A multi-feed cluster secondary frequency modulation cooperative control method, device and equipment
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN UNIV
- Filing Date
- 2026-06-30
- Publication Date
- 2026-08-07
AI Technical Summary
[0004]然而，上述方案仅停留在容量上报阶段，在完成馈线集群可调容量评估后，缺乏将电网二次调频指令精准分解至各馈线集群的完整机制，未能实现从“容量评估”到“指令跟踪”的闭环协同控制
[0026]本申请提供的技术方案带来的有益效果至少包括：
Smart Images
Figure CN122532997A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power system operation and control technology, and in particular to a method, device and equipment for secondary frequency regulation coordinated control of multi-feeder clusters. Background Technology
[0002] With the large-scale grid connection of high-proportion renewable energy sources, the randomness, volatility, and uncertainty of both the power grid source and load sides have increased significantly, leading to a continuous rise in system frequency regulation demand and posing a severe challenge to frequency stability control. Traditional frequency regulation methods heavily rely on traditional thermal power units, resulting in slow response speeds, limited adjustable capacity, and high regulation costs, making it difficult to meet the practical needs of new power systems for fast, accurate, and large-capacity frequency regulation. Distribution network feeder loads include a large number of temperature-controlled loads such as air conditioners and water heaters, as well as residential and commercial electrical equipment. These loads exhibit significant voltage sensitivity characteristics, with active power responding noticeably to voltage changes, possessing inherent rapid regulation potential. They can efficiently participate in system frequency regulation using Conservation Voltage Reduction (CVR) technology, making them high-quality flexible regulation resources. Feeder loads have outstanding technical advantages and application potential in participating in power grid frequency regulation.
[0003] In related technologies, regarding feeder load regulation characteristic modeling, a CVR coefficient model is used to describe the voltage-power coupling characteristics of the feeder load, and the model parameters are identified based on the weighted least squares method. Regarding adjustable capacity assessment, the voltage sensitivity is analyzed based on the Zbus linearized power flow model, and the voltage regulation boundary is calculated considering the voltage regulation capability of the reactive power compensation equipment, thereby assessing the adjustable capacity of the feeder cluster.
[0004] However, the above-mentioned scheme only reaches the capacity reporting stage. After completing the adjustable capacity assessment of the feeder clusters, it lacks a complete mechanism for accurately decomposing the secondary frequency regulation commands of the power grid to each feeder cluster, thus failing to achieve closed-loop coordinated control from "capacity assessment" to "command tracking". Therefore, there is an urgent need for a secondary frequency regulation control scheme that can achieve coordinated participation of feeder clusters. Summary of the Invention
[0005] This application provides a method, apparatus, and equipment for secondary frequency modulation coordinated control of a multi-feeder trunking system. This method minimizes the impact on user power quality while ensuring frequency modulation accuracy, ultimately achieving safe, efficient, and precise secondary frequency modulation through the coordinated participation of multiple feeder trunking systems. The technical solution includes at least the following: On the one hand, a secondary frequency regulation coordinated control method for multiple feeder clusters based on voltage-active power coupling characteristics is provided, including: using a CVR coefficient model to describe the voltage-active power coupling characteristics of the feeder cluster to construct a feeder cluster regulation characteristic model, and identifying the CVR coefficients of the feeder cluster based on voltage and power data during grid operation; using grid node voltage security as a constraint, solving the voltage regulation boundary based on power flow optimization, and calculating the adjustable capacity of the feeder cluster in combination with the CVR coefficients; based on the adjustable capacity, constructing a secondary frequency regulation command decomposition optimization model that takes into account both frequency regulation accuracy and power quality, and optimally decomposing the grid secondary frequency regulation command to each feeder cluster based on the discrete regulation characteristics of reactive power compensation equipment, and achieving coordinated response of multiple feeder clusters by adjusting the reactive power compensation equipment.
[0006] Optionally, the CVR coefficient of the feeder cluster identified based on voltage and power data during power grid operation is expressed as:
[0007] in, The CVR coefficient of the feeder cluster. This represents the measured value of the feeder voltage at the i-th sampling time. This represents the measured value of active power at the i-th sampling time. To identify the number of sampling points within a period.
