A virtual power plant regulation capability analysis method, electronic device and storage medium

Abstract

The present invention provides a virtual power plant regulation capacity analysis method, electronic device, and storage medium, which can be applied to the field of virtual power plant scheduling and optimization technology. The method includes: using the usage characteristics of a single type of distributed energy resource to determine the response potential factor of the single type of distributed energy resource, and determining the adjustable capacity limit of the single type of distributed energy resource based on the response potential factor; aggregating the adjustable capacity limits of all types of distributed energy resources to obtain the regulation capacity boundary of the virtual power plant under multiple scheduling scenarios; constructing a multi-type resource aggregation and interaction capacity evaluation optimization model for the virtual power plant, solving the multi-type resource aggregation and interaction capacity evaluation optimization model to obtain the multi-type resource aggregation and interaction regulation capacity of the virtual power plant; and dynamically analyzing the multi-type resource aggregation and interaction regulation capacity of the virtual power plant that changes dynamically over time.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant scheduling and optimization, and in particular to a virtual power plant regulation capability analysis method, electronic equipment, and storage medium. Background Art

[0002] A virtual power plant (VPP) is an intelligent management system that integrates and optimizes distributed energy resources (DERs) through information technology and software systems. Its core goal is to enable them to participate in electricity markets and grid operations as aggregators. Current VPP regulation capacity assessment technologies rely primarily on independent DER modeling, resource responsiveness assessment, economic target optimization, and market mechanism optimization. These technologies use historical data and optimization algorithms to predict DER regulation capacity and employ various bidding strategies to maximize revenue.

[0003] However, the existing VPP regulation capacity assessment technology solutions have the following main technical problems: (1) There is a lack of a unified quantitative evaluation index system, which makes it difficult to comprehensively measure the overall regulation capacity of VPP; (2) The timing characteristics and network constraints of DERs are not fully considered, resulting in a large deviation between the evaluation results and actual operation; (3) The optimization method mainly focuses on economic goals and fails to directly reflect the dynamic regulation capacity of VPP; (4) The interactions after DERs aggregation are complex, and the existing methods are difficult to effectively model, resulting in insufficient adaptability of scheduling and optimization strategies. Summary of the Invention

[0004] In view of the above problems, the present invention provides a virtual power plant regulation capability analysis method, electronic device and storage medium, which are used to solve at least one of the problems in the prior art.

[0005] According to a first aspect of the present invention, a method for analyzing regulation capability of a virtual power plant is provided, comprising:

[0006] Performing multiple unsupervised cluster analyses on the power curves of a single type of distributed energy resource to obtain usage characteristics of the single type of distributed energy resource, determining a response potential factor of the single type of distributed energy resource using the usage characteristics, and determining an adjustable capacity limit of the single type of distributed energy resource based on the response potential factor;

[0007] Aggregate the adjustable capacity limits of all types of distributed energy resources based on time series to obtain the adjustment capacity boundaries of virtual power plants under different scheduling scenarios;

[0008] Based on the regulation capacity boundaries of virtual power plants in different scheduling scenarios, an optimization model for evaluating the multi-type resource aggregation and interaction capacity of virtual power plants is constructed. This model is then solved to obtain the multi-type resource aggregation and interaction regulation capacity of virtual power plants.

[0009] Dynamic analysis is conducted on the interactive regulation capability of multi-type resource aggregation of virtual power plants that changes dynamically over time.

[0010] A second aspect of the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0011] The third aspect of the present invention further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0012] The virtual power plant regulation capability analysis method provided by the present invention improves the standardization and accuracy of VPP regulation capability assessment by constructing a unified quantitative evaluation index system. It optimizes resource classification based on a multi-stage clustering method to improve the assessment accuracy of different types of DERs. It adopts a multi-objective optimization algorithm to improve the reliability of VPP regulation capability assessment while taking into account both economic benefits and operational stability. It also combines the entropy weight method to conduct dynamic regulation capability assessment, enabling the VPP to more accurately adapt to changes in demand in external scenarios and optimize scheduling strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0014] Figure 1 2. This is an application scenario diagram of the virtual power plant regulation capability analysis method according to an embodiment of the present invention;

[0015] Figure 2 is a flow chart of a method for analyzing regulation capability of a virtual power plant according to an embodiment of the present invention;

[0016] Figure 3 is an architectural diagram of a multi-stage virtual power plant regulation capability quantitative evaluation method according to an embodiment of the present invention;

[0017] Figure 4 is a schematic diagram of a theoretical potential assessment model for a single type of distributed energy resource according to an embodiment of the present invention;

[0018] Figure 5is a flow chart of a multi-objective optimization solution method based on AGA-MOPSO and comprehensive membership method according to an embodiment of the present invention;

[0019] Figure 6 4 is a block diagram of an electronic device suitable for implementing a virtual power plant regulation capability analysis method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0021] The terms used herein are intended solely to describe specific embodiments and are not intended to limit the present invention. Terms such as "comprising" and "including" indicate the presence of a specified feature, step, operation, and / or component, but do not preclude the presence or addition of one or more other features, steps, operations, or components. All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having meanings consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner. When expressions such as "at least one of A, B, and C," etc., are used, they should generally be interpreted as commonly understood by those skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but are not limited to, a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.).

[0022] The present invention belongs to the technical field of virtual power plant (VPP) scheduling and optimization, involving the aggregated scheduling of distributed energy resources (DERs), regulation capability assessment, demand response optimization, and power market interaction. It is specifically applied to smart grids, energy internet, and power market transactions, and is used to enhance the VPP's ability to coordinate and control distributed resources, thereby improving grid operation efficiency and market competitiveness.

[0023] Currently, the regulation capacity assessment of VPPs mainly relies on independent modeling of DERs, resource response capacity assessment, economic target optimization, and market mechanism optimization. The regulation capacity of DERs is predicted through historical data and optimization algorithms, and different bidding strategies are adopted to improve profits.

[0024] First, existing research has established mathematical models for different types of DERs (such as photovoltaics, wind power, energy storage, electric vehicles, and adjustable loads) to analyze their physical characteristics and regulatory effects on the power grid. For example, photovoltaic and wind power modeling typically uses probabilistic statistical methods or machine learning models to predict the output characteristics of renewable energy and integrates factors such as weather and season for evaluation. Energy storage system modeling uses charge and discharge models to describe the operating characteristics of energy storage and optimize scheduling strategies. Demand response (DR) modeling constructs user load behavior models to analyze the flexibility and responsiveness of adjustable loads. However, due to the complex interactions between different DERs, individual modeling cannot fully reflect the overall regulatory capacity of DERs after aggregation, resulting in a lack of integrity and scalability in VPP evaluation.

