Method for dynamic construction of multi-level control unit considering multi-element resources and related device

Abstract

This invention belongs to the field of power automation technology and discloses a method and related apparatus for dynamically constructing a multi-level control unit considering multiple resources. The method includes: acquiring a power grid control scenario; dynamically determining the resource aggregation range based on the power grid control scenario; screening the resource aggregation range to obtain an optimized resource set; constructing a multi-resource optimization combination model with the goal of minimizing regulation costs; and solving the multi-resource optimization combination model based on the optimized resource set to obtain a multi-level control unit considering multiple resources. On the one hand, this invention fully considers the differences in regulation characteristics among multiple resources and utilizes their spatiotemporal complementary characteristics for optimized combination, fully exploring and utilizing the regulation potential of multiple resources. On the other hand, it dynamically screens resources for different control scenarios, ensuring that the multi-resource optimization combination model constructed by this method meets the technical constraints of the corresponding scenarios in terms of technical performance, better supporting power grid control and operation decisions.

Description

Technical Field

[0001] This invention belongs to the field of power automation technology, and specifically relates to a method and related apparatus for dynamically constructing a multi-level control unit that takes into account multiple resources. Background Technology

[0002] Distributed power sources, energy storage, and adjustable loads possess abundant regulation potential, but their small individual capacity and dispersed spatial distribution make direct participation in grid regulation difficult. Therefore, aggregation is necessary to form virtual regulation units to reduce control complexity. Different grid regulation needs (such as frequency regulation, peak shaving, and section control) impose varying requirements on resource regulation periods, response speeds, and regulation capacities, leading to changes in the range of resources to be selected. Furthermore, the regulation capability of distributed resources is significantly time-varying due to factors such as natural conditions and operating status. The optimal aggregation unit composition should be dynamically adjusted to meet regulation needs at different times. Therefore, to achieve efficient and economical grid regulation, it is essential to fully consider the temporal and spatial matching characteristics of regulation needs and resources, dynamically constructing aggregation units to adapt to changing operating scenarios and fully leverage the collaborative regulation advantages of distributed resources.

[0003] Current power grid modeling for distributed regulation resources primarily follows the traditional power system analysis approach, exhibiting two typical practices. The first is equipment-level refined modeling, which constructs detailed physical models and dynamic characteristic equations for individual devices such as distributed power sources, energy storage, and temperature-controlled loads. While this approach offers high accuracy, the influx of massive resources leads to a curse of dimensionality, exceeding the computational and communication capabilities of the main control station and hindering real-time optimization. The second approach employs topologically-based aggregation modeling. This method statically aggregates resources within the same electrical node or power supply area according to the "distribution transformer—feeder—substation" hierarchy, forming equivalent "virtual machine groups" to participate in main grid scheduling. While this reduces model complexity, its aggregation boundaries are fixed and electrically oriented, neglecting the diverse needs of regulation scenarios. In fact, different power grid regulation scenarios (such as frequency regulation, peak shaving, section congestion management, and voltage and reactive power control) have vastly different requirements for technical parameters such as resource response delay, regulation rate, duration, and regulation direction. Existing modeling methods fail to reflect this difference: on the one hand, rigid aggregation by topology may lead to an excessively large aggregation range, forcibly binding resources with different technical characteristics, making the overall response performance unable to meet the rapid adjustment needs of specific scenarios; on the other hand, it may also lead to the omission of high-quality resources located on other feeders but with matching technical performance due to overly conservative aggregation boundaries, resulting in a serious mismatch between the resource aggregation range and the demand scenario in time and space, which restricts the flexible adjustment capability of distributed resources.

[0004] How to overcome the limitations of existing equipment-level fine-grained modeling (curse of dimensionality, inability to optimize in real time) and topology-oriented static aggregation modeling (ignoring scenario differences and spatiotemporal mismatch) while taking into account the diverse control needs of the power grid and the characteristics of time-varying distributed control resources, and dynamically construct virtual control aggregation units that are spatiotemporally matched with control needs, so as to efficiently leverage the synergistic control advantages of distributed resources and support the efficient and economical control of the power grid, has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method and related apparatus for dynamically constructing multi-level control units that take into account multiple resources, so as to solve at least one of the technical problems of existing equipment-level fine modeling leading to the curse of dimensionality and inability to optimize in real time, and topology-oriented static aggregation modeling ignoring scene differences and spatiotemporal mismatch.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for dynamically constructing a multi-level control unit that takes into account multiple resources, including: Obtain power grid control scenarios; Dynamically determine the scope of resource aggregation based on power grid control scenarios; Filter the scope of resource aggregation to obtain an optimized resource set; With the goal of minimizing adjustment costs, a multi-resource optimization combination model is constructed; the multi-resource optimization combination model is solved based on the optimized resource set to obtain the optimal solution that includes resource call variables and resource adjustment amounts; the resources used in the optimal solution are aggregated to form a multi-level control unit that takes into account multiple resources.