[0008] Optionally, the step of using grid node voltage security as a constraint, solving the voltage regulation boundary based on power flow optimization, and calculating the adjustable capacity of the feeder cluster in conjunction with the CVR coefficient includes: considering power flow constraints, node voltage constraints, and reactive power compensation equipment operation constraints, constructing an optimization model based on power flow analysis for both voltage increase and voltage decrease operating scenarios, with the goal of maximizing the voltage regulation range of the low-voltage side bus of the main transformer, and calculating the adjustable boundary and the adjustable boundary of the low-voltage side bus voltage of the main transformer; and calculating the adjustable capacity and the adjustable capacity of the feeder cluster in conjunction with the CVR coefficient based on the adjustable boundary and the adjustable boundary of the low-voltage side bus voltage of the main transformer.
[0009] Optionally, the reactive power compensation equipment includes an on-load tap changer (OLTC) and a capacitor bank (CB); the objective function of the optimization model aimed at maximizing the voltage regulation range of the low-voltage side bus of the main transformer is expressed as:
[0010]
[0011] in, and These represent the voltage changes on the low-voltage side bus of the main transformer under two operating scenarios: voltage increase and voltage decrease. This represents the voltage change corresponding to a change in the tap position of an on-load tap-changing transformer. This refers to the tap position of the on-load tap-changing transformer at the current moment. and These refer to the tap positions of the on-load tap-changing transformers corresponding to the upper and lower adjustable boundaries of the low-voltage bus voltage of the main transformer. and These represent the voltage rise and fall on the low-voltage side bus of the main transformer caused by capacitor bank switching, respectively; the formulas for calculating the adjustable capacity and adjustable capacity of the feeder cluster are as follows:
[0012]
[0013]
[0014]
[0015] in, and These are the adjustable upper and lower limits of the low-voltage bus voltage of the main transformer, respectively. Measured voltage of the low-voltage side busbar of the main transformer. and These refer to the adjustable range of the low-voltage bus voltage of the main transformer and the adjustable range of the low-voltage side voltage, respectively. The CVR coefficient of the feeder cluster at the current moment. and These refer to the adjustable capacity and adjustable capacity of the feeder cluster, respectively.
[0016] Optionally, the construction of the secondary frequency regulation command decomposition optimization model that takes into account both frequency regulation accuracy and power quality includes: establishing a power quality classification function based on voltage deviation; and constructing a secondary frequency regulation command decomposition optimization model based on the power quality classification function with the goal of minimizing the combined impact of the power grid secondary frequency regulation command response deviation and power quality.
[0017] Optionally, the expression for the power quality classification function is:
[0018] in, Power quality level, This represents the node voltage deviation.
[0019] Optionally, the objective function of the secondary frequency modulation command decomposition and optimization model is expressed as:
[0020] in, This refers to the secondary frequency regulation command issued by the power grid during time period t. Let be the active power adjustment of the i-th feeder cluster during time period t. The number of feeder clusters participating in the regulation. Let be the power quality level of the i-th feeder cluster node j during time period t. Let j be the power quality level of the i-th feeder cluster node j during time period t-1. Let be the number of nodes in the i-th feeder cluster. and These are the preset weighting coefficients.
[0021] Optionally, the secondary frequency regulation command decomposition and optimization model considers power flow constraints, node voltage constraints, and grid secondary frequency regulation command constraints; the grid secondary frequency regulation command constraints are expressed as:
[0022]
[0023] in, The sum of the power response values of the feeder cluster during time period t. For the maximum permissible response deviation, Let be the CVR coefficient of the i-th feeder cluster. Let be the bus voltage regulation of the i-th feeder cluster during time period t.
[0024] On the other hand, a multi-feeder cluster secondary frequency regulation coordinated control device based on voltage-active power coupling characteristics is provided, comprising: an identification module, used to describe the voltage-active power coupling characteristics of the feeder cluster using a CVR coefficient model to construct a feeder cluster regulation characteristic model, and to identify the CVR coefficient of the feeder cluster based on voltage and power data during grid operation; a calculation module, used to solve the voltage regulation boundary based on power flow optimization with grid node voltage security as a constraint, and to calculate the adjustable capacity of the feeder cluster in combination with the CVR coefficient; and a construction module, used to construct a secondary frequency regulation command decomposition optimization model that takes into account both frequency regulation accuracy and power quality based on the adjustable capacity, and to optimally decompose the grid secondary frequency regulation command to each feeder cluster based on the discrete regulation characteristics of reactive power compensation equipment, and to achieve multi-feeder cluster coordinated response by adjusting the reactive power compensation equipment.
