Power distribution network optimal scheduling recovery method considering in-ring distributed resources

Abstract

The invention discloses a power distribution network optimal scheduling recovery method considering in-ring distributed resources, and belongs to the technical field of power system operation control. The method comprises the following steps: constructing a power distribution network internal flexible resource model based on output prediction, physical parameters and operation constraints of power distribution network internal flexible resources; according to the power grid operation state data, the system state parameters and the power-losing load parameters, constructing an optimal recovery model after the power distribution network fault; constructing a model constraint condition according to the working parameter and the performance parameter of each node in the nodes and the power parameter in the distributed photovoltaic system; and according to the power distribution network internal flexible resource model, the power distribution network post-fault optimal recovery model, the model constraint condition and the optimization algorithm, generating a power supply recovery scheme. The power supply recovery scheme generated by the invention can provide optimal recovery for the power distribution network.

Description

Technical Field

[0001] This invention relates to an optimal scheduling and restoration method for distribution networks that takes into account distributed resources within the ring, belonging to the technical field of power system operation control. Background Technology

[0002] With the accelerated green transformation of the energy structure, the distribution network, as the core hub of the energy internet, has seen a continuous increase in the penetration rate of distributed renewable energy. Flexible interconnection technology has been promoted and applied in the distribution network, which has changed the operation mode of the distribution network: the strong randomness and volatility of new energy power generation spread to the whole network through the flexible interconnection network, resulting in complex and variable power flow distribution.

[0003] Under the condition of flexible interconnection of ring networks, the problem of three-phase power imbalance is further amplified, and recovery methods are lacking, making it difficult for traditional control methods to achieve optimal recovery. At the same time, existing solutions lack the ability to coordinate and optimize flexible resources, cannot fully utilize the role of energy storage in smoothing fluctuations, and are difficult to achieve precise control of flexible loads. This results in a lag in system response speed, low power supply reliability, and optimization effects that are limited to a single objective, failing to provide an optimal recovery solution. Summary of the Invention

[0004] This invention provides an optimal scheduling and recovery method for distribution networks that takes into account distributed resources within the ring. It generates a power supply recovery scheme by constructing a flexible resource model within the distribution network, an optimal recovery model after a distribution network fault, and model constraints, and then using the Blackwing Kite optimization algorithm.

[0005] The technical solution of this invention is:

[0006] A method for optimal dispatch and restoration of a distribution network considering distributed resources within a ring includes: constructing a flexible resource model within the distribution network based on output prediction, physical parameters, and operational constraints; constructing an optimal restoration model after a distribution network fault based on grid operating status data, system status parameters, and power outage load parameters; constructing model constraints based on the operating parameters, performance parameters, and power parameters of each node in each node and distributed photovoltaic power; and generating a power restoration scheme based on the flexible resource model within the distribution network, the optimal restoration model after a distribution network fault, the model constraints, and the optimization algorithm.

[0007] Furthermore, the output prediction of the flexible resources within the distribution network includes the predicted active power output of the j-th phase and the i-th photovoltaic distributed power source within the distribution network; the physical parameters include voltage deviation, actual voltage of each node in each node, reference voltage of each node in each node, upper voltage limit of each node in each node, lower voltage limit of each node in each node, and basic data; the operational constraints include wind power node network loss, power of non-faulty nodes, and maximum current of branches within the distribution network; the flexible resource model within the distribution network includes at least one or more of the following: photovoltaic output function, wind power loss function, node voltage offset function, ring network internal network loss function, power transfer function, and rated current function.

[0008] Furthermore, the construction of the flexible resource model within the distribution network based on the output prediction, physical parameters, and operational constraints of the flexible resources within the distribution network includes: constructing a photovoltaic output function based on the predicted active power output of the j-th phase and the i-th distributed photovoltaic power source within the distribution network; and / or, constructing a wind power loss function based on wind power node network losses and voltage deviations; and / or, constructing a node voltage offset function based on the actual voltage, reference voltage, upper voltage limit, and lower voltage limit of each node; and / or, constructing a network loss function within the ring network based on basic data; and / or, constructing a power transfer function based on the power of non-faulty nodes; and / or, constructing a rated current function based on the maximum current of the branches within the distribution network.

[0009] Furthermore, the optimal recovery model after a distribution network fault includes at least one or more of the following: a line network loss function, a power outage time function, and a power outage load function; the step of constructing the optimal recovery model after a distribution network fault based on power grid operating status data, system state parameters, and power outage load parameters includes: constructing a line network loss function based on power grid operating status data; constructing a power outage time function based on system state parameters; and constructing a power outage load function based on power outage load parameters.

[0010] Furthermore, the step of constructing model constraints based on the operating parameters and performance parameters of each node in each node and the power parameters in the distributed photovoltaic system includes: constructing line transmission constraints based on the operating parameters of each node in each node; and / or, constructing output regulation constraints for the distributed photovoltaic system based on the power parameters in the distributed photovoltaic system; and / or, energy storage power constraints; and / or, constructing energy storage power flow constraints based on the performance parameters of each node in each node; and / or, constructing constraints that ensure the reconstructed distribution network satisfies the radial topology structure.

