Virtual power plant dispatching system based on multivariate information fusion

Abstract

The invention relates to the technical field of virtual power plants, and particularly discloses a virtual power plant scheduling system based on multivariate information fusion, which is used for solving the problems that in the prior art, a virtual power plant difficultly schedules distributed resources which are widely distributed in a balanced manner, so that resources in some regions are excessively transferred, and resources in other regions are not fully utilized. According to the method, the first multivariate data and the second multivariate data are obtained, the first multivariate data are stored in the virtual power plant resource type library, and the second multivariate data are stored in the resource adjustment feature library; based on the first multivariate data, the regulation capability value of the wind power resource, the regulation capability value of the photoelectric resource and the regulation capability value of the energy storage equipment are analyzed respectively, and then the industrial load regulation and control balance value, the commercial load regulation and control balance value and the resident load regulation and control balance value are evaluated through the second multivariate data. Balanced regulation and control of industrial, commercial and resident loads can be realized, and the stability and resource utilization efficiency of a power grid can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and more specifically, to a virtual power plant dispatching system based on multi-information fusion. Background Art

[0002] A virtual power plant is a virtual entity that integrates and optimizes the scheduling of distributed energy resources (such as wind power, photovoltaics, energy storage equipment, etc.) and adjustable loads (such as industrial loads, commercial loads, and residential loads) through advanced information and communication technologies. It can participate in the scheduling and trading of the power market like traditional power plants, while having higher flexibility and response speed. In the scheduling process of virtual power plants, it is necessary to comprehensively consider multiple information sources to improve the accuracy and effectiveness of scheduling decisions. However, in the existing technology, due to the lack of detailed resource regulation feature data and regulation capacity evaluation models, it is difficult for virtual power plants to accurately predict and schedule distributed energy resources, resulting in insufficient accuracy in resource scheduling, thereby affecting the stability of the power grid and resource utilization efficiency; and it is difficult for virtual power plants to evenly schedule widely distributed distributed resources, resulting in excessive mobilization of resources in some areas and underutilization of resources in other areas. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a virtual power plant dispatching system based on multivariate information fusion, which analyzes the regulation capability value of wind power resources, the regulation capability value of photovoltaic resources and the regulation capability value of energy storage equipment based on the first multivariate data, and then evaluates the industrial load regulation balance value, commercial load regulation balance value and residential load regulation balance value through the second multivariate data, thereby realizing balanced regulation of industrial, commercial and residential loads to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A virtual power plant dispatching system based on multivariate information fusion includes a data source acquisition module, a resource aggregation and analysis module, and a resource balance evaluation module. The data source acquisition module is used to acquire first multivariate data and second multivariate data, and store the first multivariate data in a virtual power plant resource type library, and store the second multivariate data in a resource adjustment feature library; the resource aggregation and analysis module analyzes the stability of resource scheduling based on the first multivariate data; the resource aggregation and analysis module includes a third resource adjustable capacity analysis submodule, and the third resource adjustable capacity analysis submodule is used to import the energy storage device adjustable data in the first multivariate data into a third resource adjustable capacity analysis model to analyze the energy storage device resource adjustable capacity value. The formula for the third resource adjustable capacity analysis is:

[0006]

[0007] Where: t zc is the adjustable capacity value of energy storage device resources, n is the total number of data collection intervals, c a is the real-time available capacity of the energy storage device when the data collection interval is a, c az is the total storage capacity of the energy storage device, is the maximum real-time discharge power of the energy storage device when the data collection interval is a, is the minimum real-time discharge power of the energy storage device when the data collection interval is a, is the maximum real-time charging power of the energy storage device when the data collection interval is a, It is the minimum real-time charging power of the energy storage device when the data collection interval is a.

[0008] As a further solution of the present invention, the first multivariate data includes wind power adjustable energy data, photovoltaic adjustable energy data and energy storage equipment adjustable data; the second multivariate data includes industrial load control data, commercial load control data and residential load control data.