[0025] In another aspect, a computer device is provided, the computer device comprising: a memory and a processor, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to implement the aforementioned multi-feeder cluster secondary frequency modulation coordinated control method based on voltage-active power coupling characteristics.
[0026] The beneficial effects of the technical solution provided in this application include at least the following: In this embodiment, a feeder cluster regulation characteristic model is first constructed, and the CVR coefficient of the feeder cluster is identified based on voltage and power data during grid operation. Then, with grid node voltage security as a constraint, the voltage regulation boundary is solved based on power flow optimization, and the adjustable capacity of the feeder cluster is calculated in combination with the CVR coefficient. On this basis, a secondary frequency regulation command decomposition optimization model that takes into account both frequency regulation accuracy and power quality is further constructed. Considering the discrete regulation characteristics of reactive power compensation equipment, the secondary frequency regulation command of the power grid is optimally decomposed to each feeder cluster. By adjusting the reactive power compensation equipment, the coordinated response of multiple feeder clusters is achieved. A complete closed-loop control process is realized from accurate assessment of adjustable capacity, reception of dispatch command and decomposition of secondary frequency regulation command to coordinated response of actuators. This can minimize the impact on the power quality of users while ensuring frequency regulation accuracy. Ultimately, it achieves safe, efficient and accurate secondary frequency regulation with the coordinated participation of multiple feeder clusters. Moreover, the adjustment process can be carried out without the user's awareness, without the need for communication, negotiation or signing of compensation agreements with each user. This helps to reduce the coordination cost and implementation difficulty of flexible resource allocation. Attached Figure Description
[0027] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0028] Figure 1 This is a flowchart of a secondary frequency modulation collaborative control method for a multi-feeder cluster based on voltage-active power coupling characteristics, provided in an embodiment of this application.
[0029] Figure 2 This is a structural block diagram of a multi-feeder cluster secondary frequency modulation collaborative control device based on voltage-active power coupling characteristics, provided in an embodiment of this application.
[0030] Figure 3 This is a schematic diagram of the structure of the computer device provided in the embodiments of this application. Detailed Implementation
[0031] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects.
[0032] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0033] Figure 1 This is a flowchart illustrating a secondary frequency modulation coordinated control method for a multi-feeder cluster based on voltage-active power coupling characteristics, provided in an embodiment of this application. Figure 1 As shown, the method includes: Step S1: The voltage-active power coupling characteristics of the feeder cluster are described using the CVR coefficient model to construct the feeder cluster regulation characteristic model, and the CVR coefficient of the feeder cluster is identified based on the voltage and power data during grid operation.
[0034] Step S2: Using the voltage security of the power grid nodes as a constraint, solve the voltage regulation boundary based on power flow optimization, and calculate the adjustable capacity of the feeder cluster by combining the CVR coefficient.
[0035] Step S3: Based on adjustable capacity, construct a secondary frequency regulation command decomposition and optimization model that takes into account both frequency regulation accuracy and power quality. Considering the discrete adjustment characteristics of reactive power compensation equipment, the secondary frequency regulation command of the power grid is optimally decomposed to each feeder cluster. The coordinated response of multiple feeder clusters is achieved by adjusting the reactive power compensation equipment.
