Virtual power plant resource flexibility aggregation method and device based on linear programming projection

Abstract

The application relates to a virtual power plant resource flexibility aggregation method and device based on linear programming projection, which comprises the following steps: constructing a linear state equation corresponding to each to-be-aggregated resource according to resource characteristic data of each to-be-aggregated resource, and performing linear transformation; aggregating the energy use flexibility feasible region of each to-be-aggregated resource, solving a coordinate projection problem, and obtaining a preliminary aggregated energy use feasible region; constructing a network security constraint and embedding the solving process of the preliminary aggregated energy use feasible region to obtain an aggregated energy use feasible region considering the network security constraint; and constructing a multi-parameter programming model, integrating an aggregation cost function into the solving process of the aggregated energy use feasible region considering the network security constraint, and obtaining a virtual power plant resource flexibility aggregation result. Thus, the problems that the energy use feasible region of a large amount of heterogeneous resources cannot be aggregated and adjusted at the same time in the prior art, network security constraints cannot be embedded, and high-dimensional aggregation error is large are solved, and higher calculation efficiency and compatibility are achieved.

Description

Technical Field

[0001] This application relates to the field of virtual power plant resource aggregation technology, and in particular to a method and apparatus for virtual power plant resource flexibility aggregation based on linear programming projection. Background Technology

[0002] With the increasing penetration of renewable energy, the randomness and volatility of power systems have increased significantly, posing a major challenge to system operation and highlighting the growing shortage of flexibility. Against this backdrop, the flexibility of massive distributed energy resources (DERs) is considered to have enormous potential value. In traditional system operation, electricity demand is considered rigid and must be balanced by power sources in real time. However, in reality, much electricity demand exhibits flexibility. These flexible loads can work with generators to provide energy balancing and regulation services to the system. However, these flexible resources are characterized by their small scale, distributed nature, and heterogeneity, making their management and utilization very difficult. Virtual power plants (VPPs) offer an effective method for managing DERs in a clustered manner. Leveraging advanced big data and IoT technologies, VPPs make the dispersed massive DERs observable and controllable, aggregating them into a unified whole to participate in the interaction with the power grid. The core of this process lies in the standardized modeling of heterogeneous DERs and the efficient aggregation of flexibility models for massive DERs.

[0003] In related technologies, existing techniques for aggregating the flexibility of virtual power plant resources generally begin by standardizing the model of DERs (Resources Expected to Work) using a flexible feasible region model described by a set of linear constraints. Then, by eliminating internal variables, the polyhedra of heterogeneous resources are unified into the port variable space. Based on this, the mathematical essence of aggregating the flexibility model is reduced to finding the Minkowski sum of the polyhedra. Currently, a series of estimation algorithms have been developed, such as the representative scaling and translation method.

[0004] However, although the above-mentioned methods for modeling polyhedra are widely used, they still have significant shortcomings. First, existing aggregation models can only aggregate within the feasible region and cannot provide the adjustment cost of aggregation. Second, when calculating the Minkowski sum, the shape of the aggregated polyhedron is pre-assumed for internal estimation, which easily accumulates large errors in high dimensions. Furthermore, Minkowski aggregation cannot embed cybersecurity constraints, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for virtual power plant resource flexibility aggregation based on linear programming projection, which solves the problems in the prior art that it is impossible to simultaneously aggregate the energy use feasible domain and adjustment cost of massive heterogeneous resources, cannot embed network security constraints, and has large high-dimensional aggregation errors, thus achieving higher computational efficiency and compatibility.

[0006] The first aspect of this application provides a method for virtual power plant resource flexibility aggregation based on linear programming projection, comprising the following steps: The resource characteristic data of multiple resources to be aggregated in the virtual power plant are obtained, and a linear state equation corresponding to each resource to be aggregated is constructed based on the resource characteristic data of each resource to be aggregated. The linear state equation corresponding to each resource to be aggregated is linearly transformed to unify the flexible and feasible energy use domain of each resource to be aggregated to a preset standard space. Aggregate the energy-use flexible feasible domains of each resource to be aggregated in the preset standard space, transform the current aggregation problem into a coordinate projection problem from a preset high-dimensional space to a subspace, and solve the coordinate projection problem to obtain the preliminary aggregated energy-use feasible domain of the virtual power plant. The network parameters of the power distribution network to which the virtual power plant belongs are obtained, network security constraints are constructed based on the preset linearized power flow equations, and the network security constraints are embedded into the solution process of the preliminary aggregated energy consumption feasible region to obtain the aggregated energy consumption feasible region considering the network security constraints. The operation and adjustment cost of each resource to be aggregated is obtained. A multi-parameter planning model is constructed based on the operation and adjustment cost of each resource to be aggregated. The multi-parameter planning model is simplified to obtain a piecewise linear aggregation cost function. The aggregation cost function is then incorporated into the solution process of the aggregated energy use feasible region considering network security constraints to obtain the virtual power plant resource flexibility aggregation result.

[0007] According to one embodiment of this application, constructing the linear state equation corresponding to each resource to be aggregated based on the resource characteristic data of each resource to be aggregated includes: The resource type of each resource to be aggregated is determined based on the resource characteristic data of each resource to be aggregated, wherein the resource type includes power generation resources and energy storage resources. The linear state equation corresponding to the aforementioned type of power generation resource is: ; ; in, For port output power, The climbing rate, This is the lower bound of the power. This is the upper limit of power. For time indexing, To lower the boundary, The power adjustment boundary; The linear state equation corresponding to the aforementioned energy storage resource is: ; ; ; ; ; ; ; in, For port output power, For equivalent residual energy, The rate of ascent; For charging power efficiency, For charging efficiency, For discharge efficiency, For charging power, This represents the upper limit of charging power. For discharge power, This is the upper limit of the discharge power. Electrical adjustment boundary, To adjust the charging boundary, To adjust the discharge threshold, To adjust the charging boundary, The lower limit of energy. As the upper limit of energy, For time indexing, For time intervals.

