DERs full-time-domain aggregation operation domain calculation method and system based on PVE

Abstract

The invention provides a DERs full time domain aggregation operation domain calculation method and system based on PVE, and the method comprises the steps: constructing a VB model, and calculating a time coupling constraint; decoupling the time coupling constraint to obtain a low-dimensional operation domain; key projection variables are extracted in the low-dimensional operation domain, and a general aggregation model is formed and solved; and reconstructing the time coupling constraint in the general aggregation model to obtain an equivalent aggregation operation domain. According to the method, only one-time projection calculation is needed, and the method is particularly suitable for an online calculation application scene; the operation domain combination process at different moments does not involve the optimization process, the calculation time is not increased along with the increase of the dimension, and compared with an existing method, the calculation complexity and difficulty are greatly reduced; according to the method, eight-dimensional and more than eight-dimensional operation domains can be calculated, the equivalent operation domain obtained through solving is basically consistent with the original operation domain in space, and the calculation precision is guaranteed while the applicability is improved.

Description

Technical Field

[0001] The present invention belongs to the field of multivariate flexibility resource aggregation, and specifically, relates to a PVE-based DERs full-time domain aggregation operation domain calculation method and system, and especially to the establishment of a universal aggregation model of distributed resources and a universal model parameter solution algorithm. Background Art

[0002] The proliferation of distributed energy resources (DERs) has created huge potential for flexible regulation of power systems. In order to reduce the difficulty of regulation and management of a large number of DERs, both system operators and DERs aggregators have found that characterizing the operational domain of aggregated DERs is an effective approach.

[0003] Distributed resource aggregation has been a hot topic in recent years. According to whether a normalized shape template is used, existing distributed resource aggregation methods can be divided into progressive vertex enumeration algorithm (PVE), high-dimensional cube model (HDC), high-dimensional ellipsoid model with outer edge (CHDE), and virtual generator and virtual battery model (VG-VB). At present, how to combine the time coupling characteristics of distributed resources with the uncertainty regulation characteristics of source and load resources is a bottleneck that needs to be solved in the research of distributed resource aggregation methods. The PVE algorithm forms an operating domain by searching for each vertex of a convex polyhedron, thereby obtaining the most accurate description of the aggregated operating domain. However, since the time complexity of the convex hull algorithm grows exponentially with the time granularity, PVE can only be used for low-dimensional operating domain calculations. Unlike the PVE algorithm, other methods formulate a normalized shape template for the generated convex polyhedron to reduce the computational complexity. The VG-VB model adopts a unified prototype model of generator-like and battery-like resources to obtain a more accurate operating domain than the HDC and CHDE methods. Although the VG-VB model can characterize the time coupling characteristics, it also needs to calculate all DER related parameters, which not only complicates the calculation process, but also affects practical applications due to the possible violation of user privacy. In addition, from the perspective of uncertainty, most of the existing distributed resource aggregation methods rely on the day-ahead forecast of renewable energy generation output and load demand to estimate the overall operating domain. Due to the inevitable prediction error, the estimated operating domain cannot be used directly. It requires continuous repeated calculation of the complete operating domain based on the real-time updated source-load forecast data, resulting in high computational cost and unable to meet the intraday and real-time scheduling requirements.

[0004] The patent document "A feasible domain projection equivalent method and system" (CN114329960A) discloses that the time domain coupled feasible domain solution problem is converted into an integer linear programming solution problem through dual transformation and large M method, and the parallel umbrella constraint algorithm is used to screen and eliminate the redundant constraints in the constraints, and the outer approximation algorithm is used to obtain the accurate time domain coupled feasible domain. Although the calculation scale is reduced and the calculation efficiency is improved, the calculation time increases with the increase of dimension, and distributed resources with energy constraints such as distributed energy storage are not included, and the applicable application scenarios are relatively limited.

