A virtual power plant aggregation method and system considering spatio-temporal coupling characteristics

Abstract

The present invention relates to the technical field of virtual power plant aggregation, and specifically discloses a virtual power plant aggregation method and system considering spatio-temporal coupling characteristics. The method includes: selecting a single network node in the virtual power plant area; selecting a single type of distributed resource connected to the node to obtain a control variable parameter matrix and a vector composed of constrained constants; according to the power-energy characteristics of the distributed resource at different time sequences, selecting a set of search directions; taking the maximization of the sum of the extension lengths in each dimension direction as the optimization objective, establishing an optimal linear decision model for aggregation parameters; solving the model to obtain an optimal Chino polyhedron approximate feasible region; and further obtaining the Chino polyhedron approximate aggregation feasible region of each type of resource at the node. The present invention transforms the Minkowski sum process of the polyhedron into a linear superposition by selecting different search directions, providing feasibility for the feasible region aggregation of massive and diverse distributed resources in a high-dimensional space.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant aggregation, and particularly to a virtual power plant aggregation method and system considering spatio-temporal coupling characteristics. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] A virtual power plant (VPP) is a model that aggregates dispersed distributed energy resources (DERs) through advanced information and communication technology (ICT) and an energy management system to form a power supply or demand response capability that can be uniformly dispatched.

[0004] Regarding the virtual power plant aggregation method, existing research has proposed using the half-plane representation form of convex polytopes (Polytopes) to describe resource flexibility. Compared with the traditional vertex-based representation form, the computational efficiency is improved, but it still cannot meet the aggregation requirements of large-scale resources; the accurate aggregation of the high-dimensional feasible region space still faces the problem of inaccurate calculation.

[0005] Existing technologies have proposed an aggregation method for approximate processing of the feasible region. However, in the approximate solution process, the following technical problems often exist:

[0006] The differences in electricity demand and physical characteristics of various resources in the virtual power plant and their spatio-temporal coupling result in a complex process of describing the general resource model; at the same time, the high-dimensional space of the feasible regions of various distributed resources easily leads to problems such as low computational efficiency in feasible region aggregation methods such as Minkowski summation. Summary of the Invention

[0007] To solve the above problems, the present invention proposes a virtual power plant aggregation method and system considering spatio-temporal coupling characteristics. Considering the time characteristics of different distributed resources and the coupling characteristics of their power and energy, using the concept of an affine-based safety domain, it effectively processes the spatial coupling relationship between resources, improves the computational efficiency, and at the same time realizes the effective characterization of the virtual power plant aggregation feasible region in the time / space dimension.

[0008] In some embodiments, the following technical solutions are adopted:

[0009] A virtual power plant aggregation method considering spatio-temporal coupling characteristics, comprising:

[0010] Select a single type of distributed resource accessed by the virtual power plant regional network node, and based on the principle of approximation of the Chino polyhedron, select a set of search directions according to the power-energy characteristics of the distributed resource at different time sequences;

[0011] Taking the maximization of the sum of the extended lengths in each dimension direction as the optimization objective, an optimal linear decision model for aggregation parameters is established; the model is solved to obtain the optimal Chino polyhedron approximate feasible region of the distributed resource;

[0012] Calculate the optimal Chino polyhedron approximate feasible region of each type of distributed resource of the network node. By selecting different search directions, the Minkowski sum process of the polyhedron is transformed into a linear superposition to obtain the optimal Chino polyhedron approximate feasible region of the network node;

[0013] Considering the spatial coupling characteristics of the virtual power plant, with the goal of maximizing the sum of the maximum extended interval lengths of the feasible region at the point of common coupling, the optimal Chino polyhedron approximate feasible region is corrected to obtain the final aggregation feasible region.

[0014] As a further solution, the distributed resources at least include: air-conditioning load, distributed energy storage, micro gas turbines, and distributed photovoltaics; for distributed photovoltaics, the opportunity constraint method is used to transform the constraints containing random variables into deterministic constraints, and using the moment information of the random variables, the opportunity constraint is transformed into a tractable linear constraint.

[0015] As a further solution, based on the Chino polyhedron approximation principle, according to the power-energy characteristics of the distributed resources at different time sequences, a set of search directions is selected, specifically:

[0016] The set of search directions for air-conditioning load and distributed energy storage consists of and constitutes, the set of search directions for distributed photovoltaics consists of constitutes, and the set of search directions for micro gas turbines consists of and constitutes;

[0017] Among them, includes 2N search directions, and each search direction represents the power operation boundary of the point of common coupling at a certain moment, which is used to approximate the extension direction of the aggregated resources at each decision moment;

[0018] includes N - 1 search directions, and each search direction can represent the difference in power between the next time period and the current time period, which is used to approximate the extension direction of the power time coupling constraint of the aggregated resources:

[0019] includes N - 1 search directions, which characterize the sum of energy changes in a certain time period, and is expressed as the sum of power values over a certain period of time, which is used to approximate the extension direction of the power-energy coupling constraint of the aggregated resources;

[0020] N is the time scale.