[0036] In this embodiment, a feeder cluster regulation characteristic model is first constructed, and the CVR coefficient of the feeder cluster is identified based on voltage and power data during grid operation. Then, with grid node voltage security as a constraint, the voltage regulation boundary is solved based on power flow optimization, and the adjustable capacity of the feeder cluster is calculated in combination with the CVR coefficient. On this basis, a secondary frequency regulation command decomposition optimization model that takes into account both frequency regulation accuracy and power quality is further constructed. Considering the discrete regulation characteristics of reactive power compensation equipment, the secondary frequency regulation command of the power grid is optimally decomposed to each feeder cluster. By adjusting the reactive power compensation equipment, the coordinated response of multiple feeder clusters is achieved. A complete closed-loop control process is realized from accurate assessment of adjustable capacity, reception of dispatch command and decomposition of secondary frequency regulation command to coordinated response of actuators. This can minimize the impact on the power quality of users while ensuring frequency regulation accuracy. Ultimately, it achieves safe, efficient and accurate secondary frequency regulation with the coordinated participation of multiple feeder clusters. Moreover, the adjustment process can be carried out without the user's awareness, without the need for communication, negotiation or signing of compensation agreements with each user. This helps to reduce the coordination cost and implementation difficulty of flexible resource allocation.
[0037] The voltage-power coupling characteristics of feeder loads are a crucial foundation for distribution network feeder regulation. Although feeder loads are complex and diverse (covering residential, commercial, industrial, and agricultural types), they all exhibit certain voltage-power coupling characteristics. Constant impedance loads (such as electric heating temperature control equipment and incandescent lamps) exhibit typical quadratic voltage-power characteristics, showing high sensitivity to voltage changes; while constant power loads (such as frequency converters and switching power supplies) maintain constant power through internal control, exhibiting weaker voltage dependence. This characteristic can be accurately characterized using the ZIP load model (constant impedance-constant current-constant power model).
[0038] in, For the active power of the load, The voltage of the load. The initial voltage, This represents the load power corresponding to the initial voltage. , , These represent the proportions of constant impedance loads, constant current loads, and constant power loads, respectively.
[0039] The typical feeder load model is represented by the voltage reduction energy saving factor model (CVR coefficient model), which is defined as follows:
[0040] in, The CVR factor for the feeder load represents the ratio of the percentage change in active power to the percentage change in voltage. This represents the percentage change in active power of the feeder load. This represents the percentage change in feeder voltage. This represents the change in active power of the feeder load. This represents the initial active power of the feeder load. This represents the change in feeder voltage. This represents the initial voltage of the feeder. This model characterizes the steady-state regulation characteristics of the load and is generally used for control applications of feeder loads in medium- to long-term time-scale scenarios such as peak shaving and secondary frequency regulation.
[0041] It should be noted that in step S1 of this embodiment, the feeder cluster regulation characteristic model considers the feeder clusters connected to the same bus as a whole regulation unit, whose active power demand includes both load power and line loss. Distribution network power flow analysis shows that the voltage amplitude and phase angle difference between adjacent nodes in the system are relatively small. In this embodiment, the network loss is linearized as a function of the node voltage and equivalently allocated to the load power at the beginning and end of the branch, expressed as:
[0042] in, For the total network loss, Let be the active power loss of the i-th branch. and Let be the node voltages at the beginning and end of the i-th branch, respectively. Let be the resistance of the i-th branch. This is the set of all branches below the main transformer. This equivalent allocation is equivalent to adjusting the constant current component of the ZIP model, while still maintaining the voltage-power coupling relationship. Therefore, the regulation characteristics of both a single load node and a feeder cluster can be characterized using the CVR coefficient model.
[0043] For example, after constructing the feeder cluster regulation characteristic model, the CVR coefficient can be identified using the least squares method based on the voltage and power waveform data recorded during the power grid operation.
[0044] For example, in step S1, the CVR coefficient of the feeder cluster is identified based on voltage and power data during grid operation, and is expressed as:
[0045] in, The CVR coefficient of the feeder cluster. This represents the measured value of the feeder voltage at the i-th sampling time. This represents the measured value of active power at the i-th sampling time. To identify the number of sampling points within a period. As the feeder load composition changes, the CVR coefficient can be iteratively updated to reflect its time-varying nature.
[0046] For example, in step S2, with the voltage security of the grid nodes as a constraint, the voltage regulation boundary is solved based on power flow optimization, and the adjustable capacity of the feeder cluster is calculated in combination with the CVR coefficient, including the following steps: Step S21: Considering power flow constraints, node voltage constraints, and reactive power compensation equipment operation constraints, based on power flow analysis, construct an optimization model with the goal of maximizing the voltage regulation range of the low-voltage side bus of the main transformer for both voltage increase and voltage decrease operation scenarios, and calculate the adjustable boundary and adjustable boundary of the low-voltage side bus voltage of the main transformer.