[0011] Furthermore, the line transmission constraints include a first line transmission constraint, a second line transmission constraint, and a third line transmission constraint; the step of constructing the line transmission constraints based on the operating parameters of each node includes: constructing the first line transmission constraint based on the electrical parameters of each node; constructing the second line transmission constraint based on the voltage parameters of each node; and constructing the third line transmission constraint based on the operating parameters of each node.

[0012] Furthermore, the step of generating a power supply restoration scheme based on the flexible resource model within the distribution network, the optimal recovery model after a distribution network fault, model constraints, and optimization algorithms includes: determining a comprehensive optimization objective function value based on the wind power loss function, node voltage offset function, ring network internal loss function, transfer power function, line network loss function, power outage time function, and power outage load function; and generating a power supply restoration scheme based on the optimization objective function value and the Black Kite optimization algorithm.

[0013] Furthermore, the expression for optimizing the objective function value is: ;in, To comprehensively optimize the objective function value, This refers to the wind power loss function, node voltage offset function, internal ring network loss function, transferred power function, line network loss function, power outage time function, or power outage load function. These are the weight values ​​corresponding to the wind power loss function, node voltage offset function, ring network internal network loss function, power transfer function, line network loss function, power outage time function, and power outage load function, respectively.

[0014] The beneficial effects of this invention are as follows: This invention considers the flexible resource model within the distribution network, the optimal recovery model after a distribution network fault, and the model constraints. It also combines the Blackwing Kite optimization algorithm to generate a multi-objective optimal power supply recovery scheme, thereby ensuring the maximum utilization of distributed resources during a distribution network fault and improving the switching action logic when the distribution network fault is cleared. This enables precise control of flexible loads, effectively smooths voltage fluctuations at various nodes within the distribution network, reduces the three-phase power imbalance problem in the ring network, improves the power quality within the distribution network, and provides optimal recovery for the distribution network. Attached Figure Description

[0015] Figure 1 The diagram shows a flowchart of an optimal scheduling and recovery method for a distribution network that takes into account distributed resources within a ring, according to an embodiment of the present invention.

[0016] Figure 2 The diagram shows a structural schematic of network loss before and after optimization according to an embodiment of the present invention.

[0017] Figure 3The diagram illustrates the voltage of each node in a power distribution network before and after optimization, according to an embodiment of the present invention.

[0018] Figure 4 The diagram shows a schematic of the structure of an optimal dispatch and recovery device for a distribution network that takes into account distributed resources within the ring, according to an embodiment of the present invention.

[0019] Figure 5 This is a schematic diagram of the structure of a device provided in an embodiment of the present invention.

[0020] Figure 6 The diagram shows a structural schematic of a computer-readable storage medium provided in an embodiment of the present invention.

[0021] Figure 7 The diagram illustrates an IEEE 33-node system with distributed resources provided in an embodiment of the present invention.

[0022] Figure 8 A photovoltaic power output prediction diagram provided by an embodiment of the present invention is shown.

[0023] Figure 9 This diagram illustrates a partial node load prediction map provided by an embodiment of the present invention.

[0024] Figure 10 The diagram shows an optimized structure of the IEEE 33-node distribution network system provided by an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0026] Example 1: As Figures 1-10 As shown, this invention provides a method, apparatus, device, and storage medium for optimal dispatch and restoration of distribution networks considering distributed resources within the ring. The method includes: constructing a flexible resource model within the distribution network based on output prediction, physical parameters, and operational constraints of the flexible resources within the distribution network; constructing an optimal restoration model after a distribution network fault based on grid operation status data, system status parameters, and power outage load parameters; constructing model constraints based on the operating parameters, performance parameters, and power parameters of each node in each node and distributed photovoltaic power; and generating a power restoration scheme based on the flexible resource model within the distribution network, the optimal restoration model after a distribution network fault, the model constraints, and the Black-winged Kite optimization algorithm.

[0027] Therefore, by constructing a flexible resource model within the distribution network, an optimal recovery model after a distribution network fault, and model constraints, and then combining this with the Blackwing Kite optimization algorithm, a power supply recovery scheme is generated.

[0028] Please see Figure 1 , Figure 1 This illustration shows a flowchart of an optimal scheduling and recovery method for a distribution network considering distributed resources within a ring, provided by an embodiment of the present invention. Figure 1 As shown, the optimal dispatch and recovery method for distribution networks that takes into account distributed resources within the ring may include steps 110 to 140.

[0029] In step 110, a flexible resource model for the distribution network is constructed based on the output prediction, physical parameters, and operational constraints of the flexible resources within the distribution network.