[0025] Second, existing technologies primarily assess the resource responsiveness of DERs using statistical analysis methods based on historical data and optimization algorithm-based evaluation methods. Statistical analysis methods extract DER responsiveness characteristics, such as maximum adjustable power (or adjustable power limit, adjustable power ceiling), response rate, and duration, through regression models or time series analysis. Optimization algorithm methods, on the other hand, utilize genetic algorithms or dynamic programming to calculate optimal scheduling solutions and use these solutions to assess overall regulation capability. However, these methods generally rely on historical data, struggle to adapt to the dynamic changes in DER operating conditions, and fail to fully consider the synergies between DERs. Furthermore, optimization algorithm methods typically focus on economic objectives and fail to directly quantify and evaluate the overall regulation capability of VPPs.

[0026] Based on the above analysis of existing technical solutions, it can be seen that the existing technologies mainly have the following problems: (1) There is a lack of a unified quantitative evaluation index system, which makes it difficult to comprehensively measure the overall regulation capability of VPP; (2) The timing characteristics and network constraints of DERs are not fully considered, resulting in a large deviation between the evaluation results and actual operation; (3) The optimization method mainly focuses on economic goals and fails to directly reflect the dynamic regulation capability of VPP; (4) The interactions after DERs aggregation are complex, and existing methods are difficult to effectively model, resulting in insufficient adaptability of scheduling and optimization strategies.

[0027] To more effectively coordinate DERs, this paper proposes an AI-based quantitative assessment method for the regulation capacity of virtual power plants (VPPs). By dynamically combining machine learning, swarm intelligence, and deep learning technologies, and targeting the dynamic interactive characteristics of DERs and the market environment, this method leverages AI to streamline the entire process from feature extraction to aggregate modeling to multi-objective optimization and dynamic assessment, significantly improving the refinement of assessment results. This method provides decision-making support for VPPs to participate in multi-type market collaboration, enabling precise quantification and dynamic optimization of flexible resource response potential for more effective coordination and utilization of distributed resources. First, resource characteristic analysis and assessment models are combined to more accurately assess flexible resource response potential through qualitative and quantitative analysis. An optimization assessment model is established with the comprehensive objectives of low operating cost, fast response, and wide regulation capacity and ramping range. Taking actual needs into account, a multi-objective particle swarm optimization algorithm is used to solve the target combination, and an entropy method is employed to reflect the dynamic changes in the aggregate response capacity of the VPP in the form of a score. The proposed method produces highly refined quantitative assessment results, providing an important foundation for VPPs to participate in multi-type market collaborative optimization.

[0028] In order to better illustrate the advantages of the method provided by the present invention, the method provided by the present invention is described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0029] Figure 1 This is an application scenario diagram of the virtual power plant regulation capability analysis method according to an embodiment of the present invention.

[0030] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a virtual power plant scheduling and optimization scenario. A network 104 is used as a medium for providing a communication link between a first terminal device 101, a second terminal device 102, a third terminal device 103, and a server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0031] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0032] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0033] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0034] It should be noted that the virtual power plant regulation capability analysis method provided in the embodiment of the present invention can generally be executed by the server 105.

[0035] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0036] The following will be based on Figure 1 The scene described by Figures 2 to 5 The virtual power plant regulation capability analysis method of the disclosed embodiment is described in detail.

[0037] Figure 2 4 is a flow chart of a method for analyzing regulation capability of a virtual power plant according to an embodiment of the present invention.

[0038] like Figure 2 As shown, the virtual power plant regulation capability analysis method of this embodiment includes operations S210 to S240.

[0039] In operation S210, multiple unsupervised cluster analyses are performed on the power curves of a single type of distributed energy resources to obtain usage characteristics of the single type of distributed energy resources, the usage characteristics are used to determine the response potential factor of the single type of distributed energy resources, and the adjustable capacity limit of the single type of distributed energy resources is determined based on the response potential factor.

[0040] The above operation S210 is used to perform multiple unsupervised cluster analyses on a single type of resource involved in the virtual power plant. For example, for resource A, first, a cluster analysis is performed on the power curve of a certain user of resource A (technical personnel in this field can use other resource or energy curves according to actual needs). Then, on the premise of obtaining the typical power curves of all users of resource A, the usage characteristics of resource A are determined, and then the response potential factor related to resource A and the adjustable capacity limit of the virtual power plant involving resource A are obtained; similarly, multi-stage cluster analysis is also adopted for other types of resources to obtain the adjustable capacity limit of each type of resource in the virtual power plant.

[0041] In operation S220, the adjustable capacity limits of all types of distributed energy resources are aggregated based on time series to obtain the adjustment capacity boundaries of the virtual power plant under different scheduling scenarios.

[0042] The above operation S210 is used to obtain the adjustable capacity limits of all types of distributed energy resources in the virtual power plant, and then operation S220 is used to aggregate all types of distributed energy resources in the virtual power plant to obtain the adjustment capacity boundaries of the virtual power plant under different scheduling scenarios, that is, the upper and lower limits of the adjustment capacity of the virtual power plant under multiple scheduling scenarios.

[0043] In operation S230, a multi-type resource aggregation interaction capability evaluation optimization model of the virtual power plant is constructed based on the regulation capability boundary of the virtual power plant in different scheduling scenarios, and the multi-type resource aggregation interaction capability evaluation optimization model is solved to obtain the multi-type resource aggregation interaction regulation capability of the virtual power plant.

[0044] The above operation S230 is used to obtain the correlation relationship between different types of resources of the virtual power plant, and based on this correlation relationship, more accurately analyze the regulation capability of the virtual power plant.

[0045] In operation S240 , a dynamic analysis is performed on the dynamic time-varying multi-type resource aggregation and interactive regulation capability of the virtual power plant.

[0046] The above operation S240 is used to analyze the dynamic changes of the regulation capability of the virtual power plant over time, and based on the analysis results, provide feedback on the multi-resource interactive regulation capability of the virtual power plant and take corresponding optimization measures.

[0047] The virtual power plant regulation capability analysis method provided by the present invention improves the standardization and accuracy of VPP regulation capability assessment by constructing a unified quantitative evaluation index system. It optimizes resource classification based on a multi-stage clustering method to improve the assessment accuracy of different types of DERs. It adopts a multi-objective optimization algorithm to improve the reliability of VPP regulation capability assessment while taking into account both economic benefits and operational stability. It also combines the entropy weight method to conduct dynamic regulation capability assessment, enabling the VPP to more accurately adapt to changes in demand in external scenarios and optimize scheduling strategies.