[0005] Therefore, there is an urgent need for a universal distributed resource aggregation method that can adapt to changing source and load scenarios. Summary of the invention

[0006] In view of the defects in the prior art, the object of the present invention is to provide a method and system for calculating the full-time domain aggregation operation domain of DERs based on PVE.

[0007] According to the present invention, a PVE-based DERs full-time domain aggregation operation domain calculation method is provided, including:

[0008] Step S1: construct the VB model and calculate the time coupling constraints;

[0009] Step S2: decouple the time coupling constraints to obtain a low-dimensional operating domain;

[0010] Step S3: extract key projection variables in the low-dimensional operation domain, construct a general aggregation model and solve it;

[0011] Step S4: Reconstruct the time coupling constraints in the general aggregation model to obtain the equivalent aggregation operation domain.

[0012] Preferably, in step S1, based on the Minkowski sum, the VB model is used to perform preliminary aggregation of DERs, and the Minkowski sum is used to solve the VB model parameters. Ω2={p A |α1 Τ p A ≤β1},

[0013] The aggregated VB model is:

[0014]

[0015] in, x t They represent variables without time coupling constraints and variables with time coupling constraints respectively;

[0016] p A represents the power of aggregated DERs;

[0017] Cx t ≤d represents the constraint set without time coupling constraints and the constraint set with time coupling constraints respectively;

[0018] A, b, C, d, E, F and g all represent the coefficient matrices of Ω1;

[0019] α1 and β1 both represent the coefficient matrix of Ω2;

[0020] Г represents the time domain set, Γ=1,...,T;

[0021] t represents the time variable;

[0022] T represents the time scale;

[0023] The superscript VB indicates the relevant parameters of the VB model;

[0024] The subscript ess indicates the energy storage system;

[0025] They represent the charging power and discharging power of the energy storage VB model respectively;

[0026] Indicates the equivalent electric quantity of VB model;

[0027] They represent the lower and upper bounds of the energy state of the VB model respectively;

[0028] Represents the energy state of the VB model at time t.

[0029] Preferably, in step S2, the VB model is eliminated Defining dummy variables replace Operational domain for time-coupling constraint decoupling

[0030]

[0031] Among them, x t represents variables without time coupling constraints;

[0032] x t represents variables with time-coupled constraints;

[0033] p A represents the power of aggregated DERs;

[0034] represents a constraint set without time coupling constraints;

[0035] A, b, E, F and g all represent the coefficient matrices of Ω1;

[0036] Г represents the time domain set, Γ=1,...,T;

[0037] t represents the time variable;

[0038] T represents the time scale;

[0039] represents the energy state of the VB model at time t;

[0040] Indicates the charging power of the VB model;

[0041] represents the discharge power of the VB model;

[0042] Indicates the equivalent electrical quantity of the VB model.

[0043] Preferably, in step S3, the time coupling constraint in Ω3 is reconstructed, and the source load variable and the time coupling variable are extracted as key projection variables. The PVE algorithm is used to project the aggregate power and key variables to obtain Change the source load variable from p A (t) separation, p F (t) = p A (t)-p RL (t), calculate the regulation domain of the original operating domain After projecting once, the source-load scene is input and combined to obtain a general aggregation model

[0044] Among them, p A represents the power of aggregated DERs;

[0045] α2 and β2 represent the matrix coefficients in Ω4;

[0046] represents the energy state of the VB model at time t;

[0047] Ω3 represents the operation domain of the decoupling of time coupling constraints;

[0048] Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables;

[0049] Represents the virtual energy storage charge state variable;

[0050] x key Represents the key variables of the projection;

[0051] p F (t), p RL (t) represent the regulated power and net load respectively;

[0052] β 3,t represents the matrix coefficients at time t;

[0053] Ω 4,t Represents the operating domain Ω4 at time t.

