Active power distribution network multi-type resource regulation capability aggregation method, device and equipment

Abstract

The application relates to the field of power system analysis, in particular to a kind of active distribution network multi-type resource regulation capability aggregation method, device and equipment, comprising: the output prediction error of the regulation characteristic of the multi-type resource is fitted using Gaussian distribution, the regulation model of each type resource is respectively constructed, the power flow model of active distribution network is constructed based on the preset linearization power flow model, and the regulation model of each type resource and the power flow model of active distribution network are constrained, the regulation range of active distribution network is obtained, the regulation capability aggregation model of multi-type resource is constructed based on the regulation range of active distribution network and the preset polyhedron, the regulation capability aggregation model of multi-type resource is solved using the preset boundary contraction algorithm, and the aggregation regulation range of active distribution network is obtained. Therefore, the technical problem that the aggregation regulation capability cannot be accurately described by the power grid due to the different regulation characteristics of distributed resources and the difficulty in unified quantitative description in the related art is solved.

Description

Technical Field

[0001] This application relates to the field of power system analysis, and in particular to a method, apparatus and equipment for aggregating the regulation capabilities of multiple types of resources in an active distribution network. Background Technology

[0002] With the increasing penetration rate of new energy sources, a large number of distributed energy sources, such as distributed photovoltaics, energy storage, electric vehicle charging stations, and temperature-controlled loads, have been connected to the active distribution network. These resources have a lot of flexible adjustment potential, enabling the active distribution network to interact with the outside world. However, these distributed energy sources have small individual capacities, are widely distributed, and are numerous, making it difficult to manage and control them in a unified manner.

[0003] In related technologies, when considering the uncertainty of distributed resource output, network power flow constraints, and time coupling characteristics in active distribution networks, the above constraints enclose the adjustment range of the active distribution network into a complex high-dimensional polyhedron, making it difficult to accurately characterize the adjustable range of the active distribution network, which urgently needs to be addressed. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for aggregating the regulation capabilities of multiple types of resources in an active distribution network, in order to solve the technical problem in related technologies that the aggregated regulation capabilities cannot be accurately characterized and efficiently utilized by the power grid due to the different characteristics of distributed resource regulation and the difficulty in uniformly quantifying them.

[0005] The first aspect of this application provides a method for aggregating the regulation capabilities of multiple types of resources in an active distribution network, including the following steps: The regulation characteristics of multiple types of resources in the active distribution network are obtained, and the output prediction error of the regulation characteristics of the multiple types of resources is fitted by Gaussian distribution. Regulation models for each type of resource are then constructed. Based on a preset linearized power flow model, a power flow model of an active distribution network is constructed. The adjustment model of each type of resource and the power flow model of the active distribution network are constrained by the first constraint method and the second constraint method to obtain the adjustment range of the active distribution network. Based on the active distribution network regulation range and the preset polyhedral construction target, a regulation capacity aggregation model of the multi-type resources is constructed. The regulation capacity aggregation model of the multi-type resources is solved using a preset boundary shrinkage algorithm to obtain the aggregation regulation range of the active distribution network.

[0006] According to one embodiment of this application, constructing a power flow model for an active distribution network based on a preset linearized power flow model includes: Based on the voltage-power coupling characteristics, the mapping relationship between the voltage of each node in the active distribution network, the injected power of each node in the active distribution network, and the power at the grid connection point is established using the preset linearized power flow model. A power flow model for the active distribution network is constructed based on the mapping relationship between the injected power of each node and the power at the grid connection point within the active distribution network.

[0007] According to one embodiment of this application, constraining the adjustment model of each type of resource and the power flow model of the active distribution network using a first constraint method and a second constraint method includes: The first constraint method is used to apply deterministic constraints to the adjustment model of each type of resource, while the second constraint method is used to apply circular constraints to the active power and reactive power output by the adjustment model of each type of resource. The form of the adjustment model for each type of resource and the power flow model for the active distribution network are uniformly constrained based on the deterministic constraints and the circular constraints.

[0008] According to one embodiment of this application, the step of solving the aggregation model of the regulation capacity of the multi-type resources using a preset boundary contraction algorithm to obtain the aggregation regulation range of the active distribution network includes: The active distribution network adjustment range enclosed by the deterministic constraints and the circular constraints is taken as a high-dimensional polyhedron. The projection polyhedron of the active distribution network is obtained based on the projection of the high-dimensional polyhedron onto the grid connection point in the active distribution network. The circumscribed polyhedron of the projected polyhedron is constructed. A preset boundary shrinkage algorithm is used to solve the aggregation model of the adjustment capabilities of the multi-type resources. Each vertex of the circumscribed polyhedron is shrunk to the projected polyhedron through iterative detection to obtain the inscribed polyhedron. The aggregation regulation range of the active distribution network is obtained based on the boundary parameters of the inscribed polyhedron.

