of the three-phase power flow branch flow model and each voltage, ensuring the integrity of the target parameter determination process.

[0069] As an alternative embodiment, the three-phase power flow branch flow model is solved by a linearization and relaxation method, including:

[0070] Assume that the phase difference between the three-phase voltage angles corresponding to the three-phase power flow branch flow model is 120° to simplify the calculation of apparent power flow in the three-phase power flow branch flow model;

[0071] Assume that the line loss of the distribution network corresponding to the three-phase power flow branch flow model is less than the branch power flow of the distribution network to linearize the calculation of the total approximate power flow of the three-phase power flow branch flow model;

[0072] Solve the simplified and linearized three-phase power flow branch flow model.

[0073] In the present invention, in the process of solving the three-phase power flow branch flow model by the linearization and relaxation method, assumptions need to be made on the three-phase voltage angles and losses of the distribution network, that is, assume that the phase difference between the three-phase voltage angles corresponding to the three-phase power flow branch flow model is 120° to simplify the calculation of apparent power flow in the three-phase power flow branch flow model, assume that the line loss of the distribution network corresponding to the three-phase power flow branch flow model is less than the branch power flow of the distribution network to linearize the calculation of the total approximate power flow of the three-phase power flow branch flow model. By the above assumptions, the calculation of apparent power flow and total approximate power flow of the three-phase power flow branch flow model are simplified respectively, and then the simplified and linearized three-phase power flow branch flow model is solved to accurately complete the calculation of optimal power flow.

[0074] As an alternative embodiment, after adjusting the parameters of the energy storage system to the first target parameters, the parameters of the photovoltaic module to the second target parameters, and the parameters of the capacitor bank to the third target parameters, it further includes:

[0075] Determine the charge and discharge power constraint, energy capacity constraint, and stored energy constraint within a unit period of the energy storage system;

[0076] Judge whether the first target parameters meet the charge and discharge power constraint, energy capacity constraint, and stored energy constraint;

[0077] If the first target parameters do not meet the charge and discharge power constraint, energy capacity constraint, and stored energy constraint, issue a corresponding warning.

[0078] In the present invention, considering that there are charging and discharging power constraints, energy capacity constraints, and stored energy constraints in the energy storage system of the distribution network, in order to reduce the loss and operating cost of the energy storage system, after adjusting the parameters of the energy storage system to the first target parameters, the parameters of the photovoltaic module to the second target parameters, and the parameters of the capacitor bank to the third target parameters, the charging and discharging power constraints, energy capacity constraints, and stored energy constraints within a unit cycle of the energy storage system are also determined, and it is judged whether the first target parameters meet the charging and discharging power constraints, energy capacity constraints, and stored energy constraints. If the first target parameters do not meet the charging and discharging power constraints, energy capacity constraints, and stored energy constraints, corresponding alarms are issued, that is, by judging the first target parameters of the energy storage system, it is determined whether the loss and operating cost of the energy storage system meet the minimum cost operation target. If not, an alarm is made to timely remind the user or operator, improving the reliability of the solution.

[0079] As an optional embodiment, after judging whether the first target parameters meet the charging and discharging power constraints, energy capacity constraints, and stored energy constraints, it further includes:

[0080] If the first target parameters meet the charging and discharging power constraints, energy capacity constraints, and stored energy constraints, calculate the system loss of the distribution network and the voltage deviation of the distribution network in the current scheduling mode of the energy storage controller in the energy storage system within a preset adjustment period;

[0081] Judge whether the system loss is less than a preset loss threshold and whether the voltage deviation is less than a preset voltage deviation threshold;

[0082] If the system loss is not less than the preset loss threshold and the voltage deviation is not less than the preset voltage deviation threshold, adjust the scheduling mode of the energy storage controller until the system loss is less than the preset loss threshold and the voltage deviation is less than the preset voltage deviation threshold.

