Coordinated control method, device and computer equipment for power grid resources

Abstract

The application relates to a power grid resource cooperative control method and device and a computer device. The method comprises the following steps: in the case of a fault of a power grid system, all controlled units of the power grid system are divided into multiple resource groups; based on the operation parameters of each controlled unit in each resource group, equivalent parameters corresponding to the resource group are determined; based on the equivalent parameters and a pre-constructed cooperative control model, a first reference power of each resource group is determined; and based on the first reference power of each resource group, each controlled unit in the resource group is cooperatively controlled. The method can improve the calculation efficiency and meet the requirement of rapid power control in the fault scenario.

Description

Technical Field

[0001] This application relates to the field of power grid control technology, and in particular to a method, apparatus and computer equipment for the coordinated control of power grid resources. Background Technology

[0002] Currently, with the accelerated pace of urbanization and the transformation of the energy structure, the scale and complexity of mega-city power grids have significantly increased. In addition to traditional power generation units, the power grids now widely incorporate massive amounts of decentralized and controllable resources, including energy storage facilities, electric vehicle charging stations, and distributed renewable energy generation equipment (such as photovoltaic and wind power). While these diverse resources enhance the flexibility of the power grid, they also bring enormous challenges to coordinated control.

[0003] In related technologies, when the power grid encounters sudden fault conditions (such as line short circuits or generator disconnection), a centralized control method is usually used for power control under fault scenarios. However, the centralized control method faces problems of low computational efficiency and slow response due to the need to process massive amounts of high-dimensional data of the controlled units (such as node voltage, power state, and charge level), making it difficult to meet the needs of rapid power control under fault scenarios. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, and computer equipment for the coordinated control of power grid resources that can improve computing efficiency in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for coordinated control of power grid resources, comprising:

[0006] In the event of a power grid system failure, all controlled units of the power grid system are divided into multiple resource groups;

[0007] Based on the operating parameters of each controlled unit in each resource group, the equivalent parameters corresponding to the resource group are determined;

[0008] Based on the equivalent parameters and the pre-built cooperative control model, a first reference power is determined for each of the resource groups;

[0009] Based on the first reference power of each resource group, each controlled unit in the resource group is subjected to coordinated control.

[0010] In one embodiment, dividing all controlled units of the power grid system into multiple resource groups includes: obtaining the operating parameters of all controlled units in the power grid system; and clustering all controlled units of the power grid system into multiple resource groups based on the operating parameters of all controlled units.

[0011] In one embodiment, the step of clustering all controlled units of the power grid system into multiple resource groups based on the operating parameters of all controlled units includes: dividing all controlled units into multiple controlled unit groups according to the type of all controlled units; and clustering each controlled unit in each controlled unit group into at least one resource group according to the similarity between the operating parameters of each controlled unit in each controlled unit group.

[0012] In one embodiment, determining the equivalent parameters corresponding to the resource group based on the operating parameters of each controlled unit in each resource group includes: determining the equivalent capacity corresponding to the resource group based on the capacity of each controlled unit in each resource group; determining the equivalent load power corresponding to the resource group based on the power of each load in each resource group; and determining the equivalent line impedance corresponding to the resource group based on the active power and line impedance of each controlled unit in each resource group.

[0013] In one embodiment, determining the first reference power of each resource group based on the equivalent parameters and a pre-built cooperative control model includes: inputting the equivalent parameters into the constraints of the cooperative control model; and solving the cooperative control model based on the constraints with the objective of minimizing grid operating costs to obtain the first reference power of each resource group.

[0014] In one embodiment, the equivalent parameters include equivalent capacity, equivalent line impedance, and equivalent load power; the step of inputting the equivalent parameters into the constraints of the cooperative control model includes: determining the equivalent node susceptance matrix based on the equivalent line impedance; inputting the equivalent load power and the equivalent node susceptance matrix into the power flow constraints of the cooperative control model; and inputting the equivalent capacity into the equipment operation constraints of the cooperative control model.

[0015] In one embodiment, the step of coordinating control of each controlled unit in the resource group based on a first reference power of each resource group includes: determining a second reference power of each controlled unit in the resource group according to a first reference power and a power allocation coefficient of each resource group; and sending the second reference power to the corresponding controlled unit so that the controlled unit performs control according to the second reference power.

[0016] In one embodiment, the method further includes: determining the power allocation coefficient for each of the resource groups based on the total available power of the power grid system and the available power of each of the resource groups.

[0017] Secondly, this application also provides a coordinated control device for power grid resources, comprising:

[0018] The partitioning module is used to divide all controlled units of the power grid system into multiple resource groups in the event of a power grid system failure.

[0019] The first determining module is used to determine the equivalent parameters corresponding to the resource group based on the operating parameters of each controlled unit in each resource group;

[0020] The second determining module is used to determine the first reference power of each of the resource groups based on the equivalent parameters and the pre-built cooperative control model.

[0021] The control module is used to perform coordinated control of each controlled unit in the resource group based on a first reference power for each resource group.

