Distributed coordinated regulation and control method for multiple virtual power plants

Abstract

The 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, which comprises the following steps of: acquiring power exchange data and communication frequency data of each virtual power plant, constructing a dynamic interaction incidence matrix of the virtual power plant based on a coupling relationship between the power exchange data and the communication frequency data; based on the dynamic interaction incidence matrix and a preset correlation degree scoring algorithm, generating a correlation degree score between the virtual power plants; generating a multi-target collaborative combination based on the correlation score and the power generation resource type corresponding to the virtual power plant; when the association degree score between the virtual power plants is greater than a preset association degree threshold value, extracting a virtual power plant combination from the multi-target 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 coordination group. According to the invention, efficient coordination among the virtual power plants can be realized, and the efficiency of distributed coordination regulation and control of the multiple virtual power plants is improved.

Description

Technical Field

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

[0002] In the development process of today's power industry, virtual power plants, as an innovative model for integrating distributed energy, play an increasingly important role. It organically integrates distributed energy resources, energy storage devices, and controllable loads, and uses information technology and intelligent control methods to achieve unified management and coordinated operation, effectively improving the flexibility and reliability of the power system, and becoming an important supporting technology in the critical period of energy structure transformation.

[0003] However, with the increase in the number of virtual power plants and the expansion of their scale, the distributed coordination and control of multiple virtual power plants faces many challenges. The power generation characteristics of each virtual power plant vary greatly. Wind power, photovoltaic power, etc. are constrained by natural conditions and their power generation is unstable. Virtual power plants driven by traditional energy will also be affected by various factors, which makes the power exchange complex and unpredictable. At the same time, the existing evaluation system is difficult to accurately measure the relationship between virtual power plants. When generating collaborative combinations, it is impossible to fully consider resource complementarity and pairing rules. The scheduling link cannot be reasonably arranged 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 coordination 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 control method for multiple virtual power plants, the main purpose of which is to solve the problem of low efficiency in distributed coordinated control of multiple virtual power plants.

[0005] To achieve the above object, the present invention provides a distributed coordination and control method for multiple virtual power plants, comprising: 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 After the first iteration, 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; Generate a multi-objective synergistic combination based on the correlation score and the type of power generation resources corresponding to the virtual power plant; 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; The virtual power plant is time-coupled and scheduled based on the resource complementarity relationship of the collaborative group.

[0006] Optionally, collecting 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.

[0007] Optionally, 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.

[0008] Optionally, 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.

[0009] Optionally, 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.

[0010] Optionally, generating a multi-objective synergistic combination based on the correlation score and the type of power generation resources corresponding to the virtual power plant includes: Determining a resource complementarity coefficient of the virtual power plant according to the type of power generation resources corresponding to the virtual power plant; Performing a weighted summation on the association score and the resource complementarity coefficient to obtain a weighted total score 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, a multi-objective collaborative combination of the virtual power plant is generated.

[0011] Optionally, 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 virtual power plant exceeds a preset correlation threshold, the collaborative group binding of the wind power-dominated virtual power plant and the energy storage virtual power plant is triggered.

[0012] Optionally, performing time-series coupling scheduling on the virtual power plant based on the resource complementarity relationship of the coordination group includes: Based on the resource complementarity relationship 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 power consumption peak period of the energy storage virtual power plant; Constraining the power ratio of the charging power during the surplus period to the discharging power during the peak period based on the charging and discharging efficiency of the energy storage type virtual power plant; Generate a time-stamped charging and discharging instruction set of the energy storage virtual power plant according to the time mapping relationship and the power ratio; The charging and discharging instruction set is issued to the energy storage virtual power plant through the communication link corresponding to the collaborative group.

[0013] Optionally, the establishing of a time mapping relationship between a wind power surplus period of the wind power-dominated virtual power plant and a peak power consumption period of the energy storage virtual power plant based on the resource complementarity relationship of the collaborative 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.

[0014] Optionally, the acquiring of power load data of the energy storage virtual power plant in the collaborative group to determine a 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.