[0009] As a further solution of the present invention, the resource aggregation and analysis module includes a first resource adjustable capacity analysis submodule, a second resource adjustable capacity analysis submodule, and a stability judgment submodule; the resource balance evaluation module includes a first-level load control balance evaluation submodule, a second-level load control balance evaluation submodule, a third-level load control balance evaluation submodule and a control balance judgment submodule.

[0010] As a further solution of the present invention, the first resource adjustable capacity analysis submodule constructs a first resource adjustable capacity analysis model based on wind power adjustable energy data, which is used to analyze the wind power resource adjustable capacity value; the second resource adjustable capacity analysis submodule is used to import photovoltaic adjustable energy data into the second resource adjustable capacity analysis model to analyze the photovoltaic resource adjustable capacity value; the third resource adjustable capacity analysis submodule imports the energy storage device adjustable data into the third resource adjustable capacity analysis model to analyze the energy storage device resource adjustable capacity value.

[0011] As a further solution of the present invention, the first resource adjustable capacity analysis submodule constructs a first resource adjustable capacity analysis model based on wind power adjustable energy data, which is used to analyze the wind power resource adjustable capacity value. The formula of the first resource adjustable capacity analysis model is:

[0012]

[0013] Where: t ea is the adjustable capacity value of wind power resources, n is the total number of data collection intervals, is the maximum wind speed when the data collection interval is a, is the minimum wind speed when the data collection interval is a, v ae is the real-time wind speed at time e when the data collection interval is a, v′ a is the average wind speed when the data collection interval is a, is the maximum wind power generation when the data collection interval is a, is the minimum wind energy generation when the data collection interval is a, f a is the wind power generation at time e when the data collection interval is a, f a ′ is the mean value of wind power generation when the data collection interval is a.

[0014] As a further solution of the present invention, the first-level load control balance assessment submodule is used to import the extracted industrial load control data into the first-level load control balance assessment model to evaluate the industrial load control balance value; the second-level load control balance assessment submodule is used to import the commercial load control data into the second-level load control balance assessment model to evaluate the commercial load control balance value; the third-level load control balance assessment submodule is used to import the residential load control data into the third-level load control balance assessment model to evaluate the residential load control balance value.

[0015] As a further solution of the present invention, the first-level load control balance evaluation submodule is used to import the extracted industrial load control data into the first-level load control balance evaluation model to evaluate the industrial load control balance value. The formula of the first-level load control balance evaluation model is:

[0016]

[0017] Where: p tg is the industrial load control balance value, m is the total number of parameter collection intervals, t′ xi is the mean value of the characteristic range parameter of industrial load response adjustment duration when the parameter collection interval is i, t xiq is the characteristic range parameter of industrial load response adjustment duration at parameter collection interval i and q, is the maximum industrial load response adjustment duration characteristic range parameter when the parameter collection interval is i, is the minimum industrial load response adjustment time characteristic range parameter when the parameter collection interval is i, r x ' i is the mean value of the characteristic range parameter of industrial adjustable load capacity when the parameter collection interval is i, r xiq is the characteristic range parameter of industrial adjustable load capacity at the parameter collection interval i and time q, is the characteristic range parameter of the maximum industrial adjustable load capacity when the parameter collection interval is i, It is the minimum industrial adjustable load capacity characteristic range parameter when the parameter collection interval is i.