[0022] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the coordinated control method for power grid resources provided in the first aspect of this application.

[0023] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the coordinated control method for power grid resources provided in the first aspect of this application.

[0024] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the coordinated control method for power grid resources provided in the first aspect of this application.

[0025] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for coordinated control of power grid resources, in the event of a power grid system failure, divide all controlled units of the power grid system into multiple resource groups; determine the equivalent parameters corresponding to the resource group based on the operating parameters of each controlled unit in each resource group; determine the first reference power of each resource group based on the equivalent parameters and a pre-built coordinated control model; and perform coordinated control on each controlled unit in the resource group based on the first reference power of each resource group. Therefore, this embodiment divides a massive number of dispersed controlled units into a certain number of resource groups, and achieves coordinated control of power grid power based on equivalent parameters characterizing the overall characteristics of the groups and a coordinated control model. By reducing the high-dimensional control of massive controlled units to low-dimensional control, it fundamentally overcomes the problem of low computational efficiency caused by high-dimensional data; and by characterizing the overall characteristics of the resource groups through equivalent parameters for coordinated control, it eliminates the need to model each controlled unit within the group, simplifying control complexity. Therefore, this embodiment can improve power calculation efficiency and meet the needs of rapid power control in fault scenarios. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is an application environment diagram of a collaborative control method for power grid resources in one embodiment;

[0028] Figure 2 This is a flowchart illustrating a method for coordinated control of power grid resources in one embodiment;

[0029] Figure 3 This is a flowchart illustrating step 201 in one embodiment;

[0030] Figure 4 This is a flowchart illustrating step 202 in one embodiment;

[0031] Figure 5 This is a flowchart illustrating step 203 in one embodiment;

[0032] Figure 6 This is a flowchart illustrating step 204 in one embodiment;

[0033] Figure 7 This is a flowchart illustrating a collaborative control method for power grid resources in an example.

[0034] Figure 8 This is a structural block diagram of a coordinated control device for power grid resources in one embodiment;

[0035] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0037] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0038] In related technologies, the dynamic characteristics and geographical distribution correlation of resources are often ignored when dealing with multi-resource collaborative control. Conventional clustering methods are difficult to efficiently integrate multi-dimensional indicators, resulting in insufficient accuracy of resource grouping; and the lack of effective grid simplification methods leads to an explosion of dimensionality in the optimization model, making it unable to support online decision-making. In addition, power control after a fault often relies on single-layer optimization algorithms, which are prone to getting trapped in local optima or slow convergence when balancing economic objectives (such as generation costs and start-up losses) and complex operational constraints (node ​​power flow balance, generator output limits, and energy storage charge state boundaries), making it difficult to achieve rapid optimal recovery and stable operation of system power under fault conditions.

[0039] Therefore, there is an urgent need for a collaborative control method for fault scenarios in ultra-large urban power grids, which can reduce model complexity through intelligent clustering and simplification, and integrate efficient optimization algorithms to achieve fast, economical, and stable control of multi-resource collaboration, filling the technological gap in this field.

[0040] To address the aforementioned issues, this application provides a collaborative control method for power grid resources, targeting ultra-large urban power grids that integrate diverse resources such as energy storage stations, electric vehicle charging facilities, distributed photovoltaic power generation systems, and wind power generation systems. When a fault occurs in the ultra-large urban power grid, the method achieves optimal power control and stable operation of the urban power grid by rapidly controlling the massive resources of the urban power grid.

[0041] The coordinated control method for power grid resources provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located on a cloud or other network server. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. Server 104 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0042] In one exemplary embodiment, such as Figure 2As shown, a method for coordinated control of power grid resources is provided, which is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 201 to 204. Wherein:

[0043] Step 201: In the event of a fault in the power grid system, all controlled units of the power grid system are divided into multiple resource groups.

[0044] The controlled units include various types of energy storage piles, electric vehicle charging piles, and new energy power generation equipment. Faults include, but are not limited to, line short circuits, generator disconnection from the grid, abnormal voltage drops on the bus, and system frequency exceeding limits.

[0045] For example, when a power grid system, such as a mega-city power grid system, experiences a fault, the collaborative control method of this application embodiment is automatically triggered. First, all controlled units of the power grid system are divided into multiple resource groups. Optionally, based on the connection relationships, geographical distribution, and operating parameters of each controlled unit, a clustering algorithm is used to divide all controlled units into multiple resource groups. Each resource group includes multiple controlled units of the same type, which have similar electrical characteristics.

[0046] The ultra-large urban power grid in this embodiment covers a variety of power grid resources, including traditional power sources, loads, energy storage piles, electric vehicle charging piles, and distributed new energy power generation equipment. A large number of controlled resources, such as multiple energy storage piles, electric vehicle charging piles, and distributed new energy power generation equipment, are distributed across multiple nodes of the ultra-large urban power grid.

[0047] Step 202: Determine the equivalent parameters corresponding to each resource group based on the operating parameters of each controlled unit in each resource group.