[0030] In some implementations, the output prediction, physical parameters, and operational constraints of the flexible resources within the distribution network may include the predicted active power output of the j-th phase and the i-th photovoltaic distributed generation within the distribution network, wind power node losses, voltage deviation, the actual voltage of each node in each node, the reference voltage of each node in each node, the upper voltage limit of each node in each node, the lower voltage limit of each node in each node, basic data, the power of non-faulty nodes, and the maximum current of branches within the distribution network. The output prediction corresponds to the predicted active power output of the j-th phase and the i-th photovoltaic distributed generation within the distribution network. The physical parameters include voltage deviation, the actual voltage of each node in each node, the reference voltage of each node in each node, the upper voltage limit of each node in each node, the lower voltage limit of each node in each node, and basic data. The operational constraints correspond to: wind power node losses, the power of non-faulty nodes, and the maximum current of branches within the distribution network.

[0031] In some implementations, the flexible resource model within the distribution network includes at least one or more of the following: photovoltaic output function, wind power loss function, node voltage offset function, ring network internal loss function, power transfer function, and rated current function.

[0032] Specifically, in some implementations, the step "constructing a flexible resource model for the distribution network based on the output prediction, physical parameters, and operational constraints of the flexible resources within the distribution network" may include the following steps:

[0033] (1) Construct a photovoltaic output function based on the predicted active power output of the j-th phase and the i-th photovoltaic distributed power source within the distribution network;

[0034] (2) and / or, construct the wind power loss function based on the wind power node network loss and voltage deviation;

[0035] (3) and / or, construct a node voltage offset function based on the actual voltage, reference voltage, upper voltage limit and lower voltage limit of each node in each node;

[0036] (4) and / or, based on the basic data, construct the internal network loss function of the ring network;

[0037] (5) and / or, construct a power transfer function based on the power of the non-faulty node;

[0038] (6) and / or, construct the rated current function based on the maximum current of the internal branches of the distribution network.

[0039] In order to fully leverage the advantages of distributed photovoltaic energy, a photovoltaic output function is constructed based on the predicted active power output of the j-th phase and the i-th distributed photovoltaic power source within the distribution network. This requires ensuring that the output of each distributed photovoltaic power source within the distribution network follows the predicted active power output.

[0040] In one specific implementation, the photovoltaic output function can be expressed as:

[0041] .

[0042] in," "For the j-th phase and the j-th phase within the distribution network" The reduction in active power of each photovoltaic distributed power source; "This represents the predicted active power output of the j-th phase and the i-th distributed photovoltaic power source within the distribution network." "This represents the actual active power output of the j-th phase and the i-th photovoltaic distributed power source within the distribution network."

[0043] Based on this, the minimum wind power loss is determined by the wind power loss function when the wind power flexibility resources within the distribution network fluctuate, so as to comprehensively consider the voltage fluctuations and system losses brought to the wind farm by the fluctuation of wind turbine output, and ensure that the wind power loss is minimized when the wind power flexibility resources within the distribution network fluctuate.

[0044] In one specific implementation, the wind power loss function can be expressed as:

[0045] .

[0046] in," " is the wind power loss function, " "This is the demeanor loss coefficient," "During the wind turbine's power output period," "for The network loss of the wind power node corresponding to the i-th wind turbine at time " "Total network loss during the period when the distribution network's wind turbines are operating," "is the voltage fluctuation coefficient, " "This refers to the number of fans within the power distribution network." "for The voltage deviation of the wind power node corresponding to the i-th wind turbine at time i.

[0047] Meanwhile, since connecting distributed photovoltaic resources to the grid may cause voltage spikes or even exceed limits at high nodes, a node voltage offset function is used to ensure that the voltage offset of each node remains within a safe margin.

[0048] In one specific implementation, the node voltage offset function can be expressed as:

[0049] .

[0050] in," " is the node voltage offset function, " "This represents the total number of nodes in the distribution network." " represents the actual voltage at node i," " is the reference voltage for node i, " "This represents the upper limit of the operating voltage of node i." "This is the lower limit of the operating voltage of node i.

[0051] Furthermore, the minimum internal network loss is determined through the internal network loss function of the ring network. In a specific implementation, the basic data may include the total number of nodes in the distribution network, the power flowing into each node, the number of photovoltaic distributed power sources, the upper limit of the power flowing into the photovoltaic distributed power sources, the power fluctuation of the photovoltaic distributed power sources, the number of load nodes, the active power of the load nodes, the back-to-back transfer power of each phase, the number of energy storage devices, and the power flowing into the energy storage devices.

[0052] In a specific implementation, the network loss function inside the ring network can be expressed as:

[0053] .

[0054] in," "This is the network loss function within the ring network." "This represents the total number of nodes in the distribution network." "This represents the inflow power of the j-th phase and the i-th node." "The number of distributed photovoltaic power sources," "This represents the upper limit of the incoming power of the j-th phase and the i-th distributed photovoltaic power source." "This represents the power fluctuation of the j-th phase and the i-th distributed photovoltaic power source." The number of load nodes, "This represents the active power of the j-th phase and the i-th load node." "This represents the back-to-back transfer power of the j-th phase." "The number of energy storage devices," " is the inflow power of the j-th phase and the i-th energy storage device; j=1,2,3 correspond to phase A, phase B, and phase C respectively.

