Method, device, equipment and medium for quantitative evaluation of adjustable capacity of virtual power plant

Abstract

The present invention relates to a method, device, equipment and medium for quantitatively evaluating the adjustable capacity of a virtual power plant, wherein the method comprises: dividing the resources aggregated by the virtual power plant into Class I resources according to type, and determining the output technical upper limit and security load of each resource at a preset time; determining the operation plan for the next day according to the production plan; determining the output of each type of resource t The theoretical technical upper and lower limits and output plans at the time, as well as the theoretical t The maximum upward and downward adjustment capabilities at any time; determine the key factors affecting the adjustable capacity of various resources according to the characteristics of various resources, and determine the comprehensive impact of various resources; determine the virtual power plant t The ability to adjust up and down at any time, and the use of the virtual power plant t The upward and downward adjustment capabilities at each moment are used to modify the next day's operation plan to obtain the adjustable domain of the virtual power plant based on the plan curve.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a method, device, equipment and medium for quantitatively evaluating the adjustable capacity of a virtual power plant. Background Art

[0002] Through advanced digital technology, control technology, Internet of Things technology and information and communication technology, virtual power plants aggregate management and optimize control of distributed resources such as distributed power sources, energy storage and adjustable loads, tap the regulation potential of distributed resources, provide adjustable capabilities for power grid operation, and have good application prospects.

[0003] As an important market player, virtual power plants need to effectively assess their own regulatory capabilities to improve their market profitability. Bidding beyond their capabilities will result in being assessed or purchased at a higher price, resulting in increased costs. Bidding below their capabilities will result in reduced profit margins. A scientific, reasonable and accurate assessment of their own adjustable capabilities will help them better participate in electricity trading and improve their profitability.

[0004] The existing patent document CN117394459A discloses a method for quantitatively evaluating the adjustable capacity of a virtual power plant, and its specific implementation scheme is: based on a piecewise linear function, a nonlinear relationship between the adjustment amount and the adjustment cost parameter value of the adjustable resources of the virtual power plant is constructed to establish an adjustable capacity evaluation model of the virtual power plant; the preset minimum value of the overall adjustment cost parameter of the virtual power plant is input into the adjustable capacity evaluation model of the virtual power plant, the overall adjustment amount of the virtual power plant is decomposed to obtain the control adjustment amount of each adjustable resource, and the optimal solution for quantitatively evaluating the adjustable capacity of the virtual power plant is obtained by solving the established optimization control model. Although this method can accurately reflect the nonlinear relationship between the adjustment amount and the adjustment cost, it does not comprehensively consider the actual operation situation, so there is still some deviation in the evaluation result. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for quantitatively evaluating the adjustable capacity of a virtual power plant, which can quickly and accurately evaluate the adjustment potential of a virtual power plant.

[0006] The technical solution adopted by the present invention to solve the technical problem is: to provide a method for quantitatively evaluating the adjustable capacity of a virtual power plant, comprising the following steps:

[0007] The directly controlled resources aggregated by the virtual power plant are divided into Class I resources according to their types, and the output technical upper limit and security load of each resource at the preset time are determined;

[0008] Determine the next day's operation plan according to the production plan, wherein the next day's operation plan includes the planned output components of each resource at a preset time;

[0009] Determine the output of each resource type based on the output technical upper limit, security load and planned output composition at the preset time. t The theoretical technical upper limit, theoretical technical lower limit and output plan at the moment, and output according to each type of resource t The theoretical technical upper limit, theoretical technical lower limit and output plan at the moment determine the theoretical t The maximum upward adjustment capacity and the maximum downward adjustment capacity at any moment;

[0010] Determine the key factors that affect the adjustable capacity of each type of resources based on their characteristics, and determine the comprehensive impact of each type of resources;

[0011] According to the comprehensive impact of all key factors affecting various resources, the average historical fulfillment rate of various resources and the theoretical t The maximum upward and downward adjustment capabilities at the moment determine the virtual power plant t The ability to adjust up and down at any time, and the use of the virtual power plant t The upward and downward adjustment capabilities at each moment are used to modify the next day's operation plan to obtain the adjustable domain of the virtual power plant based on the plan curve.

[0012] The planned output composition of each resource at a preset time is composed of an output plan determined by a user according to an actual operation plan, or determined through load forecasting, or calculated according to a baseline calculation formula.