[0015] The present invention constructs a dynamic interactive correlation matrix by collecting power exchange data and communication frequency data to comprehensively reflect the closeness of power exchange and information interaction between virtual power plants. Based on this, the correlation score is calculated, and the damping coefficient, resource complementarity weight factor and other factors are comprehensively considered to accurately quantify the correlation between power plants. In addition, a multi-objective collaborative combination is generated in combination with the correlation score and the type of power generation resources to ensure that the power plant resources in the combination are complementary and meet certain rules, thereby improving the overall control effect. A virtual power plant combination with a high correlation score is extracted from the multi-objective collaborative combination as a collaborative group, focusing on the advantageous combination, exerting the synergistic effect, reducing energy waste, and improving resource utilization efficiency. Time-series coupling scheduling is performed based on the resource complementarity relationship of the collaborative group, and a time mapping relationship between a wind power surplus period and a peak power consumption period of an energy storage type virtual power plant is established. The charging and discharging power ratio is constrained, and an accurate charging and discharging instruction set is generated, so that the energy storage type virtual power plant can reasonably store and release electric energy in different periods, balance power supply and demand, enhance the stability and reliability of the power system, optimize the configuration of electric resources in the time dimension, and improve the control efficiency. Therefore, the present invention proposes a distributed coordinated control method for multiple virtual power plants, which can solve the problem of low efficiency of distributed coordinated control of multiple virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a distributed coordinated control method for multiple virtual power plants provided in one embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] 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.

[0018] An embodiment of the present application provides a distributed coordination and control method for multiple virtual power plants. The execution subject of the distributed coordination and control method for multiple virtual power plants includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the distributed coordination and control method for 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 it can be 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 networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0019] Reference Figure 1FIG. 1 is a flow chart of a distributed coordination and control method for multiple virtual power plants provided by an embodiment of the present invention. In this embodiment, the distributed coordination and control method for multiple virtual power plants includes: S1. Collect power exchange data and communication frequency data of each virtual power plant, and construct 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.

[0020] In the embodiment of the present invention, 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.

[0021] In detail, the collection period of the power exchange data can be set to 5 to 15 minutes, and the collection period of the communication frequency data can be set to 1 to 5 minutes.

[0022] In detail, it is assumed that there are three virtual power plants VPP1, VPP2, and VPP3. At a certain moment, the preset sampling frequency is once every 10 minutes, and the power transmission value from VPP1 to VPP2 is 50MW, the transmission direction is from VPP1 to VPP2, and the timestamp is recorded as 10:00; at the same time, the communication frequency between VPP1 and VPP2 controllers is recorded as once every 5 minutes, the communication delay is 50 milliseconds, and the data integrity rate is 98%. When the dynamic interactive correlation matrix is ​​constructed later, these data can reflect the closeness of power exchange and communication between VPP1 and VPP2. If the correlation score is high and the resources are complementary, a collaborative group may be formed for joint scheduling to improve the efficiency of power resource utilization.

[0023] In detail, the preset sampling frequency refers to the pre-set frequency for acquiring data, which determines the time interval for collecting data and can be determined according to actual needs and system characteristics; the transmission power value indicates the power of the virtual power plant during the power transmission process, reflecting the scale and intensity of power exchange; the transmission direction refers to the direction of power transmission, which clarifies which virtual power plant the power flows from to which virtual power plant, 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 virtual power plant controllers, reflecting the activeness of information exchange between virtual power plants; the communication delay is the time delay experienced by the signal from the sender to the receiver, which affects the timeliness of information transmission; the data integrity rate is an indicator to measure the completeness of the collected data, reflecting the data loss during the communication process.

[0024] In detail, accurate collection of power exchange data and communication frequency data is the basis for the subsequent construction of a dynamic interaction matrix. These data can be used to analyze the power exchange relationship and information interaction between virtual power plants, providing a basis for determining the correlation between virtual power plants, resource complementarity, and subsequent collaborative combination and scheduling.

[0025] In detail, by obtaining power exchange data such as transmission power value and transmission direction, the power supply and demand situation of each virtual power plant can be accurately grasped, providing a basis for the rational allocation of power resources; communication frequency data reflects the status of information interaction between virtual power plants, which helps to judge the effectiveness and timeliness of information transmission.

[0026] In an embodiment of the present invention, 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.