[0008] According to one embodiment of this application, the initial aggregated energy consumption feasible domain of the virtual power plant is: ; in, The coefficient matrix, This is the dimension vector retained after the polyhedron is projected. This is the scaling factor. Let the right-hand vector be undetermined. This is the translation coefficient.

[0009] According to one embodiment of this application, the network security constraint is: ; in, for The corresponding left-hand matrix, for The corresponding left-hand matrix, This is the branch power loss vector. Inject complex function vectors into nodes. This is the vector on the right side.

[0010] According to one embodiment of this application, the aggregated energy utilization feasible domain considering network security constraints is: ; in, The left-hand matrix of the aggregate polyhedron. For the root node complex power of the time-extended extension, is the right-hand vector of the aggregated polyhedron.

[0011] According to one embodiment of this application, the multi-parameter programming model is as follows: ; The aggregation cost function is: ; in, For aggregation cost function, Let k be the power vector of resource k. For resource labeling, For the set of all resource labels, The slope of the linear piecewise cost function. The linear piecewise intercept of the cost function. The left-hand matrix of the polyhedron Let the vector be the right-hand vector of the polyhedron. For polymerization power, For row labels, For the corresponding The set of negative row labels, denoted as . j , The j-th element of the right-hand vector of the aggregated polyhedron. and These are the j-th rows of the left-end matrix blocks of the aggregated polyhedron.

[0012] The virtual power plant resource flexibility aggregation method based on linear programming projection provided in this application involves constructing a linear state equation for each resource to be aggregated based on its resource characteristic data, and then performing a linear transformation. The energy-use flexibility feasible domains of each resource are aggregated, and the coordinate projection problem is solved to obtain a preliminary aggregated energy-use feasible domain. Network security constraints are constructed and embedded into the solution process of the preliminary aggregated energy-use feasible domain to obtain an aggregated energy-use feasible domain considering network security constraints. A multi-parameter programming model is constructed, and the aggregation cost function is integrated into the solution process of the aggregated energy-use feasible domain considering network security constraints to obtain the virtual power plant resource flexibility aggregation result. This solves the problems in existing technologies, such as the inability to simultaneously aggregate the energy-use feasible domains and adjustment costs of massive heterogeneous resources, the inability to embed network security constraints, and large high-dimensional aggregation errors, achieving higher computational efficiency and compatibility.

[0013] A second aspect of this application provides a virtual power plant resource flexibility aggregation device based on linear programming projection, comprising: The modeling and standardization module acquires resource characteristic data of multiple resources to be aggregated in the virtual power plant, constructs a linear state equation corresponding to each resource to be aggregated based on the resource characteristic data of each resource to be aggregated, and performs a linear transformation on the linear state equation corresponding to each resource to be aggregated to unify the flexible and feasible energy use domain of each resource to be aggregated to a preset standard space. The feasible region aggregation optimization module is used to aggregate the energy-use flexible feasible region of each resource to be aggregated in the preset standard space, transform the current aggregation problem into a coordinate projection problem from a preset high-dimensional space to a subspace, and solve the coordinate projection problem to obtain the preliminary aggregated energy-use feasible region of the virtual power plant. The network security constraint aggregation module is used to obtain the network parameters of the power distribution network to which the virtual power plant belongs, construct network security constraints based on the preset linearized power flow equations, and embed the network security constraints into the solution process of the preliminary aggregated energy consumption feasible region to obtain the aggregated energy consumption feasible region considering network security constraints. The resource flexibility aggregation module obtains the operation and adjustment cost of each resource to be aggregated, constructs a multi-parameter planning model based on the operation and adjustment cost of each resource to be aggregated, and simplifies the multi-parameter planning model to obtain a piecewise linear aggregation cost function. The aggregation cost function is then incorporated into the solution process of the aggregated energy use feasible region considering network security constraints to obtain the virtual power plant resource flexibility aggregation result.

[0014] According to one embodiment of this application, the modeling and standardization module is used for: The resource type of each resource to be aggregated is determined based on the resource characteristic data of each resource to be aggregated, wherein the resource type includes power generation resources and energy storage resources. The linear state equation corresponding to the aforementioned type of power generation resource is: ; ; in, For port output power, The climbing rate, This is the lower bound of the power. This is the upper limit of power. For time indexing, To lower the boundary, The power adjustment boundary; The linear state equation corresponding to the aforementioned energy storage resource is: ; ; ; ; ; ; ; in, For port output power, For equivalent residual energy, The rate of ascent; For charging power efficiency, For charging efficiency, For discharge efficiency, For charging power, This represents the upper limit of charging power. For discharge power, This is the upper limit of the discharge power. Electrical adjustment boundary, To adjust the charging boundary, To adjust the discharge threshold, To adjust the charging boundary, The lower limit of energy. As the upper limit of energy, For time indexing, For time intervals.

[0015] According to one embodiment of this application, the initial aggregated energy consumption feasible domain of the virtual power plant is: ; in, The coefficient matrix, This is the dimension vector retained after the polyhedron is projected. This is the scaling factor. Let the right-hand vector be undetermined. This is the translation coefficient.

[0016] According to one embodiment of this application, the network security constraint is: ; in, for The corresponding left-hand matrix, for The corresponding left-hand matrix, This is the branch power loss vector. Inject complex function vectors into nodes. This is the vector on the right side.

[0017] According to one embodiment of this application, the aggregated energy utilization feasible domain considering network security constraints is: ; in, The left-hand matrix of the aggregate polyhedron. For the root node complex power of the time-extended extension, is the right-hand vector of the aggregated polyhedron.