[0048] The following is a specific implementation method and combined with the attached Figure 3 The above operations S210 to S240 are further described in detail.

[0049] Figure 3 4 is an architectural diagram of a multi-stage virtual power plant regulation capability quantitative evaluation method according to an embodiment of the present invention.

[0050] First, if Figure 3 As shown in the figure, AI-enabled feature mining and potential prediction: Based on the unsupervised learning K-Means algorithm, the theoretical potential of a single type of distributed energy resource is evaluated. First, a cluster analysis is performed on the power curves and energy usage patterns (one of the usage features, the same below) of different types of DERs to extract their key features and calculate their response potential factors to determine the maximum adjustable capacity of a single type of resource (i.e., the adjustable capacity limit or adjustable capacity upper limit, the same below).

[0051] Secondly, if Figure 3 As shown in the figure, the evaluation of the aggregated interactive regulation capability of multiple types of distributed energy resources: according to the characteristics of different types of DERs, an aggregated response capability evaluation model based on time series and optimization methods is constructed to analyze the regulation capability boundaries of VPP under different scheduling scenarios.

[0052] Again, if Figure 3 As shown in the figure, the AI-enhanced multi-objective global optimization based on the MOPSO algorithm (Multi-Objective Particle Swarm Optimization) and the Pareto frontier: an optimization evaluation model is established based on the MOPSO algorithm. The multi-objective evaluation model takes low operating cost, fast response, and large regulation capacity as the optimization goals. The multi-objective algorithm in artificial intelligence is used, combined with the Pareto optimal solution method, to optimize the overall regulation capability of the VPP.

[0053] Finally, if Figure 3As shown in the figure, dynamic regulation capability assessment: the entropy weight method is used to analyze the changing trend of the regulation capability of DERs, and the deep learning algorithm in artificial intelligence is used to reflect the dynamic changes of the regulation capability of VPP over time through the scoring mechanism, thereby improving the adaptability and robustness of the assessment method.

[0054] The following is a specific implementation method and the attached Figure 4 Attachment Figure 3 The cluster response potential analysis process involving a single type of distributed energy resources is further described in detail.

[0055] Figure 4 4 is a schematic diagram of a theoretical potential assessment model for a single type of distributed energy resources according to an embodiment of the present invention.

[0056] like Figure 4 As shown, a theoretical potential assessment of a single type of flexible resource (distributed energy resources, DERs, hereinafter referred to as "DERs") is conducted. First, the K-means clustering method is used to extract the DER power curve and energy usage patterns. Five characteristic parameters are extracted, and the power of DERs of the same type is aggregated to calculate their response potential factors. The average response potential factors of the power of DERs of the same type are the response potential factors of that DER power type. This process mainly involves three steps: selecting typical power curves for DERs, clustering these typical power curves, and obtaining demand response potential factors.

[0057] According to an embodiment of the present invention, the above-mentioned multiple unsupervised clustering analyses are performed on the power curve of a single type of distributed energy resource to obtain the usage characteristics of the single type of distributed energy resource, including: performing an initial unsupervised clustering analysis on the power curve of a single user of a single type of distributed energy resource to obtain the typical power curve and energy usage pattern of a single user of a single type of distributed energy resource; based on the specific characteristics of the selected power curve, performing a secondary unsupervised clustering analysis on the typical power curves of all users of a single type of distributed energy resource to obtain the energy usage pattern of a single type of distributed energy resource, wherein the usage characteristics include energy usage pattern and energy usage pattern, and the specific characteristics include load rate, daily peak-to-valley difference rate, peak period rate, flat period rate and valley period rate.

[0058] In the above embodiment, cluster analysis is first performed on the daily power curve of a single type of distributed energy resource. Depending on the response of the single type of distributed energy resource, this can be a curve with an annual, monthly, or weekly period. A typical power curve and energy usage pattern (i.e., one of the usage characteristics, the same below) for the single type of distributed energy resource are obtained. The energy usage pattern of the single type of distributed energy resource is represented by the clustering upper limit. The higher the clustering system value, the fewer different types of power curves are clustered, the more overlapped the curves are, and the better the clustering effect. Resources with lower clustering upper limits have stronger energy usage patterns.

[0059] According to an embodiment of the present invention, the above-mentioned initial unsupervised clustering analysis of the power curve of a single user of a single type of distributed energy resource to obtain the typical power curve and energy consumption pattern of a single user of a single type of distributed energy resource includes: obtaining the power curve of a single user of a single type of distributed energy resource within a preset period, and based on a preset interval, obtaining the initial clustering points of the power curve of the single user within the preset period; based on the initial clustering points, using the K-means mean clustering method to perform an initial unsupervised clustering analysis on the power curve of the single user within the preset period to obtain an initial clustering result; using the silhouette index method to perform clustering quality evaluation on the initial clustering result, and obtaining the typical power curve and energy consumption pattern of the single user within the preset period based on the clustering quality evaluation result.

[0060] The following further describes in detail the process of acquiring the above-mentioned typical power curves and energy usage patterns and the process of clustering and analyzing the typical power curves of the equipment through specific implementation methods.

[0061] The typical power curve of a single type of resource can be obtained by calculation. The calculation steps are as follows.

[0062] Step 1: Select the power curve of this type of flexible resource for a certain period , which can usually be a year, a month, or a week. The daily power within that period is calculated as one point every 15 minutes, for a total of 96 points.

[0063] Step 2: Use the K-means algorithm to analyze the device The power curves within the period are clustered, and the number of clusters is continuously increased starting from K=2. , until a curve is clustered into one category and the clustering stops. The number of clusters at this time is -1 is the upper limit of resource clustering .

[0064] Step 3: Calculate the number of clusters The following clustering quality evaluation metrics are provided. Assessing the effectiveness of clustering results, also known as cluster evaluation or validation, is crucial to the success of clustering applications. It ensures that the clustering algorithm identifies meaningful clusters in the data. It can also be used to determine which clustering algorithm is best suited for a specific dataset and task, and to tune the hyperparameters of these algorithms.

[0065] Since clustering is an unsupervised learning method, there is no ground truth label to compare the clustering results. The minimum satisfied constraint of Scatter Index (SI) is used as the optimal number of clusters. , the optimal number of clusters The cluster center with the most daily power curves of the equipment is the typical power curve. The calculation method of the silhouette index is shown in formula (1).

[0066] (1),

[0067] in, Indicates the period Vector of daily equipment power curves; Represents the mean value of all daily power curves; Indicates the Cluster center vectors; Represents the Euclidean distance between two vectors.