[0027] In detail, the matrix elements in the dynamic interaction matrix are used to measure the virtual power plant With the virtual power plant The greater the value of the comprehensive correlation between the two virtual power plants, the closer the connection between them is.

[0028] In detail, the data fusion weight factor , with a value range of 0-1, is used to adjust the relative importance of power exchange and communication frequency in the calculation of comprehensive correlation degree. When it is close to 1, it means that more attention is paid to the impact of power exchange on the degree of correlation; When it is close to 0, the impact of communication frequency is more emphasized; virtual power plant With the virtual power plant The amount of electricity exchange between the two virtual power plants intuitively reflects the actual scale of electricity interaction between the two virtual power plants and is a key indicator to measure the closeness of their electricity connection.

[0029] Specifically, the maximum value of the power exchange between all virtual power plants is used as a normalized benchmark to make the power exchange between different virtual power plants comparable; With the virtual power plant The communication frequency between them reflects the frequency of information exchange 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 frequency between all virtual power plants is also used to normalize the communication frequency so as to measure the relative high and low communication frequencies between virtual power plants under a unified standard.

[0030] In detail, firstly, the value of the data fusion weight factor is determined, and the power exchange amount between each pair of virtual power plants, the maximum power exchange amount between all virtual power plants, the communication frequency between the pair of virtual power plants and the maximum communication frequency between all virtual power plants are obtained, and these values ​​are substituted into the formula for calculation to obtain the association matrix elements between each pair of virtual power plants, thereby constructing a complete dynamic interactive association matrix.

[0031] In detail, 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 between power plants A and B is 50 MWh, and the maximum power exchange between all power plants is 100 MWh; the communication frequency between A and B is 8 times per hour, and the maximum communication frequency between 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 B and C can be calculated, thereby constructing a complete dynamic interactive correlation matrix to provide data basis for subsequent regulation.

[0032] S2. Generate a correlation score between the virtual power plants based on the dynamic interaction correlation matrix and a preset correlation scoring algorithm.

[0033] In an embodiment of the present invention, 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.

[0034] In detail, the preset correlation scoring algorithm is: ; in, It is After the first iteration, 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.

[0035] Specifically, the power exchange correlation is determined based on the dynamic interaction correlation matrix, which reflects the closeness of the power exchange connection between virtual power plants. The higher the value, the closer the power exchange connection. The resource complementarity weight factor is derived by comprehensively considering the power exchange correlation and the resource complementarity coefficient. It is used to measure the degree of resource complementarity between virtual power plants and reflects the potential for collaborative cooperation between the two in terms of resources. The correlation score is used to quantify the virtual power plant The degree of correlation 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 and resource complementarity. The damping coefficient is usually between 0 and 1. It is used to adjust the influence of random browsing factors in the correlation scoring algorithm to avoid excessive dependence of the calculation results on a certain part of factors, making the scoring results more stable and reasonable. The total number of virtual power plants refers to the total number of virtual power plants participating in distributed coordinated regulation, which is a basic parameter in the correlation scoring algorithm. The associated virtual power plant collection contains all virtual power plants Virtual power plants that have actual connections in terms of power exchange or other aspects clearly define the participants Scope of relevant objects for calculation of virtual power plant relevance score; Virtual power plant The total number of outgoing links indicates the virtual power plant The number of associations with other virtual power plants is used to normalize the related items in the association score calculation to make the impact of different virtual power plants comparable; the number of iterations of the association score is because the association score algorithm is iteratively calculated. The current calculation round is recorded. As the number of iterations increases, the correlation score gradually converges to a more accurate value.

[0036] In detail, 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.

[0037] In detail, virtual power plants Virtual Power Plant The resource complementarity weight factor comprehensively measures the degree of mutual complementation and collaborative cooperation between 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 greater the potential for better resource optimization allocation in subsequent coordinated regulation.

[0038] In detail, the weight coefficient The value range of is between 0 and 1, which is used to adjust the relative importance of power exchange correlation and resource complementarity coefficient in calculating resource complementarity weight factor. When it is closer to 1, it means that the When the power exchange correlation The impact is greater when When it is closer to 0, the resource complementarity coefficient right The impact is greater.