[0047] In this embodiment, the reactive power compensation equipment includes an on-load tap-changing transformer and a capacitor bank.
[0048] For example, the objective function of the optimization model aimed at maximizing the voltage regulation range of the low-voltage side bus of the main transformer is expressed as:
[0049]
[0050] in, and These represent the voltage changes on the low-voltage side bus of the main transformer under two operating scenarios: voltage increase and voltage decrease. This represents the voltage change corresponding to a change in the tap position of an on-load tap-changing transformer. This refers to the tap position of the on-load tap-changing transformer at the current moment. and These refer to the tap positions of the on-load tap-changing transformers corresponding to the upper and lower adjustable boundaries of the low-voltage bus voltage of the main transformer. and These represent the increase and decrease in voltage on the low-voltage side bus of the main transformer caused by capacitor bank switching, respectively. The voltage change on the low-voltage side bus of the main transformer caused by capacitor bank switching can be accurately solved through power flow calculations or estimated using the following approximate formula:
[0051] in, This refers to the change in low-voltage bus voltage of the main transformer caused by capacitor bank switching. The system's rated voltage. X For the system equivalent reactance, The amount of reactive power injected into the capacitor bank.
[0052] For example, power flow constraints can be expressed as:
[0053]
[0054] In this context, branch ij points from the first node i to the last node j, and m represents the number of last nodes. and Let be the active power injected and the reactive power injected at node i at time t, respectively. and Let be the voltage amplitude at the beginning and end of line ij at time t, respectively. and The conductance and susceptance of line ij respectively. Let be the voltage phase angle difference between the beginning and end of line ij at time t.
[0055] For example, the node voltage constraint can be expressed as:
[0056]
[0057] in, Let be the voltage at node i before adjustment at time t. The voltage change at node i is caused by the operation of the reactive power compensation equipment during the regulation process. This refers to the voltage change at the root node of the low-voltage side bus of the main transformer during the regulation process. This represents the voltage sensitivity matrix of the root node to other nodes. and These are the minimum and maximum allowable voltage values for distribution network nodes, respectively. According to national standards, in distribution networks with voltage levels of 20kV and below... and They are 0.93 pu and 1.07 pu respectively.
[0058] For example, the operating constraints of reactive power compensation equipment include operating constraints of on-load tap-changing transformers and operating constraints of capacitor banks.
[0059] Specifically, the operating constraints of on-load tap-changing transformers can be expressed as:
[0060]
[0061]
[0062] in, This refers to the low-voltage side voltage of an on-load tap-changing transformer when it is in its original tap position. Let t be the low-voltage side voltage after adjustment by the on-load tap-changing transformer. Let t be the tap position of the on-load tap-changing transformer. This refers to the tap position of the on-load tap-changing transformer at time t-1. and These are the upper and lower limits of the tap position for on-load tap-changing transformers, respectively. The threshold for the maximum number of gear changes per day is denoted by T, which represents the evaluation period.
[0063] Specifically, the operating constraints of the capacitor bank can be expressed as:
[0064]
[0065] in, Let be the reactive power input to the capacitor bank at time t. Let be the rated capacity of the capacitor bank connected at time t. Let t represent the switching state of the capacitor bank at time t. When the capacitor bank is put into use... During this period Conversely, when the capacitor bank is not , , The threshold for the maximum number of switching changes per day for the capacitor bank is denoted by , and T is the evaluation period.
[0066] Step S22: Based on the adjustable and adjustable boundaries of the low-voltage side bus voltage of the main transformer, calculate the adjustable and adjustable capacity of the feeder cluster in combination with the CVR coefficient.
[0067] For example, the formulas for calculating the adjustable capacity and adjustable capacity of a feeder cluster are as follows:
[0068]
[0069]
[0070]
[0071] in, and These are the adjustable upper and lower limits of the low-voltage bus voltage of the main transformer, respectively. Measured voltage of the low-voltage side busbar of the main transformer. and These refer to the adjustable range of the low-voltage bus voltage of the main transformer and the adjustable range of the low-voltage side voltage, respectively. The CVR coefficient of the feeder cluster at the current moment. and These represent the adjustable capacity and adjustable capacity of the feeder cluster, respectively. By calculating the adjustable capacity of the feeder cluster, a safety boundary can be provided for the power dispatching agency to issue frequency regulation commands.