[0054] Preferably, in step S4, the source load parameters are input into the general aggregation model The time coupling constraint is reconstructed and reapplied to obtain

[0055] Among them, p A represents the power of aggregated DERs;

[0056] α2 and β2 represent the matrix coefficients in Ω4;

[0057] represents the energy state of the VB model at time t;

[0058] Ω3 represents the operation domain of the decoupling of time coupling constraints;

[0059] Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables;

[0060] Represents the virtual energy storage charge state variable;

[0061] x key Represents the key variables of the projection;

[0062] Г represents the time domain set, Γ=1,...,T;

[0063] t represents the time variable;

[0064] T represents the time scale;

[0065] p F (t), p RL (t) represent the regulated power and net load respectively;

[0066] α 3,t and β 3,t represents the matrix coefficients at time t;

[0067] Ω 4,t Represents the operating domain Ω4 at time t.

[0068] According to the present invention, a DERs full-time domain aggregation operation domain computing system based on PVE is provided, comprising:

[0069] Module M1: Build VB model and calculate time coupling constraints;

[0070] Module M2: decouple time coupling constraints to obtain a low-dimensional operating domain;

[0071] Module M3: Extract key projection variables in the low-dimensional operating domain, construct a general aggregation model and solve it;

[0072] Module M4: Reconstruct the time coupling constraints in the general aggregation model to obtain the equivalent aggregation operation domain.

[0073] Preferably, in the module M1, the VB model is used to perform preliminary aggregation of DERs based on the Minkowski sum, and the Minkowski sum is used to solve the VB model parameters. Ω2={p A |α1 Τ p A ≤β1},

[0074] The aggregated VB model is:

[0075]

[0076] in, x t They represent variables without time coupling constraints and variables with time coupling constraints respectively;

[0077] p A represents the power of aggregated DERs;

[0078] Cx t ≤d represents the constraint set without time coupling constraints and the constraint set with time coupling constraints respectively;

[0079] A, b, C, d, E, F and g all represent the coefficient matrices of Ω1;

[0080] α1 and β1 both represent the coefficient matrix of Ω2;

[0081] Г represents the time domain set, Γ=1,...,T;

[0082] t represents the time variable;

[0083] T represents the time scale;

[0084] The superscript VB indicates the relevant parameters of the VB model;

[0085] The subscript ess indicates the energy storage system;

[0086] They represent the charging power and discharging power of the energy storage VB model respectively;

[0087] Indicates the equivalent electric quantity of VB model;

[0088] They represent the lower and upper bounds of the energy state of the VB model respectively;

[0089] Represents the energy state of the VB model at time t.

[0090] Preferably, the module M2 eliminates Defining dummy variables replace Operational domain for time-coupling constraint decoupling

[0091]

[0092] Among them, x t represents variables without time coupling constraints;

[0093] x t represents variables with time-coupled constraints;

[0094] p A represents the power of aggregated DERs;

[0095] represents a constraint set without time coupling constraints;

[0096] A, b, E, F and g all represent the coefficient matrices of Ω1;

[0097] Г represents the time domain set, Γ=1,...,T;

[0098] t represents the time variable;

[0099] T represents the time scale;

[0100] represents the energy state of the VB model at time t;

[0101] Indicates the charging power of the VB model;

[0102] represents the discharge power of the VB model;

[0103] Indicates the equivalent electrical quantity of the VB model.

[0104] Preferably, the module M3 reconstructs the time coupling constraint in Ω3 and extracts the source-load variable and the time coupling variable as the key projection variable The PVE algorithm is used to project the aggregate power and key variables to obtain Change the source load variable from p A (t) separation, p F (t) = p A (t)-pRL (t), calculate the regulation domain of the original operating domain After projecting once, the source-load scene is input and combined to obtain a general aggregation model

[0105] Among them, p A represents the power of aggregated DERs;

[0106] α2 and β2 represent the matrix coefficients in Ω4;

[0107] represents the energy state of the VB model at time t;

[0108] Ω3 represents the operation domain of the decoupling of time coupling constraints;

[0109] Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables;

[0110] Represents the virtual energy storage charge state variable;

[0111] x key Represents the key variables of the projection;

[0112] p F (t), p RL (t) represent the regulated power and net load respectively;

[0113] β 3,t represents the matrix coefficients at time t;

[0114] Ω 4,t Represents the operating domain Ω4 at time t.

