A flexible resource cluster adaptive droop control method and device

Abstract

The application discloses a flexible resource cluster adaptive droop control method and device, relates to the technical field of power grids, and introduces power deviation and power response variables to construct a power response model containing the synergistic effect of multiple resources in a cluster based on distribution network basic parameters and flexible resource cluster parameters. According to the power response model containing the synergistic effect of multiple resources in a cluster, the dynamic adjustment control rule of adaptive droop gain is determined in combination with the actual working condition dynamic characteristics of the distribution network and the operating state of the flexible resource cluster. The power response speed and output of the flexible resource cluster are dynamically optimized according to the dynamic adjustment control rule of adaptive droop gain. The technical problem that the control parameters of the existing flexible resource cluster droop control scheme are fixed, it is difficult to match the working condition of the power system in real time, and the power stability of the power system during multi-resource collaborative regulation is affected is solved.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a flexible resource cluster adaptive droop control method and apparatus. Background Technology

[0002] With the continuous advancement of new power system construction, flexible resource clusters, represented by electrochemical energy storage, virtual synchronous machines, and distributed power sources, are increasingly penetrating distribution networks and regional power grids, becoming core regulation resources for smoothing power fluctuations and supporting stable grid operation. These resources have advantages such as fast response speed, high regulation accuracy, and flexible deployment. They can quickly track grid power deviations, participate in system active power regulation, and effectively alleviate the regulation pressure on traditional synchronous generators.

[0003] However, the high proportion of flexible resource clusters also brings new technical challenges. Their fast power response characteristics differ significantly from those of traditional synchronous generator governors. When multiple resources collaborate in power regulation, power control interaction interference is easily triggered, leading to increased system power fluctuations and threatening grid power stability and transient security. This power interaction problem is particularly prominent in weak network, low-inertia, or high-penetration renewable energy scenarios, and has become a key bottleneck restricting the large-scale grid-connected application of flexible resources.

[0004] Existing flexible resource cluster droop control schemes are power control methods for energy storage systems based on fixed droop gain and dead-zone constraints. This method borrows from the traditional synchronous generator droop control principle, mapping grid power deviations to power regulation commands from the energy storage system through offline-tuned fixed droop coefficients. It also introduces fixed power deviation dead zones and state-of-charge constraints to achieve basic power regulation. However, the core control parameters of this method, such as droop gain and dead-zone threshold, are all offline-preset fixed values. They cannot be dynamically adjusted based on real-time conditions such as the number of synchronous generators participating in power regulation, the equivalent characteristics of the speed governor, and the strength of the grid system. This can easily lead to power control interaction interference during multi-resource coordinated regulation, exacerbating system power fluctuations. Therefore, overcoming the limitations of fixed flexible resource cluster droop control parameters and achieving real-time matching of control parameters with system operating conditions to improve power stability during multi-resource coordinated regulation is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a flexible resource cluster adaptive droop control method and apparatus to solve the technical problem that existing flexible resource cluster droop control schemes have fixed control parameters, making it difficult to match the power system's operating conditions in real time and affecting the power stability of the power system during multi-resource coordinated regulation.

[0006] In view of this, the first aspect of the present invention provides a flexible resource cluster adaptive droop control method, comprising:

[0007] Obtain basic parameters of the distribution network and parameters of the flexibility resource cluster;

[0008] Based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, power deviation and power response variables are introduced to construct a power response model that includes the synergistic effect of multiple resources within the cluster;

[0009] Based on the power response model that includes the synergistic effect of multiple resources within the cluster, and combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster, the dynamic adjustment control rules for adaptive droop gain are determined.

[0010] The control parameters of the flexible resource cluster are dynamically optimized based on the dynamic adjustment control rule of adaptive droop gain.

[0011] Optionally, the power response speed and output of the flexible resource cluster are dynamically optimized according to the dynamic adjustment control rules of the adaptive droop gain, and then the following is also included:

[0012] Based on the dynamic adjustment control rules of adaptive droop gain, a coordinated control framework integrated with the distribution network-level energy management system is constructed.

[0013] Based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions.

[0014] Optionally, control parameters include droop gain of flexibility resources, power response dead zone, and output regulation rate.

[0015] Optionally, based on the basic parameters of the distribution network and the parameters of the flexibility resource cluster, power deviation and power response variables are introduced to construct a power response model that includes the synergistic effect of multiple resources within the cluster, including:

[0016] Based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, a dynamic model of the flexible resource cluster and the distribution network is established.

[0017] Based on the dynamic model of flexible resource clusters and distribution networks, and taking the power deviation of the distribution network as the core input, the power response characteristics of flexible resources within the flexible resource cluster are coupled, and the power response increments of each flexible resource are integrated to construct a power response model that includes the synergistic effect of multiple resources within the cluster.

[0018] Optionally, the dynamic model of the flexible resource cluster and distribution network is as follows:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] in, For the power response increment of distributed energy storage, This represents the power response increment of distributed photovoltaic systems. For the power response increment of the adjustable load, For distributed energy storage, the response time constant is... Let be the equivalent droop constant for distributed energy storage. This refers to the power regulation coefficient of distributed photovoltaic power. The power regulation coefficient for adjustable loads. Where N represents the real-time power deviation of the power grid, and N is the quantity of distributed energy storage. Let be the current between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC bus voltage corresponding to the i-th distributed energy storage unit. Let be the equivalent DC capacitance of the i-th distributed energy storage device. Let be the DC-side current of the i-th distributed energy storage device. Let be the equivalent inductance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent resistance between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent capacitance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC-side capacitor of the i-th distributed energy storage.

[0026] Optionally, the dynamic adjustment control rule for the adaptive droop gain is as follows:

[0027] When distributed photovoltaic power is at full capacity and the distribution network has a power surplus, the droop gain of distributed energy storage is reduced by the first preset step size, the power response dead zone is increased to 0.15~0.2MW, and the charging power response speed of distributed energy storage is reduced by the second preset step size.

[0028] Under the condition of balanced output of distributed photovoltaic power, the distributed energy storage adopts the rated droop gain, configures the power response dead zone to 0.1MW, and keeps the power response speed unchanged.

