Distributed Coordinated Regulation Method for Multi-Virtual Power Plants

Abstract

The present invention relates to the technical field of power plant regulation and control, and discloses a multi-virtual power plant distributed coordinated regulation and control method. The method includes: collecting the power exchange data and communication frequency data of each virtual power plant, and constructing a dynamic interaction correlation matrix of the virtual power plants based on the coupling relationship between the power exchange data and the communication frequency data; generating a correlation score between the virtual power plants based on the dynamic interaction correlation matrix and a preset correlation degree scoring algorithm; generating a multi-objective collaborative combination based on the correlation score and the power generation resource types corresponding to the virtual power plants; when the correlation score between the virtual power plants is greater than a preset correlation degree threshold, extracting a virtual power plant combination from the multi-objective collaborative combination as a collaborative group; and performing time-sequence coupling scheduling on the virtual power plants based on the resource complementary relationship of the collaborative group. The present invention can achieve efficient collaboration between virtual power plants and improve the efficiency of multi-virtual power plant distributed coordinated regulation and control.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant regulation and control, and particularly to a distributed coordinated regulation and control method for multiple virtual power plants. Background Art

[0002] In the development process of the current power industry, as an innovative model for integrating distributed energy, virtual power plants play an increasingly important role. It organically integrates distributed energy resources, energy storage devices, and controllable loads, and through information technology and intelligent control means, realizes unified management and coordinated operation, effectively improving the flexibility and reliability of the power system, and becoming an important support technology during the critical period of energy structure transformation.

[0003] However, with the increase in the number and scale expansion of virtual power plants, distributed coordinated regulation and control of multiple virtual power plants face many challenges. The power generation characteristics of each virtual power plant vary greatly. Wind power, photovoltaics, etc. are restricted by natural conditions and have unstable power generation. Virtual power plants driven by traditional energy will also have their power generation power affected by various factors, resulting in complex and difficult-to-measure power exchange situations. At the same time, the existing evaluation system is difficult to accurately measure the correlation between virtual power plants. When generating collaborative combinations, resource complementarity and pairing rules cannot be fully considered, and the scheduling link cannot reasonably arrange according to power supply and demand and resource characteristics, resulting in waste of power resources, imbalance between supply and demand, and high system operation costs. Therefore, how to improve the efficiency of distributed coordinated regulation and control of multiple virtual power plants has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention provides a distributed coordinated regulation and control method for multiple virtual power plants, and its main purpose is to solve the problem of low efficiency in distributed coordinated regulation and control of multiple virtual power plants.

[0005] To achieve the above object, a distributed coordinated regulation and control method for multiple virtual power plants provided by the present invention includes:

[0006] Collect the power exchange data and communication frequency data of each virtual power plant, and construct a dynamic interaction correlation matrix of the virtual power plants based on the coupling relationship between the power exchange data and the communication frequency data;

[0007] Generate a correlation score between the virtual power plants based on the dynamic interaction correlation matrix and a preset correlation score algorithm, where the preset correlation score algorithm is:

[0008] ;

[0009] Wherein, is the correlation score of the virtual power plant after the th iteration, is the damping coefficient, is the total number of the virtual power plants, is the correlation degree score of the virtual power plant after the th iteration, is the set of virtual power plants associated with the virtual power plant ; is the resource complementary weight factor between the virtual power plant and the virtual power plant ; is the total number of out-links of the virtual power plant is the serial number of the virtual power plant, is the serial number of the virtual power plant, is the iteration number of the correlation degree score;

[0010] Generate a multi-objective collaborative combination based on the correlation degree score and the power generation resource type corresponding to the virtual power plant;

[0011] When the correlation degree score between the virtual power plants is greater than the preset correlation degree threshold, extract the virtual power plant combination from the multi-objective collaborative combination as the collaborative group;

[0012] Perform time-series coupling scheduling on the virtual power plants based on the resource complementary relationship of the collaborative group.

[0013] Optionally, collecting the power exchange data and communication frequency data of each virtual power plant includes:

[0014] Obtain the transmission power value, power transmission direction and timestamp of the virtual power plant as the power exchange data according to the preset sampling frequency;

[0015] Synchronously record the communication frequency, communication delay and data integrity rate of the controller of the virtual power plant as the communication frequency data.

[0016] Optionally, the dynamic interaction correlation matrix of the virtual power plant is:

[0017] ;

[0018] wherein, is the matrix element in the dynamic interaction correlation matrix, is the data fusion weight factor, is the power exchange amount between the virtual power plant and the virtual power plant ; is the communication frequency between the virtual power plant and the virtual power plant is the maximum communication frequency, is the serial number of the virtual power plant, is the serial number of the virtual power plant.

[0019] Optionally, generating the correlation degree score between the virtual power plants based on the dynamic interaction correlation matrix and a preset correlation degree scoring algorithm includes:

[0020] Determining the power exchange correlation degree between the virtual power plants based on the dynamic interaction correlation matrix;

[0021] Generating a resource complementary weight factor for the virtual power plants based on the power exchange correlation degree;

[0022] Generating the correlation degree score of the virtual power plants based on the resource complementary weight factor and a preset correlation degree scoring algorithm.

[0023] Optionally, the calculation formula of the resource complementary weight factor is as follows:

[0024] ;

[0025] Wherein, is the resource complementary weight factor between the virtual power plant and the virtual power plant ; is the weight coefficient, is the virtual power plant and the virtual power plant the power exchange correlation degree between; is the virtual power plant and the virtual power plant the resource complementary coefficient between; is the serial number of the virtual power plant, is the serial number of the virtual power plant.