[0018] The technical effects and advantages of a virtual power plant dispatching system based on multivariate information fusion of the present invention: the present invention obtains first multivariate data and second multivariate data, stores the first multivariate data in a virtual power plant resource type library, stores the second multivariate data in a resource regulation feature library, analyzes the regulation capability values ​​of wind power resources, photovoltaic resources and energy storage equipment based on the first multivariate data, and then evaluates the industrial load regulation balance value, commercial load regulation balance value and residential load regulation balance value through the second multivariate data, thereby achieving balanced regulation of industrial, commercial and residential loads, and helping to improve the stability of the power grid and resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A structural schematic diagram of a virtual power plant dispatching system based on multi-information fusion provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] Example 1

[0022] Figure 1 FIG. 1 shows a schematic diagram of a virtual power plant dispatching system based on multi-information fusion provided by the first embodiment of the present invention. Figure 1 As shown, a virtual power plant dispatching system based on multi-information fusion in this embodiment includes a data source acquisition module, a resource aggregation and analysis module, and a resource balance evaluation module;

[0023] The data source acquisition module is used to acquire the first multivariate data and the second multivariate data, and store the first multivariate data in the virtual power plant resource type library, and store the second multivariate data in the resource regulation feature library;

[0024] The resource aggregation and analysis module analyzes the stability of resource scheduling based on the first multivariate data;

[0025] The resource balance assessment module is used to extract the second multivariate data and assess the balance of resource mobilization.

[0026] The first multivariate data includes wind power adjustable energy data, photovoltaic adjustable energy data, and energy storage device adjustable data;

[0027] Wind power adjustable energy data includes real-time wind speed and real-time wind power generation;

[0028] Photovoltaic adjustable energy data includes real-time solar radiation intensity and real-time solar power generation;

[0029] The adjustable data of the energy storage device include the total storage capacity of the energy storage device, the real-time available capacity of the energy storage device, the maximum discharge power of the energy storage device, and the maximum charging power of the energy storage device;

[0030] The second multivariate data includes industrial load regulation data, commercial load regulation data, and residential load regulation data;

[0031] Industrial load regulation data include industrial load response regulation time characteristic range parameters and industrial adjustable load capacity characteristic range parameters;

[0032] Commercial load regulation data include commercial load response regulation duration characteristic range parameters and commercial adjustable load capacity characteristic range parameters;

[0033] The residential load regulation data include the characteristic range parameters of the residential load response regulation time and the characteristic range parameters of the residential adjustable load capacity.

[0034] The resource aggregation and analysis module includes a first resource adjustable capacity analysis submodule, a second resource adjustable capacity analysis submodule, a third resource adjustable capacity analysis submodule, and a stability determination submodule;

[0035] The first resource adjustable capacity analysis submodule constructs a first resource adjustable capacity analysis model based on wind power adjustable energy data, which is used to analyze the wind power resource adjustable capacity value;

[0036] The second resource adjustable capacity analysis submodule is used to import the photovoltaic adjustable energy data into the second resource adjustable capacity analysis model to analyze the photovoltaic resource adjustable capacity value;

[0037] The third resource adjustable capacity analysis submodule imports the energy storage device adjustable data into the third resource adjustable capacity analysis model to analyze the energy storage device resource adjustable capacity value.

[0038] The first resource adjustable capacity analysis submodule is used to extract the real-time wind speed and real-time wind energy generation from the wind power adjustable energy data, and to construct a first resource adjustable capacity analysis model based on the real-time wind speed and real-time wind energy generation, which is used to analyze the wind power resource adjustable capacity value. The formula of the first resource adjustable capacity analysis model is:

[0039]

[0040] Where: t eais the adjustable capacity value of wind power resources, n is the total number of data collection intervals, is the maximum wind speed when the data collection interval is a, is the minimum wind speed when the data collection interval is a, v ae is the real-time wind speed at time e when the data collection interval is a, v′ a is the average wind speed when the data collection interval is a, is the maximum wind power generation when the data collection interval is a, is the minimum wind energy generation when the data collection interval is a, f a is the wind power generation at time e when the data collection interval is a, f′ a is the mean value of wind power generation when the data collection interval is a.