[0048] The equivalent parameters are used to characterize the overall operating characteristics and control capabilities of the resource group. They include at least one parameter, such as equivalent capacity, equivalent load power, and equivalent line impedance.

[0049] For example, based on the operating parameters of each controlled unit in each resource group, the power grid resources and their lines can be equivalently represented (using a weighted method) to obtain the equivalent power grid model, and at the same time, the equivalent parameters of each resource group can be obtained.

[0050] Step 203: Determine the first reference power for each resource group based on equivalent parameters and a pre-built cooperative control model.

[0051] The collaborative control model is pre-built for the equivalent power grid model, including an objective function (i.e., a multi-resource economic benefit objective function) and constraints aimed at minimizing the power grid operating cost.

[0052] For example, after obtaining the equivalent parameters of each resource group, the equivalent parameters are first input into the cooperative control model. Simultaneously, the cluster center data of the resource groups can also be input into the cooperative control model. Then, the cooperative control model is solved to achieve rapid power stability control of the equivalent power grid model, obtaining the first reference power for each resource group. This first reference power can be a target value (i.e., a reference value) for either the output power or the input power. Optionally, the cooperative control model is solved based on a genetic algorithm.

[0053] Step 204: Based on the first reference power of each resource group, perform coordinated control on each controlled unit in the resource group.

[0054] For example, after obtaining the first reference power of each resource group, the first reference power is decomposed into the second reference power of each controlled unit in the corresponding resource group according to a preset control strategy, such as weighted allocation or proportional allocation. The second reference power is then sent to the corresponding controlled unit so that each controlled unit can control its own power based on the received second reference power. This enables the coordinated control of all controlled units in the power grid system and achieves rapid and stable control of the power grid under fault conditions.

[0055] In the aforementioned method for coordinated control of power grid resources, in the event of a power grid system failure, all controlled units of the power grid system are divided into multiple resource groups. Based on the operating parameters of each controlled unit within each resource group, equivalent parameters corresponding to the resource group are determined. Based on the equivalent parameters and a pre-built coordinated control model, a first reference power for each resource group is determined. Based on the first reference power of each resource group, coordinated control is performed on each controlled unit within the resource group. Therefore, this embodiment divides a massive number of dispersed controlled units into a certain number of resource groups, and achieves coordinated control of power grid power based on equivalent parameters characterizing the overall characteristics of the groups and a coordinated control model. By reducing the high-dimensional control of massive controlled units to low-dimensional control, the problem of low computational efficiency caused by high-dimensional data is fundamentally overcome. Furthermore, by characterizing the overall characteristics of the resource groups through equivalent parameters, coordinated control is performed without the need to model each controlled unit within the group, simplifying the control complexity. Therefore, this embodiment can improve power calculation efficiency and meet the needs of rapid power control in fault scenarios.

[0056] The specific implementation methods for each of the above steps are described in detail below.

[0057] In one exemplary embodiment, such as Figure 3 As shown, step 201, which involves dividing all controlled units of the power grid system into multiple resource groups, includes steps 301 and 302. Wherein:

[0058] Step 301: Obtain the operating parameters of all controlled units in the power grid system.

[0059] Step 302: Based on the operating parameters of all controlled units, cluster all controlled units of the power grid system into multiple resource groups.

[0060] For example, in the event of a power grid system failure, the operating parameters of a large number of controlled units are obtained. The operating parameters are used as clustering indicators, and an improved K-means clustering algorithm is used to perform real-time clustering of all controlled units such as energy storage piles, electric vehicle charging piles, and distributed new energy power generation equipment. This allows all controlled units to be clustered into multiple resource groups, and the cluster center of each resource group is obtained.

[0061] Optionally, taking all controlled units of the power grid system, including energy storage piles, electric vehicle charging piles, and new energy piles, as an example, multiple resource groups include virtual energy storage stations (i.e., resource groups including multiple energy storage piles), electric vehicle charging stations (i.e., resource groups including multiple electric vehicle charging piles), and new energy power plants (i.e., resource groups including multiple new energy power generation devices). When a fault occurs in the power grid system, firstly, the node voltage amplitude, node voltage phase angle, and state of charge of the energy storage piles are selected as the clustering index set for the energy storage stations; the node voltage amplitude, node voltage phase angle, and state of charge of the electric vehicle charging piles are selected as the clustering index set for the electric vehicle charging stations; and the node voltage amplitude, node voltage phase angle, node active power, and node reactive power of the distributed new energy power generation devices are selected as the clustering index set for the distributed new energy power plants. Based on the clustering indexes, each controlled unit is clustered, with energy storage piles with similar clustering indexes clustered into the same energy storage station, electric vehicle charging piles with similar clustering indexes clustered into the same electric vehicle charging station, and new energy power generation devices with similar clustering indexes clustered into the same new energy power plant.

[0062] Therefore, this embodiment clusters each controlled unit based on its operating parameters to obtain multiple resource groups, which can reduce high-dimensional data to low-dimensional data and improve computational efficiency.