[0029] When the output of distributed photovoltaic power drops sharply, the droop gain of distributed energy storage is increased by the third preset step size to reduce the power response dead zone to 0.05, and the discharge power response speed of distributed energy storage is increased by the fourth preset step size.

[0030] When the distributed photovoltaic power is insufficient, the droop gain of the distributed energy storage is increased by the fifth preset step size to reduce the power response dead zone to 0.05~0.1MW, and the power response speed of the distributed energy storage is increased by the sixth preset step size.

[0031] When the state of charge of the distributed energy storage battery reaches the lower limit, the droop gain on the discharge side of the distributed energy storage is reduced by the seventh preset step. When the state of charge of the distributed energy storage battery reaches the upper limit, the droop gain on the charging side of the distributed energy storage is reduced by the eighth preset step.

[0032] Optionally, the power response model that includes the synergistic effect of multiple resources within the cluster is as follows:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] in, Let i be the power of the i-th distributed energy storage. For power loss in distribution network lines, For state vectors, For the input vector, For the output vector, All are coefficient matrices. State vector The first derivative with respect to time is used to characterize the rate of dynamic change of the system's state. This refers to the power imbalance in the power grid. For active power loss, Let i be the power response increment of the i-th distributed energy storage. Let be the response time constant of distributed photovoltaic power. This is the response time constant for the adjustable load.

[0039] Optionally, based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions, including:

[0040] Construct a distribution network operating condition evaluation index system, combine the actual characteristics of the distribution network active network, select core quantifiable indicators to establish a multi-dimensional distribution network operating condition classification standard, quantify the power regulation requirements under different operating conditions, and optimize the control parameters under different distribution network operating conditions.

[0041] Optionally, core quantifiable indicators include distributed photovoltaic power utilization rate, distribution network load factor, average state of charge of distributed energy storage batteries, and distribution network power deviation volatility.

[0042] A second aspect of the present invention provides a flexible resource cluster adaptive droop control device, comprising:

[0043] The parameter acquisition module is used to acquire basic parameters of the distribution network and parameters of the flexibility resource cluster.

[0044] The power response model construction module is used to construct a power response model that includes the synergistic effect of multiple resources within the cluster, based on the basic parameters of the distribution network and the parameters of the flexible resource cluster.

[0045] The adaptive module is used to determine the dynamic adjustment control rules of the adaptive droop gain based on the power response model that includes the synergistic effect of multiple resources within the cluster, combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster.

[0046] The dynamic optimization module is used to dynamically optimize the control parameters of the flexible resource cluster based on the dynamic adjustment control rules of adaptive droop gain.

[0047] Optionally, the dynamic optimization of the power response speed and output of the flexible resource cluster based on the adaptive droop gain dynamic adjustment control rule also includes a closed-loop control module, which is used for:

[0048] Based on the dynamic adjustment control rules of adaptive droop gain, a coordinated control framework integrated with the distribution network-level energy management system is constructed.

[0049] Based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions.

[0050] Optionally, control parameters include droop gain of flexibility resources, power response dead zone, and output regulation rate.

[0051] Optionally, based on the basic parameters of the distribution network and the parameters of the flexibility resource cluster, power deviation and power response variables are introduced to construct a power response model that includes the synergistic effect of multiple resources within the cluster, including:

[0052] Based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, a dynamic model of the flexible resource cluster and the distribution network is established.

[0053] Based on the dynamic model of flexible resource clusters and distribution networks, and taking the power deviation of the distribution network as the core input, the power response characteristics of flexible resources within the flexible resource cluster are coupled, and the power response increments of each flexible resource are integrated to construct a power response model that includes the synergistic effect of multiple resources within the cluster.

[0054] Optionally, the dynamic model of the flexible resource cluster and distribution network is as follows:

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061] in, For the power response increment of distributed energy storage, This represents the power response increment of distributed photovoltaic systems. For the power response increment of the adjustable load, For distributed energy storage, the response time constant is... Let be the equivalent droop constant for distributed energy storage. This refers to the power regulation coefficient of distributed photovoltaic power. The power regulation coefficient for adjustable loads. Where N represents the real-time power deviation of the power grid, and N is the quantity of distributed energy storage. Let be the current between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC bus voltage corresponding to the i-th distributed energy storage unit. Let be the equivalent DC capacitance of the i-th distributed energy storage device. Let be the DC-side current of the i-th distributed energy storage device. Let be the equivalent inductance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent resistance between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent capacitance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC-side capacitor of the i-th distributed energy storage.

[0062] Optionally, the dynamic adjustment control rule for the adaptive droop gain is as follows:

[0063] When distributed photovoltaic power is at full capacity and the distribution network has a power surplus, the droop gain of distributed energy storage is reduced by the first preset step size, the power response dead zone is increased to 0.15~0.2MW, and the charging power response speed of distributed energy storage is reduced by the second preset step size.

[0064] Under the condition of balanced output of distributed photovoltaic power, the distributed energy storage adopts the rated droop gain, configures the power response dead zone to 0.1MW, and keeps the power response speed unchanged.

[0065] When the output of distributed photovoltaic power drops sharply, the droop gain of distributed energy storage is increased by the third preset step size to reduce the power response dead zone to 0.05, and the discharge power response speed of distributed energy storage is increased by the fourth preset step size.

[0066] When the distributed photovoltaic power is insufficient, the droop gain of the distributed energy storage is increased by the fifth preset step size to reduce the power response dead zone to 0.05~0.1MW, and the power response speed of the distributed energy storage is increased by the sixth preset step size.

[0067] When the state of charge of the distributed energy storage battery reaches the lower limit, the droop gain on the discharge side of the distributed energy storage is reduced by the seventh preset step. When the state of charge of the distributed energy storage battery reaches the upper limit, the droop gain on the charging side of the distributed energy storage is reduced by the eighth preset step.

[0068] Optionally, the power response model that includes the synergistic effect of multiple resources within the cluster is as follows:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] in, Let i be the power of the i-th distributed energy storage. For power loss in distribution network lines, For state vectors, For the input vector, For the output vector, All are coefficient matrices. A state vector used to characterize the dynamic rate of change of the system state. The first derivative with respect to time, This refers to the power imbalance in the power grid. For active power loss, Let i be the power response increment of the i-th distributed energy storage. Let be the response time constant of distributed photovoltaic power. This is the response time constant for the adjustable load.