[0055] Furthermore, the maximum transfer power at non-faulty nodes during a system fault can be determined using the transfer power function. In a specific implementation, the transfer power function can be expressed as:

[0056] .

[0057] in," " is the power transfer function, "This refers to the number of non-faulty nodes within the distribution network." " represents the power of the i-th non-faulty node.

[0058] In one specific implementation, the rated current function can be expressed as:

[0059] .

[0060] in," "This refers to the current flowing between nodes i and j in the distribution network." "This is the maximum current in the branch circuit within the distribution network."

[0061] Therefore, in this invention, by establishing a flexible resource model within the distribution network, specifically by establishing multiple types of optimal flexible resource models within the distribution network (e.g., photovoltaic output function, wind power loss function, node voltage offset function, ring network internal network loss function, transfer power function, and / or rated current function), the photovoltaic power generation can be fully absorbed when the penetration rate of photovoltaic distributed power generation is low, and when the penetration rate of photovoltaic distributed power generation is high, the node voltage and branch heat power do not exceed the limits.

[0062] In step 120, an optimal recovery model for the distribution network after a fault is constructed based on the power grid operating status data, system status parameters, and power outage load parameters.

[0063] In some implementations, grid operating status data may include branch resistance between different nodes, active power of nodes, reactive power of nodes, and voltage of nodes.

[0064] In some implementations, system status parameters may include the annual average power outage time and average power of the nodes.

[0065] In some implementations, the power loss load parameters may include the power loss load power of the node.

[0066] In some implementations, the optimal recovery model after a distribution network fault includes at least one or more of the following: a line network loss function, a power outage time function, and a power outage load function.

[0067] Specifically, in some implementations, the step "constructing the optimal recovery model for distribution network after a fault based on power grid operating status data, system status parameters, and power outage load parameters" may include the following steps:

[0068] (1) Construct a line network loss function based on the power grid operation status data;

[0069] (2) Construct a power outage time count function based on the system state parameters;

[0070] (3) Construct the power loss load function based on the power loss load parameters.

[0071] In one specific implementation, the expression for the line network loss function can be:

[0072] .

[0073] in," "This is the network loss function for the line;" "This represents the total number of nodes in the distribution network." "This represents the branch resistance between node i and node j." "This represents the active power of node i." "This represents the reactive power of node i." " represents the actual voltage of node i.

[0074] In one specific implementation, the power outage time count function can be expressed as:

[0075] .

[0076] in," "This is a function representing the time of power loss;" "where i is the average annual power outage time." " represents the average power of node i.

[0077] In one specific implementation, the power outage load function can be expressed as:

[0078] .

[0079] in," "This is the annual power outage load function;" "This refers to the number of fault nodes within the distribution network." " represents the power loss load of the j-th phase and the u-th fault node.

[0080] Therefore, in this invention, by constructing the line network loss function, the power outage time function, and the power outage load function, the optimal recovery model after a distribution network fault is constructed. After a system fault occurs, a more reasonable power supply recovery scheme is generated by optimizing the optimal recovery model after a distribution network fault.

[0081] In step 130, model constraints are constructed based on the operating parameters and performance parameters of each node in each node and the power parameters in the distributed photovoltaic system.

[0082] In some implementations, the operating parameters of each node may include the electrical parameters of each node, the voltage parameters of each node, and the operational parameters of each node.

[0083] In some implementations, model constraints may include line transmission constraints, output regulation constraints of distributed photovoltaics, energy storage power constraints, energy storage power flow constraints, and constraints that the reconfigured distribution network satisfies the radial topology.

[0084] Specifically, in some implementations, the step of "constructing model constraints based on the operating parameters, performance parameters of each node in each node, and power parameters in distributed photovoltaics" may include the following steps:

[0085] (1) Construct line transmission constraints based on the working parameters of each node in each node;

[0086] (2) and / or, based on the power parameters in the distributed photovoltaic system, construct the output regulation constraints of the distributed photovoltaic system, and / or, the energy storage power constraints;

[0087] (3) and / or, construct energy storage power flow output constraints based on the performance parameters of each node in each node;

[0088] (4) and / or, construct a reconstructed distribution network that satisfies the radial topology constraint conditions.

[0089] In some implementations, line transmission constraints may include a first line transmission constraint, a second line transmission constraint, and a third line transmission constraint.

[0090] In some implementations, the power parameters in distributed photovoltaic systems include a first power parameter and a second power parameter.

[0091] Specifically, in some implementations, the step "constructing line transmission constraints based on the operating parameters of each node in each node" may include the following steps:

[0092] (1) Construct the first line transmission constraint conditions based on the electrical parameters of each node in each node;

[0093] (2) Based on the voltage parameters of each node, construct the second line transmission constraint conditions;

[0094] (3) Construct the third line transmission constraints based on the operating parameters of each node in each node.

[0095] In some implementations, the electrical parameters of each node may include the actual active power output of the photovoltaic system connected to each node, the active power of the load at each node, the actual voltage of each node, the conductance of the branch between different nodes, the impedance angle of the branch between different nodes, the susceptance of the branch between different nodes, the actual reactive power output of the photovoltaic system connected to each node, and the reactive power of the load at each node.

[0096] In one specific implementation, the first line transmission constraint can be expressed as:

[0097] .

[0098] in," "This represents the actual active power output of the photovoltaic system connected at node i." "This represents the active power of the load at node i." " represents the actual voltage at node i," "This represents the total number of nodes in the distribution network." " represents the actual voltage at node j (" "and" (These are power grid operating parameters, which can be obtained through voltage measurement.) "This represents the conductance of the branch between node i and node j." "This represents the impedance angle of the branch between node i and node j." "This represents the susceptance value of the branch between node i and node j." "This represents the actual reactive power output of the photovoltaic system connected at node i." " represents the reactive power of the load at node i.

[0099] In some implementations, the voltage parameters of each node may include the apparent power of the branches between different nodes, the active power of the branches between different nodes, the reactive power of the branches between different nodes, and the upper limit of the apparent power of the branches between different nodes.

[0100] In one specific implementation, the second line transmission constraint can be expressed as:

[0101] .

[0102] in," "This represents the apparent power of the branch between node i and node j." "This represents the active power of the branch between node i and node j." "This represents the reactive power of the branch between node i and node j." "This represents the upper limit of the apparent power of the branch between node i and node j." " represents the actual voltage at node i," "This represents the conductance of the branch between node i and node j." " represents the actual voltage at node j," " is the voltage phase angle at node j, " "This represents the upper limit of active power in the branch between node i and node j." " represents the susceptance value of the branch between node i and node j.

[0103] In some implementations, the operating parameters of each node may include the minimum allowable voltage of each node, the voltage of each node, and the maximum allowable voltage of each node.

[0104] In one specific implementation, the third-line transmission constraint can be expressed as:

[0105] .

[0106] in," " represents the actual voltage at node i," "This is the lower limit of the operating voltage for node i." " represents the upper limit of the operating voltage of node i.

[0107] Furthermore, in some embodiments, the first power parameter in distributed photovoltaic systems may include the active power of the photovoltaic system connected to each node, the maximum active power of the photovoltaic system connected to each node, and the reactive power of the photovoltaic system connected to each node.

[0108] In one specific implementation, the expression for the output adjustment constraint can be:

[0109] .

[0110] .

[0111] in," "This represents the active power of the photovoltaic system connected at node i." "This represents the maximum active power of the photovoltaic system connected at node i." "This represents the reactive power of the photovoltaic system connected at node i."

[0112] Furthermore, in some implementations, the second power parameter in distributed photovoltaic systems may include... , , , as well as .

[0113] In one specific implementation, the expression for the energy storage power constraint can be:

[0114] .

[0115] in," "This represents the active power of the energy storage device connected at node i." "This represents the maximum active power of the energy storage device connected at node i." "This represents the minimum energy storage capacity of the energy storage device connected at node i." "The energy stored in the energy storage device connected at node i" " represents the maximum energy storage capacity of the energy storage device connected at node i.

[0116] Furthermore, in some implementations, the performance parameters of each node in each node may include... , Actual voltage at different nodes, conductance between different nodes, and branch impedance angle between different nodes. as well as .

[0117] In one specific implementation, the expression for the energy storage power flow output constraint can be:

[0118] .

[0119] in," "This represents the reactive power of the energy storage device connected at node i." "Let i be the reactive power of the load at node i." " represents the actual voltage at node i," " represents the actual voltage at node j," "This represents the conductance of the branch between node i and node j." "This represents the impedance angle of the branch between node i and node j." "This represents the active power of the energy storage device connected at node i." " represents the active power of the load at node i.

[0120] Finally, in a specific implementation, the expression for the reconfigured distribution network satisfying the radial topology constraint can be:

[0121] .

[0122] in," "This is the reconstructed distribution network topology." " is a feasible radial topology set.

[0123] Therefore, by constructing the first line transmission constraint condition, the second line transmission constraint condition, the third line transmission constraint condition, the output regulation constraint condition of distributed photovoltaic, the energy storage power constraint condition, the energy storage power flow output constraint condition, and the constraint condition that the reconstructed distribution network meets the radial topology constraint condition, the power supply restoration scheme generated by the system can be constrained, thereby generating a more suitable power supply restoration scheme.

[0124] In step 140, a power supply restoration scheme is generated using the Blackwing Kite optimization algorithm based on the flexible resource model within the distribution network, the optimal recovery model after a distribution network fault, and the model constraints.