[0063] In an exemplary embodiment, step 302 includes: dividing all controlled units into multiple controlled unit groups according to the type of all controlled units; and clustering each controlled unit in the controlled unit group into at least one resource group according to the similarity between the operating parameters of each controlled unit in each controlled unit group.

[0064] For example, firstly, controlled units of the same type are grouped into the same controlled unit group. For instance, all energy storage piles are grouped into one energy storage pile group, all electric vehicle charging piles into one electric vehicle charging pile group, and all new energy power generation equipment into one new energy power generation equipment group. Then, for each controlled unit group, the operating parameters of each controlled unit are used as clustering indicators to determine the similarity between clustering indicators. Based on the similarity, controlled units with similar clustering indicators are clustered into the same resource group, with each controlled unit group corresponding to at least one resource group. For example, based on the clustering indicator set, each energy storage pile in the energy storage pile group is clustered into at least one energy storage station, each energy storage pile in the electric vehicle charging pile group is clustered into at least one electric vehicle charging station, and each power generation equipment in the new energy power generation equipment group is clustered into at least one new energy power station.

[0065] Optionally, the objective function expression for real-time clustering of power grid systems containing grid resources such as energy storage piles, electric vehicle charging piles, and distributed new energy power generation equipment based on the improved k-means method is:

[0066]

[0067]

[0068] Where k is the number of clusters, p is the analysis parameter, n is the number of cluster index sets for energy storage stations, electric vehicle charging stations, and new energy power stations, and u ij Let x be the membership degree of the i-th cluster index set to the j-th cluster center. i Let c be the i-th clustering index set. j This represents the data for the j-th cluster center.

[0069] The expressions for membership degree and cluster center data are:

[0070]

[0071]

[0072]

[0073] in, The distribution characteristics of the clustering equation are represented by N, where N is the dimension of the data. The output parameters after clustering are also shown.

[0074] After performing the above clustering operation, the equivalent node voltage amplitude is obtained based on the cluster centers of the energy storage station. Node voltage phase angle and state of charge The equivalent node voltage amplitude is obtained based on the cluster centers of electric vehicle charging stations. Node voltage phase angle and state of charge The equivalent node voltage amplitude is obtained based on the cluster centers of the new energy power stations. Node voltage phase angle Active power at nodes and node reactive power .

[0075] The complete process of real-time clustering of power grid resources, including energy storage stations, electric vehicle charging stations, and distributed renewable energy power plants, based on the improved k-means method is as follows:

[0076] 1) Real-time input of clustering index sets for energy storage stations, electric vehicle charging stations, and new energy power stations;

[0077] 2) Normalize the clustering index set data;

[0078] 3) Randomly generate initial cluster centers;

[0079] 4) Iterate continuously to generate new membership degrees and cluster centers until the objective function of clustering no longer changes;

[0080] 5) Output cluster center data for energy storage stations, electric vehicle charging stations, and distributed new energy power stations.

[0081] Therefore, by first classifying according to type and then clustering according to clustering index set, multiple resource groups can be obtained, which can efficiently integrate multi-dimensional indicators, improve the grouping accuracy of resource groups, and thus ensure the accuracy of calculation.

[0082] In one exemplary embodiment, such as Figure 4 As shown, step 202 includes steps 401 to 403:

[0083] Step 401: Determine the equivalent capacity of each resource group based on the capacity of each controlled unit in each resource group.

[0084] For example, after clustering, the capacity resources of the controlled units in each resource group of the power grid system are equivalently valued to obtain the equivalent capacity corresponding to that resource group. For instance, the sum of the capacities of all controlled units in the same resource group is taken as the equivalent capacity of that resource group.

[0085] Step 402: Determine the equivalent load power corresponding to each resource group based on the power of each load in each resource group.

[0086] For example, after clustering, the load power of the controlled units in each resource group of the power grid system is equated to obtain the equivalent load power corresponding to that resource group. For instance, the sum of the load power of each controlled unit in the same resource group is taken as the equivalent capacity of that resource group.

[0087] Optionally, for a resource group (i.e., an energy storage station or an electric vehicle charging station) that includes energy storage piles or electric vehicle charging piles, the formulas for calculating its equivalent capacity and equivalent load power are as follows:

[0088]

[0089]

[0090] Where m represents the number of energy storage piles or electric vehicle charging piles in the same resource group after clustering. , , Let represent the capacity of the i-th energy storage station, the capacity of the i-th electric vehicle charging station, and the load power, respectively, within the same resource group after clustering. , , These represent the equivalent capacity of the energy storage station, the equivalent capacity of the electric vehicle charging station, and the equivalent load power of the electric vehicle charging station, respectively.

[0091] Step 403: Determine the equivalent line impedance corresponding to each resource group based on the active power and line impedance of each controlled unit in each resource group.

[0092] For example, after clustering, the connection lines between each controlled unit and the power grid in each resource group are equivalently evaluated based on the active power and line impedance in each controlled unit to obtain the equivalent line impedance of the resource group.

