Virtual power plant access regulation effect evaluation method and device, and storage medium

Abstract

The application discloses a virtual power plant access regulation effect evaluation method and device, and a storage medium, relates to the technical field of virtual power plant evaluation, and the method comprises the following steps: obtaining operation data and state information of aggregated resources from a virtual power plant monitoring system, and selecting multi-dimensional evaluation indexes of the virtual power plant access regulation effect based on a three-layer progressive framework of capability-operation-benefit; based on the multi-dimensional evaluation indexes, a multi-dimensional evaluation system of the virtual power plant access regulation effect is constructed; the entropy weight method and the coefficient of variation method are used to calculate the weight of each index in the multi-dimensional evaluation system, and the least square method is used to fuse the weight calculated by the entropy weight method and the weight calculated by the coefficient of variation method to obtain a comprehensive weight; based on the comprehensive weight, the virtual power plant access regulation effect is comprehensively evaluated by using the technique for order preference by similarity to ideal solution. The application realizes all-around, dynamic and objective evaluation of the virtual power plant operation state.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant evaluation technology, specifically to a method, equipment, and storage medium for evaluating the control effect of virtual power plant access. Background Technology

[0002] As a key innovation in new power systems, virtual power plants essentially aggregate, coordinate, and optimize massive, dispersed distributed power sources, adjustable loads, and user-side energy storage resources through advanced information and communication technologies and software systems, enabling them to participate in grid operation and the electricity market as a "virtual," stable, and controllable whole.

[0003] However, in actual operation, the regulation effectiveness of virtual power plants is affected by multiple factors such as resource characteristics, communication reliability, control strategies, and market mechanisms, resulting in problems such as inaccurate regulation capabilities and insufficient response reliability. Currently, the lack of a systematic, quantifiable, and online-executable multi-dimensional evaluation system makes it difficult to objectively and comprehensively evaluate the operational performance of virtual power plants, thus hindering their large-scale application and standardized management. Therefore, there is an urgent need to establish an evaluation scheme that can reflect the real-time operating status of virtual power plants and scientifically evaluate their regulation capabilities and comprehensive benefits to support the precise scheduling, operational optimization, and market-oriented operation of virtual power plants. Summary of the Invention

[0004] This invention addresses the problems of inaccurate regulation capabilities and insufficient response reliability in the control of virtual power plants. It provides a method, equipment, and storage medium for evaluating the control effect of virtual power plants, enabling a comprehensive, dynamic, and objective evaluation of the operating status of virtual power plants.

[0005] The present invention is achieved through the following technical solution.

[0006] Firstly, a method for evaluating the control effect of virtual power plant access is provided, the method comprising:

[0007] The system obtains the operation data and status information of aggregated resources from the virtual power plant monitoring system, and selects multi-dimensional evaluation indicators of the virtual power plant access control effect from the operation data and status information based on the three-layer progressive framework of capacity-operation-efficiency.

[0008] Based on the aforementioned multi-dimensional evaluation indicators, a multi-dimensional evaluation system for the control effect of the virtual power plant is constructed.

[0009] The weights of each indicator in the multi-dimensional evaluation system are calculated using the entropy weight method and the coefficient of variation method, respectively. The weights calculated by the entropy weight method and the weights calculated by the coefficient of variation method are then combined using the least squares method to obtain a comprehensive weight.

[0010] Based on comprehensive weights, the Top-Ideal Solution Ranking Method (TOPSIS) is used to comprehensively evaluate the control effect of the virtual power plant access.

[0011] In some embodiments, the multi-dimensional evaluation indicators include: adjustable potential indicators, operational control effect indicators, and benefit evaluation indicators.