[0039] In detail, Virtual Power Plant Virtual Power Plant The power exchange correlation between Equal to the element in the dynamic interaction matrix , integrated virtual power plant Virtual Power Plant The amount of power exchanged between the two devices and the communication frequency information, so It can reflect the closeness of the connection between the two virtual power plants at the power exchange level. The larger the value, the more frequent the power exchange between the two virtual power plants and the closer the connection.

[0040] In detail, and They are the serial numbers of the virtual power plants, which are used to clarify the resource complementarity weight factor, power exchange correlation and resource complementarity coefficient between which two virtual power plants are being discussed.

[0041] In detail, It is a virtual power plant Virtual Power Plant The resource complementarity coefficient between them is set to different values ​​according to different combinations of power generation resource types. The calculation rules include: 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, ; For other types of pairing, This coefficient measures the degree of mutual complementarity between different resource types based on the essential characteristics of the resources.

[0042] In detail, according to the value of each element in the dynamic interaction correlation matrix, the comprehensive situation of power exchange volume and communication frequency between virtual power plants is analyzed to determine the power exchange correlation between them. This step is the basis for subsequent calculations and preliminarily quantifies the power connection between virtual power plants.

[0043] In detail, based on the power exchange correlation and combined with the resource complementarity coefficient between virtual power plants, the resource complementarity weight factor is calculated, which comprehensively considers the two factors of power exchange and resource type complementarity.

[0044] In detail, using the preset correlation scoring algorithm, the resource complementarity weight factor, the initial correlation score of the virtual power plant (needs to be initialized when calculating for the first time), the damping coefficient, the total number of virtual power plants and other parameters are substituted into the formula for iterative calculation, and the correlation score is continuously updated until the score converges (the difference between the two calculation results is less than the set threshold).

[0045] In detail, by accurately calculating the correlation score, we can clearly understand the degree of correlation and resource complementarity potential between virtual power plants. When generating multi-objective synergistic combinations, we can give priority to virtual power plants with high correlation scores and strong resource complementarity. Such synergistic groups can better realize resource sharing and optimal configuration in actual operation.

[0046] In detail, in the entire multi-virtual power plant distributed coordination and control method, the resource complementarity weight factor is an important parameter of the correlation scoring algorithm, which directly affects the calculation result of the correlation score between virtual power plants and accurately calculates the correlation score between virtual power plants. It helps to more accurately measure the degree of correlation between virtual power plants and provide a reliable basis for the subsequent generation of multi-objective collaborative combinations, extraction of collaborative groups, and realization of efficient time-coupled scheduling.

[0047] In detail, by comprehensively considering the power exchange correlation and resource complementarity coefficient to calculate the resource complementarity weight factor, the complementary relationship between virtual power plants can be more comprehensively evaluated. The value of gives priority to combining virtual power plants with high resource complementarity weight factors to form a collaborative group. Such a collaborative group has more advantages in resource allocation, can better play the resource characteristics of each virtual power plant, and improve the utilization efficiency of power resources. For example, if the values ​​of two virtual power plants are calculated, The higher the value, the better the complementarity between them in terms of power exchange and resource types. When jointly dispatched, they can better balance the power supply and demand and reduce energy waste, thereby improving the overall efficiency of distributed coordinated control of multiple virtual power plants.

[0048] S3. Generate a multi-objective collaborative combination based on the correlation score and the type of power generation resources corresponding to the virtual power plant.

[0049] In an embodiment of the present invention, the generating a multi-objective synergistic combination based on the correlation score and the type of power generation resources corresponding to the virtual power plant includes: Determining a resource complementarity coefficient of the virtual power plant according to the type of power generation resources corresponding to the virtual power plant; Performing a weighted summation on the association score and the resource complementarity coefficient to obtain a weighted total score 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, a multi-objective collaborative combination of the virtual power plant is generated.

[0050] In detail, the resource complementarity coefficient is determined based on the type of power generation resources of the virtual power plant and is used to measure the degree of complementarity between different virtual power plant resources. For example, the resource complementarity coefficient is higher when wind power and energy storage are combined because the intermittent nature of wind power can be adjusted by energy storage; while the complementarity coefficient of similar power generation resources (such as wind power-wind power) is lower, and different resource combinations have different coefficient values.