[0007] A further improvement of the present invention is that it also includes: A multi-level control unit that takes into account multiple resources performs coordinated control of multiple resources.

[0008] A further improvement of the present invention is that it also includes: The steps for updating control parameters and dynamically constructing a multi-level control unit that considers multiple resources to enter the next cycle include: Update the control parameters based on the adjustment time step ΔT: Tn+1=Tn+△T, n=n+1 In the formula: Tn is the time for the nth round of screening; After updating the control parameters, the system moves to the next cycle to reacquire the power grid control scenario until a multi-level control unit considering various resources for the next week is constructed.

[0009] A further improvement of the present invention is that: in the step of obtaining the power grid control scenario, the power grid control scenario includes four typical scenarios: provincial power grid peak shaving, provincial power grid section power flow control, prefecture-level and county-level power grid peak shaving, and prefecture-level and county-level power grid equipment power flow control.

[0010] A further improvement of the present invention is that, in the step of dynamically determining the resource aggregation range based on the power grid control scenario, for provincial power grid peak regulation: taking the 220kV main transformer high-voltage side bus as the aggregation node, all adjustable resource basic control units within the province are aggregated level by level; For power flow control at provincial power grid sections: Select scenario sensitivity S i-k Greater than threshold S λ The 220kV main transformer aggregates its adjustable resource base control unit level by level; For peak shaving of county-level power grids: taking the high-voltage side of the 110kV main transformer as the aggregation node, all adjustable resource basic control units within the county are aggregated step by step; For power flow control of prefecture- and county-level power grid equipment: if the key equipment is a line, the high-voltage side bus of the main transformer with the same voltage level and a direct connection relationship is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step; if the key equipment is a main transformer, the high-voltage side bus of the main transformer with the next lower voltage level within its power supply range is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step.

[0011] A further improvement of this invention is that the step of filtering the resource aggregation range to obtain an optimized resource set specifically includes: Obtain and adjust the scene constraint parameters and adjustable resource parameters; Based on the following principles, select adjustable resources that meet the requirements and include them in the optimized resource set; Filtering criterion 1:

[0012] Filtering criterion 2:

[0013] Filtering criterion 3:

[0014] Filtering criterion 4:

[0015] in, This is the upper limit for power adjustment of resource i; The power baseline for resource i; This is the lower limit for power regulation of resource i; To adjust the start time; The start time for adjusting resource i; Let i be the response time of resource i; To adjust the duration; The duration of adjustment for resource i; When adjusting power demand At that time, the resource screening principle is: both screening conditions 1 and 3 are met, or both screening conditions 1 and 4 are met. When adjusting power demand At that time, the resource screening principle is: both screening conditions 2 and 3 are met, or both screening conditions 2 and 4 are met. According to the power adjustment requirements Select adjustable resources that meet the requirements to obtain an optimized resource set.

[0016] A further improvement of this invention is that: in the step of constructing a multi-resource optimization combination model with the objective of minimizing adjustment costs; and solving the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution including resource call variables and resource adjustment amounts, the objective function of the multi-resource optimization combination model is:

[0017] Where Ns is the number of optimized resources; U is the startup cost of resource i; i This is a variable used for resource invocation, representing the resource. i Whether it is invoked; To adjust the start time; To adjust the duration; The adjustment cost for resource i; The power adjustment for resource i during time period t; To adjust the time step; The constraints of the multi-resource optimization combination model include adjustment amount constraints, available time period constraints, adjustment speed constraints, duration constraints, and conventional constraints.

[0018] Secondly, the present invention provides a dynamic construction device for a multi-level control unit that takes into account multiple resources, comprising: The data acquisition module is used to acquire data on power grid control scenarios. The dynamic aggregation module is used to dynamically determine the scope of resource aggregation based on power grid control scenarios. The filtering module is used to filter the scope of resource aggregation to obtain an optimized resource set; The module is used to construct a multi-resource optimization combination model with the goal of minimizing adjustment costs; it solves the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution that includes resource call variables and resource adjustment amounts; the resources used in the optimal solution are aggregated to form a multi-level control unit that includes multiple resources.