[0021] As a further solution, the optimal linear decision model of the aggregation parameters is specifically:

[0022] ;

[0023] wherein, L is the sum of the extended lengths in each dimension direction, , the maximum extended length vector in each dimension direction, is the search direction matrix; is the coordinate of the center point of the polyhedron; is the non - negative Farkas multiplier, is the control variable parameter matrix, is the vector composed of the constants of the constraints, is the original feasible region of the distributed resources, i is P the identifier of the constraint in is the j th non - negative Farkas multiplier of the th dimension, j is the non - negative Farkas multiplier of the (N + th dimension, j is the th dimension of the control variable parameter matrix, j is the

[0024] As a further solution, considering the spatial coupling characteristics of the virtual power plant, with the goal of maximizing the sum of the maximum extended interval lengths of the feasible region at the point of common coupling, the approximate feasible region of the optimal Chino polyhedron is corrected, specifically:

[0025] Taking the internal resource equivalent adjustable output constraint, participation factor constraint, maximum extended amount constraint of the feasible region at the point of common coupling, virtual power plant internal network constraint, and coupling point power constraint as the constraint conditions, and with the goal of maximizing the sum of the maximum extended interval lengths of the feasible region at the point of common coupling, a target function is constructed;

[0026] The non - linear constraints in the constraint conditions are linearized; a commercial solver is used to solve the optimization model composed of the target function and the constraint conditions to obtain the corrected aggregated feasible region.

[0027] As a further solution, the target function is specifically:

[0028] ;

[0029] wherein, and are respectively the oThe lower and upper limits of the maximum extension of the feasible region; t refers to a certain time period, and N refers to the number of decision-making moments for day-ahead scheduling.

[0030] In some other embodiments, the following technical solution is adopted:

[0031] A virtual power plant aggregation system considering spatio-temporal coupling characteristics, comprising:

[0032] A search direction selection module that selects a single type of distributed resource connected to the nodes of the virtual power plant area network, and based on the principle of approximation by a Chino polyhedron, selects a set of search directions according to the power-energy characteristics of the distributed resource at different time sequences;

[0033] A feasible region solving module that takes maximizing the sum of the extension lengths in each dimension direction as the optimization goal, establishes an optimal linear decision model for aggregation parameters; solves the model to obtain the optimal Chino polyhedron approximation feasible region of the distributed resource;

[0034] A feasible region aggregation module that calculates the optimal Chino polyhedron approximation feasible region of each type of distributed resource of the network node, and transforms the Minkowski sum process of the polyhedron into a linear superposition by selecting different search directions to obtain the optimal Chino polyhedron approximation feasible region of the network node;

[0035] A feasible region correction module that considers the spatial coupling characteristics of the virtual power plant, and takes maximizing the sum of the maximum extension interval lengths of the feasible regions at the common coupling points as the goal, corrects the optimal Chino polyhedron approximation feasible region to obtain the final aggregation feasible region.

[0036] In some other embodiments, the following technical solution is adopted:

[0037] A terminal device, which includes a processor and a memory. The processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor to perform the above-mentioned virtual power plant aggregation method considering spatio-temporal coupling characteristics.

[0038] In some other embodiments, the following technical solution is adopted:

[0039] A computer-readable storage medium, in which multiple instructions are stored, and the instructions are suitable for being loaded and executed by the processor of the terminal device to perform the above-mentioned virtual power plant aggregation method considering spatio-temporal coupling characteristics.

[0040] In some other embodiments, the following technical solution is adopted:

[0041] A computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the above-mentioned virtual power plant aggregation method considering spatio-temporal coupling characteristics is implemented.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] (1) According to the linear constraints of various types of distributed resources independently participating in the optimal dispatching of the distribution network, the present invention obtains a vector composed of the control variable parameter matrix and the constants of the constraints; calculates the overall feasible region of each type of resource and the overall aggregated feasible region of a single node respectively; aiming at the difficult problem of the Minkowski sum in high-dimensional space caused by the temporal coupling characteristics of the power and energy of virtual power plant resources, based on the principle of approximation by Chino polyhedron, the present invention proposes a Chino polyhedron aggregation method with temporal coupling direction search; by selecting different search directions, the Minkowski sum process of the polyhedron is transformed into a linear superposition, providing feasibility for the aggregation of the feasible regions of a large number of diverse distributed resources in high-dimensional space.

[0044] (2) In order to make the calculated approximate Chino polyhedron approach the original feasible region to the greatest extent, the present invention constructs an optimal linear decision model for aggregation parameters with the goal of maximizing the sum of the extension lengths in each dimension direction, and realizes the solution of the optimal aggregation parameters by solving the corresponding linear programming (LP) problem, solves the problem of the complex dimensionality of the feasible region space for the aggregation regulation of multiple types of resources, and realizes the accurate characterization of the feasible region of multi-energy heterogeneous resources considering the coupling characteristics.