[0093] Optionally, for a resource group (i.e., energy storage stations, electric vehicle charging stations, and new energy power stations) that includes energy storage stations, electric vehicle charging stations, or distributed new energy power stations, the equivalent expression for the lines connected to energy storage piles, electric vehicle charging piles, and new energy power station power generation equipment in the same resource group after clustering is as follows:

[0094]

[0095]

[0096]

[0097] in, , and These represent the equivalent line impedances of energy storage stations, electric vehicle charging stations, and new energy power plants, respectively, where m represents the number of energy storage piles, electric vehicle charging piles, or new energy power generation equipment in the same resource group after clustering. , and These are the line impedances connecting to the i-th energy storage pile, electric vehicle charging pile, and distributed new energy power generation equipment within the same resource group after clustering. and These represent the active power of the i-th energy storage pile, electric vehicle charging pile, and distributed new energy power generation equipment in the same resource group after clustering.

[0098] Therefore, this embodiment performs equivalent equivalence on the power grid resources and their connecting lines of a super-large urban power grid, and obtains the equivalent parameters corresponding to the resource groups. This achieves the equivalence simplification of the power grid system, enables effective equivalence of power grid resources, greatly simplifies the maintenance of the optimization model, and thus supports online decision-making.

[0099] In one exemplary embodiment, such as Figure 5 As shown, step 203 includes steps 501 and 502:

[0100] Step 501: Input the equivalent parameters into the constraints of the collaborative control model.

[0101] For example, before performing this step, for the equivalent power grid system (including each resource group), a collaborative control model for optimizing the control of the power grid system's resources is constructed based on each resource group and its corresponding equivalent parameters. This model includes a multi-resource economic benefit objective function of the power grid and constraints. In specific collaborative control, after aggregation according to step 201 and obtaining the equivalent parameters of the resource groups according to step 202, the aggregation center data and equivalent parameters can be substituted into the constraint function of the collaborative control model to obtain the constraints for the current optimization.

[0102] Step 502: Based on the constraints, and with the goal of minimizing the power grid operating cost, the cooperative control model is solved to obtain the first reference power for each resource group.

[0103] For example, after substituting the aggregation center data and equivalent parameters into the constraints, the genetic algorithm is used to perform fast power stabilization control on the equivalent controlled resources. Specifically, the output power population of each resource group is generated, and based on the objective function and the constraints of each resource group, the power population of each resource group is iterated through crossover and mutation operations until the optimal power solution of each resource group in the power grid is output, thus obtaining the first reference power.

[0104] Therefore, in this embodiment, based on equivalent parameters and a cooperative control model, and using a genetic algorithm for fast power stabilization control, the phenomenon of getting trapped in local optima or slow convergence can be avoided, and the system power can be quickly and optimally restored and stabilized under fault conditions.

[0105] In an exemplary embodiment, the equivalent parameters include equivalent capacity, equivalent line impedance, and equivalent load power. In this embodiment, step 501 includes: determining the equivalent node susceptance matrix based on the equivalent line impedance; inputting the equivalent load power and the equivalent node susceptance matrix into the power flow constraints of the cooperative control model; and inputting the equivalent capacity into the equipment operation constraints of the cooperative control model.

[0106] For example, consider multiple resource groups including energy storage stations (i.e., resource groups including energy storage piles), electric vehicle charging stations (i.e., resource groups including electric vehicle charging piles), and new energy power stations (i.e., resource groups including new energy power stations). First, the equivalent node susceptance matrix and equivalent load power are input into the power grid flow constraints. The state of charge (SOC) from the cluster center data of the energy storage station, and the equivalent capacity corresponding to that energy storage station, are input into the energy storage station operation constraints. Similarly, the SOC from the cluster center data of the electric vehicle charging station, and the equivalent capacity corresponding to that electric vehicle charging station, are input into the electric vehicle charging station operation constraints. Then, based on the constraints and the objective function, a collaborative control model is solved to obtain the active power of the new energy power station, the charging and discharging power of the energy storage station, and the charging and discharging power of the electric vehicle charging station. These powers are used as the first reference power for the corresponding resource group; that is, the first reference power includes the active power of the new energy power station, the charging and discharging power of the energy storage station, and the charging and discharging power of the electric vehicle charging station.

[0107] Optionally, the objective function (multi-resource economic benefits) of the collaborative control model is:

[0108]

[0109] in, Let be the output power variable of the i-th generator in the power grid system at time t; It is a binary variable (takes a value of 0 or 1) that represents the on / off state of the i-th generator's connection to the grid at time t. A value of 0 indicates that it is disconnected from the grid, and a value of 1 indicates that it is connected to the grid. Let be the electricity price coefficient for the i-th generator. Let be the operating cost coefficient of the i-th generator. It is a binary variable (taking the value 0 or 1), where 1 indicates that the i-th generator starts at time t. Let be the startup cost of the i-th generator. This represents the total number of generators in the power grid. This represents the total operating time of the generator.