[0019] A further improvement of the present invention is that it also includes: The control module is used to coordinate the control of multiple resources through a multi-level control unit that takes into account multiple resources.

[0020] A further improvement of the present invention is that it also includes: The update module is used to update control parameters for the next cycle, dynamically constructing a multi-level control unit that considers various resources; specifically, it is configured as follows: Update the control parameters based on the adjustment time step ΔT: Tn+1=Tn+△T, n=n+1 In the formula: Tn is the time for the nth round of screening; After updating the control parameters, the system moves to the next cycle to reacquire the power grid control scenario until a multi-level control unit considering various resources for the next week is constructed.

[0021] A further improvement of the present invention is that: in the step of obtaining the power grid control scenario, the power grid control scenario includes four typical scenarios: provincial power grid peak shaving, provincial power grid section power flow control, prefecture-level and county-level power grid peak shaving, and prefecture-level and county-level power grid equipment power flow control.

[0022] A further improvement of the present invention is that, in the step of dynamically determining the resource aggregation range based on the power grid control scenario, for provincial power grid peak regulation: taking the 220kV main transformer high-voltage side bus as the aggregation node, all adjustable resource basic control units within the province are aggregated level by level; For power flow control at provincial power grid sections: Select scenario sensitivity S i-k Greater than threshold S λ The 220kV main transformer aggregates its adjustable resource base control unit level by level; For peak shaving of county-level power grids: taking the high-voltage side of the 110kV main transformer as the aggregation node, all adjustable resource basic control units within the county are aggregated step by step; For power flow control of prefecture- and county-level power grid equipment: if the key equipment is a line, the high-voltage side bus of the main transformer with the same voltage level and a direct connection relationship is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step; if the key equipment is a main transformer, the high-voltage side bus of the main transformer with the next lower voltage level within its power supply range is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step.

[0023] A further improvement of this invention is that the step of filtering the resource aggregation range to obtain an optimized resource set specifically includes: Obtain and adjust the scene constraint parameters and adjustable resource parameters; Based on the following principles, select adjustable resources that meet the requirements and include them in the optimized resource set; Filtering criterion 1:

[0024] Filtering criterion 2:

[0025] Filtering criterion 3:

[0026] Filtering criterion 4:

[0027] in, This is the upper limit for power adjustment of resource i; The power baseline for resource i; This is the lower limit for power regulation of resource i; To adjust the start time; The start time for adjusting resource i; Let i be the response time of resource i; To adjust the duration; The duration of adjustment for resource i; When adjusting power demand At that time, the resource screening principle is: both screening conditions 1 and 3 are met, or both screening conditions 1 and 4 are met. When adjusting power demand At that time, the resource screening principle is: both screening conditions 2 and 3 are met, or both screening conditions 2 and 4 are met. According to the power adjustment requirements Select adjustable resources that meet the requirements to obtain an optimized resource set.

[0028] A further improvement of this invention is that: in the step of constructing a multi-resource optimization combination model with the objective of minimizing adjustment costs; and solving the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution including resource call variables and resource adjustment amounts, the objective function of the multi-resource optimization combination model is:

[0029] Where Ns is the number of optimized resources; U is the startup cost of resource i; i This is a variable used for resource invocation, representing the resource. i Whether it is invoked; To adjust the start time; To adjust the duration; The adjustment cost for resource i; The power adjustment for resource i during time period t; To adjust the time step; The constraints of the multi-resource optimization combination model include adjustment amount constraints, available time period constraints, adjustment speed constraints, duration constraints, and conventional constraints.

[0030] Thirdly, the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the aforementioned multi-resource dynamic collaborative control method.

[0031] Fourthly, a computer-readable storage medium stores at least one instruction that, when executed by a processor, implements the aforementioned multi-resource dynamic collaborative control method.

[0032] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for dynamically constructing a multi-level control unit considering multiple resources, comprising: acquiring a power grid control scenario; dynamically determining the resource aggregation range based on the power grid control scenario; filtering the resource aggregation range to obtain an optimized resource set; constructing a multi-resource optimization combination model with the goal of minimizing regulation costs; solving the multi-resource optimization combination model based on the optimized resource set to obtain resource call variables and resource regulation amounts; the resource call variables and resource regulation amounts constitute a multi-level control unit considering multiple resources. This invention dynamically selects adjustable resources and optimizes the construction of a multi-resource optimization combination model for different control scenarios. Compared to traditional methods that directly superimpose resources according to the power grid topology or aggregate resources according to resource type, the multi-resource optimization combination model constructed by this method has the following two advantages: Firstly, it fully considers the differences in regulation characteristics among multiple resources, utilizing their spatiotemporal complementary characteristics for optimized combination, fully exploring and utilizing the regulation potential of multiple resources; secondly, it dynamically selects resources for different control scenarios, ensuring that the multi-resource optimization combination model constructed by this method meets the technical constraints of the corresponding scenarios in terms of technical performance, better supporting power grid control operation decisions, supporting multi-scenario collaborative decision-making of multi-level power grids at the provincial, municipal, and county levels, and improving the power system's ability to regulate distributed flexible resources. Attached Figure Description