[0026] Optionally, generating a multi-objective collaborative combination based on the correlation degree score and the power generation resource type corresponding to the virtual power plant includes:

[0027] Determining the resource complementary coefficient of the virtual power plant according to the power generation resource type corresponding to the virtual power plant;

[0028] Performing a weighted sum of the correlation degree score and the resource complementary coefficient to obtain the weighted total score of the virtual power plant;

[0029] When the weighted total score exceeds a preset total score threshold and the power plant combination of the virtual power plant meets a preset single-type pairing rule, generating a multi-objective collaborative combination of the virtual power plants.

[0030] Optionally, when the correlation score between the virtual power plants is greater than a preset correlation threshold, extracting a virtual power plant combination from the multi-objective collaborative combination as a collaborative group includes:

[0031] When the correlation score between the wind power-dominated virtual power plant and the energy storage virtual power plant exceeds the preset correlation threshold, trigger the binding of the collaborative group of the wind power-dominated virtual power plant and the energy storage virtual power plant.

[0032] Optionally, the time-series coupling scheduling of the virtual power plants based on the resource complementary relationship of the collaborative group includes:

[0033] Establish a time mapping relationship between the wind power surplus period of the wind power-dominated virtual power plant and the peak power consumption period of the energy storage virtual power plant based on the resource complementary relationship of the collaborative group;

[0034] Constrain the power ratio of the charging power during the surplus period to the discharging power during the peak period based on the charge-discharge efficiency of the energy storage virtual power plant;

[0035] Generate a timestamped charge-discharge instruction set for the energy storage virtual power plant according to the time mapping relationship and the power ratio;

[0036] Send the charge-discharge instruction set to the energy storage virtual power plant through the communication link corresponding to the collaborative group.

[0037] Optionally, establishing a time mapping relationship between the wind power surplus period of the wind power-dominated virtual power plant and the peak power consumption period of the energy storage virtual power plant based on the resource complementary relationship of the collaborative group includes:

[0038] Obtain the power generation data of the wind power-dominated virtual power plant in the collaborative group to determine the wind power surplus period;

[0039] Obtain the power consumption load data of the energy storage virtual power plant in the collaborative group to determine the peak power consumption period;

[0040] Generate the time characteristics of the wind power surplus period and the peak power consumption period;

[0041] Based on the time characteristics and the resource complementary relationship, establish a time mapping relationship between the wind power surplus period and the peak power consumption period.

[0042] Optionally, obtaining the power consumption load data of the energy storage virtual power plant in the collaborative group to determine the peak power consumption period includes:

[0043] Collect the power consumption load data of the energy storage virtual power plant within a preset time period;

[0044] Perform denoising processing on the electricity load data, and perform time series arrangement on the denoised electricity load data to obtain the electricity load sequence of the energy storage virtual power plant;

[0045] Calculate the average electricity load value within a preset window based on the sliding window algorithm and the electricity load sequence;

[0046] Determine the time period during which the average electricity load value is greater than the preset load threshold and the electricity load value within the window continues to rise as the peak electricity consumption period of the energy storage virtual power plant.

[0047] The present invention constructs a dynamic interaction correlation matrix by collecting power exchange data and communication frequency data, comprehensively reflects the tightness of power exchange and information interaction between virtual power plants, calculates the correlation score based on this, comprehensively considers factors such as damping coefficient and resource complementary weight factor, accurately quantifies the correlation degree between power plants, generates a multi-objective collaborative combination by combining the correlation score and power generation resource type, ensures that the power plant resources in the combination are complementary and meet certain rules, improves the overall regulation effect, extracts the virtual power plant combination with a high correlation score from the multi-objective collaborative combination as the collaborative group, focuses on the advantageous combination, exerts the synergistic effect, reduces energy waste, improves resource utilization efficiency, performs time series coupling scheduling according to the resource complementary relationship of the collaborative group, establishes a time mapping relationship between the wind power surplus period and the peak electricity consumption period of the energy storage virtual power plant, restricts the charge-discharge power ratio, generates an accurate charge-discharge instruction set, enables the energy storage virtual power plant to reasonably store and release electric energy at different times, balances power supply and demand, enhances the stability and reliability of the power system, optimizes the configuration of power resources in the time dimension, improves the regulation efficiency. Therefore, the present invention proposes a multi-virtual power plant distributed coordinated regulation method, which can solve the problem of low efficiency of multi-virtual power plant distributed coordinated regulation. Description of the Drawings

[0048] Figure 1 It is a schematic flowchart of a multi-virtual power plant distributed coordinated regulation method provided by an embodiment of the present invention;

[0049] The realization, functional characteristics and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiment

[0050] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] The embodiments of the present application provide a method for distributed coordinated regulation of multiple virtual power plants. The execution entities of the method for distributed coordinated regulation of multiple virtual power plants include, but are not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for distributed coordinated regulation of multiple virtual power plants can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0052] Referring to Figure 1 As shown, it is a schematic flowchart of the method for distributed coordinated regulation of multiple virtual power plants provided by an embodiment of the present invention. In this embodiment, the method for distributed coordinated regulation of multiple virtual power plants includes:

[0053] S1. Collect the power exchange data and communication frequency data of each virtual power plant, and construct a dynamic interaction correlation matrix of the virtual power plants based on the coupling relationship between the power exchange data and the communication frequency data.