[0068] The process of cluster analysis of the typical power curve of the equipment is as follows: Cluster analysis is performed on the typical power curve of the equipment to obtain different operating modes of the equipment (peak-facing type, high load rate type, peak-avoiding type).

[0069] The calculation steps are:

[0070] (1) Select a typical daily power curve for each user. Each daily power curve has one point every 15 minutes, for a total of 96 points.

[0071] (2) Since clustering is to group resources with the same peak power period into one category, if the 96-point daily power curve is selected as the feature vector, there will be a lot of redundant information, which is not conducive to the accuracy of clustering. Therefore, the resource clustering method is used to extract features. A total of five features are selected: the load rate of the curve, the daily peak-to-valley difference rate, the peak period rate, the flat period rate, and the valley period rate. Their definitions and physical meanings are shown in Table 1.

[0072] Table 1: Definition and physical meaning of the five characteristics

[0073]

[0074] (3) Using the five eigenvalues of the resource as the eigenvector, clustering is performed using the k-means algorithm, and the number of clusters is It is set to 3 categories to obtain the power consumption mode of each resource.

[0075] According to an embodiment of the present invention, the above-mentioned determination of the adjustable capacity limit of a single type of distributed energy resource based on the response potential factor includes: determining the response potential factor of a single type of distributed energy resource based on the energy consumption law and energy consumption mode of a single type of distributed energy resource; obtaining the first response potential of a single type of distributed energy resource based on the response potential factor and active power of a single type of distributed energy resource; determining the second response potential of a single type of distributed energy resource based on the peak-valley power difference of a single type of distributed energy resource; determining the theoretical response potential of a single type of distributed energy resource based on the first response potential and the second response potential of a single type of distributed energy resource; constructing a response potential evaluation model for a single type of resource by introducing a cost incentive target, a peak-valley power difference minimization target and preset constraints, wherein the preset constraints include power reduction constraints, response duration constraints, response time constraints and energy balance constraints; optimizing the theoretical response potential of a single type of distributed energy resource using the response potential evaluation model for a single type of resource to obtain the adjustable capacity limit of a single type of distributed energy resource.

[0076] The following further describes in detail the process of obtaining the adjustable capacity limit of a single type of distributed energy resource through a specific implementation method.

[0077] Calculating the adjustable capacity limit of a single type of distributed energy resource (i.e., the maximum adjustable capacity, the same below) involves the theoretical maximum adjustable capacity and the maximum adjustable capacity that takes into account the response cost of the distributed energy resource or the peak-to-valley difference minimization target. First, it is necessary to calculate the response potential factor, and based on the response potential factor and the usage characteristics (parameters of the energy consumption law, parameters of the energy consumption mode), calculate the theoretical maximum adjustable capacity, and introduce the response cost of the distributed energy resource or the peak-to-valley difference minimization target on the basis of the theoretical maximum adjustable capacity to obtain the actual maximum adjustable capacity.

[0078] Demand response potential factor calculation: Parameters and formulas for demand response potential factor calculation The demand response potential factor is a value between 0 and 1 that represents the user's demand response potential. 0 means that the resource has no demand response potential at all, and 1 means that the resource can participate in demand response at all times. The calculation of the demand response potential factor mainly involves two parameters: the resource's suitability parameters and the resource's process / equipment parameters. After determining the parameters, use formula (2) to calculate the resource's demand response potential factor:

[0079] (2).

[0080] After obtaining the response potential factor, considering the variation of response type, the response resources are determined. exist The theoretical response potential at the moment is shown in formula (3):

[0081] (3),

[0082] in, Is the response resource exist Active power at the moment, Is the response resource exist Theoretical demand response potential at the moment. Represents the average adjustment when implementing demand response.

[0083] The difference between the peak power and the daily minimum power should not exceed the theoretical potential value of the resource participating in demand response peak regulation, while the difference between the daily maximum power and the valley power should not exceed the theoretical potential value of valley filling, as shown in formula (4):

[0084] (4),

[0085] in, is the highest or lowest daily power; It has the greatest potential for peak shaving or valley filling; is the duration of the peak or trough; is the number of peak or valley periods.

[0086] Theoretical potential of demand response is the lowest value between the two, as shown in formula (5):

[0087] (5).

[0088] The achievable potential assessment model for a single type of flexible resource includes the construction of an objective function and constraints, as shown in the following steps.

[0089] (1) Objective function

[0090] Compared to previous studies that only considered a single objective, namely resource response cost or peak-to-valley difference minimization, this paper considers the objective of minimizing resource response cost or peak-to-valley difference and constructs a dual-objective function that considers both cost and peak-to-valley difference minimization, making the results more accurate. The weight coefficient method is used to simplify the multi-objective optimization problem into a single-objective optimization problem, as shown in formulas (6) to (9):

[0091] (6),

[0092] (7),

[0093] (8),

[0094] (9),

[0095] in, Refers to maximum power; Refers to the minimum power; the cost of participating in the peak demand response at time t is ; The start and end time of peak demand response are and ; is the objective function of minimizing cost; is the objective function of minimizing the peak-to-valley difference; the objective weight coefficients are .

[0096] (10),

[0097] In formula (10), Represents a user exist The amount of power increase or decrease at any moment; Represents the unit power cost.

[0098] (2) Constraints

[0099] 1. Power reduction constraints

[0100] The maximum power reduction for peak shaving demand response of each resource type shall not exceed the theoretical response potential of the resource, as shown in formula (11):

[0101] (11),

[0102] in, represents the theoretical maximum response potential; Indicates whether there is a peak shaving demand response at time t. , when there is no response .

[0103] 2. Response duration constraints

[0104] The response duration constraint must be introduced because some devices cannot respond to changes in the device operating environment, and the recovery time will be extended. Since some devices may experience extended response times when facing changes in the device operating environment, their response performance may be affected to a certain extent. Therefore, the response duration constraint needs to be considered, as shown in formula (12):

[0105] (12),

[0106] in, Indicates the maximum daily power reduction time.

[0107] 3. Response time constraints

[0108] Since the power consumption of resources varies, it may not be possible to fully meet the response requirements. This is due to the energy consumption pattern of resources, the fluctuation of demand, and the supply capacity of the system. It cannot be guaranteed that all response requirements can be met in time at all times. Therefore, the response time is subject to certain constraints, as shown in formula (13):

[0109] (13),

[0110] The time when peak shaving demand response cannot be performed is used express.

[0111] 4. Energy balance constraints

[0112] After the adjustable load resources participate in the demand response, the curve changes to a certain extent. However, the sum of the load adjustment amount cannot be greater than 0, that is, the total load value cannot increase after participating in the demand response, otherwise it cannot meet the energy balance demand, as shown in formula (14):

[0113] (14).