[0115] Preferably, the module M4 inputs the source-load parameters into the general aggregation model The time coupling constraint is reconstructed and reapplied to obtain

[0116] Among them, p A represents the power of aggregated DERs;

[0117] α2 and β2 represent the matrix coefficients in Ω4;

[0118] represents the energy state of the VB model at time t;

[0119] Ω3 represents the operation domain of the decoupling of time coupling constraints;

[0120] Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables;

[0121] Represents the virtual energy storage charge state variable;

[0122] x key Represents the key variables of the projection;

[0123] Г represents the time domain set, Γ=1,...,T;

[0124] t represents the time variable;

[0125] T represents the time scale;

[0126] p F (t), p RL (t) represent the regulated power and net load respectively;

[0127] α 3,t and β 3,t represents the matrix coefficients at time t;

[0128] Ω 4,t Represents the operating domain Ω4 at time t.

[0129] Compared with the prior art, the present invention has the following beneficial effects:

[0130] 1. The operating domain calculation method proposed in the present invention only needs to perform a one-time projection calculation to quickly solve the full-time domain aggregation operating domain according to the source load prediction data, which is particularly suitable for online computing application scenarios.

[0131] 2. The process of combining domains at different times in the present invention does not involve an optimization process, and the calculation time does not increase with the increase of dimensions. Compared with the existing DERs aggregation method, the calculation complexity and difficulty are greatly reduced.

[0132] 3. The present invention can calculate operating domains of 8 dimensions or above, and the equivalent operating domain obtained by solving is basically consistent with the original operating domain space, which improves applicability while ensuring calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0134] Figure 1 It is a flow chart of the calculation method of the full-time domain aggregation operation domain of DERs based on PVE;

[0135] Figure 2 A flow chart of the method for building a generic operational domain model;

[0136] Figure 3 Schematic diagram of reconstruction of coupling constraints for full-time aggregation operation of DERs based on PVE. DETAILED DESCRIPTION

[0137] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0138] According to a PVE-based DERs full-time aggregated operation domain calculation method provided by the present invention, the time coupling characteristics of distributed resources and source-load uncertainty are taken into consideration. Figure 1 For example, the following steps are included:

[0139] Step S1: VB model parameter calculation.

[0140] Since the number of distributed resources is large and contains time coupling constraints, the projection of all time-coupled variables of all distributed resources is not feasible. Therefore, the DERs are firstly aggregated based on the Minkowski and applied VB models to reduce the dimension of the projected variables.

[0141] The VB model parameter calculation, the specific steps are as follows:

[0142] The distributed resource aggregation problem can be described as projecting from the original high-dimensional operation domain Ω1 to the low-dimensional operation domain Ω2. Ω1 and Ω2 can be described as:

[0143]

[0144] Ω2={p A |α1 Τ p A ≤β1} (2)

[0145] in, x t They represent variables without time coupling constraints and variables with time coupling constraints respectively;

[0146] p A represents the power of aggregated DERs;

[0147] p A , and x t The relationship between ( Cx t ≤d represent the constraint set without time coupling constraint and the set with time coupling constraint respectively. );

[0148] A, b, C, d, E, F, g represent the coefficient matrix of Ω1.

[0149] α1 and β1 represent the coefficient matrices of Ω2.

[0150] Г represents the time domain set, t represents the time variable, Γ=1,...,T, and T represents the time scale.

[0151] First, the VB model is used to aggregate energy storage DERs. Aggregating energy storage DERs using the VB model will not significantly reduce the accuracy of the operating domain. The Minkowski sum is used to solve the VB model parameters. The aggregated VB model can be expressed as:

[0152]

[0153] The superscript VB represents the relevant parameters of the VB model. In fact, the above VB model only has Needs to be calculated.