[0013] Each type of resource output t The theoretical technical upper limit at the time is obtained by summing the output technical upper limits of each resource at the preset time under each resource type; the output of each resource type is t The theoretical technical lower limit of the time is obtained by summing the security load of each resource at the preset time under each resource type; the output of each resource type is t The output plan at a certain moment is obtained by summing up the planned output components of each resource at a preset moment under each type of resource.

[0014] The various resources are theoretically t The maximum upward adjustment capacity at a moment is calculated by the output of each type of resource t Theoretical technical limit of time and output of each type of resource t The output plan at each moment is obtained by making a difference; the various resources are theoretically t The maximum down-regulation capacity at a given moment is calculated by t The output plan at each time and the output of each resource type t The theoretical technical lower limit of the moment is obtained by subtracting it.

[0015] The key factors affecting the adjustable capacity of various resources are determined according to the characteristics of various resources, and the comprehensive impact of various resources is determined, specifically:

[0016] Determine the impact based on the characteristics of each resource i The adjustable capacity of class resources m Key influencing factors X i,m , and determine the m Key factors X i,m The value range of is, i is any positive integer from 1 to 1;

[0017] According to m Key influencing factors X i,m The value range of m Key factors X i,m Perform normalization processing;

[0018] Through multiple experimental data during operation, the fitting method is used to determine the m Key factors X i,m The influence function of

[0019] Each key influencing factor will be obtained X i,m Multiply the influence function of i The comprehensive impact of these resources.

[0020] The key factors affecting the adjustable capacity of various resources are determined according to the characteristics of various resources, and the comprehensive impact of various resources is determined, specifically:

[0021] Determine the impact based on the characteristics of each resource i The adjustable capacity of class resources m Key factors X i,m ,in, i is any positive integer from 1 to 1;

[0022] Conduct cluster analysis based on key influencing factors to determine the typical scenarios corresponding to each key influencing factor;

[0023] Determine the typical scenario closest to the calculation date, and calculate the scenario relevance of each key influencing factor in the typical scenario closest to the calculation date;

[0024] The membership function of the comprehensive impact degree and scenario relevance is determined based on historical data, and the fuzzy reasoning method is used to calculate the comprehensive impact degree of various resources under the typical scenario closest to the calculation date.

[0025] The Virtual Power Plant t The ability to adjust at any time It is calculated that the virtual power plant t Time to lower the ability Calculated, where Δ P up and Δ P down Virtual Power Plant t The ability to adjust up at any time and the ability to adjust down at any time in the virtual power plant, H i For the i The average historical fulfillment rate of resources of this category, F i For the i The comprehensive impact of the resource type, Δ P i,up and Δ P i,down Respectively i Class resources are theoretically t The maximum upward adjustment capability and the maximum downward adjustment capability at the moment, where: i is any positive integer from 1 to I.

[0026] The technical solution adopted by the present invention to solve the technical problem is: to provide a device for quantitatively evaluating the adjustable capacity of a virtual power plant, comprising:

[0027] The classification module is used to classify the directly controlled resources aggregated by the virtual power plant into Class I resources according to their types, and to determine the output technical upper limit and security load of each resource at the preset time;

[0028] A planning module, used to determine the next day's operation plan according to the production plan, wherein the next day's operation plan includes the planned output components of each resource at a preset time;

[0029] The determination module is used to determine the output of each type of resource according to the output technical upper limit, security load and the planned output composition at the preset time of each resource. t The theoretical technical upper limit, theoretical technical lower limit and output plan at the moment, and output according to each type of resource t The theoretical technical upper limit, theoretical technical lower limit and output plan at the moment determine the theoretical t The maximum upward adjustment capacity and the maximum downward adjustment capacity at any moment;

[0030] The comprehensive impact degree determination module is used to determine the key influencing factors that affect the adjustable capacity of various resources according to the characteristics of various resources, and obtain the comprehensive impact degree of various resources;

[0031] The correction module is used to adjust the overall impact of all key factors affecting various resources, the average historical fulfillment rate of various resources, and the theoretical t The maximum upward and downward adjustment capabilities at the moment determine the virtual power plant t The ability to adjust up and down at any time, and the use of the virtual power plant t The upward and downward adjustment capabilities at each moment are used to modify the next day's operation plan to obtain the adjustable domain of the virtual power plant based on the plan curve.