[0114] if When the load decreases, ;if When the load increases, .here Indicates the direction of load transfer, Indicates that the load The amount of load transferred at any moment.

[0115] Before building a multi-type resource aggregation interaction capability evaluation optimization model and calculating the multi-type resource aggregation interaction regulation capability of the virtual power plant, it is necessary to evaluate and analyze the multi-type resource aggregation interaction regulation capability. Figure 3 The interactive regulation capability of multi-type resource aggregation involved in the above operation S220 (i.e., the regulation capability boundary of the virtual power plant in different scheduling scenarios, the same below) is further explained in detail.

[0116] like Figure 3 As shown in the figure, the second stage involved in the virtual power plant regulation capacity analysis method is the evaluation and analysis of the regulation capacity of multi-type resource aggregation and interaction.

[0117] In order to obtain the multi-index external characteristics of the virtual power plant, it is necessary to combine the "aggregation characteristics" of the virtual power plant formed by aggregating different types of flexible resources with the aggregation regulation capability of the VPP resources. By determining key indicators such as baseline power, upper and lower boundaries of regulation capacity, ramp rate limit, and response time range, it is ensured that the capacity of each resource is fully utilized and an accurate output plan is generated while meeting grid stability and market demand.

[0118] After the flexible resource energy consumption characteristics are extracted, they are aggregated according to the characteristic parameters to characterize the aggregate characteristics of the multi-type resource aggregate, as shown in formula (15):

[0119] (15),

[0120] in, 、 、 、 Represents resource types Always The upper and lower bounds of instantaneous power and the upper and lower bounds of cumulative power consumption, The abbreviation for the access resource type, including conventional loads (power consumption is generally unchangeable, such as lighting loads), electric vehicle charging loads, distributed new energy resources, and conventional units in the system.

[0121] For the assessment of the interactive regulation capacity of multi-type resource aggregation, the time scale is first refined, and then the demand response potential of each industry is superimposed at a single time point to improve the accuracy of the multi-resource demand response potential results. The calculation formula is shown in formula (16):

[0122] (16),

[0123] Right now represents the actual demand response potential of multiple resources at time t, and Representative industries actual demand response potential.

[0124] According to an embodiment of the present invention, the above-mentioned multi-type resource aggregation interaction capability evaluation optimization model for a virtual power plant based on the regulation capability boundary of the virtual power plant in different scheduling scenarios includes: performing a baseline prediction of the active power of the virtual power plant based on the objective function of minimizing the total operating cost, the electric power balance constraint condition and the equipment constraint condition to obtain the power curve of the virtual power plant when no demand response occurs; using the power curve of the virtual power plant when no demand response occurs and the regulation capability boundary in different scheduling scenarios to obtain the regulation capacity objective function, the ramp rate objective function and the response time objective function of the multi-type resource aggregation interaction capability evaluation optimization model; taking the operation constraints and state constraints of all types of distributed energy resources as the constraint conditions of the multi-type resource aggregation interaction capability evaluation optimization model, wherein the constraint conditions of the multi-type resource aggregation interaction capability evaluation optimization model include the ramp rate constraint condition, the power output constraint condition, the capacity state constraint condition, the capacity limit constraint condition, the response power constraint condition and the response duration constraint condition.

[0125] The above embodiments involve Figure 3 The evaluation of the interactive capability of multiple types of VPP resources mainly includes VPP distributed energy resources (i.e. Figure 3 Flexible resources shown in the figure, the same below) baseline prediction, VPP multi-type resource aggregation and interaction capability evaluation optimization model (i.e. Figure 3 Multi-type resource aggregation potential assessment model shown).

[0126] Baseline forecast of flexible resource operation of virtual power plants: The baseline power refers to the overall power flow curve of the VPP in the absence of regulatory response requirements. It is calculated with the goal of minimizing the total operating cost. The corresponding objective function is expressed as shown in formulas (17) and (18):

[0127] (17),

[0128] (18),

[0129] in, A series of running moments, the set of which can be determined according to actual running requirements; for The operating cost function of VPP at the moment; Covers the total set of all equipment in VPP except fixed loads; each equipment has a unique equipment number to identify; For the Resources in Output power at all times (the load output power can be adjusted to a negative value); is the total power of fixed non-adjustable loads in the VPP; for The power purchase and sale of VPP at the moment, with the purchased power as positive; define the devices The cost function at time ; At the same time, the VPP's electricity purchase and sales price at that moment is also included in the calculation.

[0130] The current output power of VPP is shown in formula (19):

[0131] (19).

[0132] When optimizing VPP scheduling, in addition to considering the equipment constraints of DERs, it is also necessary to ensure the balance of electric power, as shown in formula (20):

[0133] (20).

[0134] The optimization model for evaluating the interactive capability of multi-type resource aggregation in virtual power plants involves objective functions and constraints, where the objective functions include upper and lower limits of regulation capacity, upper and lower limits of ramp rate, response time range, etc.

[0135] (1) Adjust the upper and lower limits of capacity

[0136] In the optimization model, the upper and lower limits of the regulation capacity can be indirectly determined by calculating the maximum and minimum power of electricity purchase and sales. 、 , that is, the upper and lower limits of the power purchase and sale reflect the extreme values of the output power range that the VPP can achieve at a specific time. The corresponding objective function is expressed as shown in formulas (21) and (22):

[0137] (twenty one),

[0138] (twenty two).

[0139] In addition, due to network constraints, the maximum and minimum power constraints of electricity purchase and sales must also be considered, as shown in formula (23):

[0140] (twenty three).

[0141] (2) Upper and lower limits of climbing rate

[0142] Climbing rate limit 、 It refers to the maximum increase or decrease in the output power of the VPP from one moment to the next when it is operating at its power baseline. The corresponding objective function can be described as shown in formulas (24) and (25):

[0143] (twenty four),

[0144] (25).

[0145] (3) Response time range

[0146] VPP response time It is defined as the time from the moment an adjustment command is received to the moment all regulation resources jointly adjust the total output power to the specified change value while following the power baseline. The shortest time required. The corresponding objective function is shown in formula (26):

[0147] (26),

[0148] Among them, the collection By equally spaced composition; are the various time points considered in the summation operation; is a binary variable indicating whether the Adjustment instructions Furthermore, when the instruction requirements are not met, the following two constraints must be followed, as shown in formulas (27) and (28):

[0149] (27),

[0150] (28).

[0151] Formulas (27) and (28) indicate that after the requirements of the regulation instruction are met at a certain moment, the subsequent moments will no longer be counted as response time.