[0075] Optionally, based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions, including:

[0076] Construct a distribution network operating condition evaluation index system, combine the actual characteristics of the distribution network active network, select core quantifiable indicators to establish a multi-dimensional distribution network operating condition classification standard, quantify the power regulation requirements under different operating conditions, and optimize the control parameters under different distribution network operating conditions.

[0077] Optionally, core quantifiable indicators include distributed photovoltaic power utilization rate, distribution network load factor, average state of charge of distributed energy storage batteries, and distribution network power deviation volatility.

[0078] A third aspect of the present invention provides a flexible resource cluster adaptive droop control device, the device comprising a processor and a memory:

[0079] The memory is used to store program code and transmit the program code to the processor;

[0080] The processor is configured to execute any of the flexible resource cluster voltage regulation methods described in the first aspect according to instructions in the program code.

[0081] A fourth aspect of the present invention provides a computer-readable storage medium for storing program code for executing any of the flexible resource cluster adaptive droop control methods described in the first aspect.

[0082] The fifth aspect of the present invention provides a computer program product including instructions that, when run on a computer, cause the computer to perform any of the flexible resource cluster adaptive droop control methods described in the first aspect.

[0083] As can be seen from the above technical solutions, the flexible resource cluster adaptive droop control method provided by the present invention has the following advantages:

[0084] The adaptive droop control method for flexible resource clusters provided by this invention, based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, introduces power deviation and power response variables to construct a power response model that includes the synergistic effect of multiple resources within the cluster. Based on this power response model, and combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster, a dynamic adjustment control rule for the adaptive droop gain is determined. This method dynamically optimizes the power response speed and output of the flexible resource cluster according to the dynamic adjustment control rule for the adaptive droop gain, overcoming the limitation of fixed droop control parameters for flexible resource clusters. It achieves real-time matching of the droop control parameters of the flexible resource clusters with the operating conditions of the power system, solving the technical problem that existing flexible resource cluster droop control schemes have fixed control parameters, making it difficult to match with the real-time operating conditions of the power system and affecting the power stability of the power system during multi-resource coordinated regulation.

[0085] The flexible resource cluster adaptive droop control method provided by this invention constructs a coordinated control framework integrated with the distribution network-level energy management system, introduces a distribution network operating condition assessment mechanism, optimizes control parameters for different distribution network operating conditions, improves the operating condition adaptability and robustness of the flexible resource cluster droop control, meets the actual application needs of large-scale power systems, and improves the power stability of the distribution network under complex scenarios such as sudden changes in photovoltaic output and peak-valley load switching.

[0086] The flexible resource cluster adaptive droop control method provided by this invention integrates distributed energy storage, distributed photovoltaic and adjustable loads in the distribution network to form a flexible resource cluster, establishes a collaborative regulation mechanism for multiple resources in the cluster, and forms a regulatory synergy. Compared with the independent control of individual resources in the prior art, the regulation efficiency of power balance in the distribution network is improved, and it is more suitable for the local consumption needs of the distribution network.

[0087] The flexible resource cluster adaptive droop control device, equipment, computer-readable storage medium, and computer program product provided by this invention are all used to execute the flexible resource cluster adaptive droop control method provided by this invention. Their principles and the technical effects achieved are the same as those of the flexible resource cluster adaptive droop control method provided by this invention, and will not be repeated here. Attached Figure Description

[0088] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0089] Figure 1This is a flowchart illustrating a flexible resource cluster adaptive droop control method provided in an embodiment of the present invention;

[0090] Figure 2 This is a comparison chart of the power deviation variation trend of the distribution network between the traditional droop control method provided in the embodiments of the present invention and the method provided in the present invention;

[0091] Figure 3 This is a schematic diagram of the structure of a flexible resource cluster adaptive droop control device provided in an embodiment of the present invention;

[0092] Figure 4 This is a schematic diagram of a flexible resource cluster adaptive droop control device provided in an embodiment of the present invention. Detailed Implementation

[0093] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0094] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0095] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of these specific details, or other methods, components, materials, devices, etc. In these cases, well-known structures, methods, devices, implementations, materials, or operations will not be shown or described in detail.

[0096] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0097] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0098] For easier understanding, please refer to Figure 1 This invention provides an embodiment of a flexible resource cluster adaptive droop control method, comprising:

[0099] Step 101: Obtain the basic parameters of the distribution network and the parameters of the flexibility resource cluster.

[0100] It should be noted that flexible resources include distributed energy storage, distributed photovoltaics, and adjustable loads. In this embodiment of the invention, the basic parameters of the distribution network and the parameters of the flexible resource cluster are first obtained to provide data support for subsequent model construction and optimization of control parameters (control parameters include the droop gain, power response dead zone, and output regulation rate of the flexible resources). The basic parameters of the distribution network include the rated voltage of the distribution network, line impedance parameters, bus power reference value, load reference value for different time periods (peak / valley / flat), allowable power deviation threshold, and distribution network EMS communication protocol. The parameters of the flexible resource cluster include the total capacity of distributed energy storage, the rated power / voltage of a single distributed energy storage unit, the upper and lower limits of the state of charge of the distributed energy storage battery, the initial droop gain, the total installed capacity of distributed photovoltaics, the maximum power point tracking adjustment range, the adjustable load adjustment capacity, the adjustable load response time, and the access bus location and communication address of each resource in the flexible resource cluster. The basic parameters of the distribution network and the parameters of the flexible resource cluster are entered into the distribution network EMS (Energy Management System), which performs parameter verification and unified management, providing basic data for subsequent control processes.

[0101] Step 102: Based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, introduce power deviation and power response variables to construct a power response model that includes the synergistic effect of multiple resources within the cluster.