[0054] In the embodiments of the present invention, the collecting of the power exchange data and communication frequency data of each virtual power plant includes:

[0055] Obtain the transmission power value, power transmission direction, and timestamp of the virtual power plant as power exchange data according to a preset sampling frequency;

[0056] Synchronously record the communication frequency, communication delay, and data integrity rate of the controller of the virtual power plant as communication frequency data.

[0057] Specifically, the collection period of the power exchange data can be set to 5 - 15 minutes, and the collection period of the communication frequency data can be set to 1 - 5 minutes.

[0058] Specifically, assume there are three virtual power plants, VPP1, VPP2, and VPP3. At a certain moment, the preset sampling frequency is once every 10 minutes. The power value transmitted from VPP1 to VPP2 is collected as 50 MW, the power transmission direction is from VPP1 to VPP2, and the timestamp is recorded as 10:00. At the same time, the communication frequency between the controllers of VPP1 and VPP2 is recorded as once every 5 minutes, the communication delay is 50 milliseconds, and the data integrity rate is 98%. When constructing the dynamic interaction correlation matrix later, these data can reflect the tightness of power exchange and communication between VPP1 and VPP2. If the correlation score is high and the resources are complementary, they may form a collaborative group for joint scheduling to improve the utilization efficiency of power resources.

[0059] Specifically, the preset sampling frequency refers to the frequency preset for obtaining data, which determines the time interval for collecting data and can be determined according to actual requirements and system characteristics; the transmitted power value represents the power magnitude during the power transmission of the virtual power plant, reflecting the scale and intensity of power exchange; the power transmission direction indicates the flow direction of power transmission, clarifying which virtual power plant the power flows from and to, and reflecting the path of power exchange; the timestamp records the specific time of data collection, providing a basis for subsequent analysis of the time series characteristics of power exchange; the communication frequency is the frequency of communication between the controllers of the virtual power plants, reflecting the activity degree of information interaction between the virtual power plants; the communication delay is the time delay experienced by the signal from the sending end to the receiving end, affecting the timeliness of information transmission; the data integrity rate is an index to measure the integrity of the collected data, reflecting the data loss situation during the communication process.

[0060] Specifically, accurately collecting power exchange data and communication frequency data is the basis for constructing the dynamic interaction correlation matrix later. Through these data, the power exchange relationship and information interaction situation between virtual power plants can be analyzed, providing a basis for determining the correlation degree, resource complementarity relationship, and subsequent collaborative combination and scheduling between virtual power plants.

[0061] Specifically, by obtaining power exchange data such as the transmitted power value and power transmission direction, the power supply and demand situations of each virtual power plant can be accurately grasped, providing a basis for reasonably allocating power resources; the communication frequency data reflects the information interaction situation between virtual power plants, helping to judge the effectiveness and timeliness of information transmission.

[0062] In the embodiment of the present invention, the dynamic interaction correlation matrix of the virtual power plant is as follows:

[0063] ;

[0064] Among them, is the matrix element in the dynamic interaction correlation matrix, is the data fusion weight factor, is the virtual power plant The power exchange volume with the virtual power plant is the maximum exchange volume, is the maximum exchange volume, is the virtual power plant The communication frequency between the virtual power plant and the virtual power plant is the maximum communication frequency, is the maximum communication frequency, is the serial number of the virtual power plant, is the serial number of the virtual power plant.

[0065] Specifically, the matrix elements in the dynamic interaction correlation matrix are used to measure the comprehensive correlation degree between the virtual power plant and the virtual power plant The larger the value, the closer the connection between the two virtual power plants.

[0066] Specifically, the data fusion weight factor , with a value range between 0 and 1, is used to adjust the relative importance of the power exchange volume and the communication frequency in the calculation of the comprehensive correlation degree. When is close to 1, it indicates that more importance is attached to the impact of the power exchange volume on the correlation degree; when is close to 0, more emphasis is placed on the impact of the communication frequency; the power exchange volume between the virtual power plant and the virtual power plant intuitively reflects the actual power interaction scale between the two virtual power plants and is a key indicator for measuring the tightness of their power connection.

[0067] Specifically, the maximum value of the power exchange volume between all virtual power plants is used as the normalization benchmark to make the power exchange volumes between different virtual power plants comparable; the communication frequency between the virtual power plant and the virtual power plant reflects the frequency of information interaction between the controllers of the two virtual power plants. The higher the communication frequency, the more timely and sufficient the information exchange; the maximum value of the communication frequencies between all virtual power plants is also used for normalizing the communication frequencies to measure the relative high and low of the communication frequencies between each virtual power plant under a unified standard.

[0068] Specifically, first determine the value of the data fusion weight factor, and obtain the power exchange volume between each pair of virtual power plants, the maximum power exchange volume between all virtual power plants, the communication frequency between this pair of virtual power plants, and the maximum communication frequency between all virtual power plants. Substitute these values into the formula for calculation to obtain the correlation matrix elements between each pair of virtual power plants, thereby constructing a complete dynamic interaction correlation matrix.

[0069] Specifically, assume that there are three virtual power plants A, B, and C, and the data fusion weight factor is set to 0.6. The power exchange volume between power plants A and B is 50 megawatt-hours, and the maximum power exchange volume among all power plants is 100 megawatt-hours; the communication frequency between A and B is 8 times per hour, and the maximum communication frequency among all power plants is 10 times per hour. According to the formula, the matrix element between A and B is 0.62. Similarly, the matrix elements between A and C, and between B and C can be calculated, thus constructing a complete dynamic interaction correlation matrix to provide a data basis for subsequent regulation.

[0070] S2. Generate the correlation degree score between the virtual power plants based on the dynamic interaction correlation matrix and a preset correlation degree scoring algorithm.

[0071] In the embodiment of the present invention, the generating the correlation degree score between the virtual power plants based on the dynamic interaction correlation matrix and a preset correlation degree scoring algorithm includes:

[0072] Determine the power exchange correlation degree between the virtual power plants based on the dynamic interaction correlation matrix;

[0073] Generate the resource complementary weight factor of the virtual power plants based on the power exchange correlation degree;

[0074] Generate the correlation degree score of the virtual power plants based on the resource complementary weight factor and a preset correlation degree scoring algorithm.

[0075] Specifically, the preset correlation degree scoring algorithm is:

[0076] ;

[0077] where is the correlation degree score of the virtual power plant after the -th iteration, is the damping coefficient, is the total number of the virtual power plants, is the correlation degree score of the virtual power plant after the -th iteration, is the set of virtual power plants associated with the virtual power plant , is the resource complementary weight factor between the virtual power plant and the virtual power plant , is the total number of out-links of the virtual power plant , is the serial number of the virtual power plant, is the serial number of the virtual power plant, is the iteration number of the correlation degree score.

[0078] Specifically, the power exchange correlation degree is determined based on the dynamic interaction correlation matrix, reflecting the tightness of the power exchange connection between virtual power plants. The higher the value, the closer the power exchange correlation. The resource complementary weight factor is obtained by comprehensively considering the power exchange correlation degree and the resource complementary coefficient, and is used to measure the degree of resource complementarity between virtual power plants, reflecting the potential of collaborative cooperation between the two in terms of resources. The correlation degree score is used to quantify the correlation degree of a virtual power plant with other virtual power plants. The higher the score, the closer the comprehensive correlation between the virtual power plant and other virtual power plants in terms of power exchange, resource complementarity, etc. The damping coefficient is usually between 0 and 1, and is used to adjust the influence of the random browsing factor in the correlation degree scoring algorithm, avoiding the calculation result being overly dependent on a certain part of the factors and making the scoring result more stable and reasonable. The total number of virtual power plants refers to the total number of virtual power plants participating in distributed coordinated control, which is a basic parameter in the correlation degree scoring algorithm; for the virtual power plant The set of virtual power plants associated with it includes all virtual power plants that have an actual association with the virtual power plant in terms of power exchange or other aspects, clarifying the scope of relevant objects participating in the calculation of the virtual power plant correlation degree score; the total number of out-links of the virtual power plant represents the number of associations between the virtual power plant and other virtual power plants, and is used to normalize the relevant items in the correlation degree scoring calculation, making the influences of different virtual power plants comparable; the number of iterations of the correlation degree score is because the correlation degree scoring algorithm is an iterative calculation and records the current calculation round. As the number of iterations increases, the correlation degree score gradually converges to a more accurate value. Specifically, the calculation formula for the resource complementary weight factor is as follows:

[0079] Specifically, the calculation formula for the resource complementary weight factor is as follows:

[0080] ;

[0081] wherein, is the resource complementary weight factor between the virtual power plant and the virtual power plant , is the weight coefficient, is the power exchange correlation degree between the virtual power plant and the virtual power plant , is the resource complementary coefficient between the virtual power plant and the virtual power plant , is the serial number of the virtual power plant, is the serial number of the virtual power plant.

[0082] Specifically, the virtual power plant and the virtual power plant The resource complementary weight factor between them comprehensively measures the degree of mutual complementarity and collaborative cooperation of the two virtual power plants in terms of resources. Its value range is usually between 0 and 1. The larger the value, the stronger the resource complementarity between the two virtual power plants, and the more potential there is to achieve better resource optimization and allocation in subsequent collaborative regulation.

[0083] Specifically, the weight coefficient The value range is between 0 - 1, which is used to adjust the relative importance of the power exchange correlation degree and the resource complementary coefficient when calculating the resource complementary weight factor. When is closer to 1, it indicates that when calculating , the influence of the power exchange correlation degree is greater; when is closer to 0, then the resource complementary coefficient has a greater influence on .

[0084] Specifically, represents the power exchange correlation degree between virtual power plant and virtual power plant , and is equal to the element in the dynamic interaction correlation matrix, which comprehensively combines the power exchange volume and communication frequency information between virtual power plant and virtual power plant . Therefore, can reflect the degree of tight correlation between the two virtual power plants at the power exchange level. The larger the value, the more frequent the power exchange and the closer the connection between the two virtual power plants.

[0085] Specifically, and are respectively the serial numbers of the virtual power plants, which are used to clarify which two virtual power plants the resource complementary weight factor, power exchange correlation degree, and resource complementary coefficient are between.

[0086] Specifically, is the resource complementary coefficient between virtual power plant and virtual power plant , and different values are set according to different combinations of power generation resource types. The calculation rule of the resource complementary coefficient includes: when the resource type is wind power - energy storage, ; when the resource type is photovoltaic - energy storage, ; when the resource type is gas turbine - energy storage, ; When other types are paired, , starting from the essential characteristics of the resources, this coefficient measures the degree of mutual complementarity between different resource types.

[0087] Specifically, according to the values of the elements in the dynamic interaction correlation matrix, analyze the comprehensive situation of the power exchange volume and communication frequency between virtual power plants, and determine the power exchange correlation degree between them. This step is the basis for subsequent calculations and preliminarily quantifies the power connection between virtual power plants.

[0088] Specifically, based on the power exchange correlation degree, combined with the resource complementarity coefficient between virtual power plants, the resource complementarity weight factor is further calculated, comprehensively considering both power exchange and resource type complementarity factors.

[0089] Specifically, using the preset correlation degree scoring algorithm, substitute parameters such as the resource complementarity weight factor, the initial correlation degree score of the virtual power plant (which needs to be initialized during the first calculation), the damping coefficient, and the total number of virtual power plants into the formula for iterative calculation, and continuously update the correlation degree score until the score converges (the difference between the results of two consecutive calculations is less than the set threshold).

[0090] Specifically, by accurately calculating the correlation degree score, the degree of connection tightness and resource complementarity potential between each virtual power plant can be clearly understood. When generating a multi-objective collaborative combination, virtual power plants with high correlation degree scores and strong resource complementarity can be preferentially selected for combination. Such a collaborative group can better achieve resource sharing and optimal allocation during actual operation.

[0091] Specifically, in the entire distributed coordinated control method for multiple virtual power plants, the resource complementarity weight factor, as an important parameter of the correlation degree scoring algorithm, directly affects the calculation result of the correlation degree score between virtual power plants. Accurate calculation helps to more accurately measure the degree of connection between virtual power plants and provides a reliable basis for subsequent generation of multi-objective collaborative combinations, extraction of collaborative groups, and realization of efficient time-series coupling scheduling.

[0092] Specifically, by comprehensively considering the power exchange correlation degree and the resource complementarity coefficient to calculate the resource complementarity weight factor, the complementary relationship between virtual power plants can be evaluated more comprehensively. When conducting collaborative control of multiple virtual power plants, virtual power plants with high resource complementarity weight factors can be preferentially selected for combination according to the value to form a collaborative group. Such a collaborative group has more advantages in resource allocation, can better exert the resource characteristics of each virtual power plant, and improve the utilization efficiency of electric power resources. For example, if the A higher value indicates that they have good complementarity in both power exchange and resource types, and can better balance power supply and demand during joint dispatching, reduce energy waste, and thus improve the overall efficiency of distributed coordinated control of multiple virtual power plants.

[0093] S3. Generate a multi-objective collaborative combination based on the correlation score and the power generation resource type corresponding to the virtual power plant.

[0094] In the embodiment of the present invention, the generating the multi-objective collaborative combination based on the correlation score and the power generation resource type corresponding to the virtual power plant includes:

[0095] Determine the resource complementarity coefficient of the virtual power plant according to the power generation resource type corresponding to the virtual power plant;

[0096] Perform weighted summation on the correlation score and the resource complementarity coefficient to obtain the weighted total score of the virtual power plant;

[0097] When the weighted total score exceeds a preset total score threshold and the power plant combination of the virtual power plant meets the preset single-type pairing rule, generate the multi-objective collaborative combination of the virtual power plant.

[0098] Specifically, the resource complementarity coefficient is determined according to the power generation resource type of the virtual power plant, and is used to measure the degree of complementarity between the resources of different virtual power plants. For example, when wind power and energy storage are paired, the resource complementarity coefficient is relatively high because the intermittency of wind power can be adjusted by energy storage; while the complementarity coefficient of the same type of power generation resources (such as wind power - wind power) is relatively low, and different resource combinations have different coefficient values.

[0099] Furthermore, the single-type pairing rule stipulates that simple combinations of the same type of power generation resources cannot appear in the virtual power plant combination, such as prohibiting combinations like wind power - wind power and photovoltaic - photovoltaic. This is to ensure that the combination has resource complementarity and improve the overall control effect.

[0100] Specifically, compare the calculated weighted total score with the preset total score threshold, and at the same time check whether the virtual power plant combination meets the single-type pairing rule. If the weighted total score exceeds the preset threshold and the combination complies with the rule, then determine the virtual power plant combination as the multi-objective collaborative combination; otherwise, do not include it in the combination.

[0101] Specifically, assume there are three virtual power plants, VPP1 is wind power-dominated, VPP2 is energy storage type, and VPP3 is photovoltaic type. It is known that the correlation score between VPP1 and VPP2 is , and the correlation score between VPP1 and VPP3 is 0.6. Set the correlation score weight , the resource complementarity coefficient weight , and the preset total score threshold is 0.7. The resource complementarity coefficient between VPP1 and VPP2 , the resource complementary coefficient of VPP1 and VPP3 .

[0102] Furthermore, calculate the weighted total scores of VPP1 and VPP2: , where and it conforms to the single - type pairing rule, so VPP1 and VPP2 can form a multi - objective collaborative combination.

[0103] Furthermore, calculate the weighted total scores of VPP1 and VPP3: , where , so VPP1 and VPP3 cannot form a multi - objective collaborative combination.

[0104] S4. When the correlation degree score between the virtual power plants is greater than the preset correlation degree threshold, extract the virtual power plant combination from the multi - objective collaborative combination as the collaborative group.

[0105] In the embodiment of the present invention, the step of when the correlation degree score between the virtual power plants is greater than the preset correlation degree threshold, extracting the virtual power plant combination from the multi - objective collaborative combination as the collaborative group includes:

[0106] When the correlation degree score between the wind - power - dominated virtual power plant and the energy - storage - type virtual power plant exceeds the preset correlation degree threshold, trigger the binding of the collaborative group of the wind - power - dominated virtual power plant and the energy - storage - type virtual power plant.

[0107] Specifically, the correlation degree score is a numerical value used to measure the overall correlation tightness between virtual power plants, comprehensively considering factors such as power exchange data, communication frequency data, and resource complementarity. The higher its value, the closer the connections between virtual power plants in various aspects, and the greater the potential for collaborative cooperation.