[0154] The subscript ess stands for energy storage system. and Respectively represent the charging power and discharging power of the energy storage VB model, represents the equivalent electric quantity of VB model, They represent the lower and upper bounds of the energy state (SOC) of the VB model respectively. Represents the energy state of the VB model at time t.

[0155] Step S2: Decoupling of time coupling constraints.

[0156] In order to solve the problem of high temporal dimension, a decoupling-projection-recoupling mechanism is established for the operation domain of aggregated DERs. The temporal coupling constraints are eliminated to achieve the decoupling of temporal coupling constraints. The original high-dimensional coupled operation domain of temporal coupling is decomposed into a single-cycle low-dimensional decoupled operation domain.

[0157] The time coupling constraint decoupling, the specific steps are as follows:

[0158] In the VB model, equation (5) is a time coupling constraint. Eliminating it can decouple the original operation domain. However, eliminating equation (5) will eliminate the relationship between power and SOC. Therefore, a dummy variable is defined. To replace the In this way, time coupling decoupling is achieved. Therefore, the operation domain Ω3 of time coupling constraint decoupling can be described as:

[0159]

[0160] Among them, x t represents variables without time coupling constraints, represents the charging power of the VB model, represents the discharge power of the VB model, Indicates the equivalent electrical quantity of the VB model.

[0161] Step S3: Key variable projection.

[0162] by Figure 2 Taking the low-dimensional decoupled operation domain as an example, source-load variables and time coupling variables are extracted as key projection variables, and a universal projection format suitable for any source-load scenario is constructed. The PVE algorithm is used to project the aggregated power and key variables, and the full-time universal DERs aggregation model solution is obtained, thus realizing complete offline calculation of the full-time operation domain.

[0163] The specific steps of obtaining the projection key variables are as follows:

[0164] Each moment in Ω3 can be solved independently, making the original problem simple. However, in order to accurately characterize the original operating domain, the time coupling constraints need to be reconstructed. Therefore, we achieve the recoupling of the decoupled operating domain by characterizing the relationship between the time coupling variables and the aggregate power. Since the model in Ω3 is a linear model, the "operating domain" of the aggregate power and SOC is a convex polyhedron, which can be solved using the PVE algorithm. The specific projection variables can be expressed as: The projection problem can be described as projecting Ω1 to Ω4, where Ω4 is:

[0165]

[0166] Among them, α2 and β2 represent the matrix coefficients in Ω4 and are the parameters that need to be solved in this problem.

[0167] Ω3 represents the operation domain of time coupling constraint decoupling, Ω4 represents the operation domain of time coupling constraint decoupling considering key variables, represents the virtual energy storage charge state variable, x key Represents the key variable of the projection.

[0168] The projection problem at each moment in Ω4 can be described as:

[0169]

[0170] Among them, due to p A (t) contains source load data, which results in different operation domains at different times. Therefore, in order to avoid repeated calculations, we change the source load variable from p A (t) Separation (p F (t) = p A (t)-p RL (t), where p F (t), p RL (t) represents the regulation power and net load respectively), that is, the regulation domain of the original operation domain is calculated. The new projection problem can be described as:

[0171]

[0172] The regulation domain in formula (10) is the same at each moment. Therefore, only one projection operation is required, and then the source-load scene is input and combined to obtain Ω4.

[0173] Among them, Ω 4,t Represents the operating domain Ω4 at time t.

[0174] Step S4: Temporal coupling constraint reconstruction.

[0175] by Figure 3 As an example, the source-load parameters are input into the above general aggregation model, and the time coupling constraints are reconstructed to obtain the equivalent DERs aggregation operation domain.