[0102] It should be noted that, based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, a dynamic model of the flexible resource cluster and the distribution network is established. Specifically, power response models for distributed energy storage, distributed photovoltaics, and adjustable loads are established separately to clarify the dynamic relationship between the output power of each resource and the power deviation of the distribution network. The core formula of the dynamic model of the flexible resource cluster and the distribution network is:

[0103]

[0104]

[0105]

[0106] in, For the power response increment of distributed energy storage, This represents the power response increment of distributed photovoltaic systems. For the power response increment of the adjustable load, For distributed energy storage, the response time constant is... Let be the equivalent droop constant for distributed energy storage. This refers to the power regulation coefficient of distributed photovoltaic power. The power regulation coefficient for adjustable loads. This represents the real-time power deviation of the power grid.

[0107] Based on this, a state-space model for distributed energy storage is constructed:

[0108]

[0109]

[0110]

[0111] Where N represents the number of distributed energy storage devices. Let be the current between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC bus voltage corresponding to the i-th distributed energy storage unit. Let be the equivalent DC capacitance of the i-th distributed energy storage device. Let be the DC-side current of the i-th distributed energy storage device. Let be the equivalent inductance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent resistance between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent capacitance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC-side capacitor of the i-th distributed energy storage.

[0112] Then, based on the dynamic model of the flexible resource cluster and the distribution network, and taking the power deviation of the distribution network as the core input, the power response characteristics of the flexible resources within the flexible resource cluster are coupled, and the power response increments of each flexible resource are integrated to construct a power response model that includes the synergistic effect of multiple resources within the cluster. Specifically, taking the power deviation of the distribution network as the core input, the power response characteristics of distributed energy storage, distributed photovoltaics, and adjustable loads within the flexible resource cluster are coupled, and the power response increments of each flexible resource are integrated to construct an overall power response model that includes the synergistic effect of multiple resources within the cluster.

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] in, Let i be the power of the i-th distributed energy storage. For power loss in distribution network lines, For state vectors, For the input vector, For the output vector, All are coefficient matrices. State vector The first derivative with respect to time is used to characterize the rate of dynamic change of the system's state. This refers to the power imbalance in the power grid. For active power loss, Let i be the power response increment of the i-th distributed energy storage. Let be the response time constant of distributed photovoltaic power. This is the response time constant for the adjustable load.

[0119] Using the A matrix, root loci are plotted based on the actual operating points of multiple distributed energy storage units within the flexible resource cluster. This evaluates the small-signal power stability of the distribution network when the controller gain changes. At the same time, key variables such as distribution network power deviation, power response amplitude and response rate of each flexible resource within the flexible resource cluster are introduced to achieve accurate modeling and characterization of the power interaction response process of multiple resources within the flexible resource cluster under distribution network power fluctuations.

[0120] Step 103: Based on the power response model that includes the synergistic effect of multiple resources within the cluster, and combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster, determine the dynamic adjustment control rules for adaptive droop gain.

[0121] It should be noted that the dynamic adjustment control rules for adaptive droop gain are determined in order to dynamically optimize the power response speed and output of the flexible resource cluster based on the output of distributed power sources, load fluctuations and the operating status of flexible resources in the distribution network.

[0122] First, a mathematical model of distributed energy storage on the DC side is established to quantify the dynamic correlation between DC-side energy storage and power output:

[0123]

[0124] Where E represents the DC-side energy storage capacity of distributed energy storage. This is the equivalent capacitance on the DC side of distributed energy storage. This is the real-time voltage of the DC bus.

[0125] For flexible resource clusters equipped with multiple grid-side converters in a distribution network, a quantitative model of the power change in the distribution network caused by the injection or absorption of power by the flexible resource cluster is constructed to clarify the correlation between power response and droop gain:

[0126]

[0127]

[0128] in, Let be the adaptive droop gain of the i-th distributed energy storage. This is the rated power of distributed energy storage. This refers to the rated power of distributed photovoltaic power. The rated power of the adjustable load, This is the reference value for the power of the distribution network bus.

[0129] Based on the above correlations, and combining the dynamic characteristics of the actual operating conditions of the distribution network with the operating status of the flexible resource cluster, the comprehensive adjustment coefficient of the cluster is obtained through weighted calculation. This is used to derive the dynamic adjustment control method for adaptive droop gain, and to establish a linkage matching rule between droop gain and the operating conditions of the distribution network and the resource status of the flexible resource cluster. In this embodiment of the invention, the dynamic adjustment control rule for adaptive droop gain is as follows:

[0130] When distributed photovoltaic power is at full capacity and the distribution network has a power surplus (i.e., the load is in a valley), the droop gain of distributed energy storage is reduced by the first preset step size, the power response dead zone is increased to 0.15~0.2MW, the charging power response speed of distributed energy storage is reduced by the second preset step size, and the power is absorbed locally in conjunction with adjustable load increase, so as to avoid power fluctuation caused by rapid resource adjustment.

[0131] Under the condition of balanced output of distributed photovoltaic power, the distributed energy storage adopts the rated droop gain, configures the power response dead zone to 0.1MW, keeps the power response speed unchanged, and maintains the power balance of the distribution network.

[0132] When the output of distributed photovoltaic power drops sharply, the droop gain of distributed energy storage is increased by the third preset step size to reduce the power response dead zone to 0.05MW. The discharge power response speed of distributed energy storage is increased by the fourth preset step size. The fastest output adjustment rate is adopted to achieve coordinated adjustment of all resources in the cluster and quickly smooth out power fluctuations in the distribution network.

[0133] When there is a power deficit in distributed photovoltaic power, the droop gain of distributed energy storage is increased by the fifth preset step size to reduce the power response dead zone to 0.05~0.1MW. The power response speed of distributed energy storage is increased by the sixth preset step size to accelerate the output adjustment rate, quickly make up for the power deficit in the distribution network, and ensure stable power supply.

[0134] When the state of charge of the distributed energy storage battery reaches the lower limit (less than or equal to 20%), the droop gain on the discharge side of the distributed energy storage is reduced by the seventh preset step size to reduce the discharge output. When the state of charge of the distributed energy storage battery reaches the upper limit (greater than or equal to 90%), the droop gain on the charging side of the distributed energy storage is reduced by the eighth preset step size to reduce the charging output and avoid overcharging and over-discharging.

[0135] Step 104: Dynamically optimize the control parameters of the flexible resource cluster according to the dynamic adjustment control rules of adaptive droop gain.