[0012] In some embodiments, the adjustable potential indicator includes:

[0013] Maximum resource upscaling capacity :

[0014] ,

[0015] in, To aggregate the total number of resources, For resources Maximum upward adjustment capability

[0016] Maximum resource reduction capability :

[0017] ,

[0018] in, To aggregate the total number of resources, For resources Maximum downward adjustment capability

[0019] Resource adjustable rate The calculation formula is:

[0020] ,

[0021] in, For the first The maximum power change value of a resource per unit time. Its rated capacity;

[0022] The operational control effectiveness indicators include:

[0023] Fast response time :

[0024] ,

[0025] in, To determine the total number of scheduling instructions received within the statistical period, No. The actual time when the response to this instruction begins. For the first The time of this instruction issuance

[0026] Average adjustment deviation rate :

[0027] ,

[0028] in, For the first The average power actually output by this instruction For the first The target power of this instruction.

[0029] Adjust command completion rate :

[0030] ,

[0031] in, The number of instructions given to achieve an actual adjustment amount of more than 90% of the target amount. This represents the total number of instructions received.

[0032] Operational reliability :

[0033] ,

[0034] in, This represents the total number of scheduled tasks within the statistical period. For the first The duration during which the actual adjustable capacity is greater than or equal to 80% of the committed capacity in the sub-task For the first Total planned output time for this task;

[0035] Benefit evaluation indicators include:

[0036] Economic benefits :

[0037] ,

[0038] in, For market trading of electricity and ancillary service revenue, As a government incentive subsidy, For operating costs, For the cost of the technology platform,

[0039] Social benefits :

[0040] ,

[0041] in, The load factor of the regional power grid during the evening peak period before the virtual power plant is connected. This represents the load rate during the same time period after the virtual power plant is connected.

[0042] Environmental benefits :

[0043] ,

[0044] in, To promote the consumption of renewable energy or replace thermal power by virtual power plants. The average carbon emission factor of the regional power grid. Carbon emission factors of the energy source being replaced.

[0045] In some embodiments, the weights of each indicator in the multi-dimensional evaluation system are calculated using the entropy weight method and the coefficient of variation method, respectively, and the weights calculated by the entropy weight method and the weights calculated by the coefficient of variation method are fused using the least squares method to obtain a comprehensive weight, including:

[0046] Based on the values ​​of multiple evaluation objects and multi-dimensional evaluation indicators, an original decision matrix is ​​constructed, and the values ​​of the multi-dimensional evaluation indicators in the original decision matrix are standardized to obtain a standardized matrix.

[0047] Based on the standardized matrix, the first entropy weight of each indicator in the multi-dimensional evaluation index is calculated using the entropy weight method.

[0048] Based on the original decision matrix, the second entropy weight of each indicator in the multidimensional evaluation index is calculated using the coefficient of variation method.

[0049] The first entropy weight and the second entropy weight are fused to obtain a comprehensive weight.

[0050] In some embodiments, the first entropy weight and the second entropy weight are fused to obtain a comprehensive weight, including:

[0051] A least squares optimization model is established, and the first entropy weight and the second entropy weight are fused based on the least squares optimization model, wherein the least squares optimization model is as follows:

[0052] ,

[0053] The constraints are as follows: ,and , , For preference coefficients, Indicates the first The comprehensive weight of the indicators to be solved For the first entropy weight, For the second entropy weight, Indicates the number of evaluation indicators;

[0054] Solving the least squares optimization model yields the comprehensive weight vector. ,in, Indicates transpose. Indicates the number of indicators.

[0055] In some embodiments, based on the comprehensive weights, TOPSIS is used to comprehensively evaluate the control effect of the virtual power plant access, including:

[0056] The standardized matrix is ​​combined with the comprehensive objective weights to obtain a weighted matrix;

[0057] Based on the weighting matrix, positive ideal solutions and negative ideal solutions are defined;

[0058] Calculate the grey relational coefficients between the plurality of evaluation objects and the positive ideal solution and the negative ideal solution, respectively;

[0059] Based on the grey relational coefficient, the grey relational degree between the plurality of evaluation objects and the positive ideal solution and the negative ideal solution is calculated, and the grey relational closeness is calculated based on the grey relational degree between the positive ideal solution and the negative ideal solution.