[0033] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating a method for dynamically constructing a multi-level control unit that takes into account multiple resources, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating a method for dynamically constructing a multi-level control unit that takes into account multiple resources, according to another embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a dynamic construction device for a multi-level control unit that takes into account multiple resources, according to an embodiment of the present invention. Figure 4This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0034] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0035] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0036] This invention proposes a method for dynamically constructing a multi-level control unit that considers multiple resources, comprising the following steps: First, acquiring the power grid control operation scenario; then, dynamically setting the aggregation scale for different power grid control scenarios; further, optimizing and screening the resource aggregation range based on scenario technical constraints; and finally, constructing a multi-resource optimization combination model with the goal of minimizing adjustment costs, thereby optimizing and constructing a control unit for the power grid scenario, supporting multi-level power grid collaborative decision-making at the provincial, municipal, and county levels, and improving the power system's ability to regulate distributed flexible resources.

[0037] Please see Figure 1 As shown, this embodiment of the invention proposes a method for dynamically constructing a multi-level control unit that considers multiple resources, specifically including the following steps: Step 1: Obtain the power grid control scenario; The power grid control scenarios involved in this invention include four typical scenarios: provincial power grid peak shaving, provincial power grid section power flow control, prefecture-level and county-level power grid peak shaving, and prefecture-level and county-level power grid equipment power flow control. The power grid control scenarios, as input parameters for this invention, can be manually input by dispatching and operation personnel, or they can be pre-set based on system reserve, equipment power flow, and section power flow data.

[0038] Step 2: Dynamically determine the scope of resource aggregation; The principle for dynamically determining the scope of resource aggregation proposed in this invention is as follows: Provincial power grid peak shaving: Using the 220kV main transformer high-voltage side bus as the aggregation node, all adjustable resource basic control units within the province are aggregated step by step.

[0039] Provincial power grid section power flow control: Selecting scenario sensitivity S i-k Greater than threshold S λ The 220kV main transformer aggregates its subordinate adjustable resource infrastructure control units level by level. The scenario sensitivity S... i-kThis represents the active power flow sensitivity of the main transformer i to section k.

[0040] Peak shaving of prefecture- and county-level power grids: taking the high-voltage side of the 110kV main transformer as the aggregation node, all adjustable resource basic control units within the prefecture- and county-level areas are aggregated step by step.

[0041] Power flow control for county-level power grid equipment: If the critical equipment is a line, the high-voltage busbar of the main transformer with the same voltage level and direct connection is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated level by level; if the critical equipment is a main transformer, the high-voltage busbar of the main transformer with the next lower voltage level within its power supply range is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated level by level. Here, critical equipment refers to equipment that dispatching and operation personnel focus on, designated by the dispatching and operation personnel. It is usually a line or main transformer that frequently experiences heavy overload during power transmission or reverse power transmission. The specific method for determining critical equipment is based on existing technology.

[0042] Following the principles outlined above, after defining the scope of resource aggregation based on the power grid control scenario, proceed to step 3.

[0043] Step 3: Filter the scope of resource aggregation; Obtaining the constraint parameters for the adjustment scenario includes: adjustment start time constraint T. s Adjusting resource response time constraints T r Adjusting the duration constraint T c ; Obtain adjustable resource parameters, including: the adjustment start time t of resource i. s_i The response time t of resource i r_i The adjustment duration t of resource i c_i The upper limit of power adjustment for resource i, p max_i The lower limit of power regulation for resource i, p min_i The power baseline p of resource i base_ i .

[0044] After obtaining the adjustment scenario constraint parameters and adjustable resource parameters, select the adjustable resources that meet the requirements according to the following principles and include them in the optimization resource set.

[0045] Filtering criterion 1:

[0046] Filtering criterion 2:

[0047] Filtering criterion 3:

[0048] Filtering criterion 4:

[0049] When adjusting power demand At that time, the resource screening principle is: both screening conditions 1 and 3 are met, or both screening conditions 1 and 4 are met. When adjusting power demand At that time, the resource screening principle is: both screening conditions 2 and 3 are met, or both screening conditions 2 and 4 are met. According to the power adjustment requirements Select the adjustable resources that meet the requirements to obtain the optimized resource set, and proceed to step 4.