[0136] It should be noted that, through real-time adjustment The value of is used to achieve dynamic optimization of the power response speed and output of flexible resource clusters, adapting to the actual power regulation needs of the distribution network.

[0137] The adaptive droop control method for flexible resource clusters provided by this invention, based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, introduces power deviation and power response variables to construct a power response model that includes the synergistic effect of multiple resources within the cluster. Based on the power response model including the synergistic effect of multiple resources within the cluster, and combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster, a dynamic adjustment control rule for the adaptive droop gain is determined. The control parameters of the flexible resource cluster power are dynamically optimized according to the dynamic adjustment control rule for the adaptive droop gain. This method overcomes the limitation of fixed droop control parameters for flexible resource clusters, achieving real-time matching between the droop control parameters of the flexible resource cluster and the operating conditions of the power system. It solves the technical problem that existing flexible resource cluster droop control schemes have fixed control parameters, making it difficult to match with the real-time operating conditions of the power system, thus affecting the power stability of the power system during multi-resource coordinated regulation.

[0138] In one embodiment, step 104 is followed by:

[0139] Step 105: Based on the dynamic adjustment control rules of adaptive droop gain, construct a coordinated control framework that is integrated with the distribution network-level energy management system.

[0140] It should be noted that a coordinated control framework integrating with the distribution network-level energy management system (EMS) is constructed based on the dynamic adjustment control rules of adaptive droop gain. The core control principle of the coordinated control framework is as follows: when there are sufficient power regulation resources in the distribution network, a smaller droop gain is initially configured to actively slow down the power response speed of the flexible resource cluster. After the power deviation in the distribution network has been initially allocated by the EMS, the droop gain is dynamically increased to enable the flexible resource cluster to respond quickly to the power regulation demand. When there are insufficient regulation resources in the distribution network, a higher droop gain is directly configured. Combined with the maximum regulation capacity of distributed photovoltaic and adjustable loads, the power deviation is quickly smoothed out, realizing the step-by-step coordinated power regulation of multiple resources within the cluster.

[0141] Based on the actual operational safety requirements of the power distribution network, three types of core constraints are incorporated into the coordinated control framework to ensure the engineering practicality of the control strategy. These three types of core constraints are:

[0142] Flexible resource operation constraints: upper and lower limits of state of charge of distributed energy storage batteries, limits of charging and discharging power, maximum tracking power output range of distributed photovoltaics, and limits of regulation capacity and regulation rate of adjustable loads.

[0143] Constraints for safe operation of distribution networks: allowable deviation of distribution network bus voltage (±7%), limit of line power transmission, and allowable threshold of power deviation.

[0144] Cluster coordinated regulation constraints: The power regulation direction of each resource within the cluster is consistent, and the total regulation amount matches the power deviation of the distribution network.

[0145] To address the dynamic variation characteristics of power deviation in distribution networks, a global dynamic solution for adaptive droop gain is constructed to achieve precise matching of droop gain with distribution network operating conditions and cluster resource status. The core formula is as follows:

[0146]

[0147]

[0148]

[0149] Deadband is the power deviation dead zone threshold. Adaptive droop control is triggered when the power deviation exceeds this threshold. For the actual droop gain of the i-th flexibility resource, For the rated droop gain of the i-th flexibility resource, This is the weighted average equivalent droop gain of the power generation equipment within the system. Let be the power reset amount for the i-th flexibility resource. This is a positive adjustment constant used to optimize the stability of gain adjustment. This is a lower bound constraint on the droop gain of the i-th flexibility resource. This is an upper limit constraint on the droop gain of the i-th flexibility resource.

[0150] As can be seen from the above formula, a larger equivalent governor droop coefficient This means the speed controller responds quickly, and its flexible resources can prevent rapid changes in output by adjusting the droop coefficient. Reduce active power commands to prevent control interference. Conversely, when the equivalent governor droop coefficient... When the speed is low, the overall response of the speed controller is slow, prompting the flexibility resources to increase the droop gain in order to achieve a faster response.

[0151] The coordinated control framework is deeply integrated with the distribution network-level EMS, relying on the EMS to achieve closed-loop control of data acquisition, command calculation, command issuance, and status feedback, which closely matches the actual management and control process of the distribution network.

[0152] Data Acquisition: EMS collects data in real time, such as power deviation of the distribution network, operating status of each resource in the flexibility resource cluster, and voltage / power of the distribution network bus, through the distribution network terminal.

[0153] Command calculation: The EMS substitutes the collected data into the dynamic adjustment control rules of the adaptive droop gain, and calculates in real time the power adjustment commands of each flexible resource in the cluster and the adaptive droop gain of the distributed energy storage.

[0154] Command issuance: EMS issues adjustment commands to the local controllers of each flexible resource within the cluster via communication protocols.

[0155] Status feedback: After each flexible resource local controller issues a qualitative adjustment command, it feeds back the actual operating status to the EMS. The EMS monitors the adjustment effect in real time. If the deviation exceeds the threshold or the resource exceeds the limit, the control parameter correction is triggered immediately.

[0156] Step 106: Based on the coordinated control framework, introduce a distribution network operating condition assessment mechanism to optimize control parameters for different distribution network operating conditions.

[0157] It should be noted that, based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced. This abandons the traditional generalized parameter design and instead designs differentiated control parameter optimization strategies for typical operating conditions of the actual distribution network. This effectively suppresses power control interaction and power fluctuation phenomena, improving the adaptability of the strategy in real-world distribution network scenarios. Specifically, a distribution network operating condition assessment index system is constructed. Combining the actual characteristics of the distribution network's active network, core quantifiable indicators are selected to establish a multi-dimensional distribution network operating condition classification standard. This quantifies the power regulation requirements under different operating conditions and optimizes the control parameters under different distribution network operating conditions. The core quantifiable indicators include distributed photovoltaic power utilization rate, distribution network load factor, average state of charge of distributed energy storage batteries, and distribution network power deviation volatility.

[0158] Distributed photovoltaic power utilization rate: Actual photovoltaic power output / Rated installed capacity (used to reflect the photovoltaic power output status).