[0051] Furthermore, the single-type pairing rule stipulates that simple combinations of similar power generation resources cannot appear in the virtual power plant combination, such as prohibiting combinations such as wind power-wind power and photovoltaic-photovoltaic. This is to ensure that the combination has resource complementarity and improve the overall regulation effect.

[0052] Specifically, the calculated weighted total score is compared with the preset total score threshold, and at the same time, it is checked whether the virtual power plant combination meets the single type pairing rule. If the weighted total score exceeds the preset threshold and the combination meets the rule, then the virtual power plant combination is determined as a multi-objective synergistic combination; otherwise, it is not included in the combination.

[0053] In detail, assume that there are three virtual power plants, VPP1 is wind power-dominated, VPP2 is energy storage-dominated, and VPP3 is photovoltaic-dominated. It is known that the correlation scores of VPP1 and VPP2 are , the correlation score of VPP1 and VPP3 is 0.6. Set the correlation score weight , resource complementarity coefficient weight The preset total score threshold is 0.7. The resource complementarity coefficient between VPP1 and VPP2 , the resource complementarity coefficient of VPP1 and VPP3 .

[0054] Furthermore, the weighted total score of VPP1 and VPP2 is calculated: ,in, And it complies with the single type pairing rule, so VPP1 and VPP2 can form a multi-target collaborative combination.

[0055] Furthermore, the weighted total score of VPP1 and VPP3 is calculated: ,in , so VPP1 and VPP3 cannot form a multi-target collaborative combination.

[0056] S4. 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.

[0057] In an embodiment of the present invention, 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 includes: When the correlation score between the wind power-dominated virtual power plant and the energy storage virtual power plant exceeds a preset correlation threshold, the collaborative group binding of the wind power-dominated virtual power plant and the energy storage virtual power plant is triggered.

[0058] In detail, the correlation score is used to measure the comprehensive correlation between virtual power plants, taking into account factors such as power exchange data, communication frequency data, and resource complementarity. The higher the value, the closer the connection between virtual power plants in all aspects, and the greater the potential for collaborative cooperation.

[0059] In detail, the preset correlation threshold is a pre-set reference value, which serves as a standard for judging whether the correlation between virtual power plants is close enough, and then deciding whether to combine them as a collaborative group. The setting of this threshold is based on actual application needs and expectations for the overall performance of the system. Different application scenarios may set different thresholds.

[0060] In detail, the multi-objective collaborative combination is a combination set consisting of multiple virtual power plants, which are generated by considering the correlation scores of the virtual power plants, the resource complementarity coefficients, and whether the preset total score thresholds and single-type pairing rules are met. It aims to achieve collaborative optimization of multiple objectives (such as stable power supply, efficient resource utilization, etc.).

[0061] In detail, the coordination group is a combination of virtual power plants further screened from the multi-objective coordination combination. These combinations are more prominent in terms of correlation and resource complementarity, and can better play a synergistic role in actual distributed coordinated control to achieve more efficient power dispatch and resource allocation.

[0062] In detail, 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 fluctuating 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.

[0063] In detail, a storage-type virtual power plant is a virtual power plant equipped with energy storage equipment and related technologies, which can store excess electricity and release it when needed. The storage-type virtual power plant can store electricity when there is a surplus of electricity and provide electricity when there is a shortage of electricity, thereby playing a role in regulating the balance of electricity supply and demand.

[0064] In detail, collaborative group binding means that when the correlation score between the wind power-dominated virtual power plant and the energy storage virtual power plant exceeds the preset correlation threshold, the two virtual power plants will be identified as a collaborative group and a collaborative cooperation relationship will be established between them to facilitate subsequent joint scheduling and coordinated regulation.

[0065] In detail, selecting combinations with higher correlation from multi-objective synergistic combinations as synergistic groups can focus on the virtual power plant combinations with the greatest synergistic potential, providing clear targets for subsequent more precise and efficient regulation.