[0072] In this embodiment, since on-load tap-changing transformers and capacitor banks are discrete regulating devices, the feeder load cluster will generate power response deviations when responding to the secondary frequency regulation command of the power grid. Considering that the feeder load regulation behavior directly affects the power quality on the user side, in step S3, a secondary frequency regulation command decomposition and optimization model that balances frequency regulation accuracy and power quality is constructed, including the following steps: Step S31: Establish a power quality classification function based on voltage deviation.
[0073] In this embodiment, the expression for the power quality classification function is:
[0074] in, Power quality level, This refers to the node voltage deviation. In other words, power quality can be divided into three levels, with an allowable voltage fluctuation range of no more than 7%. A higher power quality level indicates better power quality and a smaller deviation between the node voltage and the rated voltage.
[0075] Step S32: Construct a secondary frequency regulation command decomposition and optimization model based on the power quality classification function, with the goal of minimizing the combined impact of the power grid secondary frequency regulation command response deviation and power quality.
[0076] For example, the objective function of the secondary frequency modulation command decomposition optimization model is expressed as:
[0077] in, This refers to the secondary frequency regulation command issued by the power grid during time period t. Let be the active power adjustment of the i-th feeder cluster during time period t. The number of feeder clusters participating in the regulation. Let be the power quality level of the i-th feeder cluster node j during time period t. Let j be the power quality level of the i-th feeder cluster node j during time period t-1. Let be the number of nodes in the i-th feeder cluster. and These are preset weighting coefficients. In this embodiment, Greater than ,For example, and These can be 0.8 and 0.2 respectively. In the above objective function, the first term aims to reduce the response deviation of the power grid's secondary frequency regulation command, while the second term improves the power quality for users by reducing the voltage offset at each feeder load node. The second term is only greater than 0 when the power quality level decreases after regulation.
[0078] For example, the secondary frequency regulation command decomposition optimization model considers power flow constraints, node voltage constraints, and grid secondary frequency regulation command constraints. Power flow constraints and node voltage constraints can be found in the aforementioned expressions and will not be repeated here.
[0079] In this embodiment, it is considered that the on-load tap-changing transformer capacitor bank is a discrete control device, and its response process is allowed to have a certain deviation, but the deviation amplitude needs to be controlled within a reasonable range. Therefore, a maximum allowable response deviation rate can be introduced to quantitatively characterize this constraint.
[0080] For example, the constraint of the power grid secondary frequency regulation command is expressed as:
[0081]
[0082] in, The sum of the power response values of the feeder cluster during time period t. For the maximum permissible response deviation, Let be the CVR coefficient of the i-th feeder cluster. Let be the bus voltage regulation of the i-th feeder cluster during time period t. In this embodiment, the maximum permissible response deviation is... You can take 5%.
[0083] The following are device embodiments of this application. For details not described in detail in the device embodiments, please refer to the above method embodiments.
[0084] Figure 2 This is a structural block diagram of a multi-feeder trunking secondary frequency modulation collaborative control device based on voltage-active power coupling characteristics, provided in an embodiment of this application. Figure 2 As shown, the multi-feeder cluster secondary frequency modulation collaborative control device 100 based on voltage-active power coupling characteristics includes an identification module 101, a calculation module 102, and a construction module 103.
[0085] The identification module 101 is used to describe the voltage-active power coupling characteristics of the feeder cluster using the CVR coefficient model to construct the feeder cluster regulation characteristic model, and to identify the CVR coefficient of the feeder cluster based on the voltage and power data during the grid operation.
[0086] The calculation module 102 is used to solve the voltage regulation boundary based on power flow optimization with the voltage security of the power grid nodes as a constraint, and to calculate the adjustable capacity of the feeder cluster in combination with the CVR coefficient.
[0087] The construction module 103 is used to build a secondary frequency regulation command decomposition and optimization model based on adjustable capacity, taking into account both frequency regulation accuracy and power quality. Considering the discrete adjustment characteristics of reactive power compensation equipment, the secondary frequency regulation command of the power grid is optimally decomposed to each feeder cluster, and the coordinated response of multiple feeder clusters is achieved by adjusting the reactive power compensation equipment.