[0018] According to one embodiment of this application, the multi-parameter programming model is as follows: ; The aggregation cost function is: ; in, For aggregation cost function, Let k be the power vector of resource k. For resource labeling, For the set of all resource labels, The slope of the linear piecewise cost function. The linear piecewise intercept of the cost function. The left-hand matrix of the polyhedron Let the vector be the right-hand vector of the polyhedron. For polymerization power, For row labels, For the corresponding The set of negative row labels, denoted as . j , The j-th element of the right-hand vector of the aggregated polyhedron. and These are the j-th rows of the left-end matrix blocks of the aggregated polyhedron.

[0019] According to the virtual power plant resource flexibility aggregation device based on linear programming projection provided in this application, a linear state equation corresponding to each resource to be aggregated is constructed based on the resource characteristic data of each resource to be aggregated, and a linear transformation is performed; the energy utilization flexible feasible domain of each resource to be aggregated is aggregated, and the coordinate projection problem is solved to obtain a preliminary aggregated energy utilization feasible domain; network security constraints are constructed and embedded in the solution process of the preliminary aggregated energy utilization feasible domain to obtain an aggregated energy utilization feasible domain considering network security constraints; a multi-parameter programming model is constructed, and the aggregation cost function is integrated into the solution process of the aggregated energy utilization feasible domain considering network security constraints to obtain the virtual power plant resource flexibility aggregation result. Thus, the problems of existing technologies, such as the inability to simultaneously aggregate the energy utilization feasible domain and adjustment cost of massive heterogeneous resources, the inability to embed network security constraints, and large high-dimensional aggregation errors, are solved, achieving higher computational efficiency and compatibility.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the virtual power plant resource flexibility aggregation method based on linear programming projection as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the virtual power plant resource flexibility aggregation method based on linear programming projection as described in the above embodiments.

[0022] The beneficial effects of this application are: (1) This invention eliminates redundant variables such as output power and input power from the original energy use feasible domain model by using an algorithm based on coordinate transformation and projection, and constructs the port net power as a standardized state variable, thereby achieving better computational efficiency and compatibility; (2) The projection-based aggregation algorithm proposed in this invention can solve the problem efficiently while ensuring accuracy, which is superior to other methods in the current technology; (3) The present invention provides a piecewise linear function of total regulation cost in the aggregation, which together with the aggregated energy feasible domain forms a complete description of the regulation flexibility of the virtual power plant, and has good application prospects.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a virtual power plant resource flexibility aggregation method based on linear programming projection provided in an embodiment of this application; Figure 2 This is a flowchart of a virtual power plant resource flexibility aggregation method based on linear programming projection according to an embodiment of this application; Figure 3 This is a block diagram of a virtual power plant resource flexibility aggregation device based on linear programming projection according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following describes, with reference to the accompanying drawings, a method and apparatus for virtual power plant resource flexibility aggregation based on linear programming projection, according to embodiments of this application.

[0027] Given the limitations of the prior art, such as the inability to simultaneously aggregate the energy-feasible domain and adjustment costs of massive heterogeneous resources, the inability to embed cybersecurity constraints, and the large errors in high-dimensional aggregation, it is necessary to propose a more general projection perspective modeling within the existing framework of polyhedral modeling and Minkowski aggregation. This model would extend the embedding of cybersecurity constraints and the aggregation of adjustment costs, while simultaneously reducing the estimation error of massive resource aggregation.

[0028] This application provides a virtual power plant resource flexibility aggregation method based on linear programming projection. In this method, a linear state equation is constructed for each resource to be aggregated based on its resource characteristic data, and a linear transformation is performed. The energy-use flexibility feasible domains of each resource are aggregated, and the coordinate projection problem is solved to obtain a preliminary aggregated energy-use feasible domain. Network security constraints are constructed and embedded into the solution process of the preliminary aggregated energy-use feasible domain to obtain an aggregated energy-use feasible domain considering network security constraints. A multi-parameter programming model is constructed, and the aggregation cost function is integrated into the solution process of the aggregated energy-use feasible domain considering network security constraints to obtain the virtual power plant resource flexibility aggregation result. This solves the problems of existing technologies, such as the inability to simultaneously aggregate the energy-use feasible domains and adjustment costs of massive heterogeneous resources, the inability to embed network security constraints, and large high-dimensional aggregation errors, achieving higher computational efficiency and compatibility.

[0029] Before introducing the virtual power plant resource flexibility aggregation method based on linear programming projection proposed in this application, we will first introduce the Gaussian-Jordan elimination technique, linear programming problem solving technique and Distflow power flow equation related to this application.

[0030] Gauss-Jordan elimination is a classic method in linear algebra that can be used to solve systems of linear equations, find the rank of a matrix, and determine the maximal linearly independent set of vectors. Specifically, this method processes a matrix by first transforming it into its row-law simplified form using elementary row operations. If a row is entirely zero, the vectors corresponding to that row are linearly dependent and do not belong to the linearly independent set. Vectors corresponding to non-zero rows are linearly independent, and these vectors form the maximal linearly independent set of the matrix.

[0031] Linear programming is an important branch of mathematical optimization, aiming to optimize (maximize or minimize) an objective function under constraints. Both the objective function and constraints of a linear programming problem are linear. A linear programming problem can typically be expressed as: ; Techniques for solving linear programming problems are quite mature, with commonly used methods including the simplex method, interior-point method, and primal pair method. Many existing tools and libraries can efficiently solve linear programming problems. Open-source libraries such as the `linprog` function in Python's SciPy library, or libraries like `cvxopt`, typically offer powerful solution performance and support large-scale optimization problems.