[0083] In the present invention, considering that the scheduling mode of the energy storage controller of the energy storage system will affect the system loss and voltage deviation of the distribution network, and the voltage deviation corresponds to the stability of the node voltages in the distribution network. Therefore, in order to minimize the system loss and voltage deviation of the distribution network as much as possible, after the first target parameters satisfy the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, the system loss of the distribution network and the voltage deviation of the distribution network under the current scheduling mode of the energy storage controller in the energy storage system within a preset adjustment period are calculated, and it is determined whether the system loss is less than a preset loss threshold and whether the voltage deviation is less than a preset voltage deviation threshold. If the system loss is not less than the preset loss threshold and the voltage deviation is not less than the preset voltage deviation threshold, the scheduling mode of the energy storage controller is adjusted until the system loss is less than the preset loss threshold and the voltage deviation is less than the preset voltage deviation threshold. By adjusting the scheduling mode of the energy storage controller, the system loss and voltage deviation of the distribution network reach the minimum value, which not only reduces the system loss of the distribution network but also improves the voltage stability of the distribution network.

[0084] It should be noted that this solution comprehensively considers the scheduling of BESS (Battery Energy Storage Systems) through rule-based power flow calculation and economy-based optimal power flow calculation. By detailed modeling of each component in the distribution network system in OpenDSS (Open Distributed System Simulator, an open-source three-phase distribution network power flow simulation software), for the input parameters of each component, the working mode of the energy storage is adjusted under the condition of meeting the energy storage system constraints to achieve the goal of minimizing system loss and voltage deviation and realizing the optimal operation of the system technology. At the same time, by establishing an optimization model of the system in MATLAB with the goal of minimizing the system operation cost, the settings and outputs of each component in the system are solved through GUROBI (Gurobi Optimization, a large-scale optimizer) to achieve the optimal economic operation of the system. In addition, in practical applications, generally, the data in OpenDSS and MATLAB can be made interoperable through the COM (Cluster communication port, a serial communication port) interface, and the output results are mutually feedback, so as to realize the technical and economic operation of the system, which can effectively achieve the purpose of reducing the operation and maintenance costs, losses, and voltage fluctuations of the unbalanced distribution network.

[0085] It should also be noted that the models of each component of the distribution network are as follows: 1. Load model; 2. Photovoltaic model; 3. Energy storage system model; 4. Capacitor bank model;

[0086] I. 1. Load model: The load in the distribution network is unbalanced. The load model considered in this method is a constant power load model, and the equation is expressed as follows:

[0087] ;

[0088] Wherein, I is the complex current, and P and Q are the active and reactive powers of the load respectively, is the conjugate value of the load port voltage, and j is a preset imaginary unit.

[0089] 2. Photovoltaic model: The photovoltaic module of OpenDSS consists of a maximum power point tracking inverter and a PV array (photovoltaic array) and is used for the modeling of a three-phase PV system. The relationship between irradiance and photovoltaic output power is as follows:

[0090] ;

[0091] Wherein, represents the active power output of the photovoltaic per hour, represents the rated output, represents the irradiance, represents a specific irradiance, represents the standard irradiance.

[0092] 3. Energy storage system model: The energy stored in the energy storage is maintained between 20% and 80% of the rated value. The relationship between the capacity and power of the energy storage system is as follows:

[0093] ;

[0094] Wherein, represents the energy storage energy at time t, and represent the charge and discharge power of the energy storage, represents the charge and discharge efficiency, represents the control time interval of the energy storage system, and represent the charge and discharge states at time t, and represent the maximum power of the energy storage system, represents the rated capacity of the energy storage.

[0095] At the beginning and end of a day, the stored energy is equal and is 50% of the rated value.

[0096] ;

[0097] 4. Capacitor bank model: The reactive power output of the three-phase capacitor bank is expressed as a voltage-dependent model.