[0009] According to one embodiment of this application, the multi-type resources include at least two of distributed photovoltaic, wind power, energy storage, electric vehicle charging stations, and temperature-controlled loads.

[0010] According to the active distribution network multi-type resource regulation capability aggregation method of this application embodiment, the output prediction error of the regulation characteristics of multiple resource types is fitted by Gaussian distribution, and a regulation model for each type of resource is constructed. A power flow model of the active distribution network is constructed based on a preset linearized power flow model, and constraints are applied to the regulation model of each resource type and the power flow model of the active distribution network to obtain the regulation range of the active distribution network. Based on the regulation range of the active distribution network and a preset polyhedral construction target, a multi-type resource regulation capability aggregation model is constructed. A preset boundary shrinkage algorithm is used to solve the multi-type resource regulation capability aggregation model to obtain the aggregated regulation range of the active distribution network. This solves the technical problem in related technologies where the diverse and difficult-to-quantify regulation characteristics of distributed resources prevent the accurate characterization of the aggregated regulation capability by the power grid.

[0011] A second aspect of this application provides a device for aggregating the regulation capabilities of multiple types of resources in an active distribution network, comprising: The first construction module is used to obtain the regulation characteristics of multiple types of resources in the active distribution network, fit the output prediction error of the regulation characteristics of the multiple types of resources using a Gaussian distribution, and construct a regulation model for each type of resource respectively. The second construction module is used to construct a power flow model of an active distribution network based on a preset linearized power flow model, and to constrain the adjustment model of each type of resource and the power flow model of the active distribution network using a first constraint method and a second constraint method to obtain the adjustment range of the active distribution network. The calculation module is used to construct an aggregated model of the regulation capacity of the multi-type resources based on the regulation range of the active distribution network and a preset polyhedral construction target, and to solve the aggregated model of the regulation capacity of the multi-type resources using a preset boundary shrinkage algorithm to obtain the aggregated regulation range of the active distribution network.

[0012] According to one embodiment of this application, the second building module includes: The first building unit is used to establish the mapping relationship between the voltage of each node in the active distribution network, the injected power of each node in the active distribution network, and the power of the grid connection point based on the voltage-power coupling characteristics and the preset linearized power flow model. The second construction unit is used to construct a power flow model of the active distribution network based on the mapping relationship between the injected power of each node and the power at the grid connection point within the active distribution network.

[0013] According to one embodiment of this application, the second building module includes: The first constraint unit is used to apply deterministic constraints to the adjustment model of each type of resource using a first constraint method, and at the same time, apply circular constraints to the active power and reactive power output by the adjustment model of each type of resource using a second constraint method. The second constraint unit is used to uniformly constrain the form of the adjustment model for each type of resource and the power flow model for the active distribution network based on the deterministic constraints and the circular constraints.

[0014] According to one embodiment of this application, the computing module includes: The first acquisition unit is used to take the active distribution network adjustment range enclosed by the deterministic constraint and the circular constraint as a high-dimensional polyhedron, and obtain the projected polyhedron of the active distribution network based on the projection of the high-dimensional polyhedron onto the grid connection point in the active distribution network. The computing unit is used to construct the circumscribed polyhedron of the projected polyhedron, solve the aggregation model of the adjustment capability of the multi-type resources using a preset boundary shrinkage algorithm, and shrink each vertex of the circumscribed polyhedron to the projected polyhedron through iterative detection to obtain the inscribed polyhedron. The second acquisition unit is used to obtain the aggregation adjustment range of the active distribution network based on the boundary parameters of the inscribed polyhedron.

[0015] According to one embodiment of this application, the multi-type resources include at least two of distributed photovoltaic, wind power, energy storage, electric vehicle charging stations, and temperature-controlled loads.

[0016] According to the active distribution network multi-type resource regulation capability aggregation device of this application embodiment, the output prediction error of the regulation characteristics of multiple types of resources is fitted by Gaussian distribution, and a regulation model for each type of resource is constructed. A power flow model of the active distribution network is constructed based on a preset linearized power flow model, and constraints are applied to the regulation model of each type of resource and the power flow model of the active distribution network to obtain the regulation range of the active distribution network. Based on the regulation range of the active distribution network and a preset polyhedral construction target, a multi-type resource regulation capability aggregation model is constructed. A preset boundary shrinkage algorithm is used to solve the multi-type resource regulation capability aggregation model to obtain the aggregated regulation range of the active distribution network. This solves the technical problem in related technologies where the aggregation regulation capability cannot be accurately characterized by the power grid due to the diverse and difficult-to-quantify characteristics of distributed resource regulation.

[0017] 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 active distribution network multi-type resource regulation capability aggregation method as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the active distribution network multi-type resource regulation capability aggregation method as described in the above embodiments.

[0019] A fifth aspect of this application provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the active distribution network multi-type resource regulation capability aggregation method as described in the above embodiments.