[0159] Distribution network load factor: Actual bus load / Rated load (used to reflect peak and valley load conditions).

[0160] Average State of Charge (SOC) of Distributed Energy Storage Batteries: The arithmetic mean of the SOC of ESS within the cluster (used to reflect energy storage regulation capability).

[0161] Distribution network power deviation fluctuation rate: The magnitude of change in distribution network power deviation per unit time (used to reflect the degree of power fluctuation).

[0162] Based on the value range of each indicator, the distribution network operating conditions are divided into four typical categories: full photovoltaic (PV) generation to load valley, flat PV generation to load flat, low PV generation to load peak, and severe power fluctuation. For each of these four typical operating conditions, and considering the actual operational requirements of local power consumption and safe and stable operation of the distribution network, differentiated dynamic optimization is performed on control parameters such as droop gain, power response dead zone, and output regulation rate of the flexible resource cluster. All optimization strategies are automatically executed by the distribution network EMS without manual intervention.

[0163] Under the conditions of full photovoltaic power generation and load trough (power surplus), the droop gain is reduced to 0.5 to 1.0 times the rated value, while the power deviation dead zone is increased to 0.15 to 0.2MW, and the output adjustment rate is slowed down, so as to realize the local absorption of power surplus in the distribution network and avoid power fluctuations caused by rapid resource adjustment.

[0164] Under the photovoltaic power generation-load balance (power balance) condition, the rated droop gain (1.0 times) is adopted, and a conventional power deviation dead zone of 0.1MW is configured to maintain the conventional output regulation rate and maintain the power balance of the distribution network.

[0165] During periods of low photovoltaic power generation and peak load (power deficit), the droop gain is increased to 1.5 to 2.0 times the rated value, while the power deviation dead zone is reduced to 0.05 to 0.1 MW. The output adjustment rate is also accelerated to quickly make up for the power deficit in the distribution network and ensure stable power supply.

[0166] Under conditions of severe power fluctuations (sudden changes in photovoltaic / load), the droop gain is dynamically adjusted in real time to reduce the power deviation dead zone to 0.05MW. The fastest output regulation rate is adopted to achieve coordinated regulation of all resources in the cluster and quickly smooth out power fluctuations in the distribution network.

[0167] Through the above-mentioned distribution network operating condition assessment, the control parameters are adapted to the actual operating conditions of the distribution network in real time, suppressing power control interaction within the flexible resource cluster and power fluctuations in the distribution network, significantly improving the operating condition adaptability and robustness of the control strategy, and ensuring that the system maintains good power stability under the complex and ever-changing actual operating conditions of the distribution network.

[0168] The flexible resource cluster adaptive droop control method provided by this invention integrates distributed energy storage, distributed photovoltaic and adjustable loads in the distribution network to form a flexible resource cluster, establishes a collaborative regulation mechanism for multiple resources in the cluster, and forms a regulatory synergy. Compared with the independent control of individual resources in the prior art, the regulation efficiency of power balance in the distribution network is improved, and it is more suitable for the local consumption needs of the distribution network.

[0169] To verify the technical effectiveness of the flexible resource cluster adaptive droop control method provided in this invention, a specific calculation example is provided:

[0170] This study uses an improved power system test model adapted to the characteristics of the distribution network as the research object to verify the power deviation suppression effect under peak load conditions. The system has a rated voltage of 10kV and a total load demand of 102.488MW. Key bus nodes of the distribution network are selected as core power regulation nodes, and a flexible resource cluster with a total capacity of 1.5GW is connected at multiple feedback line nodes. The initial droop gain of the cluster is configured with a fixed value set offline. The distribution network energy management system (EMS) realizes real-time data interaction and command issuance with the cluster through the IEC61850-90-5 communication protocol.

[0171] According to the adaptive droop control method for flexible resource clusters provided in this invention, the basic parameters of the distribution network and the parameters of the flexible resource cluster are first initialized and entered, and a power response model and an ESS state-space model incorporating the synergistic effect of multiple resources are established. Then, based on the real-time power deviation of the distribution network, the output of distributed power sources, and the operating status of resources, the dynamic calculation results of the adaptive droop gain are derived, and the adjustment range of the cluster droop gain is determined. Subsequently, a coordinated control framework deeply integrated with the distribution network-level EMS is built, incorporating three core constraints: resource operation, grid security, and cluster collaboration. Finally, simulations are conducted under peak load conditions to compare the power deviation control effects of traditional control and the method of this patent. The power deviation change trends of the distribution network under the traditional fixed droop gain + dead zone constraint control method and the adaptive droop control method for flexible resource clusters described in this invention are compared, and the results are as follows: Figure 2 As shown. Figure 2 In the diagram, the horizontal axis represents time (s), and the vertical axis represents the power deviation of the distribution network (MW). The closer the power deviation is to 0, the more balanced the active power supply and demand of the distribution network, and the better the regulation effect. The simulation conditions are peak load conditions of the distribution network. The results show that within 2.2~4s, the traditional control method, due to the inability of fixed droop parameters to adapt to the operating conditions, causes interactive interference between the power control of flexible resources and generators, resulting in significant power deviation oscillations with a fluctuation range of ±2~3MW. However, the method of this invention, through dynamic calculation of adaptive droop gain, achieves coordinated power regulation between flexible resources and traditional generators, effectively suppressing oscillations, and the power deviation remains stable within ±0.5MW. After 6s, the traditional control method, due to the constraint of the state of charge (SOC) of energy storage, has a weakened power regulation capability, resulting in a continuous expansion of the power deviation. The method provided by this invention takes into account both resource operation constraints and grid power regulation needs, always keeping the power deviation close to 0. This verifies that the method provided by this invention can effectively avoid power control interaction, smooth power fluctuations, and significantly improve the stability of the active power of the distribution network under peak load conditions.

[0172] For easier understanding, please refer to Figure 3 This invention provides an embodiment of a flexible resource cluster adaptive droop control device, comprising:

[0173] The parameter acquisition module is used to acquire basic parameters of the distribution network and parameters of the flexibility resource cluster.

[0174] The power response model construction module is used to construct a power response model that includes the synergistic effect of multiple resources within the cluster, based on the basic parameters of the distribution network and the parameters of the flexible resource cluster.