[0032] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for quantitatively evaluating the adjustable capacity of a virtual power plant when executing the computer program.

[0033] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method for quantitatively evaluating the adjustable capacity of a virtual power plant are implemented.

[0034] Beneficial Effects

[0035] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention takes into account the user's own operating characteristics and production plan, and constructs a more accurate comprehensive influence degree of various resources based on influencing factors, thereby more accurately considering the influencing factors of adjustable capacity, and obtaining a more accurate virtual power plant adjustable capacity by accurately correcting the theoretical value. The comprehensive influence degree of various resources in the present invention can be calculated in different ways through the selection of key influencing factors or typical scenarios, getting rid of the influence of data learning being overly dependent on historical data or inaccurate data, and is more flexible and simple, and more suitable for actual production and life needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of a method for quantitatively evaluating the adjustable capacity of a virtual power plant according to a first embodiment of the present invention;

[0037] Figure 2 It is a principle diagram of a method for quantitatively evaluating the adjustable capacity of a virtual power plant according to a first embodiment of the present invention;

[0038] Figure 3 is a curve diagram of the regulation capacity of a virtual power plant obtained by using the first embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the use of the device for quantitatively evaluating the adjustable capacity of a virtual power plant according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0041] The first embodiment of the present invention relates to a method for quantitatively evaluating the adjustable capacity of a virtual power plant. Figure 1 and Figure 2 As shown, the following steps are included:

[0042] Step 1: Classify the directly controlled resources aggregated by the virtual power plant into Class I resources according to their types, and determine the output technical upper limit and security load of each resource at a preset time.

[0043] The resources aggregated by the virtual power plant can be divided into self-declared and directly controlled categories. For the self-declared category, users can declare the adjustable capacity on their own. For the directly controlled category, this step further divides it into Class I resources according to the resource type. For example, it can be divided into three categories: distributed power sources, energy storage, and adjustable loads. Each type of resource can be further divided into distributed photovoltaics, electrochemical energy storage, cold and heat storage devices, electric vehicle charging piles, building air conditioners, battery swap stations, industrial and commercial interruptible loads, industrial and commercial adjustable loads, etc. This step also determines the output technical upper limit of 96 points for each resource. P i,n,t,max and security load P i,n,t,min ,in, i is any positive integer from 1 to I, that is, i ={1,2,…, I}, which is the resource type serial number, n For the i The resource sequence number in the class resource, t The time sequence number.

[0044] Step 2: Determine the next day's operation plan according to the production plan (i.e., declare the operation plan), where the next day's operation plan includes the planned output components of each resource at 96 moments. P i,n,t,plan The planned output of each resource at 96 moments in this step is composed of P i,n,t,planThe output plan determined by the user based on the actual operation plan. If there is no relevant information, the next day's operation plan can be determined by load forecasting or calculated according to the baseline calculation formula. Among them. Load forecasting methods include but are not limited to unit consumption method, time series method, trend extrapolation method, regression analysis method, etc.; the baseline calculation formula can be calculated according to the average load value of the previous T days excluding the adjustment day.

[0045] Step 3: Based on the output technical upper limit of each resource P i,n,t,max , Security load P i,n,t,min And the planned output composition of 96 moments P i,n,t,plan Determine the output of each resource t Theoretical technical limit of time P i,t,max , Theoretical technical lower limit P i,n,min and output plan P i,t,plan (i.e. output planning curve), and output according to each type of resource t Theoretical technical limit of time P i,t,max , Theoretical technical lower limit P i,n,min and output plan P i,t,plan Determine the theoretical t Maximum upward adjustment capability at the moment Δ P i,up and maximum downregulation capability Δ P i,down .

[0046] In this step, each type of resource output t Theoretical technical limit of time P i,t,max By setting the output technical limit of each resource under each resource category at a preset time P i,n,t,max Summing it up, we get ,in, N For the i The total number of resources in the resource category; the output of each resource category t Theoretical technical lower limit of time P i,n,min By presetting the security load of each resource under each resource type at a certain time P i,n,t,min Summing it up, we get ; Each type of resource output t Timely output plan P i,t,planThe planned output of each resource at 96 moments under each resource type is composed P i,n,t,plan Summing it up, we get ; The various resources described are theoretically t Maximum upward adjustment capability at the moment Δ P i,up By contributing to each of the resources t Theoretical technical limit of time P i,t,max Contribute to each resource t Timely output plan P i,t,plan By subtracting, we get ; The various resources described are theoretically t Maximum down-regulation capability at the moment Δ P i,down By contributing to each of the resources t Timely output plan P i,t,plan Contribute to each resource type t Theoretical technical lower limit of time P i,n,min By subtracting, we get .