[0020] 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

[0021] 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 illustrating a method for aggregating the regulation capabilities of multiple types of resources in an active distribution network according to an embodiment of this application. Figure 2 This is a topology diagram according to one embodiment of the present application; Figure 3 This is a schematic diagram of a solution flow according to an embodiment of this application; Figure 4 This is a schematic diagram of boundary contraction according to an embodiment of this application; Figure 5 This is a flowchart illustrating the solution process according to one embodiment of this application; Figure 6 This is a diagram showing the aggregation result according to one embodiment of this application; Figure 7 This is an example diagram of an active distribution network multi-type resource regulation capacity aggregation device according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of the 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.

[0023] The following description, with reference to the accompanying drawings, outlines a method, apparatus, and device for aggregating the regulation capabilities of multiple types of resources in an active distribution network, according to embodiments of this application. Addressing the problem mentioned in the background art where the varying regulation characteristics of distributed resources make it difficult to uniformly quantify and describe them, resulting in the inability to accurately characterize and efficiently utilize aggregated regulation capabilities by the power grid, this application provides a method for aggregating the regulation capabilities of multiple types of resources in an active distribution network. In this method, the output prediction error of the regulation characteristics of multiple resource types is fitted using a Gaussian distribution, and a regulation model for each type of resource is constructed. A power flow model for the active distribution network is constructed based on a preset linearized power flow model, and constraints are applied to the regulation model for each type of resource and the power flow model for the active distribution network to obtain the regulation range of the active distribution network. Based on the regulation range of the active distribution network and a preset polyhedral construction target, a model for aggregating the regulation capabilities of multiple resource types is constructed. A preset boundary shrinkage algorithm is used to solve the model for aggregating the regulation capabilities of multiple resource types to obtain the aggregated regulation range of the active distribution network. This solves the technical problem in related technologies where the varying regulation characteristics of distributed resources make it difficult to uniformly quantify and describe them, resulting in the inability to accurately characterize the aggregated regulation capabilities by the power grid.

[0024] Specifically, Figure 1 This is a flowchart illustrating a method for aggregating the regulation capabilities of multiple types of resources in an active distribution network, as provided in an embodiment of this application.

[0025] like Figure 1 As shown, the method for aggregating the regulation capabilities of multiple types of resources in an active distribution network includes the following steps: In step S101, the regulation characteristics of multiple types of resources in the active distribution network are obtained, and the output prediction error of the regulation characteristics of multiple types of resources is fitted by Gaussian distribution to construct a regulation model for each type of resource.

[0026] Specifically, in an active distribution network, the first step is to obtain the regulation characteristics of multiple resource types in a typical active distribution network. These multiple resource types include at least two of distributed photovoltaic (PV), wind power, energy storage, electric vehicle charging stations, and temperature-controlled loads. In other words, the regulation characteristics of these multiple resource types can include the output uncertainty and adjustability of distributed PV, as well as the adjustability of wind power, energy storage, electric vehicles, and temperature-controlled loads. Distributed PV, wind power, and other new energy sources exhibit significant randomness and intermittency in their output, resulting in discrepancies between actual and predicted output data. Ignoring this uncertainty will prevent the constructed resource regulation model from accurately reflecting the actual adjustability of distributed resources, thus affecting the accuracy and reliability of subsequent aggregation results. Therefore, accurately characterizing the uncertainty of new energy output is the primary technical challenge in constructing a distributed adjustable resource model.

[0027] Therefore, in order to solve the above-mentioned technical problems, the embodiments of this application use a Gaussian distribution to fit the uncertainty of the distributed new energy power output prediction error.

[0028] Specifically, such as Figure 2 and Figure 3 As shown, firstly, annual predicted and actual output data of distributed photovoltaic and wind power in typical regions, as well as data on energy storage, electric vehicle charging stations, temperature-controlled loads, and typical daily operation, and line parameters and topology in typical regions are acquired and organized in time series format. The time resolution can be set according to scheduling requirements, such as 15 minutes, 30 minutes, or 1 hour, without specific limitations. Secondly, for each renewable energy unit, prediction error data samples are collected for all moments within the scheduling cycle, and the uncertainty of distributed renewable energy output prediction error is fitted using a Gaussian distribution to obtain the mean and variance of the prediction error. Finally, based on the fitted Gaussian distribution, chance constraints are used to describe the uncertainty of renewable energy output, thereby constructing a regulation model for each type of resource based on the completed modeling of renewable energy output uncertainty.

[0029] In step S102, based on the preset linearized power flow model, a power flow model of the active distribution network is constructed, and the adjustment model of each type of resource and the power flow model of the active distribution network are constrained by the first constraint method and the second constraint method to obtain the adjustment range of the active distribution network.

[0030] According to one embodiment of this application, a power flow model of an active distribution network is constructed based on a preset linearized power flow model, including: establishing a mapping relationship between the voltage of each node in the active distribution network, the injected power of each node in the active distribution network, and the power at the grid connection point using a preset linearized power flow model based on voltage-power coupling characteristics; and constructing a power flow model of the active distribution network based on the mapping relationship between the injected power of each node in the active distribution network and the power at the grid connection point.

