Elastic load regulation and control optimization method and device

Abstract

The invention provides an elastic load regulation and control optimization method and device. The voltage of each node in the power distribution network can be calculated according to the power utilization characteristics of the elastic load and the set particle swarm initial code. And solving a pre-constructed regulation and control optimization model by adopting a particle swarm optimization algorithm to obtain a time shift normalization coefficient and a power adjustment coefficient. Wherein the regulation and control optimization model comprises an objective function and constraint conditions. The target function aims at minimizing the sum of the adjustment costs of the elastic loads, and the constraint condition is constructed according to the voltage of each node. In the process of constructing the regulation and control optimization model, not only is the regulation cost of each elastic load considered, but also the power utilization characteristics of the elastic loads are considered, solving is performed in combination with the particle swarm optimization algorithm, traversal in the whole solving space is ensured, the optimal solution can be found, the reliability of the optimization result is improved, the optimization process is relatively simple, and the optimization efficiency is improved. The realization is easy.

Description

Technical Field

[0001] This application relates to the field of power system technology, specifically to a method and apparatus for optimizing flexible load regulation. Background Technology

[0002] Due to the needs of power grid peak shaving and voltage control, the behavior of some adjustable-power loads, i.e., flexible loads, can be intervened through means such as electricity price optimization and additional incentives to cooperate with power grid regulation. Flexible loads are large in scale, have good regularity, and a high level of control automation, thus possessing enormous adjustment potential. Flexible loads are mainly divided into loads that can be shifted, loads that can be transferred, and loads that can be reduced.

[0003] In the process of regulating and optimizing resilient loads, related technologies typically use the increase or decrease in active power of the resilient load at a certain moment as the regulation variable, and employ multi-objective non-integer optimization or artificial intelligence methods. However, because the operational needs of resilient loads vary, their active power changes at different time points are interrelated. Therefore, adjusting each time point separately ignores the correlation between power consumption at different time points, which conflicts with the power needs of resilient loads and does not conform to practical application scenarios. In multi-objective non-integer optimization methods, when the optimization objective has a high dimensionality, such as considering each time point and each load, the methods provided by related technologies may suffer from convergence problems, making it difficult to obtain the optimal solution. Artificial intelligence methods require a large amount of training data, but due to limitations in the number of resilient loads and operating conditions in a given scenario, obtaining a large amount of differentiated training data is challenging. Therefore, the optimization results obtained by the related technologies are unreliable, complex, and difficult to implement. Summary of the Invention

[0004] To address the problem of low reliability and difficulty in achieving optimization results in existing technologies, this application provides a method and apparatus for optimizing flexible load regulation.

[0005] Firstly, this application provides a method for optimizing flexible load regulation, which may include: The voltage of each node in the distribution network is calculated based on the power consumption characteristics of the flexible load and the initial particle swarm encoding.

[0006] The pre-constructed regulation optimization model was solved using the particle swarm optimization algorithm to obtain the time shift normalization coefficient and the power adjustment coefficient.

[0007] The regulation optimization model includes an objective function and constraints. The objective function aims to minimize the sum of regulation costs of the elastic load, and the constraints are constructed based on the voltage of each node.

[0008] In some possible implementations, the voltage of each node in the distribution network is calculated based on the electrical characteristics of the resilient load and the set initial particle swarm encoding, including: The first correspondence between the historical power and the power consumption time of the flexible load is generated based on the power consumption characteristics of the flexible load.

[0009] The first correspondence is shifted based on the initial particle swarm encoding to obtain the second correspondence between the historical power of the elastic load and the power consumption time.

[0010] Obtain the node admittance matrix based on the topology of the distribution network.

[0011] Based on the second correspondence and the node admittance matrix, the power flow of the distribution network is calculated to obtain the voltage of each node.

[0012] Optional, flexible loads include shiftable loads, transferable loads, and reduceable loads.

[0013] The initial particle swarm encoding includes the initial values ​​of the time-shift normalization coefficients for transferable loads, the initial values ​​of the power adjustment coefficients for transferable loads, and the initial values ​​of the power adjustment coefficients for loads that can be reduced.