[0108] Specifically, the preset correlation degree threshold is a pre - set reference value, used as a standard to judge whether the correlation degree between virtual power plants is tight enough, and then determine whether to combine them as a collaborative group. The setting of this threshold is based on actual application requirements and expectations for the overall system performance, and different application scenarios may set different thresholds.

[0109] Specifically, the multi - objective collaborative combination is a combination set composed of multiple virtual power plants. These combinations are generated under the conditions of considering the correlation degree score, resource complementary coefficient of virtual power plants, and whether they meet the preset total score threshold and single - type pairing rule, aiming to achieve the collaborative optimization of multiple objectives (such as stable power supply, efficient resource utilization, etc.).

[0110] Specifically, the collaborative group is a virtual power plant combination further selected from the multi-objective collaborative combinations. These combinations are more prominent in terms of correlation and resource complementarity, etc., and can better play a collaborative role in actual distributed coordinated regulation, achieving more efficient power dispatching and resource allocation.

[0111] Specifically, a wind power-dominated virtual power plant is a virtual power plant whose main power generation resource is wind power. Due to the intermittent and volatile characteristics of wind power, its power generation is greatly affected by natural conditions (such as wind speed), and there are certain challenges in the stability of power supply.

[0112] Specifically, a storage-type virtual power plant is a virtual power plant equipped with energy storage devices and related technologies, which can store excess electric energy and release electric energy when needed. The storage-type virtual power plant can store electric energy when the power is in excess and provide electric energy when the power is in shortage, playing a role in regulating the balance between power supply and demand.

[0113] Specifically, collaborative group binding means that when the correlation score between a wind power-dominated virtual power plant and a storage-type virtual power plant exceeds the preset correlation threshold, these two virtual power plants are determined as a collaborative group, and a collaborative cooperation relationship is established between them for subsequent joint dispatching and collaborative regulation.

[0114] Specifically, selecting combinations with higher correlation from the multi-objective collaborative combinations as the collaborative group can focus on the virtual power plant combinations with the most collaborative potential, providing a clear object for more accurate and efficient regulation in the future.

[0115] Specifically, by extracting wind power-dominated and storage-type virtual power plants with high correlation scores to form a collaborative group, the complementary advantages of the two can be fully utilized, significantly improving the efficiency of power regulation. During the period when wind power generation is large but the electricity load is low, the storage-type virtual power plant can store the excess wind power to avoid energy waste; while during the peak electricity consumption period or when the wind power output is insufficient, the storage-type virtual power plant releases the stored electric energy to ensure the stability of power supply. This collaborative working method reduces the imbalance between power supply and demand, improves the stability and reliability of the power system, and at the same time improves the energy utilization efficiency, avoiding the impact on the power system caused by the intermittency and volatility of wind power.

[0116] Specifically, assume there are 5 virtual power plants, where VPP1 is wind power-dominated, VPP2 is energy storage-based, and VPP3, VPP4, and VPP5 are of other types. After calculation, the correlation scores between the virtual power plants are obtained, and the preset correlation threshold is 0.7. The correlation score between VPP1 and VPP2 is 0.85, exceeding the preset threshold. At this time, VPP1 and VPP2 are extracted and bound as a collaborative group. In actual operation, when the wind speed is high in a certain period and the wind power generation of VPP1 is excessive, VPP2 starts to store electrical energy; while during the peak electricity consumption at night and when the wind power output is small, VPP2 releases electrical energy according to the dispatching instructions to cooperate with VPP1 to meet the electricity demand, effectively improving the stability of power supply and the resource utilization efficiency.

[0117] S5. Perform time-series coupling dispatching on the virtual power plants based on the resource complementary relationship of the collaborative group.

[0118] In the embodiment of the present invention, time-series coupling dispatching refers to coordinating and arranging the production, storage, and distribution of electricity in the time dimension according to the power supply and demand conditions in different periods, combined with the resource characteristics of the virtual power plants, so as to achieve the efficient utilization of power resources and the stable operation of the power system.

[0119] Specifically, the performing time-series coupling dispatching on the virtual power plants based on the resource complementary relationship of the collaborative group includes:

[0120] Establish a time mapping relationship between the wind power surplus period of the wind power-dominated virtual power plant and the peak electricity consumption period of the energy storage-based virtual power plant based on the resource complementary relationship of the collaborative group;

[0121] Constrain the power ratio of the charging power during the surplus period to the discharging power during the peak period based on the charge-discharge efficiency of the energy storage-based virtual power plant;

[0122] Generate a charge-discharge instruction set with timestamps for the energy storage-based virtual power plant according to the time mapping relationship and the power ratio;

[0123] Send the charge-discharge instruction set to the energy storage-based virtual power plant through the communication link corresponding to the collaborative group.

[0124] Specifically, the wind power surplus period refers to the time period in the collaborative group when the power generation of the wind power-dominated virtual power plant exceeds the local or current power demand within the collaborative group. At this time, the electrical energy generated by the wind power has a surplus and needs to be reasonably stored or distributed; the peak electricity consumption period refers to the time period when the power demand in the area where the energy storage-based virtual power plant is located or within the collaborative group reaches the peak. At this time, higher requirements are placed on the stability and sufficiency of power supply.

[0125] Specifically, the time mapping relationship refers to establishing a corresponding connection in time between the wind power surplus period and the peak electricity consumption period, and clarifying the subsequent peak electricity consumption period corresponding to the wind power surplus, so as to reasonably arrange the charge and discharge strategies of the energy storage virtual power plant.