[0031] The preset linearized power flow model can be selected by those skilled in the art based on actual resource adjustment needs, and no specific limitations are made here.

[0032] Specifically, in an active distribution network, the regulation capability of distributed resources ultimately needs to interact with the external power grid through the grid connection point. However, the relationship between the power injected by each node and the power at the grid connection point is not a simple algebraic sum, but is influenced by multiple factors such as the distribution network topology, line parameters, and voltage distribution. Therefore, it is necessary to construct a power flow model that meets engineering accuracy requirements and has a linear form to establish an explicit mapping relationship between the power injected by each node and the power at the grid connection point.

[0033] Specifically, such as Figure 3As shown, the embodiments of this application employ a pre-defined linearized power flow model, such as a Zbus linearized power flow model based on fixed-point iteration to construct the power flow model of the active distribution network. Considering the voltage-power coupling characteristics, the voltage of each node at each moment within the active distribution network is established based on the power flow model of the active distribution network. Power injected at each node and power at the grid connection point The linearized mapping relationship between them is expressed as follows: (1) (2) Where matrices A and B, vector a and vector b are all network parameters; Inject active power into each node at each time step and reactive power The vector formed, i.e. , Representation matrix The conjugate matrix.

[0034] , (3) , (4) in, Here is the nodal admittance matrix. For the root node admittance, and These are the submatrices formed by the mutual admittances between the root node and the other nodes in the nodal admittance matrix. The admittance submatrix consists of all nodes except the root node. For relaxation node voltage, Given the voltage vector in the power flow results. Indicates The components form a diagonal matrix with elements on the main diagonal. Representation matrix The inverse matrix.

[0035] Thus, through the above linearized power flow model, the linearized relationship between the voltage of each node and the power at the grid connection point in the active distribution network can be obtained.

[0036] Furthermore, during the operation of an active distribution network, the voltage at each node should be maintained within a safe range, with the voltage constraint being: (5) in, Let be the voltage at each node at time t. This is the upper limit of the node voltage. This represents the lower limit of the node voltage.

[0037] According to one embodiment of this application, the regulation model and the power flow model of the active distribution network for each type of resource are constrained using a first constraint method and a second constraint method, including: applying deterministic constraints to the regulation model of each type of resource using the first constraint method, and simultaneously applying circular constraints to the active and reactive power output by the regulation model of each type of resource using the second constraint method; and applying unified constraints to the form of the regulation model and the power flow model of the active distribution network for each type of resource based on the deterministic constraints and the circular constraints.

[0038] Specifically, in the process of aggregating the regulation capabilities of multiple types of resources in an active distribution network, two typical constraint challenges are encountered: the uncertainty of renewable energy output and the coupling constraint of active and reactive power output. Due to the significant randomness and intermittency of renewable energy output such as distributed photovoltaic and wind power, there is a deviation between the actual output and the predicted value. If deterministic constraints are adopted, the model will not be able to truly reflect the actual adjustable capacity of the resources. If the uncertainty is completely ignored, the aggregation result may be too optimistic and infeasible in actual operation. Therefore, it is necessary to reasonably handle this uncertainty in constraint modeling. At the same time, distributed renewable energy is usually connected to the grid through inverters, and its active and reactive power outputs are limited by the inverter capacity, forming a circular constraint. This constraint is quadratic and nonlinear. If it is directly retained in the optimization model, it will lead to the model being non-convex and nonlinear, making it difficult to solve efficiently. Therefore, it is necessary to linearize this circular constraint.

[0039] Therefore, to solve the above problems, the embodiments of this application adopt the first constraint method (i.e., opportunity constraint) and the second constraint method (i.e., regular polygon method) to constrain the adjustment model of each type of resource and the power flow model of the active distribution network. In other words, the uncertainty of new energy output can be handled by opportunity constraint, the circular constraint of active and reactive power output can be handled by regular polygon method, and the constraint form of the adjustment model of each type of resource and the power flow model of the active distribution network can be unified to obtain the adjustment range of the active distribution network.

[0040] Specifically, in this application embodiment, all network constraints and flexible resource operation constraints are processed into linearized forms, as shown in the following equation: (6) (7) in, Let C be a vector composed of the injected active and reactive power of all nodes in the active distribution network at all coupling time points, and let C and g be parameter matrices. In formula (6), the inequality constraints cover the operation constraints and network constraints of distributed resources. Formula (7) is the full-time form of the active power part in formula (2) at multiple times within the scheduling cycle. and These are the corresponding parameter matrices.

[0041] In step S103, a multi-type resource regulation capacity aggregation model is constructed based on the active distribution network regulation range and the preset polyhedral construction target. The multi-type resource regulation capacity aggregation model is solved using a preset boundary shrinkage algorithm to obtain the aggregation regulation range of the active distribution network.