[0175] The adaptive module is used to determine the dynamic adjustment control rules of the adaptive droop gain based on the power response model that includes the synergistic effect of multiple resources within the cluster, combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster.

[0176] The dynamic optimization module is used to dynamically optimize the control parameters of the flexible resource cluster based on the dynamic adjustment control rules of adaptive droop gain.

[0177] In one embodiment, the power response speed and output of the flexible resource cluster are dynamically optimized according to the adaptive droop gain dynamic adjustment control rule, and the system further includes a closed-loop control module, which is used for:

[0178] Based on the dynamic adjustment control rules of adaptive droop gain, a coordinated control framework integrated with the distribution network-level energy management system is constructed.

[0179] Based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions.

[0180] In one embodiment, the control parameters include the droop gain of the flexibility resources, the power response dead zone, and the output regulation rate.

[0181] In one embodiment, based on the distribution network's basic parameters and flexibility resource cluster parameters, power deviation and power response variables are introduced to construct a power response model that incorporates the synergistic effects of multiple resources within the cluster, including:

[0182] Based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, a dynamic model of the flexible resource cluster and the distribution network is established.

[0183] Based on the dynamic model of flexible resource clusters and distribution networks, and taking the power deviation of the distribution network as the core input, the power response characteristics of flexible resources within the flexible resource cluster are coupled, and the power response increments of each flexible resource are integrated to construct a power response model that includes the synergistic effect of multiple resources within the cluster.

[0184] In one embodiment, the dynamic model of the flexible resource cluster and the distribution network is as follows:

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191] in, For the power response increment of distributed energy storage, This represents the power response increment of distributed photovoltaic systems. For the power response increment of the adjustable load, For distributed energy storage, the response time constant is... Let be the equivalent droop constant for distributed energy storage. This refers to the power regulation coefficient of distributed photovoltaic power. The power regulation coefficient for adjustable loads. Where N represents the real-time power deviation of the power grid, and N is the quantity of distributed energy storage. Let be the current between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC bus voltage corresponding to the i-th distributed energy storage unit. Let be the equivalent DC capacitance of the i-th distributed energy storage device. Let be the DC-side current of the i-th distributed energy storage device. Let be the equivalent inductance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent resistance between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent capacitance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC-side capacitor of the i-th distributed energy storage.

[0192] In one embodiment, the dynamic adjustment control rule for adaptive droop gain is as follows:

[0193] When distributed photovoltaic power is at full capacity and the distribution network has a power surplus, the droop gain of distributed energy storage is reduced by the first preset step size, the power response dead zone is increased to 0.15~0.2MW, and the charging power response speed of distributed energy storage is reduced by the second preset step size.

[0194] Under the condition of balanced output of distributed photovoltaic power, the distributed energy storage adopts the rated droop gain, configures the power response dead zone to 0.1MW, and keeps the power response speed unchanged.

[0195] When the output of distributed photovoltaic power drops sharply, the droop gain of distributed energy storage is increased by the third preset step size to reduce the power response dead zone to 0.05, and the discharge power response speed of distributed energy storage is increased by the fourth preset step size.

[0196] When the distributed photovoltaic power is insufficient, the droop gain of the distributed energy storage is increased by the fifth preset step size to reduce the power response dead zone to 0.05~0.1MW, and the power response speed of the distributed energy storage is increased by the sixth preset step size.

[0197] When the state of charge of the distributed energy storage battery reaches the lower limit, the droop gain on the discharge side of the distributed energy storage is reduced by the seventh preset step. When the state of charge of the distributed energy storage battery reaches the upper limit, the droop gain on the charging side of the distributed energy storage is reduced by the eighth preset step.

[0198] In one embodiment, the power response model incorporating the synergistic effect of multiple resources within the cluster is as follows:

[0199]

[0200]

[0201]

[0202]

[0203]

[0204] in, Let i be the power of the i-th distributed energy storage. For power loss in distribution network lines, For state vectors, For the input vector, For the output vector, All are coefficient matrices. State vector The first derivative with respect to time is used to characterize the rate of dynamic change of the system's state. This refers to the power imbalance in the power grid. For active power loss, Let i be the power response increment of the i-th distributed energy storage. Let be the response time constant of distributed photovoltaic power. This is the response time constant for the adjustable load.

[0205] In one embodiment, based on a coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions, including:

[0206] Construct a distribution network operating condition evaluation index system, combine the actual characteristics of the distribution network active network, select core quantifiable indicators to establish a multi-dimensional distribution network operating condition classification standard, quantify the power regulation requirements under different operating conditions, and optimize the control parameters under different distribution network operating conditions.

[0207] In one embodiment, the core quantifiable metrics include distributed photovoltaic power utilization rate, distribution network load factor, average state of charge of distributed energy storage batteries, and distribution network power deviation volatility.

[0208] For easier understanding, please refer toFigure 4 This invention also provides an adaptive drooping control device for active resource clusters, the device comprising a processor and a memory:

[0209] The memory is used to store program code and transmit the program code to the processor;

[0210] The processor is used to execute any one of the embodiments of the adaptive droop control method for active resource clusters described in the foregoing embodiments according to the instructions in the program code.

[0211] This invention also provides a computer-readable storage medium for storing program code that executes any one of the embodiments of the adaptive droop control method for an active resource cluster described in the foregoing embodiments.

[0212] This invention also provides a computer program product including instructions that, when run on a computer, cause the computer to execute any one of the implementation methods of the adaptive drooping control method for active resource clusters described in the foregoing embodiments.

[0213] The adaptive drooping control device, computer-readable storage medium, and computer program product for active resource clusters provided by this invention are all used to execute the adaptive drooping control method for active resource clusters provided by this invention. Their principles and the technical effects achieved are the same as those of the adaptive drooping control method for active resource clusters provided by this invention, and will not be repeated here.