[0041] By considering the maximum, minimum, real-time and average values ​​of wind speed and power generation, the regulation capacity of wind power resources is comprehensively evaluated to provide more comprehensive analysis results. According to the adjustable capacity value of wind power resources, the dispatch of wind power resources can be reasonably arranged to maximize the use of wind energy and improve power generation efficiency. By accurately evaluating and dispatching wind power resources, the load of the power grid can be effectively balanced and the imbalance of power supply and demand caused by wind power volatility can be reduced. The regulation capacity of wind power resources is monitored in real time, abnormal situations can be discovered in time and corresponding measures can be taken to prevent the occurrence of power grid instability. Accurate dispatch of wind power resources makes the power grid run more smoothly, reduces frequent adjustments caused by wind speed fluctuations, and improves the reliability of the power grid.

[0042] The second resource adjustable capacity analysis submodule is used to extract the real-time solar radiation intensity and real-time solar power generation from the photovoltaic adjustable energy data, import the real-time solar radiation intensity and real-time solar power generation into the second resource adjustable capacity analysis model, and analyze the photovoltaic resource adjustable capacity value. The formula of the second resource adjustable capacity analysis model is:

[0043]

[0044] Where: t eb is the adjustable capacity value of the photoelectric resource, n is the total number of data collection intervals, is the maximum solar radiation intensity when the data collection interval is a, is the minimum solar radiation intensity when the data collection interval is a, d ac is the solar radiation intensity at time c when the data collection interval is a, d′ a is the mean solar radiation intensity when the data collection interval is a, is the maximum solar power generation when the data collection interval is a, is the minimum solar energy generation when the data collection interval is a, g acis the solar power generation at the time c when the data collection interval is a, g′ a is the average value of solar power generation when the data collection interval is a.

[0045] By using real-time solar radiation intensity and power generation data for analysis, the evaluation results can timely reflect the current status of photovoltaic resources and adapt to the dynamic changes in the power system. By considering the maximum, minimum, real-time and average values ​​of solar radiation intensity and power generation, the regulation capacity of photovoltaic resources is comprehensively evaluated to provide more comprehensive analysis results. According to the adjustable capacity value of photovoltaic resources, the scheduling of photovoltaic resources can be reasonably arranged to maximize the use of solar energy and improve power generation efficiency. Through accurate evaluation and scheduling of photovoltaic resources, the load of the power grid can be effectively balanced and the imbalance of power supply and demand caused by changes in solar radiation can be reduced. Real-time monitoring of the regulation capacity of photovoltaic resources can detect abnormal situations in time and take corresponding measures to prevent the occurrence of instability in the power grid. Accurate scheduling of photovoltaic resources makes the power grid run more smoothly, reduces frequent adjustments caused by solar radiation fluctuations, and improves the reliability of the power grid.

[0046] The third resource adjustable capacity analysis submodule is used to extract the total storage capacity of the energy storage device, the real-time available capacity of the energy storage device, the real-time discharge power of the energy storage device, and the real-time charging power of the energy storage device from the adjustable data of the energy storage device, and import the total storage capacity of the energy storage device, the real-time available capacity of the energy storage device, the real-time discharge power of the energy storage device, and the real-time charging power of the energy storage device into the third resource adjustable capacity analysis model to analyze the adjustable capacity value of the energy storage device resources. The formula for the third resource adjustable capacity analysis is:

[0047]

[0048] Where: t zc is the adjustable capacity value of energy storage device resources, n is the total number of data collection intervals, c a is the real-time available capacity of the energy storage device when the data collection interval is a, c az is the total storage capacity of the energy storage device, is the maximum real-time discharge power of the energy storage device when the data collection interval is a, is the minimum real-time discharge power of the energy storage device when the data collection interval is a, is the maximum real-time charging power of the energy storage device when the data collection interval is a, It is the minimum real-time charging power of the energy storage device when the data collection interval is a.