[0042] According to one embodiment of this application, a preset boundary shrinkage algorithm is used to solve the aggregation model of the regulation capacity of multiple types of resources to obtain the aggregation regulation range of the active distribution network. This includes: using the regulation range of the active distribution network enclosed by deterministic constraints and circular constraints as a high-dimensional polyhedron; obtaining the projected polyhedron of the active distribution network based on the projection of the high-dimensional polyhedron onto the grid connection points within the active distribution network; constructing the circumscribed polyhedron of the projected polyhedron; using the preset boundary shrinkage algorithm to solve the aggregation model of the regulation capacity of multiple types of resources; and shrinking each vertex of the circumscribed polyhedron to the projected polyhedron through iterative detection to obtain the inscribed polyhedron; and obtaining the aggregation regulation range of the active distribution network based on the boundary parameters of the inscribed polyhedron.

[0043] The preset boundary shrinkage algorithm can be selected by those skilled in the art based on actual resource adjustment needs, and no specific limitations are made here.

[0044] Specifically, after constructing the multi-type resource adjustable model and the distribution network power flow model and applying unified constraints to them, the resulting active distribution network regulation range is an original high-dimensional polyhedron. However, the external power grid is not concerned with the specific power distribution at each node within the distribution network, but only with the overall regulation capacity of the distribution network at the grid connection point, i.e., the power, ramp rate, and energy boundary presented to the external power grid. Therefore, it is necessary to project the high-dimensional regulation range onto the low-dimensional grid connection point space to obtain a projected polyhedron. The boundary parameters of this projected polyhedron represent the desired active distribution network aggregation and adjustment range. The original high-dimensional polyhedron... and projected polyhedra The expression is as follows: (8) (9) Where D and h are parameter matrices.

[0045] Furthermore, projected polyhedra (9) is still composed of a series of constraints, corresponding to the upper and lower limits of power, ramping and energy constraints of multiple types of resources. The constraints of the adjustable range of the grid connection point after aggregation also include upper and lower limit constraints of power, ramping constraints and energy constraints, as shown in expressions (10)-(14). That is, the constraint conditions shown in expression (9) are composed of expression (10): (10) Therefore, we can obtain the parameters of matrix D and the constituent elements of vector h in expression (9): (11) (12) (13) (14) in, , and All are coefficient matrices, and the vector h consists of the upper and lower limits of power, ramping, and energy of the microgrid at each time point.

[0046] Furthermore, considering The dimension of a variable may be in the tens of thousands, and it is difficult to accurately calculate its projection into a low-dimensional space. This is an extremely difficult problem. Therefore, constructing the circumscribing polyhedron of a projective polyhedron, i.e., a high-dimensional polyhedron, is crucial. , in order to pass Approximate simulation To address the challenge of projecting high-dimensional polyhedra to low-dimensional ones.

[0047] To ensure the construction Depolymerization can be performed, provided that the following conditions are met: , Should be Encircle, and at the same time, to reduce errors, Its volume should be as large as possible.

[0048] Specifically, such as Figure 3 and Figure 4 As shown, the constructed high-dimensional polyhedron First of all The circumscribed polyhedron is then used. While maintaining the slopes of each face of the circumscribed polyhedron, a pre-defined boundary shrinkage algorithm is employed to gradient-process the aggregation model of the adjustment capabilities of multiple resource types, making it easier to solve directly using the Matlab solver. Then, each plane of the circumscribed polyhedron is shrunk inwards according to its original slope until all vertices are shrunk to the projected polyhedron. The interior or boundary, to obtain inscribed polyhedron The parameters of the inscribed polyhedron are the boundary parameters of the distribution network's regulation capacity.

[0049] Furthermore, during the iterative shrinkage process, the optimization model shown in equations (15)-(18) is constructed to obtain the circumscribed polyhedrons that satisfy the condition of belonging to the boundary or interior of the inscribed polyhedrons, i.e. However, it does not belong to projective polyhedra, that is... The infeasible points are identified to facilitate subsequent boundary shrinkage: (15) (16) (17) (18) The objective function shown in formula (15) is a min-max problem, where the power of each node satisfies the constraints of formulas (17) and (18). If at point PCC, there exists a set of polyhedron vertices... If constraint (16) is satisfied, then The corresponding high-dimensional polyhedron satisfies The corresponding objective function value is 0. The corresponding high-dimensional polyhedron is the inscribed polyhedron of the high-dimensional polyhedron formed by the constraints of each node; if If the corresponding high-dimensional polyhedron vertices cannot satisfy constraint (16), then there are vertices of the polyhedron located outside the original high-dimensional polyhedron, the objective function is negative infinity, and the polyhedron vertices need to be further contracted; in formula (15), the inner max function ensures the maximization of the polyhedron, and the outer min function gives the feasibility index of whether the model has a solution.

[0050] For this min-max problem, since the objective function has no gradient and only two solutions, 0 and negative infinity, the dual method is used to transform the min-max problem into a bilinear programming problem, where the inner max problem can be expressed as: (19) (20) , ;(twenty one) in, and These are the dual variables of constraint formulas (17) and (18), respectively. When dealing with the inner layer problem, the variables of the outer layer min problem are... Treat it as a constant.

[0051] Thus, through the dual method, the original min-max problem is transformed into a min-min problem that is easier to solve. Further processing utilizes the KKT conditions to reduce the nonlinear terms. The problem is transformed into an equivalent problem as shown in Equations (22)-(28), and the mixed integer linear constraints in the complementary relaxation conditions are relaxed using the Big-M method, as shown in the following expression: ;(twenty two) ;(twenty three) , ;(twenty four) (25) (26) , , (27) (28) Among them, formula (22) is the objective function expression obtained by dualizing the original problem (19); formulas (23)-(24) are the stationary point condition and dual feasibility constraint in the KKT conditions, respectively. For dual variables; Formulas (25)-(26) use the Big-M method to transform the complementary relaxation conditions into equivalent mixed integer linear constraints, where M is a sufficiently large constant; Formulas (27)-(28) introduce binary variables s and constraint matrix W to model the logical structure in the complementary relationship, thereby ensuring that the solution after Big-M relaxation satisfies the equivalence of the original complementary conditions.

[0052] By solving this optimization model We can obtain the extreme points of the circumscribed polyhedron, and at the same time, the high-dimensional polyhedron formed by these extreme points.

[0053] Furthermore, by shrinking the boundary points inward along the original slope, we finally obtain... inscribed polyhedron Then, during the k-th iteration, the boundary shrinkage diagram is as follows: Figure 2 As shown.

[0054] For the polyhedron in the k-th iteration The top of the vertex ,exist Find the distance The nearest edge will Translate to the nearest edge, maintaining the plane slope unchanged during the translation, until the point... Falling on a polyhedron The boundary or interior, to obtain This can be obtained by solving the following optimization model: (29) (30) Sure The vertex at The corresponding point Then, based on the vertex Update polyhedron boundary parameters In order to ensure Falling Under the above conditions, maximize the inscribed polyhedron: (31) (32) (33) , (34) Where I is satisfied The set of rows is used to solve the optimization model to obtain the polyhedron after boundary shrinkage. parameters This allows the process to proceed to the next iteration.

[0055] Among them, such as Figure 5 As shown, this is an inscribed polyhedron. The solution process first initializes the fitted external polyhedron. Vertex parameters Then solve the optimization model after dual processing (22)-(28). If the model has a solution, it indicates that the circumscribed polyhedron is oriented. All vertices have shrunk to the actual polyhedron. internal, It has shrunk to The inscribed polyhedron; if the model has no solution, it indicates that the fitted polyhedron still has vertices. Located in actual polyhedron In addition, it is necessary to solve the model (29)-(30) in the polyhedron Find the distance from the vertex nearest point , and then combine Solve the model (31)-(34) and update the fitted polyhedron. Vertex parameters It participates in the next iteration until all vertices of the fitted polyhedron have shrunk into the interior of the actual polyhedron.

[0056] Furthermore, such as Figure 6 As shown, this application's embodiment yields the aggregation results of adjustable capabilities over a full 24 hours. This aggregation method is compared with other aggregation methods (linearly accumulating the adjustable range of each resource, such as using network constraints to segment the adjustable capabilities of each resource using Minkowski aggregation). Table 1 shows the coverage and solvable aggregation rate of the actual adjustable range under several aggregation results: Table 1

[0057] Therefore, it can be seen that the active distribution network multi-type resource regulation capability aggregation method of this application embodiment ensures the solvability of the aggregation result and maximizes the fitting accuracy of the aggregation result to the actual aggregation result, proving the effectiveness of the method proposed in this application embodiment.

[0058] In summary, the embodiments of this application can achieve the following beneficial effects: (1) The embodiments of this application comprehensively consider the uncertainty of distributed new energy output in active distribution networks, the time coupling constraints of distributed resource operation, and the network constraints of active distribution networks, so that the aggregation result of the active distribution network regulation capability is closer to the actual operating state of the active distribution network. (2) Based on the geometric space concept, the adjustable space of the active distribution network connection point is constructed, and the projection problem of the high-dimensional constraints of the distribution network onto the adjustable range of the connection point is handled based on the boundary shrinkage algorithm. The power, ramp and energy boundaries of the connection point are extracted, and the decomposability of the aggregation is guaranteed while maximizing the feasible domain.