[0098] ;

[0099] Wherein, is the output of the capacitor bank for the r-th phase at node i, Indicates the switching state of the capacitor of the r - th phase at node i at time t. Indicates the rated capacity of the capacitor of the r - th phase at node i. Indicates the square of the voltage of the r - th phase at node i.

[0100] II. Model Building

[0101] 1. OpenDSS Modeling: Select the corresponding components in the software to build the network, and set the parameters of each component in the distribution network according to the above - mentioned constraints.

[0102] 2. MATLAB Modeling: Write programs according to the mathematical models of each component. At the same time, the following constraints and approximations need to be made to the network. Among them, the three - phase power flow branch - flow model (Branch Flow Model, BFM) of the network is as follows:

[0103] ;

[0104] In the formula, Indicates the complex power from node j to node k. Indicates the branch impedance between node i and node j. Indicates the square of the current flowing through branch ij. Indicates the complex power injected at node j. And Respectively indicate the square of the voltages of node i and node j. Indicates the conjugate impedance of branch ij. Indicates the conjugate complex power from node i to node j.

[0105] The above - mentioned model is non - convex. The non - convex three - phase power flow formula is solved by linearization and relaxation methods, that is, the following two approximations are made:

[0106] 1) Assume that the voltage angles of the three phases are at 120°, close to balance, and the mutual apparent power flow can be written as:

[0107] ;

[0108] In the formula, , , Respectively indicate the three - phase voltages of node i. Indicates the complex rotation factor with a phase difference of 120°. Indicates the complex power between the r - th phase and the s - th phase between node i and node j. Indicates the complex power of the r - th phase between node i and node j. And Respectively indicate the square of the voltages of the s - th phase and the r - th phase at node i.

[0109] 2) Assuming the losses are relatively smaller than the branch power flows, the total approximate power flow equation is:

[0110] ;

[0111] Wherein, and respectively represent the active power and reactive power of phase r between node i and node j, and respectively represent the active power and reactive power injected by node j, and respectively represent the resistance and reactance of phase r and phase s between branch ij, represents the voltage phase difference between phase r and phase s of branch ij, and respectively represent the active power and reactive power of phase r between node j and node k.

[0112] The injected power at the node is expressed as the generation minus the load at each node. The voltage at this node is calculated as follows:

[0113] ;

[0114] Wherein, and respectively represent the active power and reactive power generated by the PV system of node j on phase r, and respectively represent the discharging active power and reactive power of the energy storage system on phase r, represents the charging active power of the energy storage system on phase r, and respectively represent the active load and reactive load of node j on phase r, represents the reactive power output by the capacitor bank of node j on phase r, and respectively represent the active power and reactive power input from the power grid to node r, and respectively represent the active power and reactive power injected by node l on phase r, and respectively represent the square of the voltage of phase r at node j and node i.

[0115] The goal of the optimization problem is to minimize the power grid input cost and the operation cost of the energy storage under the basic constraints. By controlling the PV, BESS (battery energy storage system) inverter, voltage regulator, and capacitor bank, the power consumption of the system is minimized.

[0116] ;

[0117] In the formula, and are the cost coefficients of the operation and maintenance cost of the BESS and the grid input cost respectively, is the active power input to the grid.

[0118] It should also be noted that the rule- and economy-based BESS energy management and dispatching optimization operation method provided in this application is as Figure 2 and Figure 3 shown. Among them, as Figure 3 shown in the right half, in OpenDSS, first input the system and component parameters, operate the energy storage controller according to the initial energy storage dispatching mode, then set the energy storage constraints and life cycle, and calculate whether the system loss and voltage deviation of the distribution network reach the minimum value in this working mode. If the minimum value is not reached, change the energy storage dispatching mode and re-execute the above process until the system loss and voltage deviation reach the minimum value, and then determine the charge and discharge mode of the energy storage. At the same time, Figure 3 in the left half, first input the line and load data of the distribution network, and based on the established mathematical model, run the optimal power flow to solve the optimal power flow under the minimum operation cost target, and obtain the equipment output and configuration in the economically optimal situation. The data of the left and right parts are interacted through the COM interface to update the parameters and working modes of the components in their respective models, comprehensively considering the economy and technology of the energy storage operation, and realizing the rule- and economy-based BESS energy management and dispatching optimization operation method.

[0119] As an optional embodiment, before judging whether the first target parameter meets the charge and discharge power constraint, energy capacity constraint, and stored energy constraint, it further includes:

[0120] Determine the irradiance constraint of the photovoltaic module and the voltage constraint of the capacitor bank;

[0121] Under the charge and discharge power constraint, energy capacity constraint, stored energy constraint, irradiance constraint, and voltage constraint, use OpenDSS to model the energy storage system, photovoltaic module, and capacitor bank to obtain the energy storage system model, photovoltaic model, and capacitor bank model;

[0122] Correspondingly, judging whether the first target parameter meets the charge and discharge power constraint, energy capacity constraint, and stored energy constraint includes:

[0123] Judge whether the first target parameter meets the charge and discharge power constraint, energy capacity constraint, and stored energy constraint, whether the second target parameter meets the irradiance constraint, and whether the third target parameter meets the voltage constraint;

[0124] Calculate the system loss and voltage deviation of the distribution network under the current scheduling mode of the energy storage controller in the energy storage system within a preset adjustment period, including:

[0125] Calculate the system loss and voltage deviation according to the energy storage system model, photovoltaic model, capacitor bank model, first target parameter, second target parameter, and third target parameter.

[0126] In the present invention, considering that the irradiance constraints and voltage constraints corresponding to the photovoltaic module and the capacitor bank will also affect their losses and operating costs, this solution will also determine the irradiance constraint of the photovoltaic module and the voltage constraint of the capacitor bank, and add a judgment on whether the second target parameter satisfies the irradiance constraint and whether the third target parameter satisfies the voltage constraint. In addition, considering that OpenDSS can calculate the system loss and voltage deviation of the distribution network according to the constraints of the components in the distribution network, this solution will also use OpenDSS to model the energy storage system, photovoltaic module, and capacitor bank under the charge-discharge power constraint, energy capacity constraint, stored energy constraint, irradiance constraint, and voltage constraint to obtain the energy storage system model, photovoltaic model, and capacitor bank model, and calculate the system loss and voltage deviation according to the energy storage system model, photovoltaic model, capacitor bank model, first target parameter, second target parameter, and third target parameter, ensuring the integrity of the solution.

[0127] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a distribution network optimization device provided by the present invention. The distribution network optimization device includes:

[0128] An acquisition module 11, configured to acquire the line parameters and load data of the distribution network;

[0129] A calculation module 12, configured to perform optimal power flow calculation based on the line parameters and the load data to determine the first target parameter of the energy storage system in the distribution network, the second target parameter of the photovoltaic module in the distribution network, and the third target parameter of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network;

[0130] An adjustment module 13, configured to adjust the parameters of the energy storage system to the first target parameter, adjust the parameters of the photovoltaic module to the second target parameter, and adjust the parameters of the capacitor bank to the third target parameter;

[0131] A determination unit 14, configured to determine the charge-discharge power constraint, energy capacity constraint, and stored energy constraint of the energy storage system within a unit period;

[0132] A judgment unit 15, configured to judge whether the first target parameter satisfies the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint;

[0133] An alarm unit 16, configured to issue a corresponding alarm when the first target parameter does not satisfy the charging and discharging power constraint, the energy capacity constraint, and the stored energy constraint.

[0134] As an optional embodiment, the calculation module includes:

[0135] A three-phase power flow branch flow model building unit, configured to build a three-phase power flow branch flow model in MATLAB based on the line parameters and the load data;

[0136] A calculation subunit, configured to determine the injection power of several nodes in the distribution network according to the line parameters, and calculate the voltage of each node according to each injection power;

[0137] A determination subunit, configured to solve the three-phase power flow branch flow model by a linearization and relaxation method, and determine the first target parameter, the second target parameter, and the third target parameter according to the solution result of the three-phase power flow branch flow model and each voltage.

[0138] The distribution network optimization device provided in this embodiment corresponds to the above method, so it has the same beneficial effects as the above method. Therefore, for the embodiments of the distribution network optimization device, please refer to the description of the embodiments of the method part, which will not be elaborated here.

[0139] Please refer to Figure 5 , Figure 5 FIG. is a schematic structural diagram of an electronic device provided by the present invention. The electronic device includes:

[0140] A memory 20, configured to store a computer program;

[0141] A processor 21, configured to implement the steps of the distribution network optimization method as described above when executing the computer program.

[0142] The electronic device provided in this embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, or a desktop computer, etc.

[0143] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one hardware form of a Digital Signal Processor (DSP), a Field-Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the Central Processing Unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a Graphics Processing Unit (GPU), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, and the AI processor is used to process computational operations related to machine learning.

[0144] The memory 20 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201. After the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the distribution network optimization method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be transient storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, the distribution network optimization method, etc.

[0145] In some embodiments, the electronic device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0146] Those skilled in the art can understand that Figure 5 the structure shown in

[0147] The purpose of this embodiment is to provide an electronic device, where the memory 20 is used to store a computer program, and the processor 21 is used to execute the computer program to implement the steps of the above-mentioned distribution network optimization method, making the optimization process more efficient and accurate.

[0148] The present invention also provides an embodiment corresponding to a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned distribution network optimization method are implemented.

[0149] It can be understood that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods in various embodiments of the present invention. The foregoing storage media include: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, and other media that can store program codes.

[0150] The computer-readable storage medium provided in this embodiment corresponds to the above method, so it has the same beneficial effects as the above method. Therefore, for the description of the embodiment of the computer-readable storage medium, please refer to the description of the embodiment of the method, and it will not be elaborated here for the time being.

[0151] It should be noted that in this specification, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0152] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing a distribution network, characterized in that Including: Obtain the line parameters and load data of the distribution network; Perform optimal power flow calculation based on the line parameters and the load data to determine the first target parameter of the energy storage system in the distribution network, the second target parameter of the photovoltaic module in the distribution network, and the third target parameter of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network; Adjust the parameters of the energy storage system to the first target parameter, adjust the parameters of the photovoltaic module to the second target parameter, and adjust the parameters of the capacitor bank to the third target parameter; Determine the charge and discharge power constraint, energy capacity constraint, and stored energy constraint within a unit period of the energy storage system; Judge whether the first target parameter meets the charge and discharge power constraint, the energy capacity constraint, and the stored energy constraint; If the first target parameter does not meet the charge and discharge power constraint, the energy capacity constraint, and the stored energy constraint, issue a corresponding warning.

2. The distribution network optimization method according to claim 1, wherein The performing optimal power flow calculation based on the line parameters and the load data to determine the first target parameter of the energy storage system in the distribution network, the second target parameter of the photovoltaic module in the distribution network, and the third target parameter of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network includes: Build a three-phase power flow branch flow model in MATLAB based on the line parameters and the load data; Determine the injection power of several nodes in the distribution network according to the line parameters, and calculate the voltage of each node according to each injection power; Solve the three-phase power flow branch flow model by linearization and relaxation methods, and determine the first target parameter, the second target parameter, and the third target parameter according to the solution result of the three-phase power flow branch flow model and each voltage.

3. The power distribution network optimization method according to claim 2, wherein, The solving the three-phase power flow branch flow model by linearization and relaxation methods includes: Assume that the phase difference between the three-phase voltage angles corresponding to the three-phase power flow branch flow model is 120° to simplify the calculation of the apparent power flow in the three-phase power flow branch flow model; Assume that the line loss of the distribution network corresponding to the three-phase power flow branch flow model is less than the branch power flow of the distribution network to linearize the calculation of the total approximate power flow of the three-phase power flow branch flow model; Solve the simplified and linearized three-phase power flow branch flow model.