[0125] Specifically, the step "generating a power supply restoration scheme based on the flexible resource model within the distribution network, the optimal recovery model after a distribution network fault, model constraints, and the Black-winged Kite optimization algorithm" may include the following steps:

[0126] (1) Determine the comprehensive optimization objective function value based on the wind power loss function, node voltage offset function, ring network internal network loss function, transfer power function, line network loss function, power outage time function and power outage load function;

[0127] (2) Based on the optimization objective function value and the Black-winged Kite optimization algorithm, a power supply restoration scheme is generated.

[0128] In one specific implementation, the expression for optimizing the objective function value can be:

[0129] .

[0130] in," "To comprehensively optimize the objective function value," "This refers to the wind power loss function, node voltage offset function, ring network internal loss function, power transfer function, line network loss function, power outage time function, or power outage load function." "These are the weight values ​​corresponding to the wind power loss function, node voltage offset function, ring network internal network loss function, transfer power function, line network loss function, power outage time function, and power outage load function, respectively.

[0131] First, a population of black-winged kites is randomly initialized, and its population matrix is... It can be represented as:

[0132] .

[0133] in," "Population size," "as the dimension of decision variables, Indicates the first The first individual dimension.

[0134] Furthermore, the initialization of individuals in the population matrix can be represented as follows:

[0135] .

[0136] in," "For individual Black-winged Kite" Location, " is a random function, " is the upper bound of the decision variable, " " is the lower bound of the decision variable.

[0137] In some implementations, the individual positions of the black-winged kite population are updated iteratively based on the first mathematical model and the second mathematical model.

[0138] The first mathematical model can be represented as:

[0139] .

[0140] in," "The individual black-winged kites obtained from the first mathematical model" Location, "For the t-th iteration, the individual Black-winged Kite" Location, " is a two-dimensional random variable whose value is either 0 or 1. Let i be the initial iteration position of individual black-winged kite i. " is the search radius of the first mathematical model

[0141] The second mathematical model can be expressed as:

[0142] .

[0143] in," " is the solution to the second mathematical model, " "For the t-th iteration, the individual Black-winged Kite" The initial position, " is a one-dimensional Cauchy variogram, "For the leader individual of the Black-winged Kite in the t-th iteration" Location, " is the tth iteration The fitness of each individual, "For the leader individual in iteration t..." Corresponding fitness, " is a position constant.

[0144] Based on this, the distribution balance and representativeness of the optimized solutions are used to build a high-quality solution space foundation for subsequent searches. Specifically, a threshold is first set, and feasible alternative solutions are generated based on the threshold. Then, a dynamic parameter c that changes with the number of iterations is used to ensure the balance of the algorithm during the iteration process. The individual positions of the new black-winged kites are calculated based on the value of c, and the fitness is used to determine whether to end the loop. If the loop ends, the optimal value is output, that is, the power restoration scheme is generated.

[0145] In one specific implementation, the expression for setting the threshold can be:

[0146] .

[0147] in," " is the threshold function, "t" is the number of iterations, " " represents the total number of iterations.

[0148] In one specific implementation, generating feasible alternative solutions based on a threshold can be expressed as:

[0149] .

[0150] in," "For individual Black-winged Kite" The threshold, " is the upper bound of the decision variable, " " is the lower bound of the decision variable, " "For individual Black-winged Kite" The initial value of the threshold, " is a two-dimensional logical random function, " is the threshold for the t-th iteration.

[0151] In one specific implementation, using the dynamic parameter c that varies with the number of iterations, it can be expressed as:

[0152] .

[0153] in," " is a dynamic parameter, "t" is the number of iterations, and "T" is the total iteration time. " represents the weight coefficient of the dynamic parameter in the t-th iteration.

[0154] In an embodiment of the present invention, the iteration count is 40. That is, the 40th iteration is used as the termination condition.

[0155] Furthermore, the weighting coefficients can be determined using the following formula:

[0156] .

[0157] in," " represents the weight coefficient of the dynamic parameters in the t-th iteration, where "t" is the iteration number.

[0158] Please see Figure 2 and Figure 3 , Figure 2 This invention provides a schematic diagram of network loss before and after optimization, according to an embodiment of the present invention. Figure 3 This diagram illustrates the voltage levels at various nodes of a distribution network before and after optimization, according to an embodiment of the present invention. Figure 2 and Figure 3 The power restoration scheme generated by the optimal dispatch and restoration method for distribution networks that takes into account distributed resources within the ring, as provided by this invention, greatly reduces network losses and improves power quality.

[0159] It is worth noting that the implementing entities of the optimal dispatch and restoration method for distribution networks that takes into account distributed resources within the ring include, but are not limited to, photovoltaic, wind power, and energy storage devices within the network. In other words, this optimal dispatch and restoration method for distribution networks that takes into account distributed resources within the ring can be executed by software or hardware installed on terminal equipment or server-side equipment. The software can be a blockchain platform. The server-side includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0160] Please see Figure 4 , Figure 4 This diagram illustrates the structure of a distribution network optimal scheduling and restoration device considering in-ring distributed resources according to an embodiment of the present invention. The device 200 includes: a first model building module 210, a second model building module 220, a third model building module 230, and a scheme generation module 240. Specifically:

[0161] The first model construction module 210 is used to construct a flexible resource model of the distribution network based on the output prediction, physical parameters and operating constraints of the flexible resources within the distribution network.

[0162] The second model construction module 220 is used to construct the optimal recovery model after a distribution network fault based on the power grid operation status data, system status parameters, and power outage load parameters.

[0163] The third model construction module 230 is used to construct model constraints based on the working parameters, performance parameters of each node in each node and the power parameters in distributed photovoltaics.

[0164] The scheme generation module 240 is used to generate power supply restoration schemes based on the flexible resource model inside the distribution network, the optimal recovery model after the distribution network fault, the model constraints, and the Blackwing Kite optimization algorithm.

[0165] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0166] In the several embodiments provided by the present invention, the coupling or direct coupling or communication connection between the modules shown or discussed may be an indirect coupling or communication connection through some interface, device or module, and may be electrical, mechanical or other forms.

[0167] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0168] Please see Figure 5 , Figure 5 The present invention provides a schematic diagram of the structure of a device. The device 300 in the present invention may include one or more of the following components: a processor 310, a memory 320, and one or more application programs. The one or more application programs may be stored in the memory 320 and configured to be executed by one or more processors 310. The one or more programs are configured to execute the optimal scheduling and recovery method for distribution networks taking into account distributed resources within the ring, as described in the foregoing method embodiments.

[0169] Processor 310 may include one or more processing cores. Processor 310 connects to various parts within device 300 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 320, and by calling data stored in memory 320. Optionally, processor 310 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 310 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 310 and may be implemented separately using a communication chip.

[0170] The memory 320 may include random access memory (RAM) or read-only memory (ROM). The memory 320 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 320 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the control device 300 during use.

[0171] The computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 400 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. This program code can be read from or written to one or more computer program devices. The program code 410 may be compressed, for example, in a suitable form.

[0172] Please see Figure 6 , Figure 6 The diagram illustrates the structure of a computer-readable storage medium 400 provided in an embodiment of the present invention. The computer-readable storage medium 400 stores program code, which can be called by a processor to execute the optimal scheduling and restoration method for distribution networks considering distributed resources within the ring, as described in the above method embodiment.

[0173] Please see Figure 7 , Figure 7 This invention provides a diagram of a distribution network system with distributed resources, specifically an IEEE 33-node distribution network, according to an embodiment of the invention. Figure 7 In the diagram, 1, 2, 3, ..., 33 are the node numbers within the distribution network. Circular nodes represent load nodes within the distribution network, square nodes represent substation nodes within the distribution network, dashed lines S33, S34, S35, S36, and S37 represent normally open tie switches in the distribution network, triangular nodes PV1, PV2, and PV3 represent distributed photovoltaic nodes within the distribution network, diamond nodes ESS1 and ESS2 represent distributed energy storage nodes within the distribution network, and solid lines represent normally closed circuits. Figure 7 The structure of the IEEE 33-node distribution network with distributed resources is illustrated using different symbols and lines, providing a topology reference for its operation, planning and other analyses.

[0174] Please see Figure 8 , Figure 8 The figure shows a distributed photovoltaic (PV) output curve prediction diagram provided by an embodiment of the present invention. The diagram uses time (09:00-14:00) as the horizontal axis and active power (MW) as the vertical axis, and displays the power change curves of PV1 (node ​​6), PV2 (node ​​13), PV3 (node ​​25) and total PV output. It is used to analyze the output characteristics of distributed PV and provide data support for the planning, scheduling and operation of distribution networks containing distributed PV.

[0175] Please see Figure 9 , Figure 9 The figure shows the node load prediction curve provided by the embodiment of the present invention. The figure shows the load change curves of some nodes under different scenarios such as residential, commercial and industrial, which are used to analyze the load situation of distribution network nodes and provide load data support for the planning and operation scheduling of distribution network.

[0176] Please see Figure 10 , Figure 10This diagram illustrates an optimized structure of an IEEE 33-node distribution network system provided by an embodiment of the present invention. In the diagram, 1, 2, 3, ..., 33 are the node numbers within the distribution network. Circular nodes represent load nodes within the distribution network, square nodes represent substation nodes within the distribution network, thin solid lines represent normally closed lines, thick solid lines S33 and S35 represent optimized closed tie switches, dashed lines S34, S36, and S37 represent normally open tie switches, solid triangular nodes PV1, PV2, and PV3 represent distributed photovoltaic nodes, dashed triangular node PV4 represents a newly added distributed photovoltaic node, solid rhombus nodes ESS1 and ESS2 represent distributed energy storage nodes, hollow rhombus node ESS3 represents a newly added distributed energy storage node, and fork-shaped nodes, such as node 29, represent flexible load nodes. Figure 10 Different symbols and lines are used to illustrate the optimized IEEE 33-node distribution network structure with distributed resources, providing a topology reference for the analysis of the optimized results.