[0014] Furthermore, by shifting the first correspondence based on the initial particle swarm encoding, a second correspondence between the historical power of the elastic load and the electricity consumption time is obtained, including: Based on the initial value of the time-shifting normalization coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0015] Based on the initial value of the power adjustment coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0016] The first correspondence between the historical power and electricity usage time of the load that can be reduced is shifted based on the initial value of the power adjustment coefficient of the load that can be reduced, to obtain the second correspondence between the historical power and electricity usage time of the load that can be reduced.

[0017] In some other possible implementations, the objective function satisfies: .

[0018] in, This represents the sum of the adjustment costs of the elastic load.

[0019] This indicates the amount of elastic load that increases power. Indicates the first The unit adjustment price for a flexible load with increased power. Indicates the first The power change of an elastic load with increased power.

[0020] This indicates the amount of elastic load that reduces power. Indicates the first The unit adjustment price for a flexible load with reduced power. Indicates the first The amount of power change of a flexible load with reduced power.

[0021] The constraints include voltage constraints and power flow constraints.

[0022] Voltage constraints satisfy .

[0023] in, Indicates the first Each node Voltage at time, This indicates the lower voltage limit of the node. This indicates the upper limit of the node's voltage.

[0024] Current constraints satisfy .

[0025] in, Indicates the first Nodes The injection of active power at all times, Indicates the first Nodes The injection of reactive power at any time. Indicates the first Each node Voltage at a given moment. Represents the th node in the admittance matrix Line number The real part of the column element, Represents the th node in the admittance matrix Line number The imaginary part of the column elements, Represents the th node in the admittance matrix Line number The phase angle of the column elements.

[0026] Secondly, this application provides a flexible load regulation and optimization device, which may include: The calculation module is used to calculate the voltage of each node in the distribution network based on the power consumption characteristics of the flexible load and the set initial particle swarm encoding.

[0027] The solution module is used to solve the pre-built regulation optimization model using the particle swarm optimization algorithm to obtain the time shift normalization coefficient and the power adjustment coefficient.

[0028] The regulation optimization model includes an objective function and constraints. The objective function aims to minimize the sum of regulation costs of the elastic load, and the constraints are constructed based on the voltage of each node.

[0029] In some possible implementations, the computation module is specifically used for: The first correspondence between the historical power and the power consumption time of the flexible load is generated based on the power consumption characteristics of the flexible load.

[0030] The first correspondence is shifted based on the initial particle swarm encoding to obtain the second correspondence between the historical power of the elastic load and the power consumption time.

[0031] Obtain the node admittance matrix based on the topology of the distribution network.

[0032] Based on the second correspondence and the node admittance matrix, the power flow of the distribution network is calculated to obtain the voltage of each node.

[0033] Optional, flexible loads include shiftable loads, transferable loads, and reduceable loads.

[0034] The initial particle swarm encoding includes the initial values ​​of the time-shift normalization coefficients for transferable loads, the initial values ​​of the power adjustment coefficients for transferable loads, and the initial values ​​of the power adjustment coefficients for loads that can be reduced.

[0035] Furthermore, the calculation module is specifically used for: Based on the initial value of the time-shifting normalization coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0036] Based on the initial value of the power adjustment coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0037] The first correspondence between the historical power and electricity usage time of the load that can be reduced is shifted based on the initial value of the power adjustment coefficient of the load that can be reduced, to obtain the second correspondence between the historical power and electricity usage time of the load that can be reduced.

[0038] In some other possible implementations, the objective function satisfies: .

[0039] in, This represents the sum of the adjustment costs of the elastic load.

[0040] This indicates the amount of elastic load that increases power. Indicates the first The unit adjustment price for a flexible load with increased power. Indicates the first The power change of an elastic load with increased power.

[0041] This indicates the amount of elastic load that reduces power. Indicates the first The unit adjustment price for a flexible load with reduced power. Indicates the first The amount of power change of a flexible load with reduced power.

[0042] The constraints include voltage constraints and power flow constraints.

[0043] Voltage constraints satisfy .

[0044] in, Indicates the first Each node Voltage at time, This indicates the lower voltage limit of the node. This indicates the upper limit of the node's voltage.

[0045] Current constraints satisfy .

[0046] in, Indicates the first Nodes The injection of active power at all times, Indicates the first Nodes The injection of reactive power at any time. Indicates the first Each node Voltage at a given moment. Represents the th node in the admittance matrix Line number The real part of the column element, Represents the th node in the admittance matrix Line number The imaginary part of the column elements, Represents the th node in the admittance matrix Line number The phase angle of the column elements.