[0050] Step 4: Construct the control unit For the optimized resource set selected above, a multi-resource optimization combination model is constructed as follows (taking the above adjustment scenario as an example): Objective function:

[0051] The constraints include: Adjustment amount constraint:

[0052] Available time period constraints

[0053] Adjusting speed constraints

[0054] Duration constraint

[0055] In the formula: The power adjustment for resource i during time period t; This represents the minimum regulation capacity required for participating in grid regulation during time period t. Let i be the power baseline for resource i during time period t; This represents the upper limit of power adjustment for resource i during time period t. Ns represents the optimal resource quantity; The startup cost of resource i; The adjustment cost for resource i; The adjustment speed for resource i; U i This is a variable used for resource invocation, representing the resource. i Whether it has been called, 0 means not called, 1 means called. Representing resources i Whether the function is invoked during time period t, 0 indicates not invoked, and 1 indicates invoked; This is an indicator of the availability of resource i during time period t. ∈{0,1}, where 0 is unavailable and 1 is available; To adjust the time step, the default setting is 15 minutes.

[0056] In this embodiment of the invention, the constraints also include conventional constraints; the conventional constraints include: branch power flow constraints and node voltage constraints.

[0057] Based on an optimized resource set as input, within the feasible region defined by constraints, a heuristic algorithm (such as genetic algorithm, particle swarm optimization, simulated annealing, or ant colony optimization) is used to solve the objective function and obtain the optimal solution. The optimal solution includes resource allocation variables and resource adjustment parameters. The aggregate of resources used in the optimal solution is called a control unit. The control unit controls each resource based on the resource allocation variables and resource adjustment parameters in the optimal solution to achieve dynamic collaborative control of multiple resources.

[0058] Definition: A control unit is an aggregate of distributed regulating resources, such as distributed power sources, energy storage, and adjustable loads, within a certain range.

[0059] In one specific implementation, the solution is obtained and Then, the overall regulation characteristics of the control unit are calculated using the following formula:

[0060]

[0061] The adjustable power for the control unit during time period t; To adjust the overall cost of the control unit.

[0062] After the solution is completed, proceed to step 5.

[0063] Step 5: Update control parameters; Tn+1=Tn+△T, n=n+1 In the formula: Tn is the time for the nth round of screening. After updating the control parameters, proceed to the next cycle.

[0064] In this invention, resource optimization screening is carried out by narrowing the scope of resource aggregation based on the characteristics of different regulation scenarios, taking into account the technical performance indicators of resources and the performance constraints of regulation scenarios, and considering complementary characteristics to optimize and screen the adjustment resources. This narrows the scope of optimization variables while retaining the differentiated regulation capabilities of resources.

[0065] The control unit in this invention is optimized by taking into full account the different regulatory characteristics of various types of regulatory resources and combining them in an optimized manner. This is conducive to fully exploring and utilizing the regulatory potential of micro-resources, while also optimizing the cost of the control unit.

[0066] Please see Figure 2 As shown, the present invention provides a method for dynamic collaborative control of multiple resources, comprising: S100, Obtain power grid control scenarios; S200: Dynamically determine the scope of resource aggregation based on power grid control scenarios; S300: Filter the resource aggregation range to obtain an optimized resource set; S400. With the goal of minimizing adjustment costs, construct a multi-resource optimization combination model; solve the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution that includes resource call variables and resource adjustment amounts; the resources used in the optimal solution are aggregated to form a multi-level control unit that considers multiple resources.

[0067] In one specific embodiment, the present invention provides a method for dynamically constructing a multi-level control unit that takes into account multiple resources, further comprising: A multi-level control unit that takes into account multiple resources performs coordinated control of multiple resources.

[0068] In one specific embodiment, the present invention provides a method for dynamically constructing a multi-level control unit that takes into account multiple resources, further comprising: The steps for updating control parameters and dynamically constructing a multi-level control unit that considers multiple resources to enter the next cycle include: Update the control parameters based on the adjustment time step ΔT: Tn+1=Tn+△T, n=n+1 In the formula: Tn is the time for the nth round of screening; After updating the control parameters, the system moves to the next cycle to reacquire the power grid control scenario until a multi-level control unit considering various resources for the next week is constructed.