[0049] By using the real-time data of energy storage equipment for analysis, the evaluation results can timely reflect the current status of the energy storage equipment and adapt to the needs of dynamic changes in the power system. By considering the total reserves of the energy storage equipment, the real-time available capacity, the maximum and minimum values ​​of the real-time charging and discharging power, the regulation capacity of the energy storage equipment is comprehensively evaluated to provide a more comprehensive analysis result. According to the adjustable capacity value of the energy storage equipment, the charging and discharging strategy of the energy storage equipment can be reasonably arranged to maximize the use of energy storage resources and improve power generation efficiency. Through accurate evaluation and scheduling of energy storage equipment, the load of the power grid can be effectively balanced and the imbalance of power supply and demand caused by load fluctuations can be reduced. Real-time monitoring of the regulation capacity of energy storage equipment can detect abnormal situations in time and take corresponding measures to prevent the occurrence of instability in the power grid. Accurate scheduling of energy storage equipment makes the power grid run more smoothly, reduces frequent adjustments caused by load fluctuations, and improves the reliability of the power grid.

[0050] The stability judgment submodule is used to extract the adjustable capacity value of wind power resources, the adjustable capacity value of photovoltaic resources, and the adjustable capacity value of energy storage equipment resources.

[0051] The wind power resource adjustable capacity value is compared with the preset wind power resource adjustable capacity threshold value. If the wind power resource adjustable capacity value is greater than or equal to the preset wind power resource adjustable capacity threshold value, the wind power resource adjustable capacity is stable; if the wind power resource adjustable capacity value is greater than or equal to the preset wind power resource adjustable capacity threshold value, the wind power resource adjustable capacity is unstable;

[0052] The photoelectric resource adjustable capacity value is compared with the preset photoelectric resource adjustable capacity threshold value. If the photoelectric resource adjustable capacity value is greater than or equal to the preset photoelectric resource adjustable capacity threshold value, the photoelectric resource adjustable capacity is stable; if the photoelectric resource adjustable capacity value is greater than or equal to the preset photoelectric resource adjustable capacity threshold value, the photoelectric resource adjustable capacity is unstable;

[0053] The energy storage device resource adjustable capacity value is compared with the preset energy storage device resource adjustable capacity threshold. If the energy storage device resource adjustable capacity value is greater than or equal to the preset energy storage device resource adjustable capacity threshold, the energy storage device resource adjustable capacity is stable; if the energy storage device resource adjustable capacity value is greater than or equal to the preset energy storage device resource adjustable capacity threshold, the energy storage device resource adjustable capacity is unstable.

[0054] The resource balance assessment module includes a first-level load control balance assessment submodule, a second-level load control balance assessment submodule, a third-level load control balance assessment submodule, and a control balance judgment submodule;

[0055] The primary load control balance assessment submodule is used to import the extracted industrial load control data into the primary load control balance assessment model to assess the industrial load control balance value;

[0056] The secondary load regulation balance assessment submodule is used to import commercial load regulation data into the secondary load regulation balance assessment model to assess the commercial load regulation balance value;

[0057] The three-level load control balance assessment submodule is used to import the resident load control data into the three-level load control balance assessment model to evaluate the resident load control balance value.

[0058] The first-level load regulation balance evaluation submodule is used to extract the industrial load response adjustment time characteristic range parameters and the industrial adjustable load capacity characteristic range parameters from the industrial load regulation data, and import the industrial load response adjustment time characteristic range parameters and the industrial adjustable load capacity characteristic range parameters into the first-level load regulation balance evaluation model to evaluate the industrial load regulation balance value. The formula of the first-level load regulation balance evaluation model is:

[0059]

[0060] Where: p tg is the industrial load control balance value, m is the total number of parameter collection intervals, t′ xi is the mean value of the characteristic range parameter of industrial load response adjustment duration when the parameter collection interval is i, t xiq is the characteristic range parameter of industrial load response adjustment duration at parameter collection interval i and q, is the maximum industrial load response adjustment duration characteristic range parameter when the parameter collection interval is i, is the minimum industrial load response adjustment time characteristic range parameter when the parameter collection interval is i, r x ' i is the mean value of the characteristic range parameter of industrial adjustable load capacity when the parameter collection interval is i, r xiq is the characteristic range parameter of industrial adjustable load capacity at the parameter collection interval i and time q, is the characteristic range parameter of the maximum industrial adjustable load capacity when the parameter collection interval is i, It is the minimum industrial adjustable load capacity characteristic range parameter when the parameter collection interval is i.