[0126] Specifically, the charge and discharge efficiency refers to the ratio of the actual stored or released electric energy to the consumed or input electric energy during the charging and discharging processes of the energy storage virtual power plant, which reflects the efficiency of the energy storage device during the energy conversion process and is an important basis for determining the charge and discharge power ratio.

[0127] Specifically, the power ratio refers to the proportional relationship between the charging power of the energy storage virtual power plant during the wind power surplus period and the discharging power during the peak electricity consumption period. Through this ratio, the charge and discharge processes of the energy storage device can be reasonably controlled to ensure the effective utilization of energy storage resources.

[0128] Specifically, the charge and discharge instruction set with timestamps is a set of instructions containing specific charge and discharge times and power information. Each instruction is marked with an accurate time stamp, which is used to accurately control the charge and discharge operations of the energy storage virtual power plant at different times.

[0129] Specifically, the communication link refers to the channel for data transmission and instruction communication between virtual power plants within the cooperation group, ensuring that the charge and discharge instructions can be accurately and timely transmitted from the control center to the energy storage virtual power plant.

[0130] Specifically, establishing the time mapping relationship between the wind power surplus period of the wind power-dominated virtual power plant and the peak electricity consumption period of the energy storage virtual power plant based on the resource complementary relationship of the cooperation group includes:

[0131] Obtaining the power generation data of the wind power-dominated virtual power plant within the cooperation group to determine the wind power surplus period;

[0132] Obtaining the electricity load data of the energy storage virtual power plant within the cooperation group to determine the peak electricity consumption period;

[0133] Generating the time characteristics of the wind power surplus period and the peak electricity consumption period;

[0134] Based on the time characteristics and the resource complementary relationship, establishing the time mapping relationship between the wind power surplus period and the peak electricity consumption period.

[0135] Specifically, obtaining the electricity load data of the energy storage virtual power plant within the cooperation group to determine the peak electricity consumption period includes:

[0136] Collecting the electricity load data of the energy storage virtual power plant within a preset time period;

[0137] Denoise the electricity load data, and perform time series arrangement on the denoised electricity load data to obtain the electricity load sequence of the energy storage virtual power plant;

[0138] Calculate the average electricity load value within a preset window based on the sliding window algorithm and the electricity load sequence;

[0139] Determine the time period when the average electricity load value is greater than the preset load threshold and the electricity load value within the window continuously rises as the peak electricity consumption period of the energy storage virtual power plant.

[0140] Specifically, the time characteristics refer to the time attributes used to describe the wind power surplus period and the peak electricity consumption period, such as the specific time range, duration, periodic law, etc. These characteristics help to establish the time mapping relationship between the two.

[0141] Specifically, generating time characteristics refers to extracting time characteristics from the determined wind power surplus period and peak electricity consumption period, such as the start time, end time, and duration of the statistical time period, and analyzing its occurrence law within a day or a week.

[0142] Specifically, combining the established time mapping relationship and the determined power ratio, generate an instruction set containing accurate timestamps and charge-discharge power information in chronological order, providing detailed control instructions for the charge-discharge operations of the energy storage virtual power plant.

[0143] Furthermore, use the communication link corresponding to the collaboration group to send the generated charge-discharge instruction set with timestamps to the energy storage virtual power plant, enabling it to perform charge-discharge operations according to the instructions.

[0144] Specifically, after completing the virtual power plant correlation degree scoring, multi-objective collaborative combination generation, and collaboration group extraction, it is a key link to actually regulate the virtual power plants within the collaboration group. By establishing a time mapping relationship, restricting the power ratio, and issuing the charge-discharge instruction set, the transformation from theoretical analysis to actual operation is realized, and the previously determined resource complementary relationship of the collaboration group is specifically applied to power dispatching.

[0145] Specifically, through time series coupled dispatching, the resource complementary characteristics of wind power and energy storage can be fully utilized, significantly improving the efficiency of distributed coordinated regulation of multiple virtual power plants. Specifically reflected in: during the wind power surplus period, the excess electric energy is stored in the energy storage virtual power plant, avoiding the waste of wind power and improving the energy utilization rate; during the peak electricity consumption period, the energy storage virtual power plant discharges according to the set power ratio to supplement the power supply, alleviating the power shortage situation and enhancing the stability and reliability of the power system. This precise dispatching method optimizes the allocation of power resources in the time dimension, reduces the imbalance between power supply and demand, and lowers the system operation cost.

[0146] Specifically, assume that a collaborative group includes a wind power-dominated virtual power plant VPP1 and an energy storage virtual power plant VPP2. Through data analysis, it is found that VPP1 generates far more electricity than the local electricity demand due to high wind speeds from 2 pm to 4 pm every day (wind power surplus period); while the electricity load in the area where VPP2 is located reaches the peak from 7 pm to 9 pm (peak electricity consumption period). After calculation, considering the charge and discharge efficiency of VPP2, the ratio of its charging power during the wind power surplus period to the discharging power during the peak electricity consumption period is determined to be 1:0.8. Based on this information, the following charge and discharge instruction set with timestamps is generated: At 2 pm, start charging at a power of 5 MW; at 3 pm, maintain a charging power of 5 MW; at 4 pm, stop charging; at 7 pm, start discharging at a power of 4 MW; at 8 pm, maintain a discharging power of 4 MW; at 9 pm, stop discharging. This instruction set is sent to VPP2 through the communication link of the collaborative group, achieving precise control of the charging and discharging of VPP2 at different times, effectively utilizing wind power resources, and ensuring power supply during peak electricity consumption periods.

[0147] In several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways.