[0032] The DistFlow equation is a simplified model used in power flow calculations for distribution networks, and is commonly used for the analysis and optimization of distribution systems. Compared to traditional power flow equations (such as the Newton-Raphson method, which is mainly used for power flow calculations in transmission networks), the DistFlow equation is particularly suitable for radial networks (such as typical distribution systems). It describes the operating state of the power system relatively simply by relating the power flow in the grid to physical quantities such as voltage, current, and line impedance. The DistFlow model is as follows: ; ; ; ; ; ; in, The square of the node's active power. Let be the square of the node's reactive power. The square of the node voltage magnitude; The square of the active power of the branch circuit. The square of the reactive power of the branch circuit. The square of the branch current amplitude; , These are the impedance parameters of the branch; , , All are node labels, For branch road numbers; The set of all nodes. The set of all branches, This is the lower bound of the node voltage. This is the upper bound of the node voltage. This is the upper limit of the branch current.

[0033] The following section details the proposed method for virtual power plant resource flexibility aggregation based on linear programming projection.

[0034] Specifically, Figure 1 This is a flowchart illustrating a virtual power plant resource flexibility aggregation method based on linear programming projection, provided in an embodiment of this application.

[0035] like Figure 1 As shown, the virtual power plant resource flexibility aggregation method based on linear programming projection includes the following steps: In step S101, resource characteristic data of multiple resources to be aggregated in the virtual power plant are obtained, and a linear state equation corresponding to each resource to be aggregated is constructed based on the resource characteristic data of each resource to be aggregated. A linear transformation is performed on the linear state equation corresponding to each resource to be aggregated so as to unify the flexible and feasible energy use domain of each resource to be aggregated to a preset standard space.

[0036] Furthermore, in some embodiments, constructing a linear state equation corresponding to each resource to be aggregated based on the resource characteristic data of each resource to be aggregated includes: determining the resource type of each resource to be aggregated based on the resource characteristic data of each resource to be aggregated, wherein the resource type includes power generation-like resources and energy storage-like resources.

[0037] Optionally, the resource characteristic data in this application embodiment may include the resource list to be aggregated under the virtual power plant and their respective static physical parameters or day-ahead predicted operating data, which are not specifically limited here.

[0038] Specifically, the process involves acquiring a list of resources to be aggregated under a virtual power plant, along with their respective static physical parameters or day-ahead forecast operating data. Based on the resource type, the acquired parameters or data are used to construct linear state equations for each resource. A suitable transformation matrix is ​​then constructed using the Gauss-Jordan elimination method to perform a linear transformation on the state equations of each resource, retaining only the active power at the port as the state variable. This achieves the unification of the flexible and feasible energy use domain of the resources into a standard space.

[0039] In detail, heterogeneous single-unit models are constructed based on the list of resources to be aggregated under the acquired virtual power plant and their respective static physical parameters or day-ahead predicted operating data. It is compatible with five different heterogeneous distributed resources: Distributed Generation (DG), Distributed Energy Storage System (ESS), Distributed Photovoltaic (PV), Deferrable Load (DL), and Heating, Ventilation, and Air Conditioning (HVAC). Distributed Generation (DG) and Distributed Photovoltaic (PV), as power generation-like resources, have their energy consumption characteristics uniformly modeled as follows: ; ; in, For port output power, The climbing rate, This is the lower bound of the power. This is the upper limit of power. For time indexing, To lower the boundary, This is the upper limit for power adjustment.

[0040] Distributed energy storage (ESS), deferred load (DL), and air conditioning (HVAC) systems are used as energy storage-like resources and modeled using a virtual energy storage model with ramp constraints. ; ; ; ; ; ; ; in, For port output power, For equivalent residual energy, The rate of ascent; For charging power efficiency, For charging efficiency, For discharge efficiency, For charging power, This represents the upper limit of charging power. For discharge power, This is the upper limit of the discharge power. Electrical adjustment boundary, To adjust the charging boundary, To adjust the discharge threshold, To adjust the charging boundary, The lower limit of energy. As the upper limit of energy, For time indexing, For time intervals.

[0041] For heterogeneous models constructed from different types of resources, their state variables are unified as port active power. For generation-like models, which lack internal state variables, they naturally transform into polyhedra in port space, resulting in: ; in, and It is a coefficient matrix.

[0042] The energy storage-like model needs to eliminate internal state variables and be rewritten in matrix form: ; ; ; ; in, , The matrix on the left-hand side of the inequality constraint is... , The vector on the right-hand side of the inequality constraint is... , The left-hand side matrix of the equality constraint, For charging power vector, For the discharge power vector, For energy vectors, It is an identity matrix.

[0043] Furthermore, eliminate internal state variables. , and : ; in, For the variables to be eliminated, For matrix A containing The maximal linearly independent set is obtained using Gaussian-Jordan elimination. Projection yields the polyhedral model represented by port variables: ; in, The left-hand matrix of the ESS polyhedron. For ESS power vector, Let be the right-hand vector of the ESS polyhedron.

[0044] In step S102, the energy consumption flexible feasible domains of each resource to be aggregated in the preset standard space are aggregated, the current aggregation problem is transformed into a coordinate projection problem from the preset high-dimensional space to the subspace, and the coordinate projection problem is solved to obtain the preliminary aggregated energy consumption feasible domain of the virtual power plant.

[0045] Specifically, the energy feasible domain model of massive resources obtained after standardization and unification is aggregated. This aggregation process is modeled as finding a closed expression of the Minkowski sum of all energy feasible domains, and the problem is further transformed into a first-order coordinate projection from a high-dimensional space to a subspace. For obtaining the projection result, a two-stage robust optimization is constructed, and the Lagrange duality theory is used to transform it into a linear programming problem that can be solved efficiently. Its optimal solution gives the maximum internal estimate of the coordinate projection, thereby obtaining the energy feasible domain of the virtual power plant aggregation.