[0061] By considering the load response adjustment time and the maximum, minimum, real-time and average values ​​of the adjustable load capacity, the regulation balance of the industrial load is comprehensively evaluated to provide a more comprehensive analysis result. According to the industrial load regulation balance value, the industrial load regulation strategy can be reasonably arranged to maximize the use of the adjustable load and improve resource utilization efficiency. By accurately evaluating and regulating the industrial load, the grid load can be effectively balanced to reduce the imbalance of electricity supply and demand caused by industrial load fluctuations. Real-time monitoring of the industrial load regulation balance can promptly detect abnormal situations and take corresponding measures to prevent the occurrence of grid instability. Accurate industrial load regulation makes the grid run more smoothly, reduces frequent adjustments caused by load fluctuations, and improves the reliability of the grid.

[0062] The secondary load regulation balance evaluation submodule is used to extract the commercial load response regulation time characteristic range parameters and the commercial adjustable load capacity characteristic range parameters from the commercial load regulation data, and import the commercial load response regulation time characteristic range parameters and the commercial adjustable load capacity characteristic range parameters into the secondary load regulation balance evaluation model to evaluate the commercial load regulation balance value. The formula of the secondary load regulation balance evaluation model is:

[0063]

[0064] Where: p ts is the commercial load regulation balance value, m is the total number of parameter collection intervals, t e ' i is the mean value of the characteristic range parameter of the commercial load response adjustment duration when the parameter collection interval is i, t eiq is the characteristic range parameter of the commercial load response adjustment duration at the parameter collection interval of time i and time q, is the maximum commercial load response adjustment duration characteristic range parameter when the parameter collection interval is i, is the minimum commercial load response adjustment duration characteristic range parameter when the parameter collection interval is i, r e ' i is the mean value of the characteristic range parameter of commercial adjustable load capacity when the parameter collection interval is i, r eiq is the characteristic range parameter of commercial adjustable load capacity at time i and time q when the parameter collection interval is, is the maximum commercial adjustable load capacity characteristic range parameter when the parameter collection interval is i, It is the minimum commercial adjustable load capacity characteristic range parameter when the parameter collection interval is i.

[0065] According to the commercial load regulation balance value, the commercial load regulation strategy can be reasonably arranged to maximize the use of adjustable loads and improve resource utilization efficiency. By accurately evaluating and regulating commercial loads, the grid load can be effectively balanced and the imbalance of electricity supply and demand caused by commercial load fluctuations can be reduced. Real-time monitoring of the commercial load regulation balance can promptly detect abnormal situations and take corresponding measures to prevent the occurrence of grid instability. Accurate commercial load regulation makes the grid run more smoothly, reduces frequent adjustments caused by load fluctuations, and improves the reliability of the grid.

[0066] The three-level load regulation balance assessment submodule is used to extract the characteristic range parameters of the resident load response adjustment time and the characteristic range parameters of the resident adjustable load capacity in the resident load regulation data, and import the characteristic range parameters of the resident load response adjustment time and the characteristic range parameters of the resident adjustable load capacity into the three-level load regulation balance assessment model to evaluate the resident load regulation balance value. The formula of the three-level load regulation balance assessment model is:

[0067]

[0068] Where: p ts is the balance value of residential load regulation, m is the total number of parameter collection intervals, t j ' i is the mean value of the characteristic range parameter of the residential load response adjustment duration when the parameter collection interval is i, t jiq is the characteristic range parameter of the residential load response adjustment duration at the parameter collection interval of time i and time q, is the characteristic range parameter of the maximum resident load response adjustment duration when the parameter collection interval is i, is the minimum resident load response adjustment duration characteristic range parameter when the parameter collection interval is i, r j ' i is the mean value of the characteristic range parameter of the residents’ adjustable load capacity when the parameter collection interval is i, r jiq is the characteristic range parameter of the residents' adjustable load capacity at the parameter collection interval i and time q, is the characteristic range parameter of the maximum resident adjustable load capacity when the parameter collection interval is i, It is the minimum resident adjustable load capacity characteristic range parameter when the parameter collection interval is i.