[0069] In one specific implementation, the step of obtaining the power grid control scenario includes four typical scenarios: provincial power grid peak shaving, provincial power grid section power flow control, prefecture-level and county-level power grid peak shaving, and prefecture-level and county-level power grid equipment power flow control.

[0070] In one specific implementation, in the step of dynamically determining the resource aggregation range based on the power grid control scenario, for provincial power grid peak shaving: taking the 220kV main transformer high-voltage side bus as the aggregation node, all adjustable resource basic control units within the province are aggregated level by level; For power flow control at provincial power grid sections: Select scenario sensitivity S i-k Greater than threshold Sλ The 220kV main transformer aggregates its adjustable resource base control unit level by level; For peak shaving of county-level power grids: taking the high-voltage side of the 110kV main transformer as the aggregation node, all adjustable resource basic control units within the county are aggregated step by step; For power flow control of prefecture- and county-level power grid equipment: if the key equipment is a line, the high-voltage side bus of the main transformer with the same voltage level and a direct connection relationship is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step; if the key equipment is a main transformer, the high-voltage side bus of the main transformer with the next lower voltage level within its power supply range is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step.

[0071] In one specific implementation, the step of filtering the resource aggregation range to obtain an optimized resource set specifically includes: Obtain and adjust the scene constraint parameters and adjustable resource parameters; Based on the following principles, select adjustable resources that meet the requirements and include them in the optimized resource set; Filtering criterion 1:

[0072] Filtering criterion 2:

[0073] Filtering criterion 3:

[0074] Filtering criterion 4:

[0075] in, This is the upper limit for power adjustment of resource i; The power baseline for resource i; This is the lower limit for power regulation of resource i; To adjust the start time; The start time for adjusting resource i; Let i be the response time of resource i; To adjust the duration; The duration of adjustment for resource i; When adjusting power demand At that time, the resource screening principle is: both screening conditions 1 and 3 are met, or both screening conditions 1 and 4 are met. When adjusting power demand At that time, the resource screening principle is: both screening conditions 2 and 3 are met, or both screening conditions 2 and 4 are met. According to the power adjustment requirements Select adjustable resources that meet the requirements to obtain an optimized resource set.

[0076] In one specific implementation, in the step of constructing a multi-resource optimization combination model with the objective of minimizing adjustment costs, and solving the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution including resource allocation variables and resource adjustment amounts, the objective function of the multi-resource optimization combination model is:

[0077] Where Ns is the number of optimized resources; U is the startup cost of resource i; i This is a variable used for resource invocation, representing the resource. i Whether it is invoked; To adjust the start time; To adjust the duration; The adjustment cost for resource i; The power adjustment for resource i during time period t; To adjust the time step; The constraints of the multi-resource optimization combination model include adjustment amount constraints, available time period constraints, adjustment speed constraints, duration constraints, and conventional constraints.

[0078] Please see Figure 3 As shown, the present invention provides a dynamic construction device for a multi-level control unit that considers multiple resources, comprising: The data acquisition module is used to acquire data on power grid control scenarios. The dynamic aggregation module is used to dynamically determine the scope of resource aggregation based on power grid control scenarios. The filtering module is used to filter the scope of resource aggregation to obtain an optimized resource set; The module is used to construct a multi-resource optimization combination model with the goal of minimizing adjustment costs; it solves the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution that includes resource call variables and resource adjustment amounts; the resources used in the optimal solution are aggregated to form a multi-level control unit that includes multiple resources.

[0079] Please see Figure 4 As shown, this embodiment of the invention provides an electronic device 100 for implementing a dynamic construction method of a multi-level control unit that takes into account multiple resources; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0080] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the multi-level control unit dynamic construction method considering multiple resources described in the embodiment by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0081] The at least one processor 102 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. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0082] The memory 101 in the electronic device 100 stores multiple instructions to implement a dynamic construction method for a multi-level control unit that takes into account multiple resources, and the processor 102 can execute the multiple instructions to achieve the following: Obtain power grid control scenarios; Dynamically determine the scope of resource aggregation based on power grid control scenarios; Filter the scope of resource aggregation to obtain an optimized resource set; With the goal of minimizing adjustment costs, a multi-resource optimization combination model is constructed; the multi-resource optimization combination model is solved based on the optimized resource set to obtain the optimal solution that includes resource call variables and resource adjustment amounts; the resources used in the optimal solution are aggregated to form a multi-level control unit that takes into account multiple resources.

[0083] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0084] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0085] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] 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 1 The function specified in one or more boxes.