[0060] Based on the value of the gray relational proximity, the control effect of the virtual power plant access is evaluated in a graded manner.

[0061] In some embodiments, the weighting matrix is:

[0062] ,

[0063] in, Represents the first in the normalized matrix The first assessment subject The value of each indicator.

[0064] Secondly, a virtual power plant access control effect evaluation device is provided, the device comprising:

[0065] The multi-dimensional evaluation index acquisition module is used to: acquire the operation data and status information of aggregated resources from the virtual power plant monitoring system, and select multi-dimensional evaluation indicators of the virtual power plant access control effect from the operation data and status information based on the three-layer progressive framework of capacity-operation-efficiency.

[0066] The multi-dimensional evaluation system construction module is used to: construct a multi-dimensional evaluation system for the virtual power plant access control effect based on the multi-dimensional evaluation indicators;

[0067] The comprehensive weight calculation module is used to: calculate the weights of each indicator in the multi-dimensional evaluation system using the entropy weight method and the coefficient of variation method respectively, and use the least squares method to merge the weights calculated by the entropy weight method and the weights calculated by the coefficient of variation method to obtain the comprehensive weight;

[0068] The regulation effect evaluation module is used to comprehensively evaluate the regulation effect of the virtual power plant based on comprehensive weights and using the Top-Ideal Solution Ranking Method (TOPSIS).

[0069] Thirdly, a device for evaluating the control effect of a virtual power plant is provided, the device comprising:

[0070] At least one processor;

[0071] At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions implementing the method described in any of the above when executed by the at least one processor.

[0072] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the method described in any of the preceding embodiments.

[0073] Compared with existing technologies, this invention has the following advantages and beneficial effects: First, it collects real-time operational data and status information of aggregated resources through a virtual power plant online monitoring system. Based on a three-tiered progressive architecture of "capacity-operation-benefit," it selects multi-dimensional evaluation indicators such as adjustable potential indicators, operational control effect indicators, and benefit assessment indicators. Second, relying on these multi-dimensional evaluation indicators, it constructs a multi-dimensional evaluation system for the control effect of virtual power plant access. Third, it uses the entropy weight method and the coefficient of variation method to calculate the weights of each indicator, and introduces the least squares method to fuse the two weights to obtain a comprehensive objective weight. Finally, it comprehensively evaluates the control effect of virtual power plant access based on the improved grey TOPSIS method. This allows for a systematic, objective, and real-time assessment of the control effect after virtual power plants are connected to the power grid, providing standardized and quantifiable technical support for their precise scheduling, operational optimization, market transactions, and policy evaluation. It has significant engineering application value and promising prospects for widespread application. Attached Figure Description

[0074] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a flowchart of a method for evaluating the control effect of a virtual power plant according to an embodiment of the present invention.

[0076] Figure 2 This is a schematic diagram of a multi-dimensional evaluation system according to an embodiment of the present invention.

[0077] Figure 3 This is a structural block diagram of a virtual power plant access control effect evaluation device according to an embodiment of the present invention.

[0078] Figure 4 This is a schematic diagram of a virtual power plant access control effect evaluation device according to an embodiment of the present invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0080] On the one hand, the present invention provides a method for evaluating the control effect of virtual power plant access. Figure 1 This is a flowchart illustrating a method for evaluating the control effect of a virtual power plant according to an embodiment of the present invention. (Reference) Figure 1 The evaluation method for the control effect of the virtual power plant includes: S10 to S40.

[0081] In S10, the operation data and status information of aggregated resources are obtained from the virtual power plant monitoring system, and based on the three-layer progressive framework of capacity-operation-efficiency, multi-dimensional evaluation indicators of the virtual power plant access control effect are selected from the operation data and status information.