[0214] The terms “first,” “second,” “third,” etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0215] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0216] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0217] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0218] If the integrated unit is implemented as a software functional unit 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 the present invention, 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 computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0219] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flexible resource cluster adaptive droop control method, characterized in that, include: Obtain basic parameters of the distribution network and parameters of the flexibility resource cluster; Based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, power deviation and power response variables are introduced to construct a power response model that includes the synergistic effect of multiple resources within the cluster; Based on the power response model that includes the synergistic effect of multiple resources within the cluster, and combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster, the dynamic adjustment control rules for adaptive droop gain are determined. The control parameters of the flexible resource cluster are dynamically optimized based on the dynamic adjustment control rule of adaptive droop gain.

2. The flexible resource cluster adaptive droop control method according to claim 1, characterized in that, The power response speed and output of the flexible resource cluster are dynamically optimized based on the adaptive droop gain dynamic adjustment control rule, and the following is also included: Based on the dynamic adjustment control rules of adaptive droop gain, a coordinated control framework is constructed to be integrated with the distribution network-level energy management system. Based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions.

3. The flexible resource cluster adaptive droop control method according to claim 1, characterized in that, Control parameters include droop gain of flexibility resources, power response dead zone, and output regulation rate.

4. The flexible resource cluster adaptive droop control method according to claim 1, characterized in that, Based on the basic parameters of the distribution network and the parameters of the flexibility resource cluster, power deviation and power response variables are introduced to construct a power response model that includes the synergistic effect of multiple resources within the cluster, including: Based on the basic parameters of the distribution network and the parameters of the flexible resource cluster, a dynamic model of the flexible resource cluster and the distribution network is established. Based on the dynamic model of flexible resource clusters and distribution networks, and taking the power deviation of the distribution network as the core input, the power response characteristics of flexible resources within the flexible resource cluster are coupled, and the power response increments of each flexible resource are integrated to construct a power response model that includes the synergistic effect of multiple resources within the cluster.

5. The flexible resource cluster adaptive droop control method according to claim 4, characterized in that, The dynamic model of flexible resource clusters and distribution networks is as follows: in, For the power response increment of distributed energy storage, This represents the power response increment of distributed photovoltaic systems. For the power response increment of the adjustable load, For distributed energy storage, the response time constant is... Let be the equivalent droop constant for distributed energy storage. This refers to the power regulation coefficient of distributed photovoltaic power. The power regulation coefficient for adjustable loads. Where N represents the real-time power deviation of the power grid, and N is the quantity of distributed energy storage. Let be the current between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC bus voltage corresponding to the i-th distributed energy storage unit. Let be the equivalent DC capacitance of the i-th distributed energy storage device. Let be the DC-side current of the i-th distributed energy storage device. Let be the equivalent inductance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent resistance between the i-th distributed energy storage and the j-th distributed energy storage. Let be the equivalent capacitance of the line between the i-th distributed energy storage and the j-th distributed energy storage. Let be the DC-side capacitor of the i-th distributed energy storage.

6. The flexible resource cluster adaptive droop control method according to claim 1, characterized in that, The dynamic adjustment control rule for adaptive droop gain is as follows: When distributed photovoltaic power is at full capacity and the distribution network has a power surplus, the droop gain of distributed energy storage is reduced by the first preset step size, the power response dead zone is increased to 0.15~0.2MW, and the charging power response speed of distributed energy storage is reduced by the second preset step size. Under the condition of balanced output of distributed photovoltaic power, the distributed energy storage adopts the rated droop gain, configures the power response dead zone to 0.1MW, and keeps the power response speed unchanged. When the output of distributed photovoltaic power drops sharply, the droop gain of distributed energy storage is increased by the third preset step size to reduce the power response dead zone to 0.05, and the discharge power response speed of distributed energy storage is increased by the fourth preset step size. When the distributed photovoltaic power is insufficient, the droop gain of the distributed energy storage is increased by the fifth preset step size to reduce the power response dead zone to 0.05~0.1MW, and the power response speed of the distributed energy storage is increased by the sixth preset step size. When the state of charge of the distributed energy storage battery reaches the lower limit, the droop gain on the discharge side of the distributed energy storage is reduced by the seventh preset step. When the state of charge of the distributed energy storage battery reaches the upper limit, the droop gain on the charging side of the distributed energy storage is reduced by the eighth preset step.

7. The flexible resource cluster adaptive droop control method according to claim 1, characterized in that, The power response model that includes the synergistic effect of multiple resources within the cluster is as follows: in, Let i be the power of the i-th distributed energy storage. For power loss in distribution network lines, For state vectors, For the input vector, For the output vector, All are coefficient matrices. A state vector that characterizes the dynamic rate of change of the system's state. The first derivative with respect to time, This refers to the power imbalance in the power grid. For active power loss, Let i be the power response increment of the i-th distributed energy storage. Let be the response time constant of distributed photovoltaic power. This is the response time constant for the adjustable load.

8. The flexible resource cluster adaptive droop control method according to claim 2, characterized in that, Based on the coordinated control framework, a distribution network operating condition assessment mechanism is introduced to optimize control parameters for different distribution network operating conditions, including: Construct a distribution network operating condition evaluation index system, combine the actual characteristics of the distribution network active network, select core quantifiable indicators to establish a multi-dimensional distribution network operating condition classification standard, quantify the power regulation requirements under different operating conditions, and optimize the control parameters under different distribution network operating conditions.

9. The flexible resource cluster adaptive droop control method according to claim 8, characterized in that, Key quantifiable indicators include distributed photovoltaic power utilization rate, distribution network load rate, average state of charge of distributed energy storage batteries, and distribution network power deviation fluctuation rate.

10. A flexible resource cluster adaptive droop control device, characterized in that, include: The parameter acquisition module is used to acquire basic parameters of the distribution network and parameters of the flexibility resource cluster. The power response model construction module is used to construct a power response model that includes the synergistic effect of multiple resources within the cluster, based on the basic parameters of the distribution network and the parameters of the flexible resource cluster. The adaptive module is used to determine the dynamic adjustment control rules of the adaptive droop gain based on the power response model that includes the synergistic effect of multiple resources within the cluster, combined with the dynamic characteristics of the actual operating conditions of the distribution network and the operating status of the flexible resource cluster. The dynamic optimization module is used to dynamically optimize the control parameters of the flexible resource cluster based on the dynamic adjustment control rules of adaptive droop gain.

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