[0176] The time coupling constraint reconstruction, the specific steps are as follows:

[0177] Since the energy storage variables are projected, the time coupling constraints can be re-imposed on them. Finally, the full time domain operation domain Ω6 can be described as:

[0178]

[0179] The operation and calculation method only requires one projection step, and the combination process of the operation domains at different times does not involve an optimization process. Compared with the existing DERs aggregation method, the calculation complexity is greatly reduced, and it is very suitable for scenarios where the operation domain needs to be calculated quickly.

[0180] In more preferred cases, day-ahead and real-time load data and power data of PV and wind power are scaled according to public datasets in the EU and its neighboring regions. All algorithms are developed using MATLAB R2023a and the commercial optimization solver GUROBI and executed on a PC platform with 16GB RAM and Intel Core i7-12700H cpu (2.70GHz).

[0181] The PVE algorithm is used for comparison. The PVE algorithm uses aggregate power as the projection variable, and the number of projection time periods T is the projection dimension of the method, and the maximum number of iterations is set to 100. After the source-load scenario is input into the DERs full-time aggregate operation domain calculation method based on PVE provided by the present invention, the aggregated DERs operation domain in any time domain can be obtained by combining the above-mentioned general projection model and supplementing the time coupling constraint. The calculation time and calculation accuracy of the two methods for calculating the operation domain with different number of time periods are shown in Table 1.

[0182] Table 1. Verification of calculation efficiency and accuracy:

[0183] PVE Method of the present invention T＝4 Calculation time 115.176s 10.542s Calculation accuracy 0 0 T＝8 Calculation time - 10.751s Calculation accuracy - 0 T＝12 Calculation time - 10.727s Calculation accuracy - 0 T＝24 Calculation time - 10.674s Calculation accuracy - 0

[0184] When the calculation accuracy is less than 10-10, the value is approximately considered to be 0. In terms of the operation domain calculation time, since the operation domain projection only needs to be calculated once, the calculation time does not increase with the increase of dimension, which significantly reduces the calculation difficulty of the operation domain; however, due to the inherent shortcomings of the convex hull algorithm, the PVE algorithm is difficult to calculate the operation domain of 8 dimensions and above. In terms of the operation domain calculation accuracy, the calculation accuracy is zero, and it can be considered that the equivalent operation domain obtained by the solution is basically consistent with the original operation domain space; while the PVE method can only accurately calculate the 4-dimensional operation domain.

[0185] The present invention also provides a PVE-based DERs full-time-domain aggregation operation domain computing system, which can be implemented by executing the process steps of the PVE-based DERs full-time-domain aggregation operation domain computing method, that is, those skilled in the art can understand the PVE-based DERs full-time-domain aggregation operation domain computing method as a preferred implementation of the PVE-based DERs full-time-domain aggregation operation domain computing system.

[0186] According to the present invention, a DERs full-time domain aggregation operation domain computing system based on PVE is provided, comprising:

[0187] Module M1: Build VB model and calculate time coupling constraints;

[0188] Module M2: decouple time coupling constraints to obtain a low-dimensional operating domain;

[0189] Module M3: Extract key projection variables in the low-dimensional operating domain, construct a general aggregation model and solve it;

[0190] Module M4: Reconstruct the time coupling constraints in the general aggregation model to obtain the equivalent aggregation operation domain.

[0191] In more preferred embodiments, the module M1 uses the VB model to perform preliminary aggregation of DERs based on the Minkowski sum, and uses the Minkowski sum to solve the VB model parameters. Ω2={p A |α1 Τ p A ≤β1},

[0192] The aggregated VB model is:

[0193]

[0194] in, x t They represent variables without time coupling constraints and variables with time coupling constraints respectively;

[0195] p A represents the power of aggregated DERs;

[0196] Cx t ≤d represents the constraint set without time coupling constraints and the constraint set with time coupling constraints respectively;

[0197] A, b, C, d, E, F and g all represent the coefficient matrices of Ω1;

[0198] α1 and β1 both represent the coefficient matrix of Ω2;

[0199] Г represents the time domain set, Γ=1,...,T;

[0200] t represