Power distribution network power supply recovery optimization method and system considering microgrid and voltage regulation

Abstract

The invention discloses a power distribution network power supply recovery optimization method and system considering a micro-grid and voltage regulation, and relates to the technical field of optimization management of a power system, and the method comprises the steps: collecting data of the micro-grid and voltage regulation equipment, and carrying out the data preprocessing; establishing an active power distribution network power supply recovery model considering the micro-grid and the voltage regulation equipment; and realizing modeling of an active power distribution network power supply recovery model considering the micro-grid and the voltage regulation equipment by using a mathematical modeling language AMPL, solving by using a CPLEX solver, and outputting a result. According to the invention, the practicality of the power distribution system structure is considered, application is flexible, and results are accurate; the solving efficiency of the power supply recovery model is improved; and convergence optimality is ensured through a classical optimization technology. A power supply recovery strategy of the active power distribution network can be defined according to analysis of the result. According to the method provided by the invention, each part of the system can be ensured to present a radial topological structure, and the part is allowed to be kept in a power-off state, so that other parts can be recovered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optimal management of power systems, in particular to a power distribution network power supply recovery optimization method and system considering microgrids and voltage regulation. BACKGROUND

[0002] After a permanent fault occurs in a radial distribution system, all sections downstream of the fault are de-energized. A distribution system in a region consists of nodes and branches that cannot be disconnected from each other by switching operations. Subsequently, the faulty part of the network must be isolated for repair, while the remaining de-energized part is a candidate for restoration. The purpose of fault restoration is to reduce the adverse effects of permanent faults. It must be performed without violating the physical and operational limits of the available equipment in the network, so that quality and safety are not compromised. These requirements, together with the network operation model, result in a complex mixed-integer nonlinear programming problem that is difficult to solve even for small instances.

[0003] At present, many scholars have solved the power distribution network power supply recovery problem by network reconfiguration. In addition to network reconfiguration, new restoration strategies have emerged for active distribution networks with DGs, such as forming microgrids, i.e., dividing the network into multiple microgrids after a single or multiple faults occur. With the widespread application of intelligent algorithms and mathematical programming algorithms, heuristic, metaheuristic, and mathematical programming models have begun to be used to solve fault restoration problems considering network reconfiguration and microgrid formation. Compared with mathematical programming models, radial and connectivity constraints can be easily included in the solution process of heuristic and metaheuristic models. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: how to simultaneously consider the optimal restoration problem of active distribution systems considering network reconfiguration, microgrid formation, and voltage control.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a power distribution network power supply recovery optimization method considering microgrids and voltage regulation, comprising the following steps,

[0007] Collecting data of microgrids and voltage regulation devices and performing data preprocessing.

[0008] Establishing an active distribution network power supply recovery model considering microgrids and voltage regulation devices.

[0009] Modeling the active distribution network power supply recovery model considering microgrids and voltage regulation devices in a mathematical modeling language AMPL, and solving it using a CPLEX solver to output the results.

[0010] As a preferred scheme of the power supply recovery optimization method for power distribution network considering microgrid and voltage regulation provided by the application, wherein: the data of the microgrid and voltage regulation equipment includes active power distribution network line parameters, distributed power supply parameters, group switching capacitor parameters, on-load voltage regulation transformer parameters, voltage regulator parameters and cost coefficients.

[0011] The data preprocessing is to denoise the collected state data, remove missing values, abnormal values and invalid data of wrong format, convert the original format data into the format required for analysis, and normalize the data to complete the data preprocessing.

[0012] As a preferred scheme of the power supply recovery optimization method for power distribution network considering microgrid and voltage regulation provided by the application, wherein: the establishment of the active power distribution network power supply recovery model considering microgrid and voltage regulation equipment is to establish an active power distribution network power supply recovery objective function by minimizing the total recovery cost, and the expression is:

[0013]

[0014]

[0015]

[0016] wherein, is the cost of not meeting the load reduction at node i, is the active power demand at node i under the rated voltage, denotes the section S containing node i i whether to be powered on, and when powered on otherwise is the switching action cost of branch ij, denotes the change of the switching action of branch ij, is the cost of changing the active / reactive power output of DG at node i, is the change of the active / reactive power dispatching of DG at node i, is the CB action cost at node i, is the operation change of CB at branch ij, is the cost of changing the OLTC operation at node i, is the operation change of OLTC at branch ij, is the cost of changing the VR operation at branch ij, is the operation change of VR at branch ij, is the cost of forming a microgrid, is the number of DGs with black start capability operating at node i, when at least one DG with black start capability is operating in the microgrid containing node i, is a set of demand nodes, is a set of real nodes, is a set of real substation nodes, is a set of all real branches with one switch in the system, is a set of real branches with open switches before the fault, is a set of real branches with closed switches before the fault, is a set of nodes with DGs, is a set of CBs nodes, is a set of substation on-load tap changer transformer nodes, is a set of branches with tap changers, is a set of DGs with black-start capability.

[0017] As a preferred scheme of the power distribution network power restoration optimization method considering microgrid and voltage regulation provided by the application, the active power distribution network power restoration objective function comprises linearized power flow constraints, load model constraints, DGs model constraints, CBs model constraints, VRs and OLTCs model constraints, network asset operation constraints and radial microgrid formation constraints.

[0018] As a preferred scheme of the power distribution network power restoration optimization method considering microgrid and voltage regulation provided by the application, the linearized power flow constraints are expressed as:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] wherein P ij , Qij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, j Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij,l Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, - Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij,

[0031] Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij,

[0032] Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, ij Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij,

[0033]

[0034] Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, V Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij,

[0035] Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij, Pij is the active power of branch ij, Qij is the reactive power of branch ij, Rij is the resistance of branch ij, Xij is the reactance of branch ij, Zij is the impedance of branch ij,

[0036]

[0037] where, denotes whether branch ii switch is open, open when else For branches without switches,

[0038] Length of each section of power flow square linearization, expressed as:

[0039]

[0040] where, is the current capacity of ij branch.

[0041] Slope of each section expressed as:

[0042]

[0043]

[0044] The load model constraints, expressed as:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] where, are the participation factors of constant impedance / constant current / constant power load pair active power demand at node i, respectively, is the. is the reactive power demand at node i rated voltage, V N is the rated voltage magnitude of node i, are the Big-M parameters when calculating active / reactive load at node i, respectively, are the participation factors of constant impedance / constant current / constant power load pair reactive power demand at node i, respectively, is the energized state of section S i containing node i, if energized else

[0052] The distributed generation model constraints, expressed as:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] where, are the DG active / reactive power at node i, is the apparent power capacity of the DG at node i, are the limits of the capacitive / inductive power factor of the DG at node i, are the active / reactive power injected by the DG at node i before the fault, are the changes in the DG active / reactive dispatch at node i, for the ramp-up rate limit of DG at node i, for the switch status of branch ij, open when Otherwise For branches without switches, for the artificial flow on branch ij used in the constraints related to microgrid formation, for the artificial generation at node i used in the constraints related to microgrid formation, Φ i denotes whether node i has a DG, =1 if yes i =1, otherwise Φ i =0, for the reduction factor of DG generation capacity with black-start capability installed at microgrid node i, for the number of DGs with black-start capability running at node i, when at least one DG with black-start capability is running in the microgrid containing node i.

[0074] the CBs model constraints, the expression is:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] wherein, for the total reactive power injection of CBs installed at node i, for the number of CB modules at node i, for the reactive power injected by CB module k at node i, for the susceptance of CB module at node i, for the running status of CB module k at node i, if the module is connected Otherwise for the number of CB modules at node i before the fault.

[0082] As a preferred solution of the power supply recovery optimization method of the power distribution network considering microgrid and voltage regulation according to the present application, wherein: the VRs and OLTCs model constraints, the expression is:

[0083]

[0084]

[0085] Among them, a ij For the tap of VR on branch ij, For auxiliary variables used in the VRs model, the tap is... The adjustment amount of VR for branch ij.

[0086]

[0087]

[0088]

[0089]

[0090] in, Let VR be the adjustment amount for branch ij. The maximum adjustment of VR on branch ij is For the branch ij with VR before the fault The value of .

[0091] The OLTC operating constraints are:

[0092]

[0093]

[0094] in, The maximum adjustment value of OLTC at node i. Let be the pre-fault voltage of the substation with OLTC at node i.

[0095] The network asset operation constraints are expressed as follows:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] in, Let be the current capacity of branch ij. Let be the apparent power capacity of the substation at node i.

[0109] The radial microgrid forms a constraint, expressed as follows:

[0110]

[0111] in, It is the set of all segments in the system.

[0112] If a power outage occurs in a certain section, the current node must be connected to an artificial substation. The constraint expression is as follows:

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] in, This refers to the artificial flow on branch ij used to ensure that the power outage node is disconnected from the main power grid. To generate electricity at node i using an artificial substation that ensures the disconnection of the power outage node from the main grid, Ω B The set of all branches in the system. Ω is a set of artificial branches with switches. N This refers to the set of all nodes in the system, including artificial substation nodes. It is a set that contains only artificial substation nodes.

[0121] If a certain pain point is identified, the current node will be connected to a real or artificial substation. The energized portion can only be connected to an artificial substation. The constraint expression is as follows:

[0122]

[0123]

[0124]

[0125]

[0126] in, The branch ij is used to ensure the radial flow of artificial resources between the main network and each micro-network. To ensure the artificial power generation of the main power grid and each microgrid in a radial pattern at the substation at node i.

[0127] As a preferred embodiment of the power supply restoration optimization method for distribution networks considering microgrids and voltage regulation described in this invention, the following constraint expression is used to avoid unnecessary switching actions when nodes perform connection operations:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] in, A set of faulty sections; For the segment containing node i, z ij For use in product Linearized auxiliary variables.

[0137] The active power distribution network power restoration model considering microgrids and voltage regulation equipment is modeled using the mathematical modeling language AMPL, and solved using the CPLEX solver, with the results output.

[0138] Another objective of this invention is to provide a power restoration optimization system for distribution networks that takes into account microgrids and voltage regulation. This system can optimize the power restoration process of distribution networks in the face of power outages by efficiently integrating and managing microgrid resources and voltage regulation equipment. This solves the shortcomings of existing technologies in quickly and effectively handling power restoration problems in complex distribution networks, especially when considering the needs of distributed power sources and dynamic voltage regulation.

[0139] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a power supply restoration optimization system for distribution networks considering microgrids and voltage regulation, comprising: a data collection and preprocessing module, a power supply restoration model establishment module, a model constraint setting module, a mathematical modeling and solving module, and a result output module.

[0140] The data collection and preprocessing module collects data from microgrids and voltage regulation equipment, including active distribution network line parameters, distributed power source parameters, group switching capacitor parameters, on-load tap-changing transformer parameters, voltage regulator parameters, and cost coefficients; further, it denoises the data, removes missing values, outliers, and data with incorrect formats, converts the raw data into the format required for analysis, and performs data normalization.

[0141] The power restoration model building module establishes a power restoration model for the active distribution network based on the collected data.

[0142] The model constraint setting module is used to set various model constraints, including linearized power flow constraints, load model constraints, DGs model constraints, CBs model constraints, VRs and OLTCs model constraints, network asset operation constraints, and radial microgrid formation constraints.

[0143] The mathematical modeling and solving module uses the mathematical modeling language AMPL to implement the above power restoration model and uses the CPLEX solver to solve it.

[0144] The result output module outputs the optimized results obtained by the CPLEX solver to guide the actual power supply restoration operation of the distribution network.

[0145] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the power supply restoration optimization method for a distribution network that takes into account microgrids and voltage regulation as described above.

[0146] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power supply restoration optimization method for a distribution network taking into account microgrids and voltage regulation as described above.

[0147] The beneficial effects of this invention are as follows: This invention considers the realities of power distribution system structures, is flexible in application, and yields accurate results; it accelerates the solution efficiency of power restoration models; and it ensures convergence optimality through classical optimization techniques. Based on the analysis of the results, a power restoration strategy for active distribution networks can be defined. The method proposed in this invention ensures that each part of the system exhibits a radial topology and allows some parts to remain in a power-off state so that other parts can be restored. The method proposed in this invention defines the number of distributed generation (DG) units in each microgrid and also considers the black-start capacity of different DG units and the safety reserve of DG capacity operating in the microgrid. Attached Figure Description

[0148] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0149] Figure 1 The overall flowchart of the power supply restoration optimization method for distribution networks considering microgrids and voltage regulation provided in the first embodiment of the present invention;

[0150] Figure 2 This is an optimization flowchart of the power supply restoration optimization method for distribution networks that considers microgrids and voltage regulation, provided in the first embodiment of the present invention.

[0151] Figure 3 An improved 53-node system diagram of the distribution network power supply restoration optimization method considering microgrids and voltage regulation provided in the first embodiment of the present invention;

[0152] Figure 4 The diagram below shows the structure of a power supply restoration optimization system for a distribution network that considers microgrids and voltage regulation, as provided in the second embodiment of the present invention. Detailed Implementation

[0153] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0154] Example 1

[0155] Reference Figures 1-3 As an embodiment of the present invention, a distribution network power supply restoration optimization method considering microgrids and voltage regulation is provided, characterized in that:

[0156] With the rapid development of power distribution networks, modern power distribution systems also include remotely operated voltage control devices, such as voltage regulators (VRs), substation OLTCs, and voltage control circuits (CBs). These devices increase the flexibility of system operation, improve voltage distribution, and minimize losses. Controlling these devices can be considered in fault recovery problems to increase the load that can be restored. However, the integration of numerous devices and changes in network structure lead to complex and difficult-to-solve active power distribution network power restoration models. Heuristic and metaheuristic optimization methods cannot guarantee a globally optimal solution. This patent establishes a novel MILP model for active power distribution network power restoration, aiming to solve the optimization and restoration problem of active power distribution systems that simultaneously considers network reconfiguration, microgrid formation, and voltage control.

[0157] This invention proposes an optimization method for active distribution network power restoration that considers microgrids and voltage regulation equipment. Firstly, it aims to minimize outage loads by minimizing the number of switching operations and voltage control device state modifications after one or more faults occur in the network. Considering the black-start capability of generators, multiple generators connected to the same microgrid, and various voltage regulation devices, a novel mixed-integer linear programming model for active distribution network power restoration is proposed. The method proposed in this invention can effectively improve the solution performance of active distribution network power restoration models.

[0158] The specific steps are as follows:

[0159] Step 1: Initialization. Input the parameters of the active distribution network lines, distributed generation (DGs) parameters, group switching capacitors (CBs) parameters, on-load tap changer (OLTCs) parameters, voltage regulator (VRs) parameters, and various cost coefficients.

[0160] The second step is to establish an active distribution network power restoration model that considers microgrids and voltage regulation equipment, which mainly includes the objective function and constraints for active distribution network power restoration.

[0161] (1) Establish an objective function for power supply restoration of active distribution networks to minimize the total restoration cost;

[0162] (2) The established active distribution network constraints include: linearized power flow constraints, load model constraints, DGs model constraints, CBs model constraints, VRs and OLTCs model constraints, network asset operation constraints, and radial microgrid formation constraints.

[0163] Step 3: Use the mathematical modeling language AMPL to model the active power distribution network power restoration model that considers microgrids and voltage regulation equipment, and use the CPLEX solver to solve it and output the results.

[0164] This invention provides an active power distribution network power restoration optimization method that considers microgrids and voltage regulation equipment, such as... Figure 2 As shown, taking the improved 53-node system as an example, such as... Figure 3 As shown, the active power distribution network power restoration optimization method of the present invention, which considers microgrids and voltage regulation equipment, includes the following steps:

[0165] Step 1: Initialization, inputting the parameters of the 53-node distribution network lines; the system has 3 substations and 50 load nodes, each load node representing a section, and each branch line is equipped with a switch. Six distribution gates (DGs) with capacities of 3000kVA, 6500kVA, 8000kVA, 4000kVA, 8000kVA, and 5000kVA are installed at nodes 2, 16, 20, 36, 43, and 49, respectively. Except for nodes 16 and 36, all other DGs have black-start capability. For all DGs, it is assumed that... Three branch circuits (CBs) were installed at nodes 26, 29, and 32, each with four 300kVar (rated voltage) modules. All substations were equipped with OLTCs (Optical Line Control Units) with a total regulation of 10% and tap positions of ±16. Four variable circuits (VRs) were installed on branches 4-5, 22-23, 38-39, and 42-47, with an adjustable range of 10% and tap positions of ±16. The rated voltage of the substations was 13.8kV, with minimum and maximum voltage limits of 0.95pu and 1.05pu, respectively. The objective function parameters were set as follows: κ MG =100 US$. Number of piecewise linearized segments Λ = 15.

[0166] The second step is to establish an active distribution network power restoration model that considers microgrids and voltage regulation equipment, which mainly includes the objective function and constraints for active distribution network power restoration.

[0167] (1) Establish the objective function for power supply restoration of active distribution network to minimize the total restoration cost, including the total active load cost not provided, switching operation cost, active and reactive power dispatch change cost of DGs, CBs operation cost, OLTCs and VRs operation cost, and microgrid formation cost.

[0168]

[0169] In the formula, The cost of load reduction at node i when demand is not met; P i D The active power demand at node i under its rated voltage; Indicates the segment containing node i. Is it powered on? otherwise Cost of operating the branch circuit ij switch; This indicates a change in the operation of the ij branch switch; These represent the costs of changing the active / reactive output of the DG at node i, respectively. These represent the changes in active / reactive power scheduling of the DG at node i; The cost of the CB action at node i; The operation of CB at branch ij is changed; To change the cost of running OLTC at node i; The operation of OLTC at branch ij is changed; To change the cost of VR operation at branch ij; The operation of VR at branch ij is changed; The cost of forming a microgrid; Indicates when At that time, at least one DG with black-start capability is operating in the microgrid containing node i; For the set of demand nodes, A set of real nodes; It is a set of actual substation nodes; Let be the set of all real branches in the system that have a switch. The set of actual branches whose switches were turned on before the fault occurred; This is the set of actual branches where the switch was closed before the fault occurred; It is a set of nodes containing distributed power sources; For the set of CBs nodes; This refers to the set of on-load tap-changing transformer nodes in a substation. A collection of branches with voltage regulators; This is a set of DGs nodes with black-start capability.

[0170] (2) The established active distribution network constraints include: linearized power flow constraints, load model constraints, DGs model constraints, CBs model constraints, VRs and OLTCs model constraints, network asset operation constraints, and radial microgrid formation constraints.

[0171] 1) Linearized power flow constraints

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183] In the formula, P ij Q ij R represents the active / reactive power of branch ij; ij ,X ij Z ij Let be the resistance / reactance / impedance value of branch ij; P is the square of the current in branch ij; i SS , P represents the active / reactive power input of the substation at node i; i DG , Let i be the active / reactive power of the DG at node i; The active / reactive power demand at node i; V represents the total reactive power injection of the CB installed at node i; i V i SQ Let ξ be the voltage magnitude and its square at node i; ij The relaxation variable ξ is used to calculate the branch voltage drop based on the operation of the branch switch (for branches without switches, ξ). ij =0;M ij To calculate the Big-M parameters for the voltage drop in branch ij; slack variable ξ ij It is based on The value is calculated for branches without VR. V, Let V be the minimum / maximum voltage amplitude at node i; for branches with VR, Indicates whether the branch circuit ij switch is open; when open... otherwise For branches without switches, θ i To estimate the voltage amplitude at node i from the pre-fault operation; m ij,lLet l be the slope of the piecewise linearized segment l of branch ij; For the active / reactive power segment l of branch ij, there are relevant discrete variables. The non-negative variable representing the active power of branch ij; Λ represents the non-negative variable of reactive power on branch ij; Λ is the number of segments in piecewise linearization. The larger the value of Λ, the more accurate the approximation, at the cost of increased model size and computational burden. For the length of each segment of the power flow squared linearization on branch ij, the length of each segment of the power flow squared linearization is... The slope of each segment is and Where l > 1, It is a set of real branches.

[0184] 2) Load model constraints

[0185] The load is modeled using a voltage-determined polynomial ZIP model.

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192] In the formula, The participation factor of the constant impedance / constant current / constant power load at node i on the active power demand; V represents the reactive power demand at the rated voltage of node i; N Let be the rated voltage amplitude of node i; The Big-M parameters are used to calculate the active / reactive load at node i; β i Z ,β i I ,β i P is the participation factor of the constant impedance / constant current / constant power load at node i in the reactive power demand.

[0193] 3) Constraints of the Distributed Power Generation Model

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211]

[0212]

[0213]

[0214] In the formula, Let be the apparent power capacity of the DG at node i; This is the limit value for the capacitance / inductance power factor of DG at node i; The active / reactive power generated by the DG on node i before the fault occurred; Limit the rate of ascent of DG at node i. Artificial flows on branch ij were used in the constraints related to microgrid formation; To generate artificial electricity at node i, which is used in the constraints related to microgrid formation; Φ i Indicates whether node i has a DG; if so, then Φ i =1, otherwise Φ i =0; The reduction factor for the generation capacity of a DG with black-start capability installed at node i of the microgrid is given. In each microgrid, a DG with black-start capability must maintain... Capacity reserves.

[0215] 4) CBs model constraints

[0216] The operation of CBs is represented using a voltage-determined model, as shown below:

[0217]

[0218]

[0219]

[0220]

[0221]

[0222]

[0223] In the formula, Let i be the number of CB modules at node i; The reactive power injected into CB module k at node i; Let be the susceptance of the CB module at node i; The running state of CB module k at node i: if the modules are connected otherwise This represents the number of CB modules in the state before the fault at node i.

[0224] 5) Constraints of VRs and OLTCs models

[0225]

[0226] This formula represents the functional relationship between the square of the voltage regulation amplitude at node n and the square of the voltage amplitude at node i. ij This is the tap of VR on branch ij.

[0227]

[0228] In the formula, For auxiliary variables used in VRs models (for branches without VR, ); the tap is in The adjustment amount of VR for branch ij; The definition is as follows:

[0229]

[0230] a ij It can be by and V i SQ The result is shown in the following formula:

[0231]

[0232] The operational constraints of VRs are:

[0233]

[0234]

[0235] In the formula, The maximum adjustment of VR on branch ij; For the branch ij with VR before the fault The value; because in the objective function minimize, The value will also be minimized, where the first term is about the adjustment of VR before the failure. The second item is calculated by the model that runs after the failure. Since both of these contain a common factor so The study only considered the difference in VR adjustment before and after the fault.

[0236] The OLTC operating constraints are:

[0237]

[0238]

[0239] In the formula, The maximum OLTC adjustment value at node i; This represents the pre-fault voltage of a substation with an OLTC at node i. For a typical VR or OLTC, the tap has ±16 tap positions, and The maximum error achievable with a voltage regulator is 0.31%.

[0240] 6) Network asset operation constraints

[0241]

[0242]

[0243]

[0244]

[0245]

[0246]

[0247]

[0248]

[0249]

[0250]

[0251]

[0252]

[0253] In the formula, Let be the current capacity of branch ij; Let be the apparent power capacity of the substation at node i.

[0254] 7) Constraints on the formation of radial microgrids

[0255] Consider an artificial substation node in the system and connect it to a node in each section to ensure the radial topology of the main grid and each microgrid.

[0256]

[0257] In the formula, Let be the set of all segments in the system; this formula is a sufficient condition for each node to be connected to a real or artificial substation. By ensuring that there is a path between each node and a real or artificial substation, the main grid and each microgrid can be arranged in a radial topology.

[0258] If a section of power is lost, it must be connected to an artificial substation, thus subject to the following constraints:

[0259]

[0260]

[0261]

[0262]

[0263]

[0264] In the formula, This refers to the artificial flow on branch ij used to ensure that the power outage node is disconnected from the main power grid. To generate electricity at node i, using an artificial substation to ensure the disconnection of the power outage node from the main grid; ΩB The set of all branches in the system, including artificial branches. A collection of artificial branches with switches; Ω N This refers to the set of all nodes in the system, including artificial substation nodes. It is a set that contains only artificial substation nodes.

[0265] If a section is energized, it must be connected to a physical or artificial substation. It should be noted that energized sections can only be connected to artificial substations. If its demand is met by one or more distributed generation (DGs), it operates in microgrid mode, thus subject to the following constraints:

[0266]

[0267]

[0268]

[0269]

[0270] In the formula, The branch ij is used to ensure the radial flow of artificial resources between the main network and each micro-network; To ensure the artificial power generation of the main power grid and each microgrid in a radial pattern at the substation at node i.

[0271] To avoid unnecessary switching operations in the isolated section, the following constraints apply:

[0272]

[0273]

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[0275]

[0276]

[0277]

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[0279]

[0280] In the formula, A set of faulty sections; The segment containing node i; z ij For use in product Linearized auxiliary variables.

[0281] Step 3: Use the mathematical modeling language AMPL to model the active power distribution network power restoration model that considers microgrids and voltage regulation equipment, and use the CPLEX solver to solve it and output the results.

[0282] Example 2

[0283] Reference Figure 4 As an embodiment of the present invention, a system for optimizing power supply restoration of distribution networks considering microgrids and voltage regulation is provided, characterized in that it includes a data collection and preprocessing module, a power supply restoration model establishment module, a model constraint setting module, a mathematical modeling and solving module, and a result output module.

[0284] The data collection and preprocessing module collects data from microgrids and voltage regulation equipment, including active distribution network line parameters, distributed power source parameters, group switching capacitor parameters, on-load tap-changing transformer parameters, voltage regulator parameters, and cost coefficients. It further denoises the data, removes missing values, outliers, and data with incorrect formats, converts the raw data into the format required for analysis, and performs data normalization.

[0285] The power restoration model building module is based on the collected data to build a power restoration model for the active distribution network;

[0286] The model constraint setting module is used to set various model constraints, including linearized power flow constraints, load model constraints, DGs model constraints, CBs model constraints, VRs and OLTCs model constraints, network asset operation constraints, and radial microgrid formation constraints.

[0287] The mathematical modeling and solving module uses the mathematical modeling language AMPL to implement the above power restoration model and uses the CPLEX solver to solve it.

[0288] The results output module outputs the optimized results obtained by the CPLEX solver, which guides the actual power supply restoration operation of the distribution network.

[0289] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0290] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0291] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0292] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0293] Example 3

[0294] In this embodiment, to verify the beneficial effects of the present invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments. This embodiment presents experiments comparing both existing conventional methods and the method of this embodiment.

[0295] The present invention's numerical test considers three fault scenarios. In scenario A, a fault in segments {1, 3, 11, 14} is considered, which isolates substations 101 and 102, with a recoverable load of 52740.07 kW. In scenario B, a fault in segments {1, 3, 21, 22, 30} is considered, which isolates substations 101 and 103, with a recoverable load of 34228.66 kW. In scenario C, a fault in segments {1, 3, 11, 14, 21, 22, 30} is considered, isolating all substations, with a recoverable load of 63101.81 kW. To analyze the impact of microgrid formation and voltage control on the recovery problem, three different methods were applied for each case: 1) considering network reconfiguration, microgrid formation, and voltage control; 2) considering network reconfiguration and microgrid formation in the case where voltage control is not considered, i.e., the tap positions of OLTCs and VRs and the number of CBs modules connected to each node remain unchanged from the pre-fault configuration values; 3) considering only network reconfiguration and ignoring microgrid formation.

[0296] Table 1 Data Comparison Table

[0297]

[0298]

[0299] Table 1 summarizes the main results for each scheme. The computation times for schemes A1-C3 are 22.47s, 30.08s, 2.45s, 1.73s, 0.91s, 0.66s, 0.98s, 0.91s, and 0.13s, respectively, which meet the time range requirements for fault recovery problems. The results show the importance of considering voltage control and microgrid formation in fault recovery problems, as more load can be restored when the DGs capacity decreases. The proposed model effectively solves the problem, demonstrating a good trade-off between accuracy and computation time.

[0300] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distribution network power supply restoration optimization method considering microgrids and voltage regulation, characterized in that, include: Collect data from microgrids and voltage regulation equipment, and perform data preprocessing; Establish an active power distribution network power restoration model that considers microgrids and voltage regulation equipment; The active power distribution network power restoration model considering microgrids and voltage regulation equipment is modeled using the mathematical modeling language AMPL, and solved using the CPLEX solver, with the results output.

2. The distribution network power supply restoration optimization method considering microgrids and voltage regulation as described in claim 1, characterized in that: The data collected from the microgrid and voltage regulation equipment includes active distribution network line parameters, distributed power source parameters, group switching capacitor parameters, on-load tap-changing transformer parameters, voltage regulator parameters, and cost coefficients. The data preprocessing involves denoising the collected state data, removing missing values, outliers, and invalid data with incorrect formats, converting the original format data into the format required for requirements analysis, and normalizing the data to complete the data preprocessing.

3. The distribution network power supply restoration optimization method considering microgrids and voltage regulation as described in claim 2, characterized in that: The establishment of the active distribution network power restoration model considering microgrids and voltage regulation equipment is based on minimizing the total restoration cost to establish the objective function for active distribution network power restoration, expressed as: minimizeψ in, The cost for node i where load reduction is not satisfied. The active power demand at node i under its rated voltage. S represents the segment containing node i. i Is it powered on? When powered on otherwise Cost of operating the branch circuit ij switch. This indicates that the operation of the ij branch switch has changed. These represent the costs of changing the active / reactive output of the DG at node i, respectively. These represent the changes in active / reactive power scheduling of the DG at node i. Let be the cost of the CB action at node i. The operation of CB at branch ij is changed. To change the cost of running OLTC at node i, To change the operation of OLTC at branch ij, To change the cost of VR operation at branch ij, To change the operation of VR at branch ij, The cost of forming a microgrid, Let be the number of DGs with black-start capability running at node i, when At that time, at least one distributed generation (DG) with black-start capability is operating in the microgrid containing node i. For the set of demand nodes, For the set of real nodes, This is a set of actual substation nodes. Let be the set of all real branches in the system that have a switch. For the set of actual branches that had their switches turned on before the fault, This is the set of actual branches that were opened and closed before the fault occurred. It is a set of nodes containing distributed power sources. For the set of CBs nodes, This refers to the set of on-load tap-changing transformer nodes in a substation. A collection of branches with voltage regulators. This is a set of DGs nodes with black-start capability.

4. The distribution network power supply restoration optimization method considering microgrids and voltage regulation as described in claim 3, characterized in that: The objective function for power restoration of the active distribution network includes linearized power flow constraints, load model constraints, DGs model constraints, CBs model constraints, VRs and OLTCs model constraints, network asset operation constraints, and radial microgrid formation constraints.

5. The distribution network power supply restoration optimization method considering microgrids and voltage regulation as described in claim 4, characterized in that: The linearized power flow constraint is expressed as follows: Among them, P ij Q ij R represents the active / reactive power of branch ij. ij ,X ij Z ij Let be the resistance / reactance / impedance value of branch ij. Let be the square of the current in branch ij. For the active / reactive power input of the substation at node i. Let i be the active / reactive power of the DG. Let i be the active / reactive power demand. The total reactive power injection of the CB installed at node i. Let be the voltage magnitude and its square at node i. Let ξ be the square of the voltage magnitude at node j. ij M is the slack variable used to calculate the branch voltage drop based on the operating conditions of the branch switch. ij To calculate the Big-M parameters for the voltage drop in branch ii, the slack variable ξ is used. ij It is based on The value of θ is calculated. j To estimate the voltage magnitude at node i from the operation before obstacle removal, m ij,l The probability of segmenting the linearized segment l of branch ij is given by... Let l be the discrete variable related to the active / reactive power range of branch ij. The non-negative variable representing the active power of branch ij is... Let represent the non-negative variable of reactive power on branch ij, and Λ be the number of segments in the piecewise linearization. For the length of each segment, the power on branch ij is linearized to the square of the power. For the set of real branches, ξ represents the discrete variables related to the active / reactive power flow section l of branch ij, the relaxation variables used when calculating the branch voltage drop, and the branch without switches. ij =0, the slack variable ξ ij It is based on The value is calculated, and for the branch without VR, the expression is: in, V These are the maximum and minimum voltage amplitudes of the node, respectively; For branches with VR, the expression is: in, Indicates whether the branch circuit ii switch is open; when open... otherwise For branches without switches, The length of each segment in the power flow square linearization is expressed as: in, Let be the current capacity of branch ij; The slope expression for each segment is: The load model constraint is expressed as follows: in, These are the participation factors of the constant impedance / constant current / constant power load on the active power demand of node i, respectively. for.. V represents the reactive power demand at node i under its rated voltage. N Let i be the rated voltage amplitude. These are the Big-M parameters for calculating active and reactive loads at node i, respectively. These are the participation factors of constant impedance / constant current / constant power loads at node i on reactive power demand. For segment S containing node i i The energized state, if energized otherwise The constraints of the distributed power generation model are expressed as follows: in, These represent the active and reactive power of the DG at node i, respectively. Let be the apparent power capacity of the DG at node i. Let be the limit value of the capacitance / inductance power factor of DG at node i. The active / reactive power generated by the DG on node i before the fault occurred. These represent the changes in active / reactive power scheduling of the DG at node i. Limit the rate of ascent of DG at node i. For the switch state of branch ij, when it is open otherwise For branches without switches, Artificial flows on branch ij were used in the constraints related to microgrid formation. To generate electricity artificially at node i, which is used in the constraints associated with microgrid formation, Φ i Indicates whether node i has a DG; if so, then Φ i =1, otherwise Φ i =0, The reduction factor is the generation capacity of the DG (Distributed Generator) with black-start capability installed at microgrid node i. Let be the number of DGs with black-start capability running at node i, when At that time, at least one DG with black-start capability is operating in the microgrid containing node i; The constraints of the CBs model are expressed as follows: in, The total reactive power injection of the CB installed at node i. Let i be the number of CB modules at node i. The reactive power injected into CB module k at node i. Let be the susceptance of the CB module at node i. Let CB module k be the running state at node i. If the modules are connected... otherwise This represents the number of CB modules in the state before the fault at node i.

6. The distribution network power supply restoration optimization method considering microgrids and voltage regulation as described in claim 5, characterized in that: The constraints of the VRs and OLTCs models are expressed as follows: Among them, a ij For the tap of VR on branch ij, For auxiliary variables used in the VRs model, the tap is... in, Let VR be the adjustment amount for branch ij. The maximum adjustment of VR on branch ij is For the branch ij with VR before the fault The value; The OLTC operating constraints are: in, The maximum adjustment value of OLTC at node i. The voltage before a fault in a substation with an OLTC at node i; The network asset operation constraints are expressed as follows: in, Let be the current capacity of branch ij. Let i be the apparent power capacity of the substation at node i. The radial microgrid forms a constraint, expressed as follows: in, It is the set of all segments in the system; If a power outage occurs in a certain section, the current node must be connected to an artificial substation. The constraint expression is as follows: in, This refers to the artificial flow on branch ij used to ensure that the power outage node is disconnected from the main power grid. To generate electricity at node i using an artificial substation that ensures the disconnection of the power outage node from the main grid, Ω B The set of all branches in the system. Ω is a set of artificial branches with switches. N This refers to the set of all nodes in the system, including artificial substation nodes. It is a set that contains only artificial substation nodes; If a certain pain point is identified, the current node will be connected to a real or artificial substation. The energized portion can only be connected to an artificial substation. The constraint expression is as follows: in, The branch ij is used to ensure the radial flow of artificial resources between the main network and each micro-network. To ensure the artificial power generation of the main power grid and each microgrid in a radial pattern at the substation at node i.

7. The distribution network power supply restoration optimization method considering microgrids and voltage regulation as described in claim 6, characterized in that: When nodes perform connection operations, to avoid unnecessary switching actions, the constraint expression is: in A set of faulty sections. For the segment containing node i, z ij For use in product Linearized auxiliary variables; The active power distribution network power restoration model considering microgrids and voltage regulation equipment is modeled using the mathematical modeling language AMPL, and solved using the CPLEX solver, with the results output.

8. A system employing the distribution network power supply restoration optimization method considering microgrids and voltage regulation as described in any one of claims 1 to 7, characterized in that: It includes a data collection and preprocessing module, a power restoration model establishment module, a model constraint setting module, a mathematical modeling and solving module, and a result output module; The data collection and preprocessing module collects data from microgrids and voltage regulation equipment, including active distribution network line parameters, distributed power source parameters, group switching capacitor parameters, on-load tap-changing transformer parameters, voltage regulator parameters, and cost coefficients; further, it denoises the data, removes missing values, outliers, and data with incorrect formats, converts the raw data into the format required for analysis, and performs data normalization. The power restoration model building module establishes a power restoration model for the active distribution network based on the collected data. The model constraint setting module is used to set various model constraints, including linearized power flow constraints, load model constraints, DGs model constraints, CBs model constraints, VRs and OLTCs model constraints, network asset operation constraints, and radial microgrid formation constraints. The mathematical modeling and solving module uses the mathematical modeling language AMPL to implement the above power restoration model and uses the CPLEX solver to solve it. The result output module outputs the optimized results obtained by the CPLEX solver to guide the actual power supply restoration operation of the distribution network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power supply restoration optimization method for distribution networks that takes into account microgrids and voltage regulation as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power supply restoration optimization method for distribution networks that takes into account microgrids and voltage regulation as described in any one of claims 1 to 7.

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