[0082] Specifically, the system acquires real-time operational data and status information of aggregated resources from the virtual power plant monitoring system. Based on a three-tiered progressive framework of "capacity-operation-benefits," it selects multi-dimensional evaluation indicators for the effectiveness of virtual power plant access control. These indicators include: adjustable potential indicators, operational control effectiveness indicators, and benefit evaluation indicators. Aggregated resources refer to geographically dispersed, small-capacity, and diverse distributed energy resources integrated through advanced information and communication technologies and software systems to form a virtual entity with a unified control interface, capable of participating in grid operation and electricity market transactions like a traditional large power plant.

[0083] In S20, a multi-dimensional evaluation system for the control effect of virtual power plant access is constructed based on multi-dimensional evaluation indicators.

[0084] Specifically, a multi-dimensional evaluation system for the control effect of virtual power plant access can be constructed by relying on multi-dimensional evaluation indicators such as adjustable potential indicators, operation and control effect indicators, and benefit evaluation indicators.

[0085] refer to Figure 2 Adjustable potential indicators include: maximum resource upsizing capacity. Maximum resource reduction capability Resource adjustable rate .

[0086] Maximum resource upscaling capacity The calculation formula is:

[0087] (1)

[0088] in, To aggregate the total number of resources, For resources Maximum upward adjustment capability.

[0089] Maximum resource reduction capability The calculation formula is:

[0090] (2)

[0091] in, To aggregate the total number of resources, For resources Maximum downward adjustment capability.

[0092] Resource adjustable rate The calculation formula is:

[0093] (3)

[0094] in, For the first The maximum power change value of a resource per unit time. Its rated capacity.

[0095] Operational control effectiveness indicators include: operational response speed Average adjustment deviation rate Adjustment command completion rate Operational reliability .

[0096] Fast response time The calculation formula is:

[0097] (4)

[0098] in, To determine the total number of scheduling instructions received within the statistical period, No. The actual time when the response to this instruction begins. For the first The time when the instruction is issued.

[0099] Average adjustment deviation rate The calculation formula is:

[0100] (5)

[0101] in, For the first The average power actually output by this instruction For the first The target power of this instruction.

[0102] Adjust command completion rate The calculation formula is:

[0103] (6)

[0104] in, The number of instructions given to achieve an actual adjustment amount of more than 90% of the target amount. This represents the total number of instructions received.

[0105] Operational reliability The calculation formula is:

[0106] (7)

[0107] in, This represents the total number of scheduled tasks within the statistical period. For the first The duration during which the actual adjustable capacity is greater than or equal to 80% of the committed capacity in the sub-task For the first Total planned output time for this task.

[0108] Benefit evaluation indicators include: economic benefits Social benefits Environmental benefits .

[0109] Economic benefits The calculation formula is:

[0110] (8)

[0111] in, For market trading of electricity and ancillary service revenue, As a government incentive subsidy, For operating costs, Cost of the technology platform.

[0112] Social benefits The calculation formula is:

[0113] (9)

[0114] in, The load factor of the regional power grid during the evening peak period before the virtual power plant is connected. This represents the load rate during the same period after the virtual power plant is connected.

[0115] Environmental benefits The calculation formula is:

[0116] (10)

[0117] in, To promote the consumption of renewable energy or replace thermal power by virtual power plants. The average carbon emission factor of the regional power grid. Carbon emission factors of the energy source being replaced.

[0118] In S30, the weights of each indicator in the multi-dimensional evaluation system are calculated using the entropy weight method and the coefficient of variation method, respectively. The weights calculated by the entropy weight method and the weights calculated by the coefficient of variation method are then combined using the least squares method to obtain the comprehensive weight.