4. The distribution network optimization method according to any one of claims 1 to 3, characterized in that After the judging whether the first target parameter meets the charge and discharge power constraint, the energy capacity constraint, and the stored energy constraint, it further includes: If the first target parameter meets the charge and discharge power constraint, the energy capacity constraint, and the stored energy constraint, calculate the system loss of the distribution network and the voltage deviation of the distribution network in the current scheduling mode of the energy storage controller in the energy storage system within a preset adjustment period; Judge whether the system loss is less than a preset loss threshold and whether the voltage deviation is less than a preset voltage deviation threshold; If the system loss is not less than a preset loss threshold and the voltage deviation is not less than a preset voltage deviation threshold, then adjust the scheduling mode of the energy storage controller until the system loss is less than the preset loss threshold and the voltage deviation is less than the preset voltage deviation threshold.

5. The power distribution network optimization method according to claim 4, wherein Before determining whether the first target parameter satisfies the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, it further includes: Determine the irradiance constraint of the photovoltaic module and the voltage constraint of the capacitor bank; Under the charge-discharge power constraint, the energy capacity constraint, the stored energy constraint, the irradiance constraint, and the voltage constraint, use OpenDSS to model the energy storage system, the photovoltaic module, and the capacitor bank to obtain an energy storage system model, a photovoltaic model, and a capacitor bank model.

6. The distribution network optimization method according to claim 5, characterized in that The determination of whether the first target parameter satisfies the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint includes: Determine whether the first target parameter satisfies the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint, whether the second target parameter satisfies the irradiance constraint, and whether the third target parameter satisfies the voltage constraint; The calculation of the system loss of the distribution network and the voltage deviation of the distribution network in the current scheduling mode of the energy storage controller in the energy storage system within a preset adjustment period includes: Calculate the system loss and the voltage deviation according to the energy storage system model, the photovoltaic model, the capacitor bank model, the first target parameter, the second target parameter, and the third target parameter.

7. A distribution network optimization device, characterized in that, It includes: An acquisition module for acquiring the line parameters and load data of the distribution network; A calculation module for performing optimal power flow calculation based on the line parameters and the load data to determine the first target parameter of the energy storage system in the distribution network, the second target parameter of the photovoltaic module in the distribution network, and the third target parameter of the capacitor bank in the distribution network corresponding to the minimum system power consumption target of the distribution network; An adjustment module for adjusting the parameters of the energy storage system to the first target parameter, adjusting the parameters of the photovoltaic module to the second target parameter, and adjusting the parameters of the capacitor bank to the third target parameter; A determination unit for determining the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint within a unit period of the energy storage system; A judgment unit for judging whether the first target parameter satisfies the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint; An alarm unit for sending a corresponding alarm when the first target parameter does not satisfy the charge-discharge power constraint, the energy capacity constraint, and the stored energy constraint.

8. The distribution network optimization device according to claim 7, wherein, The calculation module includes: A three-phase power flow branch flow model building unit for building a three-phase power flow branch flow model in MATLAB based on the line parameters and the load data; A calculation subunit for determining the injection power of several nodes in the distribution network according to the line parameters and calculating the voltage of each node according to each injection power. A determining subunit, configured to solve the three-phase power flow branch flow model by a linearization and relaxation method, and determine the first target parameter, the second target parameter, and the third target parameter according to the solution result of the three-phase power flow branch flow model and each of the voltages.

9. An electronic device, characterized in that, Comprising: A memory, configured to store a computer program; A processor, configured to implement the steps of the distribution network optimization method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the distribution network optimization method according to any one of claims 1 to 6 are implemented.

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