[0110] The power flow constraints for each node in the equivalent power grid model at each time step are as follows:

[0111]

[0112] in, This is the equivalent nodal susceptance matrix, where some parameters can be obtained from the equivalent line impedance. , and get, Let be the angle variable of node j at time t. Let be the output power variable of generator node j at time t. The equivalent load power at time t (derived from the equivalent load power in step 402) get), Let be the active power variable of the renewable energy power station at time t. and These represent the charging and discharging power variables at time t for the energy storage station and the electric vehicle charging station, respectively. For generator connection matrix, This is the equivalent distributed renewable energy power station connection matrix. This is the equivalent connection matrix for energy storage stations and electric vehicle charging stations. This represents the total number of nodes after the equivalence.

[0113] The generator operating constraints are:

[0114]

[0115] in, Let be the output power variable of the i-th generator at time t. The binary variable represents the on / off state of the generator. When it is 0 A value of 0 indicates that the i-th generator is disconnected from the power grid at time t. and These are the maximum and minimum output power of the i-th generator at time t. This represents the total number of generators. This represents the total operating time of the generator.

[0116] The operating constraints for energy storage stations and electric vehicle charging stations are as follows:

[0117]

[0118]

[0119] in, , The states of charge of the energy storage station and the electric vehicle charging station at time t-1 are respectively obtained from the cluster center data of the clustered resource group. These represent the state of charge of the resource groups at time t, respectively, for the energy storage station and the electric vehicle charging station. and Efficiency coefficient for energy storage stations and electric vehicle charging stations during the charging and discharging process; These represent the equivalent capacity of the energy storage station and the equivalent capacity of the electric vehicle charging station, respectively (obtained after equivalence in step 402, and here used as the rated capacity of the energy storage station and the electric vehicle charging station). m is the number of energy storage piles or electric vehicle charging piles in the same cluster after clustering. These represent the active power of the i-th energy storage pile and the i-th electric vehicle charging pile in the same group after clustering at time t-1.

[0120] The operational constraints for distributed renewable energy power stations are as follows:

[0121]

[0122] in, Let be the active power variable of the renewable energy power station at time t. After clustering, the active power of the i-th renewable energy power generation device in the same renewable energy power station at time t, m This represents the number of new energy power generation devices in the same new energy power station after clustering.

[0123] Based on the genetic algorithm and the above constraints, the charging and discharging power of the energy storage station is obtained by solving the control model. Charging and discharging power of electric vehicle charging stations and the active power of new energy power stations As the first reference power issued by the power grid to the energy storage station, electric vehicle charging station group and new energy power station, it is output to step 204 for controlling each controlled unit in the corresponding group.

[0124] In practical applications, when coordinating the control of each controlled unit, fair power allocation control can be performed on the controlled units in each resource group based on the first reference power. The following is an example description.

[0125] In one exemplary embodiment, such as Figure 6 As shown, step 204 includes steps 601 and 602:

[0126] Step 601: Determine the second reference power of each controlled unit in the resource group based on the first reference power and power allocation coefficient of each resource group.

[0127] For example, for each resource group, the product of its first reference power and power allocation coefficient is first calculated. Then, the sum of reference power values ​​for each controlled unit in the resource group is determined based on the product. Finally, the ratio between the sum of reference power values ​​and the number of controlled units is calculated to obtain the second reference power for each controlled unit, thereby achieving fair allocation among the controlled units. The second reference power can be calculated using the following formula:

[0128]

[0129] in, Let m be the second reference power of the i-th controlled unit in the a-th resource group, m be the number of controlled units in the a-th resource group, and k be the total number of resource groups. It is the power allocation coefficient for fair allocation control of the a-th resource group. It is the first reference power of the a-th resource group.

[0130] Optionally, the first reference power includes: the charging and discharging power of the energy storage station obtained in step 203. Charging and discharging power of electric vehicle charging stations and the active power of new energy power stations The second reference power includes: the charging and discharging power of energy storage piles, the charging and discharging power of electric vehicle charging piles, and the active power of new energy power generation equipment.

[0131] Step 602: The second reference power is sent to the corresponding controlled unit so that the controlled unit can perform control according to the second reference power.

[0132] For example, after obtaining the second reference power of each controlled unit, the second reference power is sent to the corresponding controlled unit. Upon receiving the second reference power, the controlled unit controls itself according to the second reference power, thus achieving coordinated control of various resources. For instance, the active power of the new energy power generation equipment is sent to the new energy power generation equipment, the charging and discharging power of the energy storage pile is sent to the energy storage pile, and the charging and discharging power of the electric vehicle charging pile is sent to the electric vehicle charging pile. The new energy power generation equipment, energy storage pile, and electric vehicle charging pile are controlled according to the received power.

[0133] Therefore, by implementing fair power allocation control for each controlled unit, coordinated control of power grid resources can be achieved, ensuring control reliability and effectiveness.

[0134] In an exemplary embodiment, the power allocation coefficient is determined by the following steps: determining the power allocation coefficient for each resource group based on the total available power of the power grid system and the available power of each resource group.