[0045] (3) Based on the efficient Zonotope solution method of Farkas lemma, the present invention constructs an optimal linear decision model for aggregation parameters, avoids the non-linear problem of using the Hausdorff distance method, and effectively improves the calculation efficiency of the virtual power plant aggregation method.

[0046] (4) The present invention considers the spatial coupling characteristics of the virtual power plant, effectively processes the spatial coupling relationship between resources, and corrects the aggregated feasible region, solves the problem that the obtained approximate feasible region ignores the influence of the spatial constraints of the distribution system network where the virtual power plant is located, and improves the calculation accuracy of the feasible region of the point of common coupling of the virtual power plant.

[0047] Other features and advantages of the present invention will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of this aspect. Brief Description of the Drawings

[0048] Figure 1 It is a flow chart of the virtual power plant aggregation method considering spatio-temporal coupling characteristics in the embodiment of the present invention. Detailed Embodiment

[0049] It should be noted that the following detailed description is illustrative and is intended to provide further description of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0051] Term Explanation:

[0052] "Aggregation parameter": That is, the matrix of matrix control variable parameters and the vector composed of constraint constants elements.

[0053] "Feasible region": Refers to the dynamic boundary range within which the distributed resources aggregated by the virtual power plant can safely, reliably, and economically respond to grid dispatching or market demand under specific operating conditions; it is a set of feasible operations within a multi-dimensional constraint space formed by mathematizing the physical constraints, response characteristics, and collaborative capabilities of dispersed resources.

[0054] Power-energy characteristic: Represents the functional relationship between power and energy.

[0055] Embodiment 1

[0056] In one or more embodiments, a virtual power plant aggregation method considering spatio-temporal coupling characteristics is disclosed, combined with Figure 1 , and specifically includes the following processes:

[0057] S101: Select a single network node in the virtual power plant area; select a single type of distributed resource connected to this node, and based on the linear constraints of the distributed resource independently participating in the distribution network optimal dispatching, obtain the control variable parameter matrix and the vector composed of constraint constants.

[0058] In this embodiment, the distributed resources considered for virtual power plant aggregation mainly include air-conditioning loads, distributed energy storage, distributed photovoltaic, micro gas turbines, etc. The linear constraints of each type of distributed resource independently participating in the distribution network optimal dispatching constitute the physical model of its own original feasible region, and the basic mathematical form is as follows:

[0059] (1)

[0060] Wherein, is the original feasible region of the distributed resource; is the vector composed of decision variables, generally the unit output or load; is the control variable parameter matrix, is the vector composed of constraint constants; denotes N an n-dimensional real vector space, which represents the time dimension hereinafter.

[0061] The specific distributed resources are as follows:

[0062] (1) Air-conditioning load

[0063] For the establishment of a single air-conditioning load model, a first-order equivalent thermal parameter model is commonly used to describe the thermal dynamic process inside and outside the room. The model is:

[0064] (2)

[0065] (3)

[0066] Among them, is the indoor temperature, is the outdoor temperature; is the cooling capacity of the air conditioner; and are both thermodynamic parameters, which are the thermal resistance of the indoor room and the heat capacity of the indoor air respectively; is the working efficiency parameter of the air conditioner; is the electric power of the air conditioner. According to Equation (2), the dynamic electrical model of the air conditioner is derived as:

[0067] (4)

[0068] (5)

[0069] (6)

[0070] Among them, is t the indoor temperature at time period t; and are the upper and lower limits of the comfortable temperature in the room respectively; is t the outdoor temperature at time period t; is t the electric power of the air conditioner at time period t; is the maximum power of the air conditioner; is the decision time step.

[0071] In the process of aggregating and regulating air conditioners as flexibility resources, since the thermodynamic parameters, efficiency, outdoor temperature, etc. of individual rooms are all regarded as constants, let , , Equation (4) can be simplified as:

[0072] (7)

[0073] (2)Distributed energy storage

[0074] The constraints of distributed energy storage include power constraints and state of charge (SOC) operation constraints. The model is as follows:

[0075] (8)

[0076] (9)

[0077] (10)

[0078] (11)

[0079] (12)

[0080] Among them, and are respectively t the discharging and charging powers of the energy storage at time is t the charge-discharge state variable of the energy storage at time, 1 for discharging and 0 for charging; and are respectively the maximum charging and discharging powers of the energy storage; is t the state of charge of the energy storage at time is the charge-discharge efficiency of the energy storage; and are respectively the minimum and maximum states of charge of the energy storage; is the initial state of charge of the energy storage within the scheduling period; is the end state of charge of the energy storage within the scheduling period.

[0081] is the power of the energy storage at time t. When it is positive, it represents discharging, and when it is negative, it represents charging.

[0082] (3)Micro gas turbine

[0083] Only the output limit and ramp rate limit of the micro gas turbine are considered.

[0084] (13)

[0085] (14)

[0086] Among them, is t the output of the micro gas turbine during the period is t the operating state of the generator at time, 1 for operating and 0 for shutdown; and are the maximum and minimum power outputs of the generator, respectively; and are the maximum upward and downward ramping capabilities of the generator in a single time period, respectively.