[0046] In detail, the aggregation problem of the standardized and unified energy feasible region model is transformed into a coordinate projection estimation problem. For the following two standardized models given in step S101, using... and : ; in, For polyhedron 1, For polyhedron 2, , For a dimension vector, , The left-hand matrix, , This is the vector on the right side.

[0047] The Minkowski aggregation result is expressed in the following form: ; in, It is an aggregated dimension vector.

[0048] Using the same Gaussian-Jordan elimination method as in step S101, the transformation matrix is ​​obtained, and the set is then subjected to coordinate transformation: ; Furthermore, the projection results are estimated based on linear programming. For the projection problem obtained from the above transformation, it is equivalent to eliminating the following polyhedral variables. : ; in, for The corresponding left-hand matrix, for The corresponding left-hand matrix, This is the vector on the right side.

[0049] Initialize the following polyhedron shape estimation projection results: ; in, Let the right-hand vector be undetermined, and the coefficient matrix be... The following set of normal vectors is given: ; ; ; ; The vector on the right side is given by the following linear programming problem: ; Based on the initialized polyhedron, solve the following linear programming problem to determine the scaling factor. Translation coefficient : ; in, ; in, To optimize variables, To replace the variable, For dimension labels, For the set of all dimensions, These are dual variables.

[0050] The projection estimation results are given by the following polyhedron: ; in, This is the scaling factor. This is the translation coefficient.

[0051] In step S103, the network parameters of the power distribution network to which the virtual power plant belongs are obtained, network security constraints are constructed based on the preset linearized power flow equations, and the network security constraints are embedded into the solution process of the preliminary aggregated energy consumption feasible region to obtain the aggregated energy consumption feasible region considering the network security constraints.

[0052] In this application embodiment, the "network parameters of the power distribution network" are defined as the topology and branch impedance parameters of the distribution network, which are recorded as the node branch management matrix and impedance matrix, respectively.

[0053] Specifically, the network parameters of the power distribution network where the virtual power plant is located are obtained, and the network security constraints and branch power flow losses are modeled using linearized Distflow power flow equations. Redundant state variables in the power flow equations are eliminated using the coordinate transformation method in step S101 and the projection estimation method in step S102, serving as the energy-feasible region that couples all resources, replacing the Minkowski method in step S102 and realizing the aggregation with embedded network security constraints. This problem remains a projection problem, and the estimation results from step S102 are used to estimate the energy-feasible region of the virtual power plant aggregation with embedded network security constraints. In detail, the process involves obtaining network distribution parameters, constructing network security constraints using Distflow, and embedding them into the aggregation process. The energy feasibility domain representation of the network root node is then constructed. ; ; in, The active power of the root node. Inject active power into node j. For the active power loss of branch (i, j), The reactive power of the root node. Inject reactive power into node j. Let (i,j) be the reactive power loss of the branch.

[0054] Written in matrix form: ; in, For the root node complex power, This is the active power loss vector of the branch. This is the branch reactive power loss vector. Inject active power vectors into nodes. Inject reactive power vectors into nodes. This is the branch power loss vector. Inject complex function vectors into nodes.

[0055] Building network security constraints using Distflow: ; in, for The corresponding left-hand matrix, for The corresponding left-hand matrix, This is the vector on the right side.

[0056] Similar to step S101, the transformation matrix is ​​obtained using the Gaussian-Jordan elimination method. This eliminates variables in the system. : ; ; in, For matrix A containing A maximal linearly independent set.

[0057] We will aggregate the constraints to consider cybersecurity. We will extend these cybersecurity constraints to a space coupled across all time periods and combine them with a standardized model: ; in, , Aggregate the left-side matrix and right-side vector of the polyhedron at node i. , To constrain network security, the left-hand matrix is ​​extended into blocks after time intervals. For the time-extended node i complex power, For the root node complex power of the time-extended extension, This involves dividing the right-hand vector into blocks to constrain network security.

[0058] By using a projection algorithm to eliminate redundant variables, the aggregation result is obtained: ; in, The left-hand matrix of the aggregate polyhedron. is the right-hand vector of the aggregated polyhedron.

[0059] In step S104, the operation and adjustment cost of each resource to be aggregated is obtained, a multi-parameter planning model is constructed based on the operation and adjustment cost of each resource to be aggregated, and the multi-parameter planning model is simplified to obtain a piecewise linear aggregation cost function. The aggregation cost function is then incorporated into the solution process of the aggregated energy use feasible domain considering network security constraints to obtain the virtual power plant resource flexibility aggregation result.

[0060] Specifically, the operation and adjustment costs of individual resources are obtained, and a multi-parameter programming model is constructed to describe the minimum total cost corresponding to the optimal solution when aggregating the total energy consumption curve of the virtual power plant. This cost is used as the aggregation cost. Combining multi-parameter programming theory and projection estimation in step S102, the multi-parameter programming model is simplified to obtain a piecewise linear aggregation cost function. This process is then embedded into the aggregation process in step S102 to obtain a virtual power plant flexibility aggregation method that simultaneously provides the feasible region of aggregated energy consumption and the aggregated adjustment cost.

[0061] In this application embodiment, the "operational adjustment cost of a single resource" is defined as a set of quantity-price pairs, and the number of quantity-price pairs corresponds to the number of segments in its adjustment cost function.

[0062] In detail, the quantity-price curves of the adjustment costs for each resource are obtained, and a model expression of the aggregate cost is constructed. The adjustment costs of each resource are represented by... The piecewise linear function expression of the multi-parameter programming problem is constructed using the following aggregate cost function: ; in, For aggregation cost function, Let k be the power vector of resource k. For resource labeling, For the set of all resource labels, , The cost function is a linear piecewise segment with slope and intercept. The left-hand matrix of the polyhedron Let the vector be the right-hand vector of the polyhedron. This refers to the polymerization power.

[0063] Furthermore, a piecewise linear expression for the aggregation cost function is given. Using a method similar to that in step S101, redundant state variables in the feasible region of the multi-parameter planning are eliminated, resulting in a simplified feasible region: ; in, , Divide the left-hand matrix of the aggregated polyhedron into blocks. For aggregation cost, For aggregated power vectors, This is the vector at the right end of the polyhedron resulting from the aggregation.

[0064] Based on the simplified feasible region, the original multi-parameter programming problem is transformed into: ; Thus, the explicit expression of the cost function can be obtained: ; in, For aggregated power vectors, , For the i-th and j-th elements of the right-hand vector of the aggregated polyhedron, and The i-th row of the left-hand matrix of the aggregated polyhedron is represented by the following block. and The j-th row represents the left-end matrix block of the polyhedron of the aggregation result. , For row labels, For the corresponding The set of positive row labels. For the corresponding The set of negative row labels.

[0065] Therefore, the virtual power plant resource flexibility aggregation method based on linear programming projection proposed in this application aims to solve the overall flexibility problem of massive heterogeneous resources in the aggregation description of virtual power plants. First, it establishes individual energy consumption feasible region models for heterogeneous resources, and uses the Gauss-Jordan elimination method to obtain the transformation matrix, unifying all individual models into a standardized state space. Then, the Minkowski aggregation problem of the energy consumption feasible region is transformed into a projection estimation problem, and the result is obtained using an efficient projection algorithm based on linear programming. Secondly, it constructs distribution network power flow security constraints based on Distflow and embeds the above aggregation framework from a projection perspective. Finally, for the adjustment cost of individual resources, it constructs an aggregation cost function expression using a multi-parameter programming method and embeds the above aggregation framework from a projection perspective. This invention ultimately provides an efficient aggregation algorithm that is compatible with massive heterogeneous resources, considers network security constraints, and has energy consumption feasible regions and adjustment costs, showing good engineering application prospects.

[0066] To facilitate a clearer and more intuitive understanding of the proposed method for virtual power plant resource flexibility aggregation based on linear programming projection, the following section combines... Figure 2 Please provide a detailed explanation.

[0067] Specifically, such as Figure 2 As shown, the virtual power plant resource flexibility aggregation method based on linear programming projection includes the following steps: First, obtain the resource list to be aggregated under the virtual power plant and their respective static physical parameters or day-ahead predicted operating data, and construct heterogeneous models describing their respective flexible feasible regions of energy use. Use Gauss-Jordan elimination to construct coordinate transformation matrices to unify the heterogeneous models into a standardized space, so that they can be uniformly described, compared, and aggregated. Then, based on the massive standardized models, perform aggregation equivalence. By constructing transformation matrices, the Minkowski aggregation problem of the standardized feasible region model is equivalent to the maximum projection in-space estimation problem. Use Lagrange duality theory to construct an efficient solution algorithm based on linear programming to obtain an aggregated standardized model describing the overall energy feasible region. To further consider the impact of network flow and security constraints of the power distribution network on the energy feasible region aggregation results, the linearized Distflow power flow model is standardized and transformed, and embedded into the maximum projection estimation process of the above aggregation. In addition to aggregating the energy feasible region, the aggregation resource adjustment cost is also considered. An aggregation cost function based on the optimal instruction solution is constructed. Combining multi-parameter programming theory and projection estimation simplification, the piecewise linear expression of the aggregation cost function is estimated and embedded into the above energy feasible region aggregation process.

[0068] Therefore, this method provides a flexible aggregation algorithm framework for virtual power plants that is compatible with massive heterogeneous resource access. It can simultaneously provide a description of the energy use feasible region of aggregation and the adjustment cost function of aggregation, and has high computational efficiency and high aggregation accuracy.

[0069] The virtual power plant resource flexibility aggregation method based on linear programming projection proposed in this application constructs a linear state equation for each resource to be aggregated based on its resource characteristic data, and performs a linear transformation. The energy-use flexibility feasible domains of each resource are aggregated, and the coordinate projection problem is solved to obtain a preliminary aggregated energy-use feasible domain. Network security constraints are constructed and embedded into the solution process of the preliminary aggregated energy-use feasible domain to obtain an aggregated energy-use feasible domain considering network security constraints. A multi-parameter programming model is constructed, and the aggregation cost function is integrated into the solution process of the aggregated energy-use feasible domain considering network security constraints to obtain the virtual power plant resource flexibility aggregation result. This solves the problems in existing technologies, such as the inability to simultaneously aggregate the energy-use feasible domains and adjustment costs of massive heterogeneous resources, the inability to embed network security constraints, and large high-dimensional aggregation errors, achieving higher computational efficiency and compatibility.

[0070] Next, referring to the accompanying drawings, a virtual power plant resource flexibility aggregation device based on linear programming projection proposed according to an embodiment of this application is described.

[0071] Figure 3 This is a block diagram of a virtual power plant resource flexibility aggregation device based on linear programming projection, according to an embodiment of this application.

[0072] like Figure 3 As shown, the virtual power plant resource flexibility aggregation device 10 based on linear programming projection includes: a modeling and standardization module 100, a feasible domain aggregation optimization module 200, a network security constraint aggregation module 300, and a resource flexibility aggregation module 400.