[0135] Each resource group has a corresponding available power.

[0136] For example, the total available power of the power grid system is first determined based on the sum of the available power of all resource groups, and then for each resource group, the power allocation factor of that resource group is determined based on the ratio between the available power of that resource group and the total available power.

[0137] Alternatively, the power allocation factor can be calculated using the following formula:

[0138]

[0139] in, For total available power, is the available power of the a-th resource group, where a is the a-th resource group and k is the total number of resource groups.

[0140] Therefore, by determining the power allocation coefficient for each resource group based on the total available power of the power grid system and the available power of each resource group, the reliability of the power allocation coefficient can be ensured, thereby ensuring the reliability of power allocation.

[0141] The following example illustrates the method for coordinated control of power grid resources according to an embodiment of this application.

[0142] like Figure 7 As shown, the cooperative control method includes the following steps:

[0143] Step 701: In the event of a fault in the power grid system, obtain the operating parameters of all controlled units in the power grid system;

[0144] Step 702: Divide all controlled units into multiple controlled unit groups according to the type of all controlled units;

[0145] Step 703: Based on the similarity between the operating parameters of each controlled unit in each controlled unit group, cluster each controlled unit in the controlled unit group into at least one resource group.

[0146] Step 704: Determine the equivalent capacity of each resource group based on the capacity of each controlled unit in each resource group;

[0147] Step 705: Determine the equivalent load power corresponding to each resource group based on the power of each load in each resource group;

[0148] Step 706: Determine the equivalent line impedance corresponding to each resource group based on the active power and line impedance of each controlled unit in each resource group.

[0149] Step 707: Determine the equivalent node susceptance matrix based on the equivalent line impedance, and input the equivalent load power and the equivalent node susceptance matrix into the power flow constraints of the cooperative control model.

[0150] Step 708: Input the equivalent capacity into the equipment operation constraints of the collaborative control model;

[0151] Step 709: Determine the second reference power of each controlled unit in the resource group based on the first reference power and power allocation coefficient of each resource group;

[0152] The power allocation factor is determined based on the total available power of the power grid system and the available power of each resource group.

[0153] Step 710: The second reference power is sent to the corresponding controlled unit so that the controlled unit can perform control according to the second reference power.

[0154] It should be noted that the specific implementation methods for steps 701 to 710 are the same as those for the corresponding steps described above. To avoid redundancy, they will not be repeated here.

[0155] In summary, the collaborative control method for power grid resources in this application, when a power grid system fault occurs, uses the node voltage amplitude, phase angle, state of charge, and available active and reactive power of a massive number of controlled units as clustering indicators. Based on an improved k-means method, it performs real-time clustering of power grid resources, including energy storage stations, electric vehicle charging stations, and distributed new energy power plants. Then, it equates the power grid resources and their connecting lines of the ultra-large urban power grid. For the equated urban power grid, it constructs a multi-resource economic benefit objective function for the power grid and power flow constraints for the power grid and controlled resources. Based on a genetic algorithm, it performs rapid power stability control on the equated controlled resources. Finally, it performs fair power allocation control on the controlled units within each equated model. For ultra-large urban power grids integrating multiple resources such as energy storage stations, electric vehicle charging facilities, distributed photovoltaic power generation systems, and wind power generation systems, when a fault occurs in the ultra-large urban power grid, it achieves optimal power control and stable operation of the urban power grid by rapidly controlling the massive resources of the urban power grid.

[0156] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0157] Based on the same inventive concept, this application also provides a power grid resource collaborative control device for implementing the aforementioned power grid resource collaborative control method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more power grid resource collaborative control device embodiments provided below can be found in the limitations of the power grid resource collaborative control method described above, and will not be repeated here.

[0158] In one exemplary embodiment, such as Figure 8 As shown, a collaborative control device for power grid resources is provided, comprising: a partitioning module 801, a first determining module 802, a second determining module 803, and a control module 804, wherein:

[0159] The partitioning module 801 is used to divide all controlled units of the power grid system into multiple resource groups in the event of a power grid system failure.

[0160] The first determining module 802 is used to determine the equivalent parameters corresponding to the resource group based on the operating parameters of each controlled unit in each resource group;

[0161] The second determining module 803 is used to determine the first reference power of each resource group based on equivalent parameters and a pre-built cooperative control model;

[0162] The control module 804 is used to perform coordinated control of each controlled unit in the resource group based on the first reference power of each resource group.

[0163] In one embodiment, the partitioning module 801 includes: an acquisition unit for acquiring the operating parameters of all controlled units in the power grid system; and a clustering unit for clustering all controlled units in the power grid system into multiple resource groups based on the operating parameters of all controlled units.

[0164] In one embodiment, the clustering unit is specifically used to: divide all controlled units into multiple controlled unit groups according to the type of all controlled units; and cluster each controlled unit in the controlled unit group into at least one resource group according to the similarity between the operating parameters of each controlled unit in each controlled unit group.