[0087] (4) Distributed PV

[0088] (15)

[0089] (16)

[0090] Among them, is t the active power output by the distributed PV at time represents the expected value of the PV output, is the maximum active power output of the distributed PV; is a random variable representing the uncertainty of the distributed PV output.

[0091] Since the optimization model cannot directly handle constraints containing random variables, in this embodiment, the chance-constraint method is used to transform the constraints containing random variables into deterministic constraints. According to the empirical results, the prediction error of the distributed PV output can be well modeled by a parametric normal distribution, that is, the random variable , where is t the covariance matrix of the distributed PV prediction error at time

[0092] (17)

[0093] Among them, represents the risk level of generator overload, and usually a relatively small value (e.g., 5%) is selected.

[0094] Since the chance-constraint formula (17) cannot be directly solved, in this embodiment, the moment information of the random variable is used to transform the chance-constraint into a tractable linear constraint for facilitating the solution of the model. The specific derivation process is as follows:

[0095] Formula (17) can be transformed into the following form:

[0096] (18)

[0097] Let , where satisfies , . According to the principle of the one-sided Chebyshev inequality, it can be obtained that:

[0098] (19)

[0099] (20)

[0100] The infimum calculation depends on When it is equivalent to the square of the distance from the origin to the hyperplane, at this time is:

[0101] (21)

[0102] Furthermore, it can be obtained that:

[0103] (22)

[0104] Among them, .

[0105] Through the above processing, the complex probability optimization problem can be simplified into a convex optimization model that can be efficiently solved, improving the efficiency.

[0106] In summary, the modeling of the internal resources of the virtual power plant includes constraints (5) - (14), (22), which respectively constitute the high-dimensional expressions of the feasible regions of various types of resources aggregated by the virtual power plant.

[0107] The above-mentioned various types of resources can all be written in the mathematical form of formula (1).

[0108] S102: Based on the principle of approximation of the Chino polyhedron, according to the power-energy characteristics of the distributed resources at different time sequences, a set of search directions is selected.

[0109] This embodiment is based on the principle of approximation of the Chino polyhedron and proposes a Chino polyhedron aggregation method for time-sequence coupling direction search. The basic model is as follows:

[0110] (23)

[0111] (24)

[0112] (25)

[0113] (26)

[0114] Among them, is the coordinate of the center point of the polyhedron; is the search direction matrix, which is composed of M direction vectors constitute, indicating that the polyhedron extends from the center point to each direction; is the extension length vector in each direction, which is limited by the maximum extension length ; Denote the 2-norm operation; is the i-th direction vector, is the M-th extension length vector.

[0115] For the selected same search direction matrix of the Chino polyhedra, due to their consistent extension directions, the Minkowski sum process of these Chino polyhedra is simplified to an exclusive OR operation between each Chino polyhedron:

[0116] (27)

[0117] (28)

[0118] Among them, is the number of distributed resource entities at a certain node; is the center point coordinate of the aggregated polyhedron; is the s-th center point coordinate; and is the sum of the total extension length and the extension length limit in the m -th search direction of the aggregated polyhedron, and is the sum of the extension length and the extension length limit of the s -th polyhedron before aggregation in the m -th search direction.

[0119] By selecting different search directions, the Minkowski sum process of the polyhedron is transformed into a simple linear superposition, providing feasibility for the aggregation of the feasible region in the high-dimensional space of massive multi-source distributed resources.

[0120] Facing the constructed various distributed resource feasible region models, the selection of the extension direction matrix is crucial for the accuracy of using the Chino polyhedron to approximate the original feasible region of the resources.

[0121] When constructing the search direction vector, in this embodiment, according to the natural attributes and regulation flexibility of the internal resources of the virtual power plant, the coupling and complementary characteristics of the power-energy of various types of resources on the time scale are extracted, fully exploiting the spatio-temporal coordination potential of the multi-type resources of the virtual power plant, and enhancing the robustness and economy of the virtual power plant in dealing with complex working conditions.

[0122] (1) Basic time series constraints

[0123] Include 2N search directions, where each search direction represents the power operation boundary of the common coupling point at a certain moment, and is used to approximate the extension direction of the aggregated resources at each decision moment:

[0124] (29)

[0125] Among them, is the Kronecker symbol, which is 1 when i = j , and 0 otherwise.

[0126] (2) Time coupling constraint

[0127] It includes N - 1 search directions, where each search direction can represent the difference in power between the next time period and the current time period, and is used to approximate the extension direction of the aggregated resource power time coupling constraint:

[0128] (30)

[0129] (3) Power - energy coupling constraint

[0130] It includes N - 1 search directions, representing the sum of energy changes in a certain time period, which is shown as the sum of power values over a certain period of time, and is used to approximate the extension direction of the aggregated resource power - energy coupling constraint:

[0131] (31)

[0132] For the temperature - controlled load constraint (7) such as air conditioners, its energy change is not the sum of the work done by the power in each time period. When the power is 0, there is also a linear relationship between the energy variables at adjacent moments, and its mathematical expression is:

[0133] (32)

[0134] (33)

[0135] Among them, is the energy variable at time t, is the power variable at time t, is the external influence parameter at time t, and are both proportional parameters.