[0177] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A power distribution network optimal dispatch restoration method considering in-loop distributed resources, characterized in that, The application relates to a power supply recovery method and device. The application comprises the following steps: Based on the output prediction, physical parameters and operation constraints of the internal flexible resources of the power distribution network, an internal flexible resource model of the power distribution network is constructed; According to the power grid operation state data, system state parameters and power loss load parameters, an optimal recovery model after the power distribution network fails is constructed; According to the working parameters, performance parameters and power parameters of each node in the nodes and the power of the distributed photovoltaic, a model constraint condition is constructed; 2. The optimal restoration method of distribution network considering in-loop distributed resources according to claim 1, wherein, According to the internal flexible resource model of the power distribution network, the optimal recovery model after the power distribution network fails, the model constraint condition and an optimization algorithm, a power supply recovery scheme is generated. The output prediction of the internal flexible resources of the power distribution network comprises an active power output prediction value of the jth phase and the ith photovoltaic distributed power supply in the internal power distribution network; the physical parameters comprise a voltage deviation, an actual voltage of each node in the nodes, a reference voltage of each node in the nodes, an upper limit of the voltage of each node in the nodes, a lower limit of the voltage of each node in the nodes and basic data; and the operation constraints comprise a wind power node loss, a non-fault node power and a maximum current of the internal branch of the power distribution network. 3.The optimal restoration method of distribution network considering in-loop distributed resources of claim 2, wherein, The internal flexible resource model of the power distribution network at least comprises one or more of a photovoltaic output function, a wind power loss function, a node voltage deviation function, an internal loop network loss function, a transfer power function and a rated current function. The internal flexible resource model of the power distribution network is constructed based on the output prediction, physical parameters and operation constraints of the internal flexible resources of the power distribution network, and the method comprises the following steps: According to the active power output prediction value of the jth phase and the ith photovoltaic distributed power supply in the internal power distribution network, a photovoltaic output function is constructed; And / or, according to the wind power node loss and the voltage deviation, a wind power loss function is constructed; And / or, according to the actual voltage, the reference voltage, the upper limit of the voltage and the lower limit of the voltage of each node in the nodes, a node voltage deviation function is constructed; And / or, according to the basic data, an internal loop network loss function is constructed; And / or, according to the non-fault node power, a transfer power function is constructed; 4. The method of claim 1, wherein, And / or, according to the maximum current of the internal branch of the power distribution network, a rated current function is constructed. The optimal recovery model after the power distribution network fails at least comprises one or more of a line loss function, a power loss time function and a power loss load function; The optimal recovery model after the power distribution network fails is constructed according to the power grid operation state data, system state parameters and power loss load parameters, and the method comprises the following steps: According to the power grid operation state data, a line loss function is constructed; According to the system state parameters, a power loss time function is constructed; 5. The method of claim 1, wherein, According to the power loss load parameters, a power loss load function is constructed. The model constraint condition is constructed according to the working parameters, performance parameters and power parameters of each node in the nodes and the power of the distributed photovoltaic, and the method comprises the following steps: According to the working parameters of each node in the nodes, a line transmission constraint condition is constructed; And / or, according to the power parameters of the distributed photovoltaic, an output adjustment constraint condition of the distributed photovoltaic and / or an energy storage power constraint condition is constructed; And / or, according to the performance parameters of each node in the nodes, an energy storage power flow output constraint is constructed; And / or, a constraint condition that the reconstructed power distribution network satisfies a radial topology structure is constructed.

6. The optimal restoration method of distribution network considering in-loop distributed resources of claim 5, wherein, The line transmission constraints include a first line transmission constraint, a second line transmission constraint, and a third line transmission constraint; The line transmission constraints are constructed according to the working parameters of each node in the nodes, and the line transmission constraints include: The first line transmission constraint is constructed according to the electrical parameters of each node in the nodes; The second line transmission constraint is constructed according to the voltage parameters of each node in the nodes; The third line transmission constraint is constructed according to the operation parameters of each node in the nodes.

7. The method of claim 1, wherein, The power supply recovery scheme is generated according to the flexible resource model inside the distribution network, the optimal recovery model after the distribution network failure, the model constraint condition, and the optimization algorithm, and the power supply recovery scheme includes: The comprehensive optimization objective function value is determined according to a wind power loss function, a node voltage offset function, a loop network internal network loss function, a transfer power function, a line network loss function, a power failure time function, and a power failure load function; The power supply recovery scheme is generated according to the optimization objective function value and the black kite optimization algorithm.

8. The method of claim 1, wherein, The expression of the optimization objective function value is: ； wherein, is a comprehensive optimization objective function value, is a wind power loss function, a node voltage deviation function, a loop network internal network loss function, a transfer power function, a line network loss function, a power failure time number function, or a power failure load function, is a weight value corresponding to the wind power loss function, the node voltage deviation function, the loop network internal network loss function, the transfer power function, the line network loss function, the power failure time number function, and the power failure load function, respectively.

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