[0069] According to the balance value of residential load regulation, the regulation strategy of residential load can be reasonably arranged to maximize the use of adjustable load and improve resource utilization efficiency. By accurately evaluating and regulating residential load, the grid load can be effectively balanced and the imbalance of electricity supply and demand caused by residential load fluctuations can be reduced. Real-time monitoring of the regulation balance of residential load can promptly detect abnormal situations and take corresponding measures to prevent the occurrence of grid instability. Accurate residential load regulation makes the grid run more smoothly, reduces frequent adjustments caused by load fluctuations, and improves the reliability of the grid.

[0070] The control balance discrimination submodule is used for industrial load control balance value, commercial load control balance value and residential load control balance value.

[0071] The industrial load control balance value is compared with the preset industrial load control balance value. If the industrial load control balance value is greater than or equal to the preset industrial load control balance value, the industrial load control is balanced; if the industrial load control balance value is less than the preset industrial load control balance value, the industrial load control is unbalanced;

[0072] The commercial load regulation balance value is compared with the preset commercial load regulation balance value. If the commercial load regulation balance value is greater than or equal to the preset commercial load regulation balance value, the commercial load regulation is balanced; if the commercial load regulation balance value is less than the preset commercial load regulation balance value, the commercial load regulation is unbalanced;

[0073] The resident load control balance value is compared with the preset resident load control balance value. If the resident load control balance value is greater than or equal to the preset resident load control balance value, the resident load control is balanced; if the resident load control balance value is less than the preset resident load control balance value, the resident load control is unbalanced.

[0074] The embodiment of the present invention obtains first multivariate data and second multivariate data, stores the first multivariate data in a virtual power plant resource type library, stores the second multivariate data in a resource regulation feature library, analyzes the regulation capability values ​​of wind power resources, the regulation capability values ​​of photovoltaic resources, and the regulation capability values ​​of energy storage equipment based on the first multivariate data, and then evaluates the industrial load regulation balance value, the commercial load regulation balance value, and the residential load regulation balance value through the second multivariate data, thereby achieving balanced regulation of industrial, commercial, and residential loads, and helping to improve the stability of the power grid and resource utilization efficiency.

[0075] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0076] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A virtual power plant dispatching system based on multi-information fusion, comprising a data source acquisition module, a resource aggregation and analysis module, and a resource balance evaluation module, characterized in that: The data source acquisition module is used to acquire the first multivariate data and the second multivariate data, and store the first multivariate data in the virtual power plant resource type library, and store the second multivariate data in the resource regulation feature library; The resource aggregation and analysis module analyzes the stability of resource scheduling based on the first multivariate data; The resource aggregation and analysis module includes a third resource adjustable capacity analysis submodule, which is used to import the adjustable data of the energy storage device in the first multivariate data into the third resource adjustable capacity analysis model to analyze the adjustable capacity value of the energy storage device resources. The formula for the third resource adjustable capacity analysis is: Where: t zc is the adjustable capacity value of energy storage device resources, n is the total number of data collection intervals, c a is the real-time available capacity of the energy storage device when the data collection interval is a, c az is the total storage capacity of the energy storage device, is the maximum real-time discharge power of the energy storage device when the data collection interval is a, is the minimum real-time discharge power of the energy storage device when the data collection interval is a, is the maximum real-time charging power of the energy storage device when the data collection interval is a, It is the minimum real-time charging power of the energy storage device when the data collection interval is a.

2. A virtual power plant dispatching system based on multi-information fusion according to claim 1, characterized in that: The first multivariate data includes wind power adjustable energy data, photovoltaic adjustable energy data and energy storage equipment adjustable data; the second multivariate data includes industrial load control data, commercial load control data and residential load control data.

3. A virtual power plant dispatching system based on multi-information fusion according to claim 1, characterized in that: The resource aggregation and analysis module also includes a first resource adjustable capacity analysis submodule, a second resource adjustable capacity analysis submodule, and a stability determination submodule; The resource balance assessment module includes a first-level load control balance assessment submodule, a second-level load control balance assessment submodule, a third-level load control balance assessment submodule and a control balance judgment submodule.

4. A virtual power plant dispatching system based on multi-information fusion according to claim 3, characterized in that: The first resource adjustable capacity analysis submodule constructs a first resource adjustable capacity analysis model based on wind power adjustable energy data, which is used to analyze the wind power resource adjustable capacity value; the second resource adjustable capacity analysis submodule is used to import photovoltaic adjustable energy data into the second resource adjustable capacity analysis model to analyze the photovoltaic resource adjustable capacity value; the third resource adjustable capacity analysis submodule imports the energy storage device adjustable data into the third resource adjustable capacity analysis model to analyze the energy storage device resource adjustable capacity value.

5. A virtual power plant dispatching system based on multi-information fusion according to claim 2, characterized in that: The first resource adjustable capacity analysis submodule constructs a first resource adjustable capacity analysis model based on wind power adjustable energy data, which is used to analyze the wind power resource adjustable capacity value. The formula of the first resource adjustable capacity analysis model is: Where: t ea is the adjustable capacity value of wind power resources, n is the total number of data collection intervals, is the maximum wind speed when the data collection interval is a, is the minimum wind speed when the data collection interval is a, v ae is the real-time wind speed at time e when the data collection interval is a, v′ a is the average wind speed when the data collection interval is a, is the maximum wind power generation when the data collection interval is a, is the minimum wind energy generation when the data collection interval is a, f ae is the wind power generation at time e when the data collection interval is a, f a ′ is the mean value of wind power generation when the data collection interval is a.

6. A virtual power plant dispatching system based on multi-information fusion according to claim 3, characterized in that: The primary load control balance assessment submodule is used to import the extracted industrial load control data into the primary load control balance assessment model to assess the industrial load control balance value; The secondary load regulation balance assessment submodule is used to import commercial load regulation data into the secondary load regulation balance assessment model to assess the commercial load regulation balance value; The three-level load control balance assessment submodule is used to import the resident load control data into the three-level load control balance assessment model to evaluate the resident load control balance value.

7. A virtual power plant dispatching system based on multi-information fusion according to claim 6, characterized in that: The first-level load control balance evaluation submodule is used to import the extracted industrial load control data into the first-level load control balance evaluation model to evaluate the industrial load control balance value. The formula of the first-level load control balance evaluation model is: Where: p tg is the balance value of industrial load regulation, m is the total number of parameter collection intervals, t′ xi is the mean value of the characteristic range parameter of industrial load response adjustment duration when the parameter collection interval is i, t xiq is the characteristic range parameter of industrial load response adjustment duration at the parameter collection interval of time i and time q, is the maximum industrial load response adjustment duration characteristic range parameter when the parameter collection interval is i, is the minimum industrial load response adjustment time characteristic range parameter when the parameter collection interval is i, r x ' i is the mean value of the characteristic range parameter of industrial adjustable load capacity when the parameter collection interval is i, r xiq is the characteristic range parameter of industrial adjustable load capacity at the parameter collection interval i and time q, is the maximum industrial adjustable load capacity characteristic range parameter when the parameter collection interval is i, It is the minimum industrial adjustable load capacity characteristic range parameter when the parameter collection interval is i.

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