[0087] 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.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for dynamically constructing multi-level control units considering multiple resources, characterized in that, include: Obtain power grid control scenarios; Dynamically determine the scope of resource aggregation based on power grid control scenarios; Filter the scope of resource aggregation to obtain an optimized resource set; With the goal of minimizing adjustment costs, a multi-resource optimization combination model is constructed; the multi-resource optimization combination model is solved based on the optimized resource set to obtain the optimal solution that includes resource call variables and resource adjustment amounts; the resources used in the optimal solution are aggregated to form a multi-level control unit that takes into account multiple resources.

2. The method for dynamically constructing a multi-level control unit considering multiple resources according to claim 1, characterized in that, Also includes: A multi-level control unit that takes into account multiple resources performs coordinated control of multiple resources.

3. The method for dynamically constructing a multi-level control unit considering multiple resources according to claim 1, characterized in that, Also includes: The next cycle involves updating control parameters and dynamically constructing a multi-level control unit that takes into account various resources. Specifically, it includes: Update the control parameters based on the adjustment time step ΔT: Tn+1=Tn+△T, n=n+1 In the formula: Tn is the time for the nth round of screening; After updating the control parameters, the system moves to the next cycle to reacquire the power grid control scenario until a multi-level control unit considering various resources for the next week is constructed.

4. The method for dynamically constructing a multi-level control unit considering multiple resources according to claim 1, characterized in that, In the step of obtaining the power grid control scenario, the power grid control scenario includes four typical scenarios: provincial power grid peak shaving, provincial power grid section power flow control, prefecture-county level power grid peak shaving, and prefecture-county level power grid equipment power flow control.

5. The method for dynamically constructing a multi-level control unit considering multiple resources according to claim 1, characterized in that, In the step of dynamically determining the scope of resource aggregation based on the power grid control scenario, for provincial power grid peak shaving: taking the 220kV main transformer high-voltage side bus as the aggregation node, all adjustable resource basic control units within the province are aggregated level by level. For provincial power grid section flow control: select scenario sensitivity S i-k greater than threshold S λ 220kV main transformer, the adjustable resource base control unit under its jurisdiction is aggregated step by step; For peak shaving of county-level power grids: taking the high-voltage side of the 110kV main transformer as the aggregation node, all adjustable resource basic control units within the county are aggregated step by step; For power flow control of prefecture- and county-level power grid equipment: if the key equipment is a line, the high-voltage side bus of the main transformer with the same voltage level and a direct connection relationship is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step; if the key equipment is a main transformer, the high-voltage side bus of the main transformer with the next lower voltage level within its power supply range is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step.

6. The method for dynamically constructing a multi-level control unit considering multiple resources according to claim 1, characterized in that, The step of filtering the resource aggregation range to obtain the optimized resource set specifically includes: Obtain and adjust the scene constraint parameters and adjustable resource parameters; Based on the following principles, select adjustable resources that meet the requirements and include them in the optimized resource set; Filtering criterion 1: Filtering criterion 2: Filtering criterion 3: Filtering criterion 4: in, This is the upper limit for power adjustment of resource i; The power baseline for resource i; This is the lower limit for power regulation of resource i; To adjust the start time; The start time for adjusting resource i; Let i be the response time of resource i; To adjust the duration; The duration of adjustment for resource i; When adjusting power demand When the resource selection criteria are met, the selection criteria 1 and 3 must be met simultaneously, or the selection criteria 1 and 4 must be met simultaneously. When adjusting power demand At that time, the resource screening principle is: both screening conditions 2 and 3 are met, or both screening conditions 2 and 4 are met. According to the power adjustment requirements Select adjustable resources that meet the requirements to obtain an optimized resource set.

7. The method for dynamically constructing a multi-level control unit considering multiple resources according to claim 1, characterized in that, In the step of constructing a multi-resource optimization combination model with the objective of minimizing adjustment costs, and solving the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution including resource call variables and resource adjustment amounts, the objective function of the multi-resource optimization combination model is: Where Ns is the number of optimized resources; U is the startup cost of resource i; i This is a variable for resource invocation, representing the resource. i Whether it is invoked; To adjust the start time; To adjust the duration; The adjustment cost for resource i; The power adjustment for resource i during time period t; To adjust the time step; The constraints of the multi-resource optimization combination model include adjustment amount constraints, available time period constraints, adjustment speed constraints, duration constraints, and conventional constraints.