[0059] According to the active distribution network multi-type resource regulation capability aggregation method of this application embodiment, the output prediction error of the regulation characteristics of multiple resource types is fitted by Gaussian distribution, and a regulation model for each type of resource is constructed. A power flow model of the active distribution network is constructed based on a preset linearized power flow model, and constraints are applied to the regulation model of each resource type and the power flow model of the active distribution network to obtain the regulation range of the active distribution network. Based on the regulation range of the active distribution network and a preset polyhedral construction target, a multi-type resource regulation capability aggregation model is constructed. A preset boundary shrinkage algorithm is used to solve the multi-type resource regulation capability aggregation model to obtain the aggregated regulation range of the active distribution network. This solves the technical problem in related technologies where the diverse and difficult-to-quantify regulation characteristics of distributed resources prevent the accurate characterization of the aggregated regulation capability by the power grid.

[0060] Next, referring to the accompanying drawings, we describe the active distribution network multi-type resource regulation capability aggregation device proposed according to the embodiments of this application.

[0061] Figure 7 This is a block diagram of an active distribution network multi-type resource regulation capability aggregation device according to an embodiment of this application.

[0062] like Figure 7 As shown, the active distribution network multi-type resource regulation capacity aggregation device 10 includes: a first construction module 100, a second construction module 200, and a calculation module 300.

[0063] The first construction module 100 is used to obtain the regulation characteristics of multiple types of resources in the active distribution network, use Gaussian distribution to fit the output prediction error of the regulation characteristics of multiple types of resources, and construct the regulation model of each type of resource respectively. The second construction module 200 is used to construct the power flow model of the active distribution network based on the preset linearized power flow model, and to constrain the adjustment model of each type of resource and the power flow model of the active distribution network using the first constraint method and the second constraint method to obtain the adjustment range of the active distribution network. The calculation module 300 is used to construct a multi-type resource regulation capacity aggregation model based on the active distribution network regulation range and a preset polyhedral construction target, and to solve the multi-type resource regulation capacity aggregation model using a preset boundary shrinkage algorithm to obtain the aggregation regulation range of the active distribution network.

[0064] According to one embodiment of this application, the second building module 200 includes: The first building unit is used to establish the mapping relationship between the voltage of each node in the active distribution network, the injected power of each node in the active distribution network and the power of the grid connection point based on the voltage-power coupling characteristics and using a preset linearized power flow model. The second building unit is used to construct a power flow model of the active distribution network based on the mapping relationship between the injected power of each node and the power at the grid connection point within the active distribution network.

[0065] According to one embodiment of this application, the second building module 200 includes: The first constraint unit is used to apply deterministic constraints to the adjustment model of each type of resource using the first constraint method, and at the same time, to apply circular constraints to the active power and reactive power output by the adjustment model of each type of resource using the second constraint method. The second constraint unit is used to uniformly constrain the form of the regulation model and the power flow model of the active distribution network for each type of resource based on deterministic constraints and circular constraints.

[0066] According to one embodiment of this application, the computing module 300 includes: The first acquisition unit is used to take the active distribution network regulation range enclosed by deterministic constraints and circular constraints as a high-dimensional polyhedron, and obtain the projected polyhedron of the active distribution network based on the projection of the high-dimensional polyhedron onto the grid connection point in the active distribution network. The computing unit is used to construct the circumscribed polyhedron of the projected polyhedron. It uses a preset boundary shrinkage algorithm to solve the aggregation model of the adjustment capabilities of multiple types of resources, and shrinks each vertex of the circumscribed polyhedron to the projected polyhedron through iterative detection to obtain the inscribed polyhedron. The second acquisition unit is used to obtain the aggregation and regulation range of the active distribution network based on the boundary parameters of the inscribed polyhedron.

[0067] According to one embodiment of this application, the multi-type resources include at least two of distributed photovoltaic, wind power, energy storage, electric vehicle charging stations, and temperature-controlled loads.

[0068] According to the active distribution network multi-type resource regulation capability aggregation device of this application embodiment, the output prediction error of the regulation characteristics of multiple types of resources is fitted by Gaussian distribution, and a regulation model for each type of resource is constructed. A power flow model of the active distribution network is constructed based on a preset linearized power flow model, and constraints are applied to the regulation model of each type of resource and the power flow model of the active distribution network to obtain the regulation range of the active distribution network. Based on the regulation range of the active distribution network and a preset polyhedral construction target, a multi-type resource regulation capability aggregation model is constructed. A preset boundary shrinkage algorithm is used to solve the multi-type resource regulation capability aggregation model to obtain the aggregated regulation range of the active distribution network. This solves the technical problem in related technologies where the aggregation regulation capability cannot be accurately characterized by the power grid due to the diverse and difficult-to-quantify characteristics of distributed resource regulation.

[0069] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0070] When the processor 802 executes the program, it implements the active distribution network multi-type resource regulation capability aggregation method provided in the above embodiments.

[0071] Furthermore, electronic devices also include: Communication interface 803 is used for communication between memory 801 and processor 802.

[0072] The memory 801 is used to store computer programs that can run on the processor 802.

[0073] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0074] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 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 8 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.

[0075] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0076] The processor 802 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.

[0077] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for aggregating the regulation capabilities of multiple types of resources in an active distribution network.