[0047] In another aspect, this application also provides a computer device, including: one or more processors.

[0048] A processor is used to execute one or more programs.

[0049] When one or more programs are executed by one or more processors, the optimization methods described above are implemented.

[0050] Furthermore, this application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, it implements the optimization method described above.

[0051] Compared with the prior art, the beneficial effects of this application are as follows: The flexible load regulation optimization method provided in this application can calculate the voltage of each node in the distribution network based on the power consumption characteristics of the flexible loads and the set initial particle swarm optimization (PSO) encoding. The pre-constructed regulation optimization model is solved using the PSO algorithm to obtain the time-shifted normalization coefficient and power adjustment coefficient. The regulation optimization model includes an objective function and constraints. The objective function aims to minimize the sum of the regulation costs of the flexible loads, and the constraints are constructed based on the voltage of each node. In constructing the regulation optimization model, this application considers not only the regulation costs of each flexible load but also its power consumption characteristics, and combines this with the PSO algorithm to ensure that the optimal solution can be found throughout the entire solution space, improving the reliability of the optimization results. Furthermore, the optimization process is relatively simple and easy to implement.

[0052] This application employs the particle swarm optimization algorithm to solve the regulation optimization model, which is fast and flexible in finding the optimal solution and has high efficiency, providing an effective means for the regulation and optimization of the power grid for flexible loads.

[0053] This application considers various flexible loads such as loads that can be shifted, loads that can be transferred, and loads that can be reduced. It fully takes into account the correspondence between the historical power and the electricity consumption time of different flexible loads, and optimizes the time shift normalization coefficient and the power adjustment coefficient, which is highly feasible. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic flowchart of a flexible load regulation optimization method in an embodiment of this application; Figure 2 This is a schematic structural diagram of a flexible load regulation and optimization device in an embodiment of this application. Detailed Implementation

[0056] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0057] The terms "first," "second," etc., used in the specification, embodiments, claims, and drawings of this application are for distinguishing purposes only and should not be construed as indicating or implying relative importance or order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as including a series of steps or units. A method, system, product, or apparatus is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0058] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0059] Example 1: This application provides a method for optimizing flexible load regulation. Flexible loads include shiftable loads, transferable loads, and reduceable loads. Shiftable loads refer to loads whose power consumption can vary according to plan during electricity use, but require overall shifting; their electricity consumption spans multiple scheduling periods, such as general or special equipment manufacturing and data centers. Transferable loads refer to loads whose total electricity consumption remains constant during demand response, but whose electricity consumption is shifted based on time-of-use pricing and other information, such as agricultural and food processing industries, electric vehicle charging stations, and 5G base stations. Reduceable loads refer to loads whose power consumption can be reduced to a certain extent at various times, but whose duration remains unchanged.

[0060] like Figure 1 As shown, optimization method 100 includes the following steps: Step S1: Calculate the voltage of each node in the distribution network based on the power consumption characteristics of the flexible load and the set initial particle swarm encoding.

[0061] Step S2: Solve the pre-constructed regulation optimization model using the particle swarm optimization algorithm to obtain the time shift normalization coefficient and power adjustment coefficient.

[0062] The regulation optimization model includes an objective function and constraints. The objective function aims to minimize the sum of regulation costs of the elastic load, and the constraints are constructed based on the voltage of each node.

[0063] In some possible implementations, step S1 involves calculating the voltage of each node in the distribution network based on the electrical characteristics of the resilient load and the set initial particle swarm encoding, including: The first correspondence between the historical power and the power consumption time of the flexible load is generated based on the power consumption characteristics of the flexible load.

[0064] The first correspondence is shifted based on the initial particle swarm encoding to obtain the second correspondence between the historical power of the elastic load and the power consumption time.

[0065] Obtain the node admittance matrix based on the topology of the distribution network.

[0066] Based on the second correspondence and the node admittance matrix, the power flow of the distribution network is calculated to obtain the voltage of each node.

[0067] Specifically, the initial particle swarm encoding includes the initial values ​​of the time-shift normalization coefficients for transferable loads, the initial values ​​of the power adjustment coefficients for transferable loads, and the initial values ​​of the power adjustment coefficients for loads that can be reduced.