[0148] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0149] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence is the theory, method, and technology that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A distributed coordination and control method for multiple virtual power plants, characterized in that: The method comprises: Collecting power exchange data and communication frequency data of each virtual power plant, and constructing a dynamic interaction correlation matrix of the virtual power plant based on the coupling relationship between the power exchange data and the communication frequency data; The correlation scores between the virtual power plants are generated based on the dynamic interaction correlation matrix and a preset correlation scoring algorithm, wherein the preset correlation scoring algorithm is: ； in, It is The virtual power plant The relevance score of is the damping coefficient, is the total number of virtual power plants, It is After the first iteration, the virtual power plant The relevance score of Virtual Power Plant There is a collection of associated virtual power plants, The virtual power plant With the virtual power plant The resource complementarity weight factor between It is a virtual power plant The total number of outgoing links, is the serial number of the virtual power plant, is the serial number of the virtual power plant, is the number of iterations of relevance scoring; Determine the resource complementarity coefficient of the virtual power plant according to the type of power generation resources corresponding to the virtual power plant, perform weighted summation on the relevance score and the resource complementarity coefficient to obtain a weighted total score of the virtual power plant, and generate a multi-objective collaborative combination of the virtual power plant when the weighted total score exceeds a preset total score threshold and the power plant combination of the virtual power plant meets a preset single type pairing rule; When the correlation score between the virtual power plants is greater than a preset correlation threshold, extracting a virtual power plant combination from the multi-objective synergy combination as a synergy group; Based on the resource complementarity of the collaborative group, a time mapping relationship is established between the wind power surplus period of the wind power-dominated virtual power plant and the peak power consumption period of the energy storage type virtual power plant. Based on the charging and discharging efficiency of the energy storage type virtual power plant, the power ratio of the charging power in the wind power surplus period to the discharging power in the peak power consumption period is constrained. According to the time mapping relationship and the power ratio, a time-stamped charging and discharging instruction set of the energy storage type virtual power plant is generated, and the charging and discharging instruction set is issued to the energy storage type virtual power plant through the communication link corresponding to the collaborative group.

2. The distributed coordination and control method of multiple virtual power plants according to claim 1, characterized in that: The collecting of power exchange data and communication frequency data of each virtual power plant includes: Acquire the transmission power value, transmission direction and timestamp of the virtual power plant as power exchange data according to a preset sampling frequency; The communication frequency, communication delay and data integrity rate of the controller of the virtual power plant are synchronously recorded as communication frequency data.

3. The distributed coordination and control method of multiple virtual power plants according to claim 1, characterized in that: The dynamic interaction matrix of the virtual power plant is: ； in, is a matrix element in the dynamic interaction matrix, is the data fusion weight factor, The virtual power plant With the virtual power plant The amount of power exchanged between is the maximum exchange amount, The virtual power plant With the virtual power plant The communication frequency between is the maximum communication frequency, is the serial number of the virtual power plant, is the serial number of the virtual power plant.

4. The distributed coordination and control method of multiple virtual power plants according to claim 1, characterized in that: The generating the correlation scores between the virtual power plants based on the dynamic interaction correlation matrix and a preset correlation scoring algorithm includes: Determining the power exchange correlation between the virtual power plants based on the dynamic interaction correlation matrix; generating a resource complementarity weight factor of the virtual power plant based on the power exchange correlation; A relevance score for the virtual power plant is generated based on the resource complementarity weight factor and a preset relevance scoring algorithm.

5. The distributed coordination and control method of multiple virtual power plants according to claim 4 is characterized in that: The calculation formula of the resource complementarity weight factor is as follows: ； in, The virtual power plant With the virtual power plant The resource complementarity weight factor between is the weight coefficient, The virtual power plant With the virtual power plant The power exchange correlation between The virtual power plant With the virtual power plant The resource complementarity coefficient between is the serial number of the virtual power plant, is the serial number of the virtual power plant.

6. The distributed coordination and control method of multiple virtual power plants according to claim 1, characterized in that: When the correlation scores between the virtual power plants are greater than a preset correlation threshold, extracting a virtual power plant combination from the multi-objective synergy combination as a synergy group includes: When the correlation score between the wind power-dominated virtual power plant and the energy storage type virtual power plant exceeds a preset correlation threshold, the collaborative group binding of the wind power-dominated virtual power plant and the energy storage type virtual power plant is triggered.

7. The distributed coordination and control method of multiple virtual power plants according to claim 1, characterized in that: The establishing of a time mapping relationship between the wind power surplus period of the wind power-dominated virtual power plant and the power consumption peak period of the energy storage virtual power plant based on the resource complementarity relationship of the coordination group includes: Obtaining power generation data of wind power-dominated virtual power plants within the coordination group to determine a period of excess wind power; Obtaining power load data of the energy storage virtual power plant within the collaborative group to determine peak power consumption periods; Generating time characteristics of the wind power surplus period and the power consumption peak period; Based on the time characteristics and the resource complementarity relationship, a time mapping relationship between the wind power surplus period and the power consumption peak period is established.

8. The distributed coordination and control method of multiple virtual power plants according to claim 7, characterized in that: The obtaining of the power load data of the energy storage type virtual power plant in the collaborative group to determine the peak power consumption period includes: Collecting power load data of the energy storage virtual power plant within a preset time period; De-noising the power load data, and arranging the de-noised power load data in time series to obtain a power load sequence of the energy storage virtual power plant; Calculate the average power load value within a preset window based on a sliding window algorithm and the power load sequence; The time period in which the average power load value is greater than a preset load threshold and the power load value in the window continues to rise is determined as the peak power consumption period of the energy storage virtual power plant.

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