[0119] For example, obtaining the comprehensive weight includes: constructing an original decision matrix based on the values ​​of multiple evaluation objects and multi-dimensional evaluation indicators, and standardizing the values ​​of the multi-dimensional evaluation indicators in the original decision matrix to obtain a standardized matrix; calculating the first entropy weight of each indicator in the multi-dimensional evaluation indicators using the entropy weight method based on the standardized matrix; calculating the second entropy weight of each indicator in the multi-dimensional evaluation indicators using the coefficient of variation method based on the original decision matrix; and fusing the first entropy weight and the second entropy weight to obtain the comprehensive weight.

[0120] Specifically, this can be achieved through the following steps.

[0121] (1) There is Each assessment object, The evaluation indicators constitute the original decision matrix. Each indicator is standardized to obtain a standardized matrix. ,in, Represents the first in the normalized matrix The first assessment subject One indicator.

[0122] (2) Calculate the weights using the entropy weight method, and calculate the first... Entropy value of the item index And entropy weight :

[0123] , , (11)

[0124] in, Indicates the first The assessment subject was in the first The value on the indicator, express The indicator in the first The feature weights of each evaluation object Indicates the first The entropy value of the indicator, The entropy weight method calculates the first... Item index weight (first entropy weight) Indicates the number of evaluation objects, This indicates the number of evaluation indicators.

[0125] (3) Calculate the weights using the coefficient of variation method, and calculate the weights of the first... Coefficient of variation of the item index And entropy weight :

[0126] , , , (12)

[0127] in, Indicates the first The mean difference of each indicator Indicates the first The standard deviation of the indicator Indicates the first The coefficient of variation of the item index The coefficient of variation method is used to calculate the first... Item index weight (second entropy weight).

[0128] (4) Based on the least squares method to fuse weights, in order to take into account the advantages of both types of weights, a least squares optimization model is established. Solving for the overall weight :

[0129] (13)

[0130] Where: the constraint condition is ,and , , For preference coefficients, Indicates the first The comprehensive weights of the indicators to be solved are determined. By solving this model, the comprehensive weight vector is obtained. .

[0131] In S40, the effect of virtual power plant access control is comprehensively evaluated based on the overall weight and the Approximate Ideal Solution Ranking Method (TOPSIS).

[0132] For example, a comprehensive evaluation of the control effect of virtual power plant access includes: combining a standardized matrix with comprehensive objective weights to obtain a weighted matrix; defining positive and negative ideal solutions based on the weighted matrix; calculating the grey relational coefficients between multiple evaluation objects and the positive and negative ideal solutions respectively; calculating the grey relational degrees of the positive and negative ideal solutions between multiple evaluation objects and the positive and negative ideal solutions respectively based on the grey relational coefficients, and calculating the grey relational proximity based on the grey relational degrees of the positive and negative ideal solutions; and conducting a graded evaluation of the control effect of virtual power plant access based on the value of the grey relational proximity.

[0133] Specifically, a weighted normalized matrix can be constructed based on the following steps: (1) A comprehensive evaluation of the control effect of virtual power plant access can be performed.

[0134] (1) Standardize the matrix With comprehensive weight By combining these, we obtain the weighted matrix. :

[0135] (14)

[0136] (2) Determine the positive and negative ideal solutions, and the positive ideal solution With negative ideal solution They are defined as follows:

[0137] , (15)

[0138] in, Indicates the first The positive ideal solution of the indicator, Indicates the first The negative ideal solution of the indicator, The set of benefit indicators includes maximum resource adjustment capacity, maximum resource reduction capacity, resource adjustability rate, operational response speed, adjustment command completion rate, operational reliability, economic benefits, social benefits, and environmental benefits. It refers to a set of cost-related indicators, including the average adjustment deviation rate.

[0139] (3) Calculate the grey relational coefficients, and calculate the grey relational coefficients between each evaluation object and the positive ideal solution and the negative ideal solution respectively:

[0140] (16)

[0141] (17)

[0142] in, The resolution coefficient is typically set to 0.5. Indicates the object of evaluation In terms of indicators The grey relational coefficient between the above and the positive ideal solution. Indicates the object of evaluation In terms of indicators The grey relational coefficient between the upper and negative ideal solutions. This represents the minimum absolute difference between all evaluated objects and the ideal solution across all indicators. This represents the maximum absolute difference between all evaluated objects and the ideal solution across all indicators.

[0143] (4) Calculate the grey relational degree between each object and the positive ideal solution and the negative ideal solution:

[0144] (18)

[0145] (19)

[0146] (5) Calculate the grey relational proximity:

[0147] (20)

[0148] in, For object The weighted grey relational degree with the positive ideal solution, For object The weighted grey relational degree with the negative ideal solution, Representation Object The gray level of closeness, The larger the value, the closer the virtual power plant's resource regulation effect is to the ideal state. Based on... By ranking all evaluation objects, a quantitative comparison and classification of the resource regulation effects of virtual power plants across multiple dimensions can be achieved.

[0149] This invention proposes a complete multi-dimensional quantitative evaluation scheme for the control effect of virtual power plants connected to the power grid. Based on a three-tiered progressive framework of "capacity-operation-benefits", the qualitative control effect is transformed into a series of measurable, calculable, and comparable indicators. An improved grey TOPSIS method is used to comprehensively evaluate the control effect of virtual power plants connected to the grid. By calculating the relative closeness of each evaluation object to the ideal solution, the control effect is ranked and classified, providing support for dynamic diagnosis and optimization decision-making of the operating status. This overcomes the problems of single indicators, strong subjectivity, and insufficient real-time performance in existing evaluation methods, and realizes a comprehensive, dynamic, and objective evaluation of the operating status of virtual power plants.

[0150] On the other hand, the present invention provides a device for evaluating the control effect of virtual power plant access. Figure 3 This is a structural block diagram of a virtual power plant access control effect evaluation device according to an embodiment of the present invention. (Reference) Figure 3 The virtual power plant is connected to the control effect evaluation equipment, which includes: a multi-dimensional evaluation indicator acquisition module, a multi-dimensional evaluation system construction module, a comprehensive weight calculation module, and a control effect evaluation module.

[0151] The multi-dimensional evaluation index acquisition module is used to: acquire the operation data and status information of aggregated resources from the virtual power plant monitoring system, and select multi-dimensional evaluation indicators of the virtual power plant access control effect from the operation data and status information based on the three-level progressive framework of capacity-operation-efficiency.

[0152] The multi-dimensional evaluation system construction module is used to: construct a multi-dimensional evaluation system for the control effect of virtual power plant access based on multi-dimensional evaluation indicators.

[0153] The comprehensive weight calculation module is used to calculate the weights of each indicator in the multi-dimensional evaluation system using the entropy weight method and the coefficient of variation method respectively, and to merge the weights calculated by the entropy weight method and the weights calculated by the coefficient of variation method using the least squares method to obtain the comprehensive weight.

[0154] The regulation effect evaluation module is used to comprehensively evaluate the regulation effect of virtual power plant access based on comprehensive weights and using the Top-Ideal Solution Ranking Method (TOPSIS).

[0155] Other details regarding the virtual power plant access control effect evaluation equipment are described in the previous section on the virtual power plant access control effect evaluation method and will not be repeated here.

[0156] In implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides a structure for the virtual power plant access control effect evaluation device involved in the above embodiments. Figure 4 This is a schematic diagram of a virtual power plant access control effect evaluation device according to an embodiment of the present invention. (Reference) Figure 4 The virtual power plant access control effect evaluation device includes: at least one processor; and at least one memory. The at least one memory is coupled to the at least one processor and stores instructions for execution by the at least one processor, which, when executed by the at least one processor, implement the method described above.

[0157] A processor can be a set of logic blocks, modules, and circuits that implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. A processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc.