[0047] Step 4: Determine the key factors that affect the adjustable capacity of each type of resource based on the characteristics of each type of resource, and determine the comprehensive impact of each type of resource.

[0048] This implementation provides two different methods to determine the comprehensive impact of various types of resources. When the key influencing factors are clearer than typical scenarios, the first method is used to determine the comprehensive impact of various types of resources; when the key influencing factors are clearer than typical scenarios, the second method is used to determine the comprehensive impact of various types of resources.

[0049] The first method is as follows:

[0050] Determine the impact based on the characteristics of each resource i The adjustable capacity of class resources m Key factors X i,m ,in, m is a positive integer. The key influencing factors include price factors, production factors, environmental factors, probability of charging pile use and other factors that are strongly related to resource regulation potential.

[0051] Next, determine the m Key factors X i,m The value range of [ X i,m,max ,X i,m,min ], and normalized, that is, .

[0052] Then, the fitting method is used to determine the first m Key factors X i,m Specifically, this embodiment calculates the influence factor of each point based on experimental data. f i , and then use the nonlinear least squares fitting method to get the error , the objective function , determine the m Key factors X i,m The influence function is: ,in, b 0. b 1. b 2. b 3 and C are all fitting constants.

[0053] Finally, each key influencing factor X i,m Multiply the influence function of i The comprehensive impact of resources F i ,Right now ,in, M To influence the i The number of key factors affecting the adjustable capacity of each type of resource. Repeat the above steps to obtain the comprehensive impact of each type of resource.

[0054] The second method is as follows:

[0055] First, determine the impact of each resource based on its characteristics. i The adjustable capacity of class resources m Key factors X i,m ,Among them, the key influencing factors include price factors, production factors, environmental factors,,probability of charging pile use, and other factors that are strongly related to the,potential of resource regulation.

[0056] Next, a cluster analysis is performed based on the key influencing factors to determine the typical scenarios corresponding to each key influencing factor. The typical scenarios include high market prices and heavy rain, medium market prices to ensure production load, and low market prices with a stable system and clear weather.

[0057] Then, the typical scenario closest to the calculation date is determined, and the scenario relevance of each key influencing factor in the typical scenario closest to the calculation date is calculated. The calculation results show that, x i,m For the m Key factors X i,m The result after normalization is x i,m,ty For a typical scenario i Class resources m The value of a key factor.

[0058] Finally, based on historical data, the membership function of the comprehensive impact degree and the scene relevance is determined, and the fuzzy reasoning method is used to calculate the comprehensive impact degree of various resources under the typical scene closest to the calculation day, that is, the fuzzy reasoning input vector is set The domain is [0,1], and the output vector The domain is [0,1], and the fuzzy relation set of the input and output data vectors and the fuzzy reasoning rules of the fuzzy reasoning unit are constructed. The fuzzy output variables are calculated based on the Mamdani maximum-minimum method based on fuzzy rules. The center of gravity method is used to solve the fuzzification problem to obtain accurate fuzzy reasoning component values. At the same time, a comprehensive harmony matrix Λ is established according to the relevant data. The obtained comprehensive harmony matrix Λ is used as a weight to perform a comprehensive weighted operation on the accurate fuzzy reasoning component values ​​to obtain the first i The comprehensive impact of resources F i Repeat the above steps to get the comprehensive impact of each type of resources.

[0059] Step 5: Calculate the comprehensive impact of all key influencing factors of various resources F i , the average historical fulfillment rate of various resources H i And various resources are theoretically t Maximum upward adjustment capability at the moment Δ P i,up and maximum downregulation capability Δ P i,down Identify virtual power plants t The ability to adjust the time Δ P up and down-regulation capability Δ P down , and adopt the virtual power plant t The ability to adjust the time Δ P up and down-regulation capability Δ P downThe next day operation plan is modified to obtain the adjustable domain of the virtual power plant based on the plan curve (see Figure 3 ). Among them, virtual power plants t The ability to adjust at any time It is calculated that the virtual power plant t Time to lower the ability Calculated. The average historical fulfillment rate of various resources H i It can be expressed based on the ratio of the actual response amount of each type of resource to the user's declared amount or the issued adjustment plan value.