[0152] (4) Other constraints

[0153] In order to further obtain the optimization model of aggregated response capability, according to the independent response capability models of various DERs, the DERs operation constraints and state constraints are considered as part of the constraints of the VPP aggregated regulation response capability optimization model.

[0154] Distributed power sources, load resources, and energy storage resources must comply with the ramp rate constraint, as shown in formula (29):

[0155] (29).

[0156] Distributed power generation and energy storage resources must comply with power output constraints, as shown in formula (30):

[0157] (30).

[0158] Energy storage resources must comply with capacity state and capacity limit constraints, as shown in equations (31) and (32):

[0159] (31),

[0160] (32).

[0161] The load resources must also meet the response power constraint and response duration constraint, as shown in formula (33):

[0162] (33).

[0163] According to an embodiment of the present invention, the above-mentioned solution of the multi-type resource aggregation interaction capability evaluation optimization model to obtain the multi-type resource aggregation interaction regulation capability of the virtual power plant includes: performing a weighted operation on the objective function of the multi-type resource aggregation interaction capability evaluation optimization model to obtain the joint objective function of the multi-type resource aggregation interaction capability evaluation optimization model; initializing the weight parameters used by the adaptive grid-based multi-objective ion swarm optimization algorithm and the required number of iterations, and generating the initial population of the adaptive grid-based multi-objective ion swarm optimization algorithm according to the joint objective function; obtaining the fitness of particles in the initial population or the current iterative population based on the joint objective function. value, and determine the dominance relationship between particles in the initial population to obtain the Pareto solution set in the current iteration round; use the multi-objective ion swarm optimization algorithm based on adaptive grid to dynamically adjust the distance between particles in the initial population or the current iteration population to obtain the updated Pareto solution set; iteratively perform fitness value acquisition operation, Pareto solution set acquisition operation, and population ion dynamic adjustment operation until the number of iterations is met, and use the linear membership function to obtain the comprehensive membership of all solutions in the updated Pareto solution set; the solution with the largest comprehensive membership in the updated Pareto solution set is used as the model target solution that characterizes the multi-type resource aggregation and interactive regulation capability of the virtual power plant.

[0164] The above embodiments relate to the Figure 3 The following is a detailed implementation of the virtual power plant interactive capability evaluation process considering multiple time scales. Figure 5 The above process is further explained in detail.

[0165] Figure 5 4 is a flowchart of a multi-objective optimization solution method based on AGA-MOPSO and comprehensive membership method according to an embodiment of the present invention.

[0166] The VPP interactive regulation capability evaluation optimization model established in this invention involves multiple constraints, including power balance relationships, power purchase and sales constraints, resource call constraints, resource physical operation characteristics constraints, etc., aiming to optimize multiple objective functions such as minimizing total cost, minimizing response time, maximizing regulation capacity and ramp rate. Therefore, the evaluation of aggregate regulation capability is a dynamic rolling, multi-variable, and multi-constrained optimization problem. Considering the relevant series of constraints, solving the objective function formula (i.e., formula (17)) in the optimization model and extracting the optimal decision variables at each moment can obtain the operating baseline. Based on the baseline power, formulas (20)-(22), (24), (25), and (26) are then obtained to obtain the corresponding other aggregate regulation response capabilities, which can be reported to the operation control center after combination.

[0167] The method of solving each objective function separately does not accurately describe the feasible adjustment range of VPP. For example, the solution of the response time objective function in some cases may be greater than In this case, it is considered that the current regulation demand VPP cannot participate in the response. To avoid such a situation, the upper and lower limits of regulation capacity, upper and lower limits of ramping, and response time range can be weighted and jointly solved to meet the needs of actual demand, as shown in formula (34):

[0168] (34),

[0169] in, 、 、 、 、 and is the weight of each objective function. Equation (34) shows that when the VPP operation control center adjusts the VPP performance requirements to suit various application scenarios, it can obtain solutions for different VPP adjustment capabilities by changing the weight coefficients of each objective. There are many strategies for finding the optimal solution to this type of multi-objective optimization problem.

[0170] This method employs a Pareto frontier and comprehensive evaluation strategy. First, it uses the Multi-objective Particle Swarm Optimization based on Adaptive Grid Algorithms (AGA-MOPSO) algorithm to generate a Pareto solution set. By adaptively adjusting the particle search strategy, it effectively explores the solution space and generates a set of non-inferior solutions that are widely distributed and evenly distributed. Subsequently, a comprehensive membership method is used to select the optimal solution from the Pareto solution set. This method calculates the membership score of each solution to evaluate its relative merits, and then selects the optimal solution based on the decision maker's specific objective preferences.

[0171] The present invention selects a linear membership function to calculate the comprehensive membership of each solution in the Pareto solution set. The solution with the largest comprehensive membership is the comprehensive optimal solution. The corresponding VPP benchmark and boundary capacity at each moment form the optimal evaluation solution. The objective function, The membership degree of each solution is shown in formula (35):

[0172] (35),

[0173] in, For the The solution for The membership degree of the objective function is greater, and the larger the value is, the better the solution is. The better the objective function is; 、 They are the first solutions corresponding to each solution in the Pareto solution set. The set of objective function values The maximum and minimum values in ; For the The first solution The objective function value.

[0174] No. The comprehensive membership of a solution As shown in formula (36):

[0175] (36),

[0176] in, is the total number of objective functions; For the The weight of an objective function in all objective functions; is the total number of solutions in the Pareto solution set.

[0177] The process of multi-objective optimization method is as follows Figure 5 As shown in the figure is the number of iterations, is the fitness function of the particle, 、 、 For the The particle swarm, Pareto solution set and objective function solution set at the iteration. Based on this flowchart, the model solution steps can be obtained.

[0178] Step 1: Basic parameter input. Enter the operating status of the VPP resources, and calculate and upload their regulation capacity and expected cost based on the regulation characteristic model.

[0179] Step 2: Population initialization. Set the population size, number of iterations and inertia weight parameters of the optimization algorithm. Generate the initial population according to the number of objective functions. and ensure that the particles meet the feasibility constraints of the system.

[0180] Step 3: Fitness evaluation. Calculate the fitness value of the particle according to the model target , determine its dominance relationship to obtain the Pareto frontier .

[0181] Step 4: Adaptive grid loop iteration. Introduce the adaptive grid mechanism to optimize the particle swarm algorithm, estimate the particle density, dynamically adjust the crowding distance, and update the particle speed and position; then determine whether the maximum number of iterations has been reached. If so, output the Pareto solution set. .