[0158] The memory may be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0159] In one implementation, the memory can exist independently of the processor. The memory can be connected to the processor via a bus and used to store instructions or program code. When the processor calls and executes the instructions or program code stored in the memory, it can implement the method provided in the embodiments of the present invention. In another implementation, the memory can also be integrated with the processor.

[0160] On the other hand, the present invention also provides a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any of the above embodiments.

[0161] Exemplary examples show that the aforementioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this invention may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0162] This invention provides a computer program that, when run on a computer, causes the computer to perform the method of any of the above embodiments.

[0163] This invention provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the method of any of the above embodiments.

[0164] In this invention, a virtual power plant online monitoring system is used to collect real-time operational data and status information of aggregated resources. Based on a three-tiered progressive architecture of "capacity-operation-benefit," multi-dimensional evaluation indicators are selected, including adjustable potential indicators, operational control effect indicators, and benefit assessment indicators. Secondly, relying on these multi-dimensional evaluation indicators, a multi-dimensional evaluation system for the control effect of virtual power plant access is constructed. Then, the entropy weight method and the coefficient of variation method are used to calculate the weights of each indicator, and the least squares method is introduced to fuse the two weights to obtain a comprehensive objective weight. Finally, the improved grey TOPSIS method is used to comprehensively evaluate the control effect of virtual power plant access. This system can systematically, objectively, and in real-time assess the control effect of virtual power plants after they are connected to the power grid, providing standardized and quantifiable technical support for their precise scheduling, operational optimization, market transactions, and policy evaluation. It has significant engineering application value and promising prospects for widespread application.

[0165] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the control effect of virtual power plant access, characterized in that, The method includes: The system obtains the operation data and status information of aggregated resources from the virtual power plant monitoring system, and selects multi-dimensional evaluation indicators of the virtual power plant access control effect from the operation data and status information based on the three-layer progressive framework of capacity-operation-efficiency. Based on the aforementioned multi-dimensional evaluation indicators, a multi-dimensional evaluation system for the control effect of the virtual power plant is constructed. The weights of each indicator in the multi-dimensional evaluation system are calculated using the entropy weight method and the coefficient of variation method, respectively. The weights calculated by the entropy weight method and the weights calculated by the coefficient of variation method are then combined using the least squares method to obtain a comprehensive weight. Based on comprehensive weights, the Top-Ideal Solution Ranking Method (TOPSIS) is used to comprehensively evaluate the control effect of the virtual power plant access.

2. The method according to claim 1, characterized in that, The multi-dimensional evaluation indicators include: adjustable potential indicators, operational control effectiveness indicators, and benefit evaluation indicators.

3. The method according to claim 2, characterized in that, The adjustable potential indicators include: Maximum resource upscaling capacity : ， in, To aggregate the total number of resources, For resources Maximum upward adjustment capability Maximum resource reduction capability : ， in, To aggregate the total number of resources, For resources Maximum downward adjustment capability Resource adjustable rate The calculation formula is: ， in, For the first The maximum power change value of a resource per unit time. Its rated capacity; The operational control effectiveness indicators include: Fast response time : ， in, To determine the total number of scheduling instructions received within the statistical period, No. The actual time when the response to this instruction begins. For the first The time of this instruction issuance Average adjustment deviation rate : ， in, For the first The average power actually output by this instruction For the first The target power of this instruction. Adjust command completion rate : ， in, The number of instructions given to achieve an actual adjustment amount of more than 90% of the target amount. This represents the total number of instructions received. Operational reliability : ， in, This represents the total number of scheduled tasks within the statistical period. For the first The duration during which the actual adjustable capacity is greater than or equal to 80% of the committed capacity in the sub-task For the first Total planned output time for this task; Benefit evaluation indicators include: Economic benefits : ， in, For revenue from trading electricity and ancillary services in the market, As a government incentive subsidy, For operating costs, For the cost of the technology platform, Social benefits : ， in, The load factor of the regional power grid during the evening peak period before the virtual power plant is connected. The load rate during the same period after the virtual power plant is connected. Environmental benefits : ， in, To promote the consumption of renewable energy or replace thermal power by virtual power plants. The average carbon emission factor of the regional power grid. Carbon emission factors of the energy source being replaced.