8. A dynamic construction device for multi-level control units considering multiple resources, characterized in that, include: The data acquisition module is used to acquire data on power grid control scenarios. The dynamic aggregation module is used to dynamically determine the scope of resource aggregation based on power grid control scenarios. The filtering module is used to filter the scope of resource aggregation to obtain an optimized resource set; The module is used to construct a multi-resource optimization combination model with the goal of minimizing adjustment costs; it solves the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution that includes resource call variables and resource adjustment amounts; the resources used in the optimal solution are aggregated to form a multi-level control unit that includes multiple resources.

9. The dynamic construction device for a multi-level control unit considering multiple resources according to claim 8, characterized in that, Also includes: The control module is used to coordinate the control of multiple resources through a multi-level control unit that takes into account multiple resources.

10. The dynamic construction device for a multi-level control unit considering multiple resources according to claim 8, characterized in that, Also includes: The update module is used to update control parameters for the next cycle, dynamically constructing a multi-level control unit that considers various resources; specifically, it is configured as follows: Update the control parameters based on the adjustment time step ΔT: Tn+1=Tn+△T, n=n+1 In the formula: Tn is the time for the nth round of screening; After updating the control parameters, the system moves to the next cycle to reacquire the power grid control scenario until a multi-level control unit considering various resources for the next week is constructed.

11. The dynamic construction device for a multi-level control unit considering multiple resources according to claim 8, characterized in that, In the step of obtaining the power grid control scenario, the power grid control scenario includes four typical scenarios: provincial power grid peak shaving, provincial power grid section power flow control, prefecture-county level power grid peak shaving, and prefecture-county level power grid equipment power flow control.

12. The dynamic construction device for a multi-level control unit considering multiple resources according to claim 8, characterized in that, In the step of dynamically determining the scope of resource aggregation based on the power grid control scenario, for provincial power grid peak shaving: taking the 220kV main transformer high-voltage side bus as the aggregation node, all adjustable resource basic control units within the province are aggregated level by level. For provincial power grid section flow control: select scenario sensitivity S i-k greater than the threshold value S λ 220kV main transformer, the adjustable resource base control unit under its jurisdiction is aggregated step by step; For peak shaving of county-level power grids: taking the high-voltage side of the 110kV main transformer as the aggregation node, all adjustable resource basic control units within the county are aggregated step by step; For power flow control of prefecture- and county-level power grid equipment: if the key equipment is a line, the high-voltage side bus of the main transformer with the same voltage level and a direct connection relationship is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step; if the key equipment is a main transformer, the high-voltage side bus of the main transformer with the next lower voltage level within its power supply range is used as the aggregation node, and the adjustable resource basic control units under its jurisdiction are aggregated step by step.

13. The dynamic construction device for a multi-level control unit considering multiple resources according to claim 8, characterized in that, The step of filtering the resource aggregation range to obtain the optimized resource set specifically includes: Obtain and adjust the scene constraint parameters and adjustable resource parameters; Based on the following principles, select adjustable resources that meet the requirements and include them in the optimized resource set; Filtering criterion 1: Filtering criterion 2: Filtering criterion 3: Filtering criterion 4: in, This is the upper limit for power adjustment of resource i; The power baseline for resource i; This is the lower limit for power regulation of resource i; To adjust the start time; The start time for adjusting resource i; Let i be the response time of resource i; To adjust the duration; The duration of adjustment for resource i; When adjusting power demand When the resource selection criteria are met, the selection criteria 1 and 3 must be met simultaneously, or the selection criteria 1 and 4 must be met simultaneously. When adjusting power demand At that time, the resource screening principle is: both screening conditions 2 and 3 are met, or both screening conditions 2 and 4 are met. According to the power adjustment requirements Select adjustable resources that meet the requirements to obtain an optimized resource set.

14. The dynamic construction device for a multi-level control unit considering multiple resources according to claim 8, characterized in that, In the step of constructing a multi-resource optimization combination model with the objective of minimizing adjustment costs, and solving the multi-resource optimization combination model based on the optimized resource set to obtain the optimal solution including resource call variables and resource adjustment amounts, the objective function of the multi-resource optimization combination model is: Where Ns is the number of optimized resources; U is the startup cost of resource i; i This is a variable for resource invocation, representing the resource. i Whether it is invoked; To adjust the start time; To adjust the duration; The adjustment cost for resource i; The power adjustment for resource i during time period t; To adjust the time step; The constraints of the multi-resource optimization combination model include adjustment amount constraints, available time period constraints, adjustment speed constraints, duration constraints, and conventional constraints.

15. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the dynamic construction apparatus for a multi-level control unit that takes into account multiple resources as described in any one of claims 1 to 8.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the dynamic construction apparatus for a multi-level control unit considering multiple resources as described in any one of claims 1 to 8.

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