[0136] According to formula (33), the search direction vector of the power - energy coupling constraint in general form can be obtained , and the specific expression is as follows:

[0137] (34)

[0138] Among them, , , are all sets of search directions. i and j represent the rows and columns in the matrix, N represents the matrix dimension, and h is the introduced auxiliary variable.

[0139] It should be noted that N is determined by the time scale considered by the model. If 1 hour is considered as the decision step, then N is 24; if 15 minutes is considered, then N is 96.

[0140] In summary, the search direction matrices for various types of resources can be obtained. Among them, the search direction matrices for air-conditioning load and distributed energy storage are composed of and , and the search direction matrix for distributed photovoltaic is composed of ; the search direction matrix for micro gas turbines is composed of and . By constructing the search direction matrices for various types of resources, the coupling problem of time and space can be solved.

[0141] S103: Taking the maximization of the sum of the extension lengths in each dimension direction as the optimization objective, establish an optimal linear decision model for aggregation parameters; solve the model to obtain the optimal approximate feasible region of the Chino polyhedron.

[0142] For each type of resource, the calculated Chino polyhedron Z and the original feasible region space P are both high-dimensional spaces generated from the same data source. In order to make the calculated approximate Chino polyhedron approach the original feasible region to the greatest extent, this embodiment adopts a high-dimensional space generation method based on the Farkas condition, and solves the corresponding linear programming (LP) problem of this method to achieve the solution of the optimal aggregation parameters.

[0143] The specific process is as follows:

[0144] The definition of the Chino polyhedron in formula (23) can be rewritten as:

[0145] (33)

[0146] Among them, and are auxiliary variables, representing the transformed decision coefficients, which are specifically as follows:

[0147] (34)

[0148] Among them, is the dimensional identity matrix.

[0149] In order to make , for any point coordinate Z in x to satisfy each constraint P of the original feasible region , i is the identifier of the constraint in P , according to Farkas' lemma, there exist non-negative Farkas multipliers such that P and Z satisfy the coefficient matching condition and the right - hand side constraint:

[0150] (35)

[0151] (36)

[0152] Substituting formula (34) gives, for the j th dimension:

[0153] (37)

[0154] (38)

[0155] Furthermore, it can be sorted out to obtain:

[0156] (39)

[0157] To make the calculated approximate Chinohedron approach the original feasible region to the greatest extent, the objective function is set to maximize the sum of the extension lengths in each dimension direction .

[0158] In summary, the aggregated parameter optimal linear decision model is sorted out as follows:

[0159] (40)

[0160] where L is the sum of the extension lengths in each dimension direction, , is the vector of the maximum extension lengths in each dimension direction, is a column vector, and each dimension direction has its own maximum extension length; is the search direction matrix; is the coordinate of the polyhedron center point; is the non - negative Farkas multiplier, is the control variable parameter matrix, is the vector composed of the constants of the constraints, is the original feasible region of the distributed resources, i is P the identifier of the constraint in; is the non - negative Farkas multiplier of the j th dimension, is the non - negative Farkas multiplier of the (N + j )th dimension, is the j th dimension of the control variable parameter matrix, is the j th dimension of the coordinates of the center point of the polyhedron.

[0161] By solving the linear programming (LP) problem corresponding to the optimal linear decision model of the aggregation parameters, the optimal aggregation parameters are obtained.

[0162] By solving, the coordinates of the center point of the optimal aggregated polyhedron can be obtained , as well as the total extension length of the aggregated polyhedron in each search direction , and the sum of the extension length limits of the aggregated polyhedron in the m th search direction , so as to obtain the optimal Chino polyhedron approximate feasible region.

[0163] In this embodiment, an optimal linear decision model for aggregation parameters is established, and the process of obtaining the optimal Chino polyhedron approximate feasible region through model solution realizes the accurate and efficient solution of the feasible region of aggregated resources.

[0164] S104: Calculate the optimal Chino polyhedron approximate feasible region for each type of resource of this node; perform Minkowski summation to form the Chino polyhedron approximate aggregation feasible region of each type of resource of this node.

[0165] Using the same method as in S101 - S103, calculate the optimal Chino polyhedron approximate feasible region for other types of distributed resources of this node; perform Minkowski summation on the optimal Chino polyhedron approximate feasible regions of all distributed resources to form the Chino polyhedron approximate aggregation feasible region of each type of resource of this node.

[0166] Through the method of this embodiment, the process of aggregating the feasible regions is transformed into simple superposition in several directions, solving the problem of difficult solution of the Minkowski sum of polyhedrons, and providing feasibility for the aggregation of feasible regions in the high - dimensional space of massive multi - source distributed resources.