[0066] In detail, by extracting wind power-dominated and energy storage virtual power plants with high correlation scores to form a collaborative group, the complementary advantages of the two can be fully utilized to significantly improve the efficiency of power regulation. During periods when wind power is high but the power load is low, energy storage virtual power plants can store excess wind power to avoid energy waste; and during peak power consumption or insufficient wind power output, energy storage virtual power plants release stored electricity to ensure stable 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 efficiency of energy utilization, avoiding the impact of the intermittent and volatile nature of wind power on the power system.

[0067] In detail, it is assumed that there are 5 virtual power plants, of which VPP1 is wind power-dominated, VPP2 is energy storage-type, and VPP3, VPP4, and VPP5 are 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, which exceeds 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 during a certain period of time and VPP1 has excess wind power generation, VPP2 begins to store electricity; and at night when the power consumption is peak and the wind power output is low, VPP2 releases electricity according to the dispatch instruction, and cooperates with VPP1 to meet the power demand, effectively improving the stability of power supply and resource utilization efficiency.

[0068] S5. Perform time-coupling scheduling on the virtual power plant based on the resource complementarity relationship of the collaborative group.

[0069] In an embodiment of the present invention, time-coupled scheduling refers to coordinating the production, storage and distribution of electricity in the time dimension according to the electricity supply and demand conditions in different time periods and the resource characteristics of the virtual power plant, so as to achieve efficient utilization of electricity resources and stable operation of the power system.

[0070] In detail, the performing time-series coupling scheduling on the virtual power plant based on the resource complementarity relationship of the coordination group includes: Based on the resource complementarity relationship 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 power consumption peak period of the energy storage virtual power plant; Constraining the power ratio of the charging power during the surplus period to the discharging power during the peak period based on the charging and discharging efficiency of the energy storage type virtual power plant; Generate a time-stamped charging and discharging instruction set of the energy storage virtual power plant according to the time mapping relationship and the power ratio; The charging and discharging instruction set is issued to the energy storage virtual power plant through the communication link corresponding to the collaborative group.

[0071] In detail, the period of surplus wind power refers to the time period in the coordination group when the power generation of the wind power-dominated virtual power plant exceeds the current power demand locally or within the coordination group. During this period, there is surplus electricity generated by wind power, which 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 virtual power plant is located or in the coordination group reaches its peak, and at this time, the stability and adequacy of the power supply are required to be higher.

[0072] In detail, the time mapping relationship refers to establishing a corresponding connection in time between the period of excess wind power and the period of peak electricity consumption, and clarifying the subsequent peak electricity consumption period corresponding to the excess wind power, so as to reasonably arrange the charging and discharging strategy of the energy storage virtual power plant.

[0073] In detail, the charging and discharging efficiency refers to the ratio of the electric energy actually stored or released to the electric energy consumed or input during the charging and discharging process of the energy storage virtual power plant. It reflects the efficiency of the energy storage equipment in the energy conversion process and is an important basis for determining the charging and discharging power ratio.

[0074] In detail, the power ratio refers to the proportional relationship between the charging power of the energy storage virtual power plant during the period of excess wind power and the discharging power during the peak electricity consumption period. This ratio can be used to reasonably control the charging and discharging process of the energy storage equipment to ensure the effective use of energy storage resources.

[0075] In detail, the charging and discharging instruction set with timestamp contains a set of instructions for specific charging and discharging time and power information, and each instruction carries a precise time mark (timestamp) for accurately controlling the charging and discharging operations of the energy storage virtual power plant at different times.

[0076] In detail, the communication link refers to the channel for data transmission and command communication between virtual power plants within the coordination group, ensuring that the charging and discharging instructions can be accurately and timely transmitted from the control center to the energy storage virtual power plant.

[0077] In detail, the 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 is established based on the resource complementarity relationship of the coordination group, including: 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.

[0078] In detail, the step of obtaining the power load data of the energy storage 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.

[0079] In detail, temporal characteristics refer to the time attributes used to describe the periods of excess wind power and peak electricity consumption, such as specific time range, duration, periodic regularity, etc. These characteristics help to establish a temporal mapping relationship between the two.

[0080] In detail, generating time features refers to extracting time features from determined wind power surplus periods and peak power consumption periods, such as counting the start time, end time, and duration of the time period, and analyzing its occurrence pattern within a day or a week.