[0078] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the active distribution network multi-type resource regulation capability aggregation method as described in the above embodiments.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0083] 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.

[0084] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0085] 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.

[0086] 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 aggregating the regulation capabilities of multiple types of resources in an active distribution network, characterized in that, Includes the following steps: The regulation characteristics of multiple types of resources in the active distribution network are obtained, and the output prediction error of the regulation characteristics of the multiple types of resources is fitted by Gaussian distribution. Regulation models for each type of resource are then constructed. Based on a preset linearized power flow model, a power flow model of an active distribution network is constructed. The adjustment model of each type of resource and the power flow model of the active distribution network are constrained by the first constraint method and the second constraint method to obtain the adjustment range of the active distribution network. Based on the active distribution network regulation range and the preset polyhedral construction target, a regulation capacity aggregation model of the multi-type resources is constructed. The regulation capacity aggregation model of the multi-type resources is solved using a preset boundary shrinkage algorithm to obtain the aggregation regulation range of the active distribution network.

2. The method according to claim 1, characterized in that, The construction of the power flow model for the active distribution network based on the preset linearized power flow model includes: Based on the voltage-power coupling characteristics, the mapping relationship between the voltage of each node in the active distribution network, the injected power of each node in the active distribution network, and the power at the grid connection point is established using the preset linearized power flow model. A power flow model for the active distribution network is constructed based on the mapping relationship between the injected power of each node and the power at the grid connection point within the active distribution network.

3. The method according to claim 1, characterized in that, The constraint method, which uses the first constraint method and the second constraint method to constrain the adjustment model of each type of resource and the power flow model of the active distribution network, includes: The first constraint method is used to apply deterministic constraints to the adjustment model of each type of resource, while the second constraint method is used to apply circular constraints to the active power and reactive power output by the adjustment model of each type of resource. The form of the adjustment model for each type of resource and the power flow model for the active distribution network are uniformly constrained based on the deterministic constraints and the circular constraints.

4. The method according to claim 1 or 3, characterized in that, The method of solving the aggregation model of the regulation capacity of the multi-type resources using a preset boundary contraction algorithm to obtain the aggregation regulation range of the active distribution network includes: The active distribution network regulation range enclosed by deterministic constraints and circular constraints is taken as a high-dimensional polyhedron. The projection polyhedron of the active distribution network is obtained based on the projection of the high-dimensional polyhedron onto the grid connection point in the active distribution network. The circumscribed polyhedron of the projected polyhedron is constructed. A preset boundary shrinkage algorithm is used to solve the aggregation model of the adjustment capabilities of the multi-type resources. Each vertex of the circumscribed polyhedron is shrunk to the projected polyhedron through iterative detection to obtain the inscribed polyhedron. The aggregation regulation range of the active distribution network is obtained based on the boundary parameters of the inscribed polyhedron.

5. The method according to claim 1, characterized in that, The multiple resource types include at least two of the following: distributed photovoltaic, wind power, energy storage, electric vehicle charging stations, and temperature-controlled loads.

6. A device for aggregating the regulation capabilities of multiple types of resources in an active distribution network, characterized in that, include: The first construction module is used to obtain the regulation characteristics of multiple types of resources in the active distribution network, fit the output prediction error of the regulation characteristics of the multiple types of resources using a Gaussian distribution, and construct a regulation model for each type of resource respectively. The second construction module is used to construct a power flow model of an active distribution network based on a preset linearized power flow model, and to constrain the adjustment model of each type of resource and the power flow model of the active distribution network using a first constraint method and a second constraint method to obtain the adjustment range of the active distribution network. The calculation module is used to construct an aggregated model of the regulation capacity of the multi-type resources based on the regulation range of the active distribution network and a preset polyhedral construction target, and to solve the aggregated model of the regulation capacity of the multi-type resources using a preset boundary shrinkage algorithm to obtain the aggregated regulation range of the active distribution network.

7. The apparatus according to claim 6, wherein the second building module comprises: The first building unit is used to establish the mapping relationship between the voltage of each node in the active distribution network, the injected power of each node in the active distribution network, and the power of the grid connection point based on the voltage-power coupling characteristics and the preset linearized power flow model. The second construction unit is used to construct a power flow model of the active distribution network based on the mapping relationship between the injected power of each node and the power at the grid connection point within the active distribution network.

8. The apparatus of claim 6, wherein the second building module comprises: The first constraint unit is used to apply deterministic constraints to the adjustment model of each type of resource using a first constraint method, and at the same time, apply circular constraints to the active power and reactive power output by the adjustment model of each type of resource using a second constraint method. The second constraint unit is used to uniformly constrain the form of the adjustment model for each type of resource and the power flow model for the active distribution network based on the deterministic constraints and the circular constraints.

9. An electronic device, characterized in that, include: The method includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the active distribution network multi-type resource regulation capability aggregation method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the active distribution network multi-type resource regulation capability aggregation method as described in any one of claims 1-5.

Images