[0073] The modeling and standardization module 100 acquires resource characteristic data of multiple resources to be aggregated in the virtual power plant, constructs a linear state equation for each resource based on its resource characteristic data, and performs a linear transformation on the linear state equation for each resource to unify the flexible feasible energy use domain of each resource to be aggregated to a preset standard space. The feasible domain aggregation and optimization module 200 aggregates the flexible feasible energy use domain of each resource to be aggregated in the preset standard space, transforming the current aggregation problem into a coordinate projection problem from a preset high-dimensional space to a subspace, and solves the coordinate projection problem to obtain the preliminary aggregated feasible energy use domain of the virtual power plant. Network security is also included. The bundle aggregation module 300 is used to obtain the network parameters of the power distribution network to which the virtual power plant belongs, construct network security constraints based on the preset linearized power flow equations, and embed the network security constraints into the solution process of the preliminary aggregated energy consumption feasible region to obtain the aggregated energy consumption feasible region considering network security constraints. The resource flexibility aggregation module 400 obtains the operation and adjustment cost of each resource to be aggregated, constructs a multi-parameter programming model based on the operation and adjustment cost of each resource to be aggregated, and simplifies the multi-parameter programming model to obtain a piecewise linear aggregation cost function. The aggregation cost function is then incorporated into the solution process of the aggregated energy consumption feasible region considering network security constraints to obtain the resource flexibility aggregation result of the virtual power plant.

[0074] Furthermore, in some embodiments, the modeling and standardization module 100 is used to: determine the resource type of each resource to be aggregated based on the resource characteristic data of each resource to be aggregated, wherein the resource type includes power generation-like resources and energy storage-like resources; the linear state equation corresponding to the power generation-like resources is: ; ; in, For port output power, The climbing rate, This is the lower bound of the power. This is the upper limit of power. For time indexing, To lower the boundary, The power adjustment boundary; The linear state equation corresponding to the energy storage resource is: ; ; ; ; ; ; ; in, For port output power, For equivalent residual energy, The rate of ascent; For charging power efficiency, For charging efficiency, For discharge efficiency, For charging power, This represents the upper limit of charging power. For discharge power, This is the upper limit of the discharge power. Electrical adjustment boundary, To adjust the charging boundary, To adjust the discharge threshold, To adjust the charging boundary, The lower limit of energy. As the upper limit of energy, For time indexing, For time intervals.

[0075] Furthermore, in some embodiments, the initial aggregated energy feasible domain of the virtual power plant is: ; in, The coefficient matrix, This is the dimension vector retained after the polyhedron is projected. This is the scaling factor. Let the right-hand vector be undetermined. This is the translation coefficient.

[0076] Furthermore, in some embodiments, network security constraints are as follows: ; in, for The corresponding left-hand matrix, for The corresponding left-hand matrix, This is the branch power loss vector. Inject complex function vectors into nodes. This is the vector on the right side.

[0077] Furthermore, in some embodiments, the aggregated energy-feasible domain considering network security constraints is: ; in, The left-hand matrix of the aggregate polyhedron. For the root node complex power of the time-extended extension, is the right-hand vector of the aggregated polyhedron.

[0078] Furthermore, in some embodiments, the multi-parameter programming model is as follows: ; The aggregation cost function is: ; in, For aggregation cost function, Let k be the power vector of resource k. For resource labeling, For the set of all resource labels, The slope of the linear piecewise cost function. The linear piecewise intercept of the cost function. The left-hand matrix of the polyhedron Let the vector be the right-hand vector of the polyhedron. For polymerization power, For row labels, For the corresponding The set of negative row labels, denoted as . j , The j-th element of the right-hand vector of the aggregated polyhedron. and These are the j-th rows of the left-end matrix blocks of the aggregated polyhedron.

[0079] It should be noted that the foregoing explanation of the virtual power plant resource flexibility aggregation method based on linear programming projection also applies to the virtual power plant resource flexibility aggregation device based on linear programming projection in this embodiment, and will not be repeated here.

[0080] The virtual power plant resource flexibility aggregation device based on linear programming projection proposed in this application constructs a linear state equation for each resource to be aggregated based on its resource characteristic data, and performs a linear transformation. It then aggregates the energy-use flexibility feasible domains of each resource and solves the coordinate projection problem to obtain a preliminary aggregated energy-use feasible domain. Finally, it constructs network security constraints and embeds them into the solution process of the preliminary aggregated energy-use feasible domain to obtain an aggregated energy-use feasible domain considering network security constraints. A multi-parameter programming model is then constructed, and the aggregation cost function is integrated into the solution process of the aggregated energy-use feasible domain considering network security constraints to obtain the virtual power plant resource flexibility aggregation result. This solves the problems of existing technologies, such as the inability to simultaneously aggregate the energy-use feasible domains and adjustment costs of massive heterogeneous resources, the inability to embed network security constraints, and large high-dimensional aggregation errors, achieving higher computational efficiency and compatibility.

[0081] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0082] When processor 402 executes the program, it implements the virtual power plant resource flexibility aggregation method based on linear programming projection provided in the above embodiments.

[0083] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0084] The memory 401 is used to store computer programs that can run on the processor 402.

[0085] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0086] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0087] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0088] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0089] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for aggregating virtual power plant resource flexibility based on linear programming projection.

[0090] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0091] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0092] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0094] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0095] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0096] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0097] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for virtual power plant resource flexibility aggregation based on linear programming projection, characterized in that, Includes the following steps: The resource characteristic data of multiple resources to be aggregated in the virtual power plant are obtained, and a linear state equation corresponding to each resource to be aggregated is constructed based on the resource characteristic data of each resource to be aggregated. The linear state equation corresponding to each resource to be aggregated is linearly transformed to unify the flexible and feasible energy use domain of each resource to be aggregated to a preset standard space. Aggregate the energy-use flexible feasible domains of each resource to be aggregated in the preset standard space, transform the current aggregation problem into a coordinate projection problem from a preset high-dimensional space to a subspace, and solve the coordinate projection problem to obtain the preliminary aggregated energy-use feasible domain of the virtual power plant. The network parameters of the power distribution network to which the virtual power plant belongs are obtained, network security constraints are constructed based on the preset linearized power flow equations, and the network security constraints are embedded into the solution process of the preliminary aggregated energy consumption feasible region to obtain the aggregated energy consumption feasible region considering the network security constraints. The operation and adjustment cost of each resource to be aggregated is obtained. A multi-parameter planning model is constructed based on the operation and adjustment cost of each resource to be aggregated. The multi-parameter planning model is simplified to obtain a piecewise linear aggregation cost function. The aggregation cost function is then incorporated into the solution process of the aggregated energy use feasible region considering network security constraints to obtain the virtual power plant resource flexibility aggregation result.

2. The method according to claim 1, characterized in that, The step of constructing the linear state equation corresponding to each resource to be aggregated based on the resource characteristic data of each resource to be aggregated includes: The resource type of each resource to be aggregated is determined based on the resource characteristic data of each resource to be aggregated, wherein the resource type includes power generation resources and energy storage resources. The linear state equation corresponding to the aforementioned type of power generation resource is: ； ； in, For port output power, The climbing rate, This is the lower bound of the power. This is the upper limit of power. For time indexing, To lower the boundary, The power adjustment boundary; The linear state equation corresponding to the aforementioned energy storage resource is: ； ； ； ； ； ； ； in, For port output power, For equivalent residual energy, The rate of ascent; For charging power efficiency, For charging efficiency, For discharge efficiency, For charging power, This represents the upper limit of charging power. For discharge power, This is the upper limit of the discharge power. Electrical adjustment boundary, To adjust the charging boundary, To adjust the discharge threshold, To adjust the charging boundary, The lower limit of energy. As the upper limit of energy, For time indexing, For time intervals.

3. The method according to claim 1, characterized in that, The initial feasible region for aggregated energy consumption of the virtual power plant is: ； in, The coefficient matrix, This is the dimension vector retained after the polyhedron is projected. This is the scaling factor. Let the right-hand vector be undetermined. This is the translation coefficient.

4. The method according to claim 1, characterized in that, The network security constraints are as follows: ； in, for The corresponding left-hand matrix, for The corresponding left-hand matrix, This is the branch power loss vector. Inject complex function vectors into nodes. This is the vector on the right side.

5. The method according to claim 1, characterized in that, The feasible region for aggregated energy consumption considering network security constraints is: ； in, The left-hand matrix of the aggregate polyhedron. For the root node complex power of the time-extended extension, is the right-hand vector of the aggregated polyhedron.

6. The method according to claim 1, characterized in that, The multi-parameter programming model is as follows: ； The aggregation cost function is: ； in, For aggregation cost function, Let k be the power vector of resource k. For resource labeling, For the set of all resource labels, The slope of the linear piecewise cost function. The linear piecewise intercept of the cost function. The left-hand matrix of the polyhedron Let the vector be the right-hand vector of the polyhedron. For polymerization power, For row labels, For the corresponding The set of negative row labels, denoted as . j , The j-th element of the right-hand vector of the aggregated polyhedron. and These are the j-th rows of the left-end matrix blocks of the aggregated polyhedron.

7. A virtual power plant resource flexibility aggregation device based on linear programming projection, characterized in that, include: The modeling and standardization module acquires resource characteristic data of multiple resources to be aggregated in the virtual power plant, constructs a linear state equation corresponding to each resource to be aggregated based on the resource characteristic data of each resource to be aggregated, and performs a linear transformation on the linear state equation corresponding to each resource to be aggregated to unify the flexible and feasible energy use domain of each resource to be aggregated to a preset standard space. The feasible region aggregation optimization module is used to aggregate the energy-use flexible feasible region of each resource to be aggregated in the preset standard space, transform the current aggregation problem into a coordinate projection problem from a preset high-dimensional space to a subspace, and solve the coordinate projection problem to obtain the preliminary aggregated energy-use feasible region of the virtual power plant. The network security constraint aggregation module is used to obtain the network parameters of the power distribution network to which the virtual power plant belongs, construct network security constraints based on the preset linearized power flow equations, and embed the network security constraints into the solution process of the preliminary aggregated energy consumption feasible region to obtain the aggregated energy consumption feasible region considering network security constraints. The resource flexibility aggregation module obtains the operation and adjustment cost of each resource to be aggregated, constructs a multi-parameter planning model based on the operation and adjustment cost of each resource to be aggregated, and simplifies the multi-parameter planning model to obtain a piecewise linear aggregation cost function. The aggregation cost function is then incorporated into the solution process of the aggregated energy use feasible region considering network security constraints to obtain the virtual power plant resource flexibility aggregation result.

8. The apparatus according to claim 7, characterized in that, The modeling and standardization module is used for: The resource type of each resource to be aggregated is determined based on the resource characteristic data of each resource to be aggregated, wherein the resource type includes power generation resources and energy storage resources. The linear state equation corresponding to the aforementioned type of power generation resource is: ； ； in, For port output power, The climbing rate, This is the lower bound of the power. This is the upper limit of power. For time indexing, To lower the boundary, The power adjustment boundary; The linear state equation corresponding to the aforementioned energy storage resource is: ； ； ； ； ； ； ； in, For port output power, For equivalent residual energy, The rate of ascent; For charging power efficiency, For charging efficiency, For discharge efficiency, For charging power, This represents the upper limit of charging power. For discharge power, This is the upper limit of the discharge power. Electrical adjustment boundary, To adjust the charging boundary, To adjust the discharge threshold, To adjust the charging boundary, The lower limit of energy. As the upper limit of energy, For time indexing, For time intervals.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the virtual power plant resource flexibility aggregation method based on linear programming projection as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the virtual power plant resource flexibility aggregation method based on linear programming projection as described in any one of claims 1-6.

Images