[0182] Step 5: Optimal solution selection. Combined with the objective function weight input , the comprehensive membership function method is used to determine the comprehensive membership of each objective function , from the solution set Select the optimal solution to obtain the objective function value , and obtain the optimal evaluation result considering the trade-offs among multiple objectives.

[0183] According to an embodiment of the present invention, the above-mentioned dynamic analysis of the multi-type resource aggregation and interactive regulation capability of the virtual power plant that changes dynamically over time includes: data standardization of the regulation capacity, ramp rate and response time of the virtual power plant that changes dynamically over time, and obtaining the information entropy and redundancy of the regulation capacity, ramp rate and response time after data standardization; using the information entropy and redundancy of the information entropy to obtain the weight coefficients of the regulation capacity, ramp rate and response time after data standardization; averaging the regulation capacity, ramp rate and response time after data standardization, and performing calculations on the regulation capacity, ramp rate and response time after average processing and the corresponding weight coefficients to obtain dynamic analysis results of the regulation capacity, ramp rate and response time.

[0184] The above embodiments relate to the Figure 3 Evaluation of the aggregated interactive regulation capability of virtual power plants based on the entropy weight method: The weights of the above-mentioned multi-objective solution method are formulated by decision makers based on their experience or preferences, which to a certain extent adapts well to different actual business needs. However, in order to more objectively and directly reflect the regulation capability of virtual power plants, weights can be assigned through the entropy weight method to quantify each capability indicator. This weight distribution takes into account the stability of the regulation capability indicators of virtual power plants in various time periods, that is, the greater the fluctuation of the regulation capability indicators, the lower the corresponding indicator weights should be. In order to objectively allocate weights, a method based on information entropy is adopted to determine the weights based on the volatility of the indicators. Lower information entropy means that the indicators change greatly, so the amount of information is large, and its weight in the comprehensive evaluation should be higher. Conversely, higher information entropy means that the indicators change little, the amount of information is small, and the weight should be relatively low. The weighting steps of the entropy weight method are as follows:

[0185] (1) Data standardization, calculate the proportion of each indicator in all evaluation objects. Assume that indicators ,in . The standardized value of the indicator data is , the standardized calculation is shown in formula (37):

[0186] (37).

[0187] (2) Calculate the information entropy of each indicator using the standardized indicator value, and then obtain the redundancy of the information entropy. According to the definition of information entropy in information theory, the information entropy of a set of data is shown in formulas (38) and (39):

[0188] (38),

[0189] (39),

[0190] like , then define .

[0191] (3) Determine the weight of each indicator. The weight is determined according to the redundancy of each indicator. The indicator with low redundancy (low information entropy) is given a higher weight. According to the calculation formula of information entropy, the information entropy of each indicator is calculated. Calculate the weight of each indicator through information entropy , as shown in formula (40):

[0192] (40).

[0193] (4) The evaluation values of each indicator are shown in formula (41):

[0194] (41).

[0195] (5) The value score of the virtual power plant's aggregate regulation capability is shown in formula (42):

[0196] (42).

[0197] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0198] (1) This paper establishes a universal quantitative evaluation index system to quantitatively analyze the typical resource scalability under different power generation mechanisms and user behaviors. The system comprehensively considers the scalability, timing characteristics, and market environment of distributed energy resources (DERs), and uses the entropy method to weight each index to ensure the objectivity and comparability of the evaluation results. This overcomes the problem of the existing technology of single evaluation index and difficulty in comprehensively measuring the overall scalability of virtual power plants (VPPs), and improves the accuracy and applicability of the evaluation.

[0199] (2) This paper proposes a two-stage flexible resource clustering-based source-load resource response capability assessment method, which combines DER resource characteristic analysis with an assessment model to achieve fine classification of resources and evaluate the resource response potential based on the classification results. The first stage analyzes the cluster response potential of a single type of flexible resource, and the second stage evaluates the interactive regulation capability of multi-type resource aggregation. When evaluating the demand response potential of flexible resources, current research focuses on modeling and analysis based on load characteristics, while ignoring other characteristics such as response cycle and response duration. Qualitative and quantitative research are the two types of existing research. The conclusions of qualitative research are often to compare the demand response potential of various flexible resources, and it is impossible to obtain an exact value; quantitative research makes up for the above shortcomings, but the result is always a number, that is, the demand response potential for the whole day adopts a fixed value, which is irrational. At the same time, a single objective function will cause the result to differ greatly from reality. In addition, when examining the demand response potential of regional multi-load aggregation, the result will have a higher error due to this failure. Therefore, the present invention comprehensively considers resource characteristics and constructs a two-stage demand response potential assessment model based on "single-unit achievable potential-multi-type resource potential", making resource classification more accurate. This method overcomes the problem that existing technologies do not fully consider the dynamic characteristics of DERs and network constraints, enhances the adaptability of the assessment model, and improves the utilization efficiency of flexible resources.

[0200] (3) The present invention establishes an optimization evaluation model with the goals of low operating cost, fast response, and large regulation capacity. This model comprehensively considers key indicators of the VPP, such as operating cost, response speed, regulation capacity, and ramp range, and adopts a multi-objective particle swarm optimization algorithm to solve it. By introducing the concept of Pareto optimal solution, different objectives are weighed and optimized to maximize the regulation capacity of the VPP while ensuring optimal economic benefits. This overcomes the problem of existing optimization methods that only focus on economic goals and fail to effectively evaluate the overall regulation capacity of the VPP, and achieves a balanced optimization of economic benefits and regulation capacity.

[0201] (4) This invention uses an entropy method to dynamically reflect changes in the aggregate responsiveness of VPPs. By calculating the scores of VPP aggregate responsiveness, the method dynamically reflects the changing trends of VPP regulation capabilities under different market environments and load conditions. The entropy method can adaptively adjust the weights of each indicator, making the scores more representative. Simultaneously, combined with a time series analysis method, it tracks and optimizes VPP regulation capabilities over the long term, thus overcoming the difficulty of existing methods in directly quantifying the overall regulation capabilities of VPPs and improving the flexibility and competitiveness of VPPs in different market environments.

[0202] Figure 6 4 is a block diagram of an electronic device suitable for implementing a virtual power plant regulation capability analysis method according to an embodiment of the present invention.

[0203] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 602 or programs loaded from a storage unit 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or related chipsets and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0204] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the programs in the ROM 602 and / or RAM 603 to perform various operations according to the method flow of the embodiment of the present invention. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the programs stored in the one or more memories to perform various operations according to the method flow of the embodiment of the present invention.

[0205] According to an embodiment of the present invention, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0206] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0207] According to an embodiment of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above, and / or one or more memories other than ROM 602 and RAM 603.