[0081] In detail, combined with the established time mapping relationship and the determined power ratio, an instruction set containing precise timestamps and charging and discharging power information is generated in chronological order, providing detailed control instructions for the charging and discharging operations of the energy storage virtual power plant.

[0082] Furthermore, the communication link corresponding to the collaborative group is used to send the generated charging and discharging instruction set containing timestamps to the energy storage virtual power plant, so that it can perform charging and discharging operations according to the instructions.

[0083] In detail, after completing the virtual power plant correlation scoring, multi-objective coordination combination generation and coordination group extraction, the key link of actual regulation of the virtual power plant in the coordination group is to establish a time mapping relationship, constrain the power ratio and issue a charging and discharging instruction set, realizing the transformation from theoretical analysis to actual operation, and applying the previously determined coordination group resource complementary relationship to power dispatch.

[0084] In detail, through time-series coupling scheduling, the resource complementarity of wind power and energy storage can be fully utilized, and the efficiency of distributed coordinated control of multiple virtual power plants can be significantly improved. Specifically, during periods of excess wind power, excess electricity is stored in energy storage virtual power plants, avoiding the waste of wind power and improving energy utilization; during peak electricity consumption periods, energy storage virtual power plants discharge according to the set power ratio to supplement power supply, alleviate the power shortage, and enhance the stability and reliability of the power system. This precise scheduling method optimizes the configuration of power resources in the time dimension, reduces the imbalance between power supply and demand, and reduces the system operation cost.

[0085] In detail, it is assumed that a certain coordination 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 power far exceeding local power demand due to high wind speed from 2:00 to 4:00 p.m. every day (wind power surplus period); while the power load in the area where VPP2 is located reaches its peak from 7:00 to 9:00 p.m. (peak power consumption period). After calculation, considering the charging and discharging efficiency of VPP2, it is determined that the ratio of its charging power during the wind power surplus period to its discharging power during the peak power consumption period is 1:0.8. Based on this information, the following charging and discharging instruction set with timestamps is generated: at 2:00 p.m., start charging with 5MW power; at 3:00 p.m., maintain 5MW charging power; at 4:00 p.m., stop charging; at 7:00 p.m., start discharging with 4MW power; at 8:00 p.m., maintain 4MW discharging power; at 9:00 p.m., stop discharging. The instruction set is sent to VPP2 through the communication link of the collaborative group, realizing precise control of VPP2 charging and discharging at different time periods, effectively utilizing wind power resources and ensuring power supply during peak hours.

[0086] In several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways.

[0087] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0088] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method and technology of using digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution 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 After the first iteration, 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; Generate a multi-objective synergistic combination based on the correlation score and the type of power generation resources corresponding to the virtual power plant; 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; The virtual power plant is time-coupled and scheduled based on the resource complementarity relationship of 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: The generating a multi-objective synergistic combination based on the correlation score and the type of power generation resources corresponding to the virtual power plant includes: Determining a resource complementarity coefficient of the virtual power plant according to the type of power generation resources corresponding to the virtual power plant; Performing a weighted summation on the association score and the resource complementarity coefficient to obtain a weighted total score 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, a multi-objective collaborative combination of the virtual power plant is generated.

7. 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 virtual power plant exceeds a preset correlation threshold, the collaborative group binding of the wind power-dominated virtual power plant and the energy storage virtual power plant is triggered.

8. The distributed coordination and control method of multiple virtual power plants according to claim 7, characterized in that: The performing time-series coupling scheduling on the virtual power plant based on the resource complementarity relationship of the coordination group includes: Based on the resource complementarity relationship 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 power consumption peak period of the energy storage virtual power plant; Constraining the power ratio of the charging power during the surplus period to the discharging power during the peak period based on the charging and discharging efficiency of the energy storage type virtual power plant; Generate a time-stamped charging and discharging instruction set of the energy storage virtual power plant according to the time mapping relationship and the power ratio; The charging and discharging instruction set is issued to the energy storage virtual power plant through the communication link corresponding to the collaborative group.

9. The distributed coordination and control method of multiple virtual power plants according to claim 8, 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.

10. The distributed coordination and control method of multiple virtual power plants according to claim 9, 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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