[0068] Optionally, generating a first correspondence between the historical power and consumption time of the flexible load based on its electrical characteristics may include: The type of flexible load can be determined based on its electrical characteristics.

[0069] Obtain historical power consumption data of the transferable load, and generate the first correspondence between the historical power and power consumption time of the transferable load based on the historical power consumption data of the transferable load.

[0070] Obtain historical electricity consumption data of transferable loads, and generate a first correspondence between historical power and electricity consumption time of transferable loads based on the historical electricity consumption data of transferable loads.

[0071] Obtain historical electricity consumption data of loads that can be reduced, and generate a first correspondence between historical power and electricity consumption time of loads that can be reduced based on the historical electricity consumption data of loads that can be reduced.

[0072] In this embodiment of the application, the first correspondence (which can be a curve) between the historical power of the transferable load and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve of the shiftable load. Indicates that the load can be moved. Historical power at any given moment.

[0073] The first correspondence between the historical power of transferable loads and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve of the transferable load. Indicates transferable load in Historical power at any given moment.

[0074] The first correspondence between the historical power of the load that can be reduced and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve indicating where load reduction is possible. This indicates that the load can be reduced in Historical power at any given moment.

[0075] Optionally, shifting the first correspondence based on the initial particle swarm encoding to obtain a second correspondence between the historical power of the elastic load and the electricity consumption time may include: Based on the initial value of the time-shifting normalization coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0076] Based on the initial value of the power adjustment coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0077] The first correspondence between the historical power and electricity usage time of the load that can be reduced is shifted based on the initial value of the power adjustment coefficient of the load that can be reduced, to obtain the second correspondence between the historical power and electricity usage time of the load that can be reduced.

[0078] In this embodiment of the application, the second correspondence (which can be a curve) between the historical power of the transferable load and the electricity consumption time can be expressed as follows: . This represents the initial value of the time-shift normalization coefficient for the transferable load. This represents the historical power curve after the shiftable load has been shifted. Indicates that the load can be moved. Historical power at any given moment. If... If positive, the historical power curve shifts to the right overall after the load shift. Duration. If If the value is negative, the historical power curve after the load shift will shift to the left as a whole. Duration.

[0079] The second correspondence between the historical power of transferable loads and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve after the transferable load has been shifted. Indicates that the load can be moved. Historical power at any given moment This is the initial value of the power adjustment factor for transferable loads. This is the sum of the power values ​​at all times in the first correspondence between the historical power of the transferable load and the electricity consumption time. It can be seen that the total power of the load at each time time remains unchanged before and after the transfer.

[0080] The second correspondence between the historical power of the load that can be reduced and the electricity consumption time can be expressed as follows: .in, Indicates that the load can be moved. Historical power at any given moment It is the initial value of the power adjustment factor that can reduce load, and it exists. . This indicates that the load can be reduced in Historical power at any given time. It can be seen that the load was reduced after the cut.

[0081] In some other possible implementations, the objective function satisfies: .

[0082] in, This represents the sum of the adjustment costs of the elastic load.

[0083] This indicates the amount of elastic load that increases power. Indicates the first The unit adjustment price for a flexible load with increased power. Indicates the first The power change of an elastic load with increased power.

[0084] This indicates the amount of elastic load that reduces power. Indicates the first The unit adjustment price for a flexible load with reduced power. Indicates the first The amount of power change of a flexible load with reduced power.

[0085] The constraints include voltage constraints and power flow constraints.

[0086] Voltage constraints satisfy .

[0087] in, Indicates the first Each node Voltage at time, This indicates the lower voltage limit of the node. This indicates the upper limit of the node's voltage.

[0088] Current constraints satisfy .

[0089] in, Indicates the first Nodes The injection of active power at all times, Indicates the first Nodes The injection of reactive power at any time. Indicates the first Each node Voltage at a given moment. Represents the th node in the admittance matrix Line number The real part of the column element, Represents the th node in the admittance matrix Line number The imaginary part of the column elements, Represents the th node in the admittance matrix Line number The phase angle of the column elements.