[0208] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0209] It will be understood by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention may be combined and / or coupled in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or couplings fall within the scope of the present invention.

[0210] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A virtual power plant regulation capability analysis method, characterized in that: The method comprises: Performing an initial unsupervised cluster analysis on the power curve of a single user of a single type of distributed energy resource to obtain a typical power curve and energy usage pattern of the single user of the single type of distributed energy resource; Based on the specific characteristics of the selected power curves, a secondary unsupervised cluster analysis is performed on the typical power curves of all users of the single type of distributed energy resource to obtain an energy usage pattern of the single type of distributed energy resource, a response potential factor of the single type of distributed energy resource is determined using the usage characteristics including the energy usage law and the energy usage pattern, and an adjustable capacity limit of the single type of distributed energy resource is determined based on the response potential factor; By refining the time scale operation and superimposing the response potential factors of all types of distributed energy resources at a single time point, the adjustable capacity limits of all types of distributed energy resources are aggregated based on time series, and the adjustment capacity boundaries of virtual power plants under different scheduling scenarios are obtained; Constructing an optimization model for evaluating the multi-type resource aggregation and interaction capability of the virtual power plant based on the regulation capability boundaries of the virtual power plant in different scheduling scenarios, and solving the optimization model for evaluating the multi-type resource aggregation and interaction capability to obtain the multi-type resource aggregation and interaction regulation capability of the virtual power plant; A dynamic analysis is conducted on the multi-type resource aggregation and interactive regulation capabilities of the virtual power plant that change dynamically over time.

2. The method according to claim 1, characterized in that The specific characteristics include load rate, daily peak-to-valley rate, peak rate, flat rate and valley rate.

3. The method according to claim 1, characterized in that Performing an initial unsupervised cluster analysis on the power curve of a single user of the single type of distributed energy resource, obtaining a typical power curve and energy usage pattern of the single user of the single type of distributed energy resource, including: Obtaining a power curve of a single user of the single type of distributed energy resource within a preset period, and obtaining an initial clustering point of the power curve of the single user within the preset period based on a preset interval; Based on the initial clustering points, an initial unsupervised cluster analysis is performed on the power curve of a single user within the preset period using a K-means mean clustering method to obtain an initial clustering result; The initial clustering result is evaluated for clustering quality using a silhouette index method, and a typical power curve and energy usage pattern of a single user within the preset period are obtained based on the clustering quality evaluation result.

4. The method according to claim 1, wherein Determining the adjustable capacity limit of the single type of distributed energy resource based on the response potential factor includes: Determining a response potential factor of the single type of distributed energy resource based on the energy usage law and energy usage pattern of the single type of distributed energy resource; Obtaining a first response potential of the single type of distributed energy resource based on the response potential factor and active power of the single type of distributed energy resource; determining a second response potential of the distributed energy resource of the single type based on a difference between peak and valley power of the distributed energy resource of the single type; A theoretical response potential of the single type of distributed energy resource is determined based on the first response potential and the second response potential of the single type of distributed energy resource.

5. The method according to claim 4, characterized in that Also includes: A response potential assessment model for a single type of resource is constructed by introducing a cost incentive target, a peak-valley power difference minimization target, and preset constraints, wherein the preset constraints include a power reduction constraint, a response duration constraint, a response time constraint, and an energy balance constraint. The theoretical response potential of the single type of distributed energy resource is optimized using the response potential evaluation model of the single type of resource to obtain the adjustable capacity limit of the single type of distributed energy resource.

6. The method according to claim 1, characterized in that Constructing a multi-type resource aggregation and interaction capability evaluation optimization model of the virtual power plant based on the regulation capability boundary of the virtual power plant in different scheduling scenarios includes: Performing a baseline prediction of the active power of the virtual power plant based on an objective function of minimizing total operating cost, electric power balance constraints, and equipment constraints to obtain a power curve of the virtual power plant when no demand response occurs; The power curve of the virtual power plant in the absence of demand response and the regulation capacity boundary under different scheduling scenarios are used to obtain the regulation capacity objective function, the ramp rate objective function and the response time objective function of the multi-type resource aggregation interaction capability evaluation optimization model; The operating constraints and state constraints of all types of distributed energy resources are used as constraints of the multi-type resource aggregation and interaction capability evaluation optimization model, wherein the constraints of the multi-type resource aggregation and interaction capability evaluation optimization model include climbing rate constraints, power output constraints, capacity state constraints, capacity limit constraints, response power constraints and response duration constraints.

7. The method according to claim 6, characterized in that The multi-type resource aggregation and interaction capability evaluation optimization model is solved to obtain the multi-type resource aggregation and interaction regulation capability of the virtual power plant, which includes: Performing a weighted operation on the objective function of the multi-type resource aggregation and interaction capability evaluation optimization model to obtain a joint objective function of the multi-type resource aggregation and interaction capability evaluation optimization model; Initializing the weight parameters and the required number of iterations used by the adaptive grid-based multi-objective ion swarm optimization algorithm, and generating an initial population of the adaptive grid-based multi-objective ion swarm optimization algorithm according to the joint objective function; Obtaining fitness values of particles in the initial population or the current iterative population based on the joint objective function, and determining the dominance relationship between particles in the initial population to obtain a Pareto solution set in the current iterative round; Dynamically adjusting the distances between particles of the initial population or the current iterative population using a multi-objective ion swarm optimization algorithm based on an adaptive grid to obtain an updated Pareto solution set; Iteratively performing a fitness value acquisition operation, a Pareto solution set acquisition operation, and a population ion dynamic adjustment operation until the number of iterations is satisfied, and then using a linear membership function to obtain the comprehensive membership of all solutions in the updated Pareto solution set; The solution with the largest comprehensive membership in the updated Pareto solution set is used as the model target solution that characterizes the multi-type resource aggregation and interactive regulation capability of the virtual power plant.

8. The method according to claim 5, characterized in that Dynamic analysis of the multi-type resource aggregation and interactive regulation capability of the virtual power plant that changes dynamically over time includes: Normalizing the data of the regulation capacity, ramp rate, and response time of the virtual power plant that dynamically changes over time, and obtaining information entropy of the regulation capacity, ramp rate, and response time after data standardization and the redundancy of the information entropy; Using the information entropy and the redundancy of the information entropy, respectively, weight coefficients of the regulation capacity, ramp rate, and response time after the data is standardized are obtained; The regulation capacity, ramp rate and response time after data standardization are respectively averaged, and the regulation capacity, ramp rate and response time after averaged processing are respectively calculated with the corresponding weight coefficients to obtain dynamic analysis results of the regulation capacity, ramp rate and response time.

9. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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