[0088] It should be noted that the above embodiments of the multi-feeder trunking secondary frequency modulation coordinated control device based on voltage-active power coupling characteristics are only illustrated by the division of the functional modules described above. In practical applications, the functions described above can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the above embodiments of the multi-feeder trunking secondary frequency modulation coordinated control device based on voltage-active power coupling characteristics and the embodiments of the multi-feeder trunking secondary frequency modulation coordinated control method based on voltage-active power coupling characteristics belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.
[0089] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods are possible. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.
[0090] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal device (which may be a personal computer, mobile phone, or communication device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0091] Figure 3 This is a schematic diagram of the structure of the computer device provided in an embodiment of this application. Figure 3 As shown, the computer device 200 includes a processor 201 and a memory 202.
[0092] Processor 201 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 201 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 201 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 201 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 201 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.
[0093] The memory 202 may include one or more computer-readable storage media, which may be non-transitory. The memory 202 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 202 is used to store at least one instruction, which is executed by the processor 201 to implement the multi-feeder cluster secondary frequency modulation cooperative control method based on voltage-active power coupling characteristics provided in the embodiments of this application.
[0094] Those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the computer device 200, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.
[0095] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a computer device, enables the computer device to execute the multi-feeder cluster secondary frequency modulation collaborative control method based on voltage-active power coupling characteristics provided in this application.
[0096] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the multi-feeder cluster secondary frequency modulation collaborative control method based on voltage-active power coupling characteristics provided in this application.
[0097] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A secondary frequency modulation coordinated control method for multi-feeder clusters based on voltage-active power coupling characteristics, characterized in that, include: The voltage-active power coupling characteristics of the feeder cluster are described by the CVR coefficient model to construct the feeder cluster regulation characteristic model, and the CVR coefficient of the feeder cluster is identified based on the voltage and power data during grid operation. With the voltage security of power grid nodes as a constraint, the voltage regulation boundary is solved based on power flow optimization, and the adjustable capacity of the feeder cluster is calculated in combination with the CVR coefficient. Based on the adjustable capacity, a secondary frequency regulation command decomposition and optimization model is constructed that takes into account both frequency regulation accuracy and power quality. Considering the discrete adjustment characteristics of reactive power compensation equipment, the secondary frequency regulation command of the power grid is optimally decomposed to each feeder cluster, and the coordinated response of multiple feeder clusters is achieved by adjusting the reactive power compensation equipment.
2. The method for secondary frequency modulation and coordinated control of multi-feeder clusters based on voltage-active power coupling characteristics according to claim 1, characterized in that, The CVR coefficient of the feeder cluster identified based on voltage and power data during power grid operation is expressed as follows: in, The CVR coefficient of the feeder cluster. This represents the measured value of the feeder voltage at the i-th sampling time. This represents the measured value of active power at the i-th sampling time. To identify the number of sampling points within a period.
3. The multi-feeder cluster secondary frequency modulation coordinated control method based on voltage-active power coupling characteristics according to claim 1, characterized in that, The process of using grid node voltage security as a constraint, solving the voltage regulation boundary based on power flow optimization, and calculating the adjustable capacity of the feeder cluster in conjunction with the CVR coefficient includes: Considering power flow constraints, node voltage constraints, and reactive power compensation equipment operation constraints, based on power flow analysis, an optimization model is constructed to maximize the voltage regulation range of the low-voltage side bus of the main transformer for two operating scenarios: voltage increase and voltage decrease. The adjustable boundary and adjustable boundary of the low-voltage side bus voltage of the main transformer are calculated. Based on the adjustable upper and lower limits of the low-voltage side bus voltage of the main transformer, the adjustable upper and lower limits of the feeder cluster are calculated in conjunction with the CVR coefficient.