[0060] It is not difficult to find that the present invention takes into account the user's own operating characteristics and production plans, and constructs a more accurate comprehensive influence degree of various resources based on influencing factors, thereby more accurately considering the influencing factors of adjustable capacity, and obtaining a more accurate virtual power plant adjustable capacity through accurate correction of theoretical values. The comprehensive influence degree of various resources in the present invention can be calculated in different ways through the selection of key influencing factors or typical scenarios, getting rid of the influence of data learning being overly dependent on historical data or inaccurate data, and is more flexible and simple, and more suitable for actual production and life needs.

[0061] A second embodiment of the present invention relates to a device for quantitatively evaluating the adjustable capacity of a virtual power plant, comprising:

[0062] The classification module is used to classify the directly controlled resources aggregated by the virtual power plant into Class I resources according to their types, and to determine the output technical upper limit and security load of each resource at the preset time;

[0063] A planning module, used to determine the next day's operation plan according to the production plan, wherein the next day's operation plan includes the planned output components of each resource at a preset time;

[0064] The determination module is used to determine the output of each type of resource according to the output technical upper limit, security load and the planned output composition at the preset time of each resource. t The theoretical technical upper limit, theoretical technical lower limit and output plan at the moment, and output according to each type of resource t The theoretical technical upper limit, theoretical technical lower limit and output plan at the moment determine the theoretical t The maximum upward adjustment capacity and the maximum downward adjustment capacity at any moment;

[0065] The comprehensive impact degree determination module is used to determine the key influencing factors that affect the adjustable capacity of various resources according to the characteristics of various resources, and obtain the comprehensive impact degree of various resources;

[0066] The correction module is used to adjust the overall impact of all key factors affecting various resources, the average historical fulfillment rate of various resources, and the theoretical t The maximum upward and downward adjustment capabilities at the moment determine the virtual power plant t The ability to adjust up and down at any time, and the use of the virtual power plant t The upward and downward adjustment capabilities at each moment are used to modify the next day's operation plan to obtain the adjustable domain of the virtual power plant based on the plan curve.

[0067] The planned output composition of each resource at a preset time is composed of an output plan determined by a user according to an actual operation plan, or determined through load forecasting, or calculated according to a baseline calculation formula.

[0068] Each type of resource output t The theoretical technical upper limit at the time is obtained by summing the output technical upper limits of each resource at the preset time under each resource type; the output of each resource type is t The theoretical technical lower limit of the time is obtained by summing the security load of each resource at the preset time under each resource type; the output of each resource type is t The output plan at a certain moment is obtained by summing up the planned output components of each resource at a preset moment under each type of resource.

[0069] The various resources are theoretically t The maximum upward adjustment capacity at a moment is calculated by the output of each type of resource t Theoretical technical limit of time and output of each type of resource t The output plan at each moment is obtained by making a difference; the various resources are theoretically t The maximum down-regulation capacity at a given moment is calculated by t The output plan at each time and the output of each resource type t The theoretical technical lower limit of the moment is obtained by subtracting it.

[0070] The comprehensive impact degree determination module includes:

[0071] The key influencing factor determination unit is used to determine the key influencing factors according to the characteristics of various resources. i The adjustable capacity of class resources m Key factors X i,m , and determine the m Key factors X i,m The value range of

[0072] A normalization processing unit is used to m Key factors X i,mThe value range of m Key factors X i,m Perform normalization processing;

[0073] The fitting unit is used to determine the first m Key factors X i,m The influence function of

[0074] The multiplication unit is used to obtain each key influencing factor X i,m Multiply the influence function of i The comprehensive impact of these resources.

[0075] The comprehensive impact degree determination module includes:

[0076] The key influencing factor determination unit determines the key influencing factors according to the characteristics of various resources. i The adjustable capacity of class resources m Key factors X i,m ;

[0077] Clustering unit, used to perform cluster analysis based on key influencing factors and determine the typical scenario corresponding to each key influencing factor;

[0078] A calculation unit, used to determine a typical scenario closest to the calculation date, and calculate the scenario relevance of each key influencing factor in the typical scenario closest to the calculation date;

[0079] The fuzzy processing unit is used to determine the membership function of the comprehensive impact degree and the scene relevance based on historical data, and use the fuzzy reasoning method to calculate the comprehensive impact degree of various resources in the typical scene closest to the calculation day.

[0080] The Virtual Power Plant t The ability to adjust at any time It is calculated that the virtual power plant t Time to lower the ability Calculated, where Δ P up and Δ P down Virtual Power Plant t The ability to adjust up at any time and the ability to adjust down at any time in the virtual power plant, H i For the i The average historical fulfillment rate of resources of this category, F i For the iThe comprehensive impact of the resource class, Δ P i,up and Δ P i,down Respectively i Class resources are theoretically t The maximum upward adjustment capacity and maximum downward adjustment capacity at all times.

[0081] like Figure 4 As shown, the input parameters of the virtual power plant adjustable capacity quantitative evaluation device can be user information, basic resource information, historical resource operation data, load forecast results or operation plans, user-declared adjustable capacity, etc., and the output parameters are the maximum adjustable capacity and maximum adjustable capacity of the virtual power plant at different times. The resource users aggregated by the virtual power plant report the basic information and operation plan of the aggregated resources of the virtual power plant through the user interface, and the virtual power plant operator calculates the virtual power plant adjustable capacity quantitative evaluation device based on the information reported by the user to obtain the overall adjustable capacity of the virtual power plant.

[0082] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for quantitatively evaluating the adjustable capacity of a virtual power plant of the first embodiment when executing the computer program.

[0083] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for quantitatively evaluating the adjustable capacity of a virtual power plant of the first embodiment.

[0084] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0085] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0088] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for quantitatively evaluating the adjustable capacity of a virtual power plant, characterized in that: The following steps are involved: The directly controlled resources aggregated by the virtual power plant are classified into Class I resources according to their types, and the output technical upper limit and security load of each resource at the preset time are determined; Determine the next day's operation plan according to the production plan, wherein the next day's operation plan includes the planned output components of each resource at a preset time; Determine the theoretical technical upper limit, theoretical technical lower limit and output plan of each type of resource at the output time t according to the output technical upper limit, security load and planned output composition of each type of resource at the preset time, and determine the maximum upward adjustment capacity and maximum downward adjustment capacity of each type of resource at the theoretical time t according to the theoretical technical upper limit, theoretical technical lower limit and output plan of each type of resource at the output time t; wherein, the maximum upward adjustment capacity of each type of resource at the theoretical time t is obtained by subtracting the theoretical technical upper limit of each type of resource at the output time t from the output plan of each type of resource at the output time t; the maximum downward adjustment capacity of each type of resource at the theoretical time t is obtained by subtracting the output plan of each type of resource at the output time t from the theoretical technical lower limit of each type of resource at the output time t; Determine the key factors that affect the adjustable capacity of each type of resources based on their characteristics, and determine the comprehensive impact of each type of resources; The upward and downward adjustment capabilities of the virtual power plant at time t are determined according to the comprehensive impact of all key influencing factors of various resources, the average historical fulfillment rate of various resources, and the theoretical maximum upward and downward adjustment capabilities of various resources at time t, and the upward and downward adjustment capabilities of the virtual power plant at time t are used to correct the next day's operation plan to obtain the adjustable domain of the virtual power plant based on the plan curve; wherein the upward adjustment capacity of the virtual power plant at time t is obtained by It is calculated that the virtual power plant's capacity reduction at time t is Calculated, where ΔP up and ΔP down are the upward adjustment capacity and downward adjustment capacity of the virtual power plant at time t, respectively. i is the average historical fulfillment rate of the i-th type of resources, F i is the comprehensive impact of the i-th type of resources, ΔP i,up and ΔP i,down are respectively the maximum upward adjustment capacity and the maximum downward adjustment capacity of the i-th type of resource at time t in theory, where i is any positive integer from 1 to I.

2. The method for quantitatively evaluating the adjustable capacity of a virtual power plant according to claim 1, characterized in that: The planned output composition of each resource at a preset time is composed of an output plan determined by a user according to an actual operation plan, or determined through load forecasting, or calculated according to a baseline calculation formula.

3. The method for quantitatively evaluating the adjustable capacity of a virtual power plant according to claim 1, characterized in that: The theoretical technical upper limit of the output of each type of resource at time t is obtained by summing the output technical upper limits of each resource under each type of resource at a preset time; the theoretical technical lower limit of the output of each type of resource at time t is obtained by summing the security load of each resource under each type of resource at a preset time; The output plan of each type of resource at time t is obtained by summing the planned output components of each resource under each type of resource at a preset time.

4. The method for quantitatively evaluating the adjustable capacity of a virtual power plant according to claim 1, characterized in that: The key factors affecting the adjustable capacity of various resources are determined according to the characteristics of various resources, and the comprehensive impact of various resources is determined, specifically: Determine the mth key influencing factor X that affects the adjustable capacity of the i-th resource based on the characteristics of each resource i,m , and determine the mth key influencing factor X i,m The value range of According to the mth key influencing factor X i,m The value range of the mth key influencing factor X i,m Perform normalization processing; use the fitting method to determine the mth key influencing factor X through multiple experimental data during the operation process i,m The influence function of Each key influencing factor X i,m Multiply the impact function of the i-th resource to get the comprehensive impact of the i-th resource.

5. The method for quantitatively evaluating the adjustable capacity of a virtual power plant according to claim 1, characterized in that: The key factors affecting the adjustable capacity of various resources are determined according to the characteristics of various resources, and the comprehensive impact of various resources is determined, specifically: Determine the mth key influencing factor X that affects the adjustable capacity of the i-th resource based on the characteristics of each resource i,m ; Conduct cluster analysis based on key influencing factors to determine the typical scenarios corresponding to each key influencing factor; Determine the typical scenario closest to the calculation date, and calculate the scenario relevance of each key influencing factor in the typical scenario closest to the calculation date; The membership function of the comprehensive impact degree and scenario relevance is determined based on historical data, and the fuzzy reasoning method is used to calculate the comprehensive impact degree of various resources under the typical scenario closest to the calculation date.

6. A device for quantitatively evaluating the adjustable capacity of a virtual power plant, characterized in that: include: A classification module is used to classify the directly controlled resources aggregated by the virtual power plant into Class I resources according to their types, and determine the output technical upper limit and security load of each resource at a preset time; A planning module, used to determine the next day's operation plan according to the production plan, wherein the next day's operation plan includes the planned output components of each resource at a preset time; A determination module, used to determine the theoretical technical upper limit, theoretical technical lower limit and output plan of each type of resource at the output time t according to the output technical upper limit, security load and planned output composition of each type of resource at the preset time, and determine the maximum upward adjustment capacity and maximum downward adjustment capacity of each type of resource at the theoretical time t according to the theoretical technical upper limit, theoretical technical lower limit and output plan of each type of resource at the output time t; wherein the maximum upward adjustment capacity of each type of resource at the theoretical time t is obtained by subtracting the theoretical technical upper limit of each type of resource at the output time t from the output plan of each type of resource at the output time t; the maximum downward adjustment capacity of each type of resource at the theoretical time t is obtained by subtracting the output plan of each type of resource at the output time t from the theoretical technical lower limit of each type of resource at the output time t; The comprehensive impact degree determination module is used to determine the key influencing factors that affect the adjustable capacity of various resources according to the characteristics of various resources, and obtain the comprehensive impact degree of various resources; The correction module is used to determine the upward adjustment capacity and downward adjustment capacity of the virtual power plant at time t according to the comprehensive influence degree of all key influencing factors of various resources, the average historical fulfillment rate of various resources, and the maximum upward adjustment capacity and maximum downward adjustment capacity of various resources at time t in theory, and use the upward adjustment capacity and downward adjustment capacity of the virtual power plant at time t to correct the next day's operation plan, and obtain the adjustable domain of the virtual power plant based on the plan curve, wherein the upward adjustment capacity of the virtual power plant at time t is calculated by It is calculated that the virtual power plant's capacity reduction at time t is Calculated, where ΔP up and ΔP down are the upward adjustment capacity and downward adjustment capacity of the virtual power plant at time t, respectively. i is the average historical fulfillment rate of the i-th type of resources, F i is the comprehensive impact of the i-th type of resources, ΔP i,up and ΔP i,down are respectively the maximum upward adjustment capacity and the maximum downward adjustment capacity of the i-th type of resource at time t in theory, where i is any positive integer from 1 to I.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for quantitatively evaluating the adjustable capacity of a virtual power plant as described in any one of claims 1-5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for quantitatively evaluating the adjustable capacity of a virtual power plant as described in any one of claims 1 to 5 are implemented.

Images