[0090] Example 2: Based on the same inventive concept, this application also provides a flexible load regulation and optimization device. Flexible loads include shiftable loads, transferable loads, and reduceable loads. Shiftable loads refer to loads whose power consumption can change according to plan during electricity use, but which require overall shifting, and whose electricity consumption spans multiple scheduling periods, such as general or special equipment manufacturing, data centers, etc. Transferable loads refer to loads whose total electricity consumption remains constant during demand response, but whose electricity consumption is shifted according to information such as time-of-use pricing, such as agricultural and food processing industries, electric vehicle charging stations, 5G base stations, etc. Reduceable loads refer to loads whose power consumption can be reduced to a certain extent at various times, but whose duration remains unchanged.

[0091] like Figure 2 The optimization device 200 may include: The calculation module 201 is used to calculate the voltage of each node in the distribution network based on the power consumption characteristics of the flexible load and the set initial particle swarm encoding.

[0092] The solver module 202 is used to solve the pre-built regulation optimization model using the particle swarm optimization algorithm to obtain the time shift normalization coefficient and the power adjustment coefficient.

[0093] The regulation optimization model includes an objective function and constraints. The objective function aims to minimize the sum of regulation costs of the elastic load, and the constraints are constructed based on the voltage of each node.

[0094] In some possible implementations, the computing module 201 is specifically used for: The first correspondence between the historical power and the power consumption time of the flexible load is generated based on the power consumption characteristics of the flexible load.

[0095] The first correspondence is shifted based on the initial particle swarm encoding to obtain the second correspondence between the historical power of the elastic load and the power consumption time.

[0096] Obtain the node admittance matrix based on the topology of the distribution network.

[0097] Based on the second correspondence and the node admittance matrix, the power flow of the distribution network is calculated to obtain the voltage of each node.

[0098] Optionally, the initial particle swarm encoding includes initial values ​​for the time-shift normalization coefficient of the transferable load, initial values ​​for the power adjustment coefficient of the transferable load, and initial values ​​for the power adjustment coefficient of the load that can be reduced.

[0099] The calculation module 201 is further used for: The type of flexible load can be determined based on its electrical characteristics.

[0100] Obtain historical power consumption data of the transferable load, and generate the first correspondence between the historical power and power consumption time of the transferable load based on the historical power consumption data of the transferable load.

[0101] Obtain historical electricity consumption data of transferable loads, and generate a first correspondence between historical power and electricity consumption time of transferable loads based on the historical electricity consumption data of transferable loads.

[0102] Obtain historical electricity consumption data of loads that can be reduced, and generate a first correspondence between historical power and electricity consumption time of loads that can be reduced based on the historical electricity consumption data of loads that can be reduced.

[0103] In this embodiment of the application, the first correspondence (which can be a curve) between the historical power of the transferable load and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve of the shiftable load. Indicates that the load can be moved. Historical power at any given moment.

[0104] The first correspondence between the historical power of transferable loads and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve of the transferable load. Indicates transferable load in Historical power at any given moment.

[0105] The first correspondence between the historical power of the load that can be reduced and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve indicating where load reduction is possible. This indicates that the load can be reduced in Historical power at any given moment.

[0106] In another embodiment, the computing module 201 is further configured to: Based on the initial value of the time-shifting normalization coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0107] Based on the initial value of the power adjustment coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load.

[0108] The first correspondence between the historical power and electricity usage time of the load that can be reduced is shifted based on the initial value of the power adjustment coefficient of the load that can be reduced, to obtain the second correspondence between the historical power and electricity usage time of the load that can be reduced.

[0109] In this embodiment of the application, the second correspondence (which can be a curve) between the historical power of the transferable load and the electricity consumption time can be expressed as follows: . This represents the initial value of the time-shift normalization coefficient for the transferable load. This represents the historical power curve after the shiftable load has been shifted. Indicates that the load can be moved. Historical power at any given moment. If... If positive, the historical power curve shifts to the right overall after the load shift. Duration. If If the value is negative, the historical power curve after the load shift will shift to the left as a whole. Duration.

[0110] The second correspondence between the historical power of transferable loads and the electricity consumption time can be expressed as follows: .in, This represents the historical power curve after the transferable load has been shifted. Indicates that the load can be moved. Historical power at any given moment This is the initial value of the power adjustment factor for transferable loads. This is the sum of the power values ​​at all times in the first correspondence between the historical power of the transferable load and the electricity consumption time. It can be seen that the total power of the load at each time time remains unchanged before and after the transfer.