4. The multi-feeder cluster secondary frequency modulation coordinated control method based on voltage-active power coupling characteristics according to claim 3, characterized in that, The reactive power compensation equipment includes an on-load tap-changing transformer and a capacitor bank; The objective function of the optimization model, which aims to maximize the voltage regulation range of the low-voltage side bus of the main transformer, is expressed as: in, and These represent the voltage changes on the low-voltage side bus of the main transformer under two operating scenarios: voltage increase and voltage decrease. This represents the voltage change corresponding to a change in the tap position of an on-load tap-changing transformer. This refers to the tap position of the on-load tap-changing transformer at the current moment. and These refer to the tap positions of the on-load tap-changing transformers corresponding to the upper and lower adjustable boundaries of the low-voltage bus voltage of the main transformer. and These represent the increase and decrease in voltage on the low-voltage side bus of the main transformer caused by capacitor bank switching, respectively. The formulas for calculating the adjustable capacity and adjustable capacity of the feeder cluster are as follows: in, and These are the adjustable upper and lower limits of the low-voltage bus voltage of the main transformer, respectively. Measured voltage of the low-voltage side busbar of the main transformer. and These refer to the adjustable range of the low-voltage bus voltage of the main transformer and the adjustable range of the low-voltage side voltage, respectively. The CVR coefficient of the feeder cluster at the current moment. and These refer to the adjustable capacity and adjustable capacity of the feeder cluster, respectively.
5. The method for secondary frequency modulation and coordinated control of multi-feeder clusters based on voltage-active power coupling characteristics according to claim 1, characterized in that, The construction of the secondary frequency modulation command decomposition optimization model that takes into account both frequency modulation accuracy and power quality includes: Establish a power quality classification function based on voltage deviation; Based on the power quality classification function, a secondary frequency regulation command decomposition and optimization model is constructed with the goal of minimizing the combined impact of the power grid secondary frequency regulation command response deviation and power quality.
6. The multi-feeder cluster secondary frequency modulation coordinated control method based on voltage-active power coupling characteristics according to claim 5, characterized in that, The expression for the power quality classification function is: in, Power quality level, This represents the node voltage deviation.
7. The multi-feeder cluster secondary frequency modulation coordinated control method based on voltage-active power coupling characteristics according to claim 6, characterized in that, The objective function of the secondary frequency modulation command decomposition and optimization model is expressed as: in, This refers to the secondary frequency regulation command issued by the power grid during time period t. Let be the active power adjustment of the i-th feeder cluster during time period t. The number of feeder clusters participating in the regulation. Let be the power quality level of the i-th feeder cluster node j during time period t. Let j be the power quality level of the i-th feeder cluster node j during time period t-1. Let be the number of nodes in the i-th feeder cluster. and These are the preset weighting coefficients.
8. The multi-feeder cluster secondary frequency modulation coordinated control method based on voltage-active power coupling characteristics according to claim 7, characterized in that, The secondary frequency regulation command decomposition and optimization model considers power flow constraints, node voltage constraints, and grid secondary frequency regulation command constraints. The power grid secondary frequency regulation command constraint is expressed as follows: in, The sum of the power response values of the feeder cluster during time period t. For the maximum permissible response deviation, Let be the CVR coefficient of the i-th feeder cluster. Let be the bus voltage regulation of the i-th feeder cluster during time period t.
9. A multi-feeder cluster secondary frequency modulation coordinated control device based on voltage-active power coupling characteristics, characterized in that, include: The identification module is used to describe the voltage-active power coupling characteristics of the feeder cluster using the CVR coefficient model to construct the feeder cluster regulation characteristic model, and to identify the CVR coefficient of the feeder cluster based on the voltage and power data during grid operation. The calculation module is used to solve the voltage regulation boundary based on power flow optimization with the voltage security of the power grid nodes as a constraint, and to calculate the adjustable capacity of the feeder cluster in combination with the CVR coefficient. The module is used to construct a secondary frequency regulation command decomposition and optimization model that takes into account both frequency regulation accuracy and power quality based on the adjustable capacity. It decomposes the secondary frequency regulation command of the power grid to each feeder cluster in the optimal way, taking into account the discrete adjustment characteristics of reactive power compensation equipment, and realizes the coordinated response of multiple feeder clusters by adjusting the reactive power compensation equipment.
10. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the multi-feeder cluster secondary frequency modulation collaborative control method based on voltage-active power coupling characteristics as described in any one of claims 1 to 8.