[0167] S105: Characterize the output boundary of the point of common coupling of the virtual power plant considering the spatial coupling characteristics.

[0168] The external aggregation characteristics of the virtual power plant should project the high - dimensional model with high coupling characteristics into the external power characteristics of the point of common coupling (PCC). However, the approximate feasible region obtained in the above process ignores the influence of the network space constraints of the distribution system where the virtual power plant is located. In fact, the network power flow will cause a large deviation in the calculation of the feasible region of the virtual power plant's point of common coupling. Therefore, based on the optimal feasible region obtained above, this embodiment corrects the aggregation feasible region considering the spatial coupling characteristics of the virtual power plant.

[0169] (I) Constraint conditions

[0170] (1)Internal resource equivalent adjustable output constraint

[0171] The upper and lower limits of the output of resources such as internal air-conditioning loads, distributed photovoltaics, energy storage, and micro gas turbines in the virtual power plant are calculated The boundaries are:

[0172] (41)

[0173] For node n The explicit expression of the display of the feasible region of the Chino polyhedron approximation is as follows:

[0174] (42)

[0175] Among them, " " represents the parameters corresponding to any one of the resources such as internal air-conditioning loads, distributed photovoltaics, energy storage, and micro gas turbines in the virtual power plant; represents the power at the center point of the feasible region of a certain type of resource, represents the extension length of the feasible region of this type of resource, represents the operating power of this type of resource; is the extension length, is the upper limit of the extension length.

[0176] (2)Participation factor constraint

[0177] The external power of the PCC is distributed among the resource units of various types at each node according to the participation factor. Since the power adjustment amount of the unit needs to match the external power of the PCC, the sum of the participation factors is 1.

[0178] (43)

[0179] Among them, represents the column vector of the participation factor, represents the column vector with all elements being 1.

[0180] (3)Maximum extension amount constraint of the feasible region of the point of common coupling

[0181] (44a)

[0182] (44b)

[0183] (45a)

[0184] (45b)

[0185] Among them, and respectively represent node n of thes The physical upper and lower limits of the output of distributed resources. and respectively represent the maximum upward and downward regulation capacities of a certain type of distributed resource connected to node n ; and are respectively the maximum extension amounts of the feasible region of the point of common coupling o ; represents the participation factor of a certain resource, represents the power at the center point of the feasible region of a certain type of resource, represents the extension length of the feasible region of this type of resource, represents the number of distributed resource entities on a certain node.

[0186] (4) Internal network constraints of the virtual power plant

[0187] The network power flow constraints of the distribution system where the virtual power plant is located mainly consider line power flow constraints, node voltage constraints, node power balance constraints, and node power regulation range constraints.

[0188] (46)

[0189] (47)

[0190] (48)

[0191] (49)

[0192] (50)

[0193] Among them, , , , and are respectively the active power, reactive power, conductance, susceptance, and maximum apparent power of the line with both ends being nodes , ; is the square value of the voltage of node n ; and are respectively the upper and lower limits of the square of the voltage; is the voltage phase angle of node n ; , , are respectively the active power, reactive power, and power factor angle of node n ; is the adjustment amount of the output of a certain type of resource on node n . is the set of branch connections on the node n ; is the square of the voltage of node n 1, is the square of the voltage of node n 2, is the phase angle of node n 1, is the phase angle of node n 2, is the output of a certain type of resource on node n ; The two end nodes are , is the active power of the line.

[0194] (4) Coupling point power constraint

[0195] (51)

[0196] (52)

[0197] Among them, is the active power output by the common coupling point o ; is each node When the common coupling point o Output active power; is the end node of the line connected to the common coupling point o The active power flowing from n to the common coupling point o .

[0198] (II) Objective function

[0199] The objective of this embodiment is to maximize the volume of the feasible region of the virtual power plant at the common coupling point o in the distribution system. To avoid the non-linear problems caused by using the volume formula of a super polyhedron, the objective function uses the sum of the maximum extension interval lengths of the feasible region of the common coupling point to approximately replace the objective function form of maximizing the volume of the feasible region in the high-dimensional space.

[0200] (54)

[0201] Among them, and are respectively the lower and upper limits of the maximum extension of the feasible region of the common coupling point o ; t refers to a certain time period, and N refers to the number of decision moments for day-ahead scheduling (when each 1 hour is a decision moment, N is 24).

[0202] (III) Model linearization processing

[0203] In the above model, equations (44) and (47) are non - linear constraints. In this embodiment, the McCormick envelope method and the inscribed - circle linearization method are respectively used to linearize these two groups of constraints.

[0204] The constraint conditions of equation (44) contain bilinear variables, and all bilinear terms are continuous variables. Therefore, the big - M method cannot be directly applied to achieve linear processing. It is planned to relax the constraint (44) by constructing a McCormick envelope. However, and do not have upper and lower limits in the physical sense and cannot directly apply the McCormick envelope method. Therefore, the relaxation boundaries of and are first proposed.

[0205] (55)

[0206] Wherein, and satisfy the following formula:

[0207] (56)

[0208] Wherein, represents the set of network nodes except node o .

[0209] Subsequently, equations (44a) and (44b) are respectively transformed into linear constraint groups (57a) and (57b):

[0210] (57a)

[0211] (57b)

[0212] For the constraint conditions of equation (47), according to the inscribed - circle linearization method, in this paper, the outer circumference of the constraint region represented by equation (47) K is equally divided, and each division point is successively connected to obtain a regular K - sided polygon inscribed in the circle. That is, the region enclosed by the regular K - sided polygon inscribed in the circle is used to approximately replace the region represented by the original constraint. Therefore, equation (47) can be transformed into the following formula.

[0213] (58)

[0214] Wherein,

[0215] (59)

[0216] Through linearization processing, the solution difficulty of the model can be reduced, an MILP problem that can be directly solved by a commercial solver can be obtained, and then the corrected aggregated feasible region can be obtained, improving the solution efficiency.

[0217] Example Two

[0218] In one or more embodiments, a virtual power plant aggregation system considering spatio-temporal coupling characteristics is disclosed, including:

[0219] A search direction selection module that selects a single type of distributed resource connected to the network nodes in the virtual power plant area, and based on the principle of approximation of the Chino polyhedron, selects a set of search directions according to the power-energy characteristics of the distributed resource at different time sequences;

[0220] A feasible region solution module that establishes an optimal linear decision model for aggregation parameters with the goal of maximizing the sum of the extended lengths in each dimension direction; solves the model to obtain the optimal Chino polyhedron approximation feasible region of the distributed resource;

[0221] A feasible region aggregation module that calculates the optimal Chino polyhedron approximation feasible region of each type of distributed resource of the network node, and transforms the Minkowski sum process of the polyhedron into a linear superposition by selecting different search directions to obtain the optimal Chino polyhedron approximation feasible region of the network node;

[0222] A feasible region correction module that corrects the optimal Chino polyhedron approximation feasible region with the goal of maximizing the sum of the maximum extended interval lengths of the feasible region at the point of common coupling, considering the spatial coupling characteristics of the virtual power plant, to obtain the final aggregated feasible region.

[0223] It should be noted that the specific implementation manners of the above modules are the same as those in Example One and will not be elaborated here.

[0224] Example Three

[0225] In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory. The processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor to perform the virtual power plant aggregation method considering spatio-temporal coupling characteristics described in Example One.

[0226] Example Four

[0227] In one or more embodiments, a computer-readable storage medium is disclosed, in which multiple instructions are stored, and the instructions are suitable for being loaded and executed by the processor of the terminal device to perform the virtual power plant aggregation method considering spatio-temporal coupling characteristics described in Example One.

[0228] Example Five

[0229] In one or more embodiments, a computer program product is disclosed, including a computer program / instructions, which, when executed by a processor, implement the virtual power plant aggregation method considering spatio-temporal coupling characteristics described in the first embodiment.

[0230] Although the specific embodiments of the present invention are described above in conjunction with the accompanying drawings, they do not limit the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. A virtual power plant aggregation method considering spatio-temporal coupling characteristics, characterized in that, Including: Select a single type of distributed resource accessed by the virtual power plant regional network nodes. Based on the principle of approximation by the Chino polyhedron, select a set of search directions according to the power-energy characteristics of the distributed resource at different time sequences. Taking the maximization of the sum of the extension lengths in each dimension direction as the optimization objective, establish an optimal linear decision model for aggregation parameters; solve the model to obtain the optimal Chino polyhedron approximation feasible region of the distributed resource. Calculate the optimal Chino polyhedron approximation feasible region of each type of distributed resource of the network node. By selecting different search directions, transform the Minkowski sum process of the polyhedron into a linear superposition to obtain the optimal Chino polyhedron approximation feasible region of the network node. Considering the spatial coupling characteristics of the virtual power plant, with the goal of maximizing the sum of the maximum extension interval lengths of the common coupling point feasible region, correct the optimal Chino polyhedron approximation feasible region to obtain the final aggregation feasible region. Considering the spatial coupling characteristics of the virtual power plant, with the goal of maximizing the sum of the maximum extension interval lengths of the common coupling point feasible region, the correction of the optimal Chino polyhedron approximation feasible region is specifically as follows: Taking the internal resource equivalent adjustable output constraint, participation factor constraint, maximum extension amount constraint of the common coupling point feasible region, virtual power plant internal network constraint, and coupling point power constraint as constraint conditions, and taking the maximization of the sum of the maximum extension interval lengths of the common coupling point feasible region as the goal, construct an objective function. Linearize the non-linear constraints in the constraint conditions; use a commercial solver to solve the optimization model composed of the objective function and constraint conditions to obtain the corrected aggregation feasible region. The specific form of the objective function is: ； Among them, and are respectively the lower limit and the upper limit of the maximum extension of the feasible region of the common coupling point o ; t refers to a certain time period, and N refers to the number of decision-making moments for day-ahead scheduling.

2. The virtual power plant aggregation method considering spatio-temporal coupling characteristics according to claim 1, wherein, The distributed resource at least includes: air-conditioning load, distributed energy storage, micro gas turbine, and distributed photovoltaic; for distributed photovoltaic, use the chance constraint method to transform the constraint containing random variables into a deterministic constraint, and use the moment information of the random variable to transform the chance constraint into a tractable linear constraint.

3. The virtual power plant aggregation method considering spatio-temporal coupling characteristics according to claim 1, characterized in that Based on the principle of approximation by the Chino polyhedron, select a set of search directions according to the power-energy characteristics of the distributed resource at different time sequences, specifically as follows: The air-conditioning load and distributed energy storage search direction sets are composed of and The distributed PV search direction set is composed of The micro gas turbine search direction set is composed of and constitute; Among them, it includes 2N search directions, and each search direction represents the power operation boundary of the common coupling point at a certain moment, and is used to approximately represent the extension direction of the aggregated resources at each decision moment; Including N - 1 search directions, where each search direction can represent the difference in power between the next time period and the current time period, and is used to approximate the extended direction of the time coupling constraint of the aggregated resource power: It includes N - 1 search directions, representing the sum of energy changes in a certain time period, and the display expression is the summation of power values within a certain period of time, which is used to approximately aggregate the extended direction of the power - energy coupling constraint of resources; N is the time scale.

4. A virtual power plant aggregation method considering spatio-temporal coupling characteristics as described in claim 1, characterized in that The optimal linear decision model for aggregation parameters is specifically: ； Among them, L is the sum of the extension lengths in each dimension direction, , the maximum extension length vector in each dimension direction, is the search direction matrix; is the coordinate of the center point of the polyhedron; is the non - negative Farkas multiplier, is the control variable parameter matrix, is the vector composed of the constants of the constraints, is the original feasible region of the distributed resources, i is P the identifier of the constraint in; is the j non - negative Farkas multiplier of the th dimension, j is the non - negative Farkas multiplier of the (N + th dimension of the control variable parameter matrix, j is the th dimension of the coordinate of the center point of the polyhedron, j th dimension.

5. A virtual power plant aggregation system considering spatio-temporal coupling characteristics, characterized in that, Including: A search direction selection module that selects a single type of distributed resource accessed by the virtual power plant regional network nodes. Based on the principle of approximation by the Chino polyhedron, select a set of search directions according to the power-energy characteristics of the distributed resource at different time sequences. A feasible region solving module that takes the maximization of the sum of the extension lengths in each dimension direction as the optimization objective, establishes an optimal linear decision model for aggregation parameters; solves the model to obtain the optimal Chino polyhedron approximation feasible region of the distributed resource. A feasible region aggregation module that calculates the optimal Chino polyhedron approximation feasible region of each type of distributed resource of the network node. By selecting different search directions, transform the Minkowski sum process of the polyhedron into a linear superposition to obtain the optimal Chino polyhedron approximation feasible region of the network node. The feasible region correction module takes into account the spatial coupling characteristics of the virtual power plant, and aims to maximize the sum of the maximum extension interval lengths of the feasible regions at the point of common coupling, and corrects the approximate feasible region of the optimal Chino polyhedron to obtain the final aggregated feasible region; Taking into account the spatial coupling characteristics of the virtual power plant, and aiming to maximize the sum of the maximum extension interval lengths of the feasible regions at the point of common coupling, the correction of the approximate feasible region of the optimal Chino polyhedron is specifically as follows: Taking the equivalent adjustable output constraint of internal resources, participation factor constraint, maximum extension amount constraint of the feasible region at the point of common coupling, internal network constraint of the virtual power plant, and power constraint at the coupling point as constraint conditions, and aiming to maximize the sum of the maximum extension interval lengths of the feasible regions at the point of common coupling, a target function is constructed; Linearize the non-linear constraints in the constraint conditions; apply a commercial solver to solve the optimization model composed of the target function and constraint conditions to obtain the corrected aggregated feasible region; The specific form of the target function is: ； Among them, and are respectively the lower limit and the upper limit of the maximum extension of the feasible region of the common coupling point o ; t refers to a certain time period, and N refers to the number of decision-making moments for day-ahead scheduling.

6. A terminal device, comprising a processor and a memory, the processor being configured to implement instructions; the memory being configured to store a plurality of instructions, characterized in that, The instruction is suitable for being loaded and executed by a processor to perform the virtual power plant aggregation method considering spatio-temporal coupling characteristics according to any one of claims 1-4.

7. A computer-readable storage medium storing multiple instructions, characterized in that, The instruction is suitable for being loaded and executed by a processor of a terminal device to perform the virtual power plant aggregation method considering spatio-temporal coupling characteristics according to any one of claims 1-4.

8. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by a processor, it implements the virtual power plant aggregation method considering spatio-temporal coupling characteristics according to any one of claims 1-4.

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