[0111] The second correspondence between the historical power of the load that can be reduced and the electricity consumption time can be expressed as follows: .in, Indicates that the load can be moved. Historical power at any given moment It is the initial value of the power adjustment factor that can reduce load, and it exists. It is evident that the load was reduced after the cuts.

[0112] In some other possible implementations, the objective function satisfies: .

[0113] in, This represents the sum of the adjustment costs of the elastic load.

[0114] This indicates the amount of elastic load that increases power. Indicates the first The unit adjustment price for a flexible load with increased power. Indicates the first The power change of an elastic load with increased power.

[0115] This indicates the amount of elastic load that reduces power. Indicates the first The unit adjustment price for a flexible load with reduced power. Indicates the first The amount of power change of a flexible load with reduced power.

[0116] The constraints include voltage constraints and power flow constraints.

[0117] Voltage constraints satisfy .

[0118] in, Indicates the first Each node Voltage at time, This indicates the lower voltage limit of the node. This indicates the upper limit of the node's voltage.

[0119] Current constraints satisfy .

[0120] in, Indicates the first Nodes The injection of active power at all times, Indicates the first Nodes The injection of reactive power at any time. Indicates the first Each node Voltage at a given moment. Represents the th node in the admittance matrix Line number The real part of the column element, Represents the th node in the admittance matrix Line number The imaginary part of the column elements, Represents the th node in the admittance matrix Line number The phase angle of the column elements.

[0121] Example 3: Based on the same inventive concept, this application also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the optimized method provided in the above embodiments.

[0122] Example 4: Based on the same inventive concept, this application also provides a computer-readable storage medium, specifically a computer-readable storage medium (Memory). A computer-readable storage medium is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the optimized method provided in the above embodiments.

[0123] Those skilled in the art will understand that the embodiments of the application can be provided as a method, system, or computer program product. Therefore, the application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] The above are merely examples of the application and are not intended to limit the application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the application shall be included within the scope of the claims of the pending application.

Claims

1. A method for optimizing flexible load regulation, characterized in that, The optimization method includes: The voltage of each node in the distribution network is calculated based on the power consumption characteristics of the flexible load and the set initial particle swarm encoding. The pre-constructed regulation optimization model is solved using the particle swarm optimization algorithm to obtain the time shift normalization coefficient and power adjustment coefficient. The regulation optimization model includes an objective function and constraints; the objective function aims to minimize the sum of the regulation costs of the elastic load, and the constraints are constructed based on the voltage of each node.

2. The optimization method according to claim 1, characterized in that, The calculation of the voltage at each node in the distribution network based on the power consumption characteristics of the flexible load and the set initial particle swarm encoding includes: A first correspondence between the historical power and power consumption time of the elastic load is generated based on the power consumption characteristics of the elastic load; The first correspondence is shifted according to the initial particle swarm encoding to obtain a second correspondence between the historical power and electricity consumption time of the elastic load. Obtain the node admittance matrix based on the topology of the power distribution network; Based on the second correspondence and the node admittance matrix, power flow calculation is performed on the distribution network to obtain the voltage of each node.

3. The optimization method according to claim 2, characterized in that, The resilient load includes shiftable load, transferable load, and reduceable load; The initial particle swarm encoding includes the initial value of the time-shift normalization coefficient of the transferable load, the initial value of the power adjustment coefficient of the transferable load, and the initial value of the power adjustment coefficient of the load that can be reduced.

4. The optimization method according to claim 3, characterized in that, The step of shifting the first correspondence based on the initial particle swarm encoding to obtain a second correspondence between the historical power and electricity consumption time of the elastic load includes: Based on the initial value of the time-shifting normalization coefficient of the transferable load, the first correspondence between the historical power and electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and electricity consumption time of the transferable load. Based on the initial value of the power adjustment coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load; The first correspondence between the historical power and electricity usage time of the load that can be reduced is shifted based on the initial value of the power adjustment coefficient of the load that can be reduced, to obtain the second correspondence between the historical power and electricity usage time of the load that can be reduced.