[0165] In one embodiment, the first determining module 802 is specifically used to: determine the equivalent capacity of the resource group based on the capacity of each controlled unit in each resource group; determine the equivalent load power of the resource group based on the power of each load in each resource group; and determine the equivalent line impedance of the resource group based on the active power and line impedance of each controlled unit in each resource group.

[0166] In one embodiment, the second determining module 803 includes: an input unit for inputting equivalent parameters into the constraints of the cooperative control model; and a solving unit for solving the cooperative control model based on the constraints and with the objective of minimizing the grid operating cost, to obtain the first reference power for each resource group.

[0167] In one embodiment, the equivalent parameters include equivalent capacity, equivalent line impedance, and equivalent load power; the input unit is specifically used to: determine the equivalent node susceptance matrix based on the equivalent line impedance; input the equivalent load power and the equivalent node susceptance matrix into the power flow constraints of the cooperative control model; and input the equivalent capacity into the equipment operation constraints of the cooperative control model.

[0168] In one embodiment, the control module 804 is specifically configured to: determine the second reference power of each controlled unit in the resource group based on the first reference power and power allocation coefficient of each resource group; and send the second reference power to the corresponding controlled unit so that the controlled unit can perform control according to the second reference power.

[0169] In one embodiment, the control module 804 is further configured to: determine the power allocation coefficient of each resource group based on the total available power of the power grid system and the available power of each resource group.

[0170] Each module in the aforementioned coordinated control device for power grid resources can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0171] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data related to the coordinated control of power grid resources. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for the coordinated control of power grid resources.

[0172] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0173] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for coordinated control of power grid resources.

[0174] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a method for coordinated control of power grid resources.

[0175] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements a method for coordinated control of power grid resources.

[0176] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0177] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0178] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for coordinated control of power grid resources, characterized in that, The method includes: In the event of a power grid system failure, all controlled units of the power grid system are divided into multiple resource groups; Based on the operating parameters of each controlled unit in each resource group, the equivalent parameters corresponding to the resource group are determined; Based on the equivalent parameters and the pre-built cooperative control model, a first reference power is determined for each of the resource groups; Based on the first reference power of each resource group, each controlled unit in the resource group is subjected to coordinated control.

2. The method according to claim 1, characterized in that, The division of all controlled units of the power grid system into multiple resource groups includes: Obtain the operating parameters of all controlled units in the power grid system; Based on the operating parameters of all the controlled units, all the controlled units of the power grid system are clustered into multiple resource groups.

3. The method according to claim 2, characterized in that, Based on the operating parameters of all controlled units, all controlled units of the power grid system are clustered into multiple resource groups, including: All controlled units are divided into multiple controlled unit groups according to their types; Based on the similarity between the operating parameters of each controlled unit in each controlled unit group, each controlled unit in the controlled unit group is clustered into at least one resource group.

4. The method according to claim 1, characterized in that, The step of determining the equivalent parameters corresponding to the resource group based on the operating parameters of each controlled unit in each resource group includes: The equivalent capacity of each resource group is determined based on the capacity of each controlled unit in each resource group; The equivalent load power corresponding to each resource group is determined based on the power of each load in each resource group; The equivalent line impedance corresponding to each resource group is determined based on the active power and line impedance of each controlled unit in each resource group.

5. The method according to claim 1, characterized in that, The determination of the first reference power for each resource group based on the equivalent parameters and the pre-built cooperative control model includes: The equivalent parameters are input into the constraints of the cooperative control model; Based on the constraints, and with the goal of minimizing grid operating costs, the cooperative control model is solved to obtain the first reference power for each resource group.

6. The method according to claim 5, characterized in that, The equivalent parameters include equivalent capacity, equivalent line impedance, and equivalent load power; the constraints for inputting the equivalent parameters into the cooperative control model include: The equivalent node susceptance matrix is ​​determined based on the equivalent line impedance. The equivalent load power and the equivalent node susceptance matrix are input into the power flow constraints of the cooperative control model; The equivalent capacity is input into the equipment operation constraints of the collaborative control model.

7. The method according to any one of claims 1 to 6, characterized in that, The coordinated control of each controlled unit in the resource group based on a first reference power of each resource group includes: The second reference power of each controlled unit in the resource group is determined based on the first reference power and power allocation coefficient of each resource group; The second reference power is sent to the corresponding controlled unit so that the controlled unit can perform control according to the second reference power.

8. The method according to claim 7, characterized in that, The method further includes: The power allocation coefficient for each resource group is determined based on the total available power of the power grid system and the available power of each resource group.

9. A collaborative control device for power grid resources, characterized in that, The device includes: The partitioning module is used to divide all controlled units of the power grid system into multiple resource groups in the event of a power grid system failure. The first determining module is used to determine the equivalent parameters corresponding to the resource group based on the operating parameters of each controlled unit in each resource group; The second determining module is used to determine the first reference power of each of the resource groups based on the equivalent parameters and the pre-built cooperative control model. The control module is used to perform coordinated control of each controlled unit in the resource group based on a first reference power for each resource group.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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