4. The method according to any one of claims 1 to 3, characterized in that, The weights of each indicator in the multi-dimensional evaluation system are calculated using the entropy weight method and the coefficient of variation method, respectively. The weights calculated using the entropy weight method and the weights calculated using the coefficient of variation method are then combined to obtain a comprehensive weight, including: Based on the values ​​of multiple evaluation objects and multi-dimensional evaluation indicators, an original decision matrix is ​​constructed, and the values ​​of the multi-dimensional evaluation indicators in the original decision matrix are standardized to obtain a standardized matrix. Based on the standardized matrix, the first entropy weight of each indicator in the multi-dimensional evaluation index is calculated using the entropy weight method. Based on the original decision matrix, the second entropy weight of each indicator in the multidimensional evaluation index is calculated using the coefficient of variation method. The first entropy weight and the second entropy weight are fused to obtain a comprehensive weight.

5. The method according to claim 4, characterized in that, The first entropy weight and the second entropy weight are fused using the least squares method to obtain a comprehensive weight, including: A least squares optimization model is established, and the first entropy weight and the second entropy weight are fused based on the least squares optimization model, wherein the least squares optimization model is as follows: ， The constraints are as follows: ,and , , For preference coefficients, Indicates the first The comprehensive weight of the indicators to be solved For the first entropy weight, For the second entropy weight, Indicates the number of evaluation indicators; Solving the least squares optimization model yields the comprehensive weight vector. ,in, Indicates transpose. Indicates the number of indicators.

6. The method according to claim 5, characterized in that, Based on the aforementioned comprehensive weights, TOPSIS is used to comprehensively evaluate the control effect of the virtual power plant access, including: The standardized matrix is ​​combined with the comprehensive objective weights to obtain a weighted matrix; Based on the weighting matrix, positive ideal solutions and negative ideal solutions are defined; Calculate the grey relational coefficients between the plurality of evaluation objects and the positive ideal solution and the negative ideal solution, respectively; Based on the grey relational coefficient, the grey relational degree between the plurality of evaluation objects and the positive ideal solution and the negative ideal solution is calculated, and the grey relational closeness is calculated based on the grey relational degree between the positive ideal solution and the negative ideal solution. Based on the value of the gray relational proximity, the control effect of the virtual power plant access is evaluated in a graded manner.

7. The method according to claim 6, characterized in that, The weighting matrix is: ， in, Represents the first in the normalized matrix The first assessment subject The value of each indicator.

8. A device for evaluating the control effect of a virtual power plant, characterized in that, The device includes: The multi-dimensional evaluation index acquisition module is used to: acquire the operation data and status information of aggregated resources from the virtual power plant monitoring system, and select multi-dimensional evaluation indicators of the virtual power plant access control effect from the operation data and status information based on the three-layer progressive framework of capacity-operation-efficiency. The multi-dimensional evaluation system construction module is used to: construct a multi-dimensional evaluation system for the virtual power plant access control effect based on the multi-dimensional evaluation indicators; The comprehensive weight calculation module is used to: calculate the weights of each indicator in the multi-dimensional evaluation system using the entropy weight method and the coefficient of variation method respectively, and use the least squares method to merge the weights calculated by the entropy weight method and the weights calculated by the coefficient of variation method to obtain the comprehensive weight; The regulation effect evaluation module is used to comprehensively evaluate the regulation effect of the virtual power plant based on comprehensive weights and using the Top-Ideal Solution Ranking Method (TOPSIS).

9. A device for evaluating the control effect of a virtual power plant access, characterized in that, The device includes: At least one processor; At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions implementing the method of any one of claims 1 to 7 when executed by the at least one processor.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7.

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