5. The optimization method according to claim 1, characterized in that, The objective function satisfies: ； in, This represents the sum of the adjustment costs of the elastic load; This indicates the amount of elastic load that increases power. Indicates the first The unit adjustment price for a flexible load with increased power. Indicates the first The power change of an elastic load with increased power; This indicates the amount of elastic load that reduces power. Indicates the first The unit adjustment price for a flexible load with reduced power. Indicates the first The amount of power change of a flexible load with reduced power.

6. The optimization method according to claim 1, characterized in that, The constraints include voltage constraints and power flow constraints; The voltage constraint satisfies ; in, Indicates the first Each node Voltage at time, This indicates the lower voltage limit of the node. Indicates the upper limit of the node's voltage; The power flow constraints satisfy ; in, Indicates the first Nodes The injection of active power at all times, Indicates the first Nodes The injection of reactive power at any given moment; Indicates the first Each node Voltage at any given moment; Represents the node admittance matrix of the nth node. Line number The real part of the column element, Represents the node admittance matrix of the nth node. Line number The imaginary part of the column elements, Represents the node admittance matrix of the nth node. Line number The phase angle of the column elements.

7. A flexible load regulation and optimization device, characterized in that, include: The calculation module is used to calculate the voltage of each node in the distribution network based on the power consumption characteristics of the flexible load and the set initial particle swarm encoding. The solution module is used to solve the pre-constructed regulation optimization model using the particle swarm optimization algorithm to obtain the time shift normalization coefficient and the power adjustment coefficient. The regulation and optimization model includes an objective function and constraints. The objective function aims to minimize the sum of the adjustment costs of the elastic load, and the constraints are constructed based on the voltage of each node.

8. The optimization device according to claim 7, characterized in that, The calculation module is specifically used for: A first correspondence between the historical power and power consumption time of the elastic load is generated based on the power consumption characteristics of the elastic load; The first correspondence is shifted according to the initial particle swarm encoding to obtain a second correspondence between the historical power and electricity consumption time of the elastic load. Obtain the node admittance matrix based on the topology of the power distribution network; Based on the second correspondence and the node admittance matrix, power flow calculation is performed on the distribution network to obtain the voltage of each node.

9. The optimization device according to claim 8, characterized in that, The resilient load includes shiftable load, transferable load, and reduceable load; The initial particle swarm encoding includes the initial value of the time-shift normalization coefficient of the transferable load, the initial value of the power adjustment coefficient of the transferable load, and the initial value of the power adjustment coefficient of the load that can be reduced.

10. The optimization device according to claim 9, characterized in that, The calculation module is specifically used for: Based on the initial value of the time-shifting normalization coefficient of the transferable load, the first correspondence between the historical power and electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and electricity consumption time of the transferable load. Based on the initial value of the power adjustment coefficient of the transferable load, the first correspondence between the historical power and the electricity consumption time of the transferable load is shifted to obtain the second correspondence between the historical power and the electricity consumption time of the transferable load; The first correspondence between the historical power and electricity usage time of the load that can be reduced is shifted based on the initial value of the power adjustment coefficient of the load that can be reduced, to obtain the second correspondence between the historical power and electricity usage time of the load that can be reduced.

11. The optimization device according to claim 7, characterized in that, The objective function satisfies: ； in, This represents the sum of the adjustment costs of the elastic load; This indicates the amount of elastic load that increases power. Indicates the first The unit adjustment price for a flexible load with increased power. Indicates the first The power change of an elastic load with increased power; This indicates the amount of elastic load that reduces power. Indicates the first The unit adjustment price for a flexible load with reduced power. Indicates the first The amount of power change of a flexible load with reduced power.

12. The optimization device according to claim 7, characterized in that, The constraints include voltage constraints and power flow constraints; The voltage constraint satisfies ; in, Indicates the first Each node Voltage at time, This indicates the lower voltage limit of the node. Indicates the upper limit of the node's voltage; The power flow constraints satisfy ; in, Indicates the first Nodes The injection of active power at all times, Indicates the first Nodes The injection of reactive power at any given moment; Indicates the first Each node Voltage at any given moment; Represents the node admittance matrix of the nth node. Line number The real part of the column element, Represents the node admittance matrix of the nth node. Line number The imaginary part of the column elements, Represents the node admittance matrix of the nth node. Line number The phase angle of the column elements.

13. A computer device, characterized in that, include: One or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the optimization method as described in any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the optimization method as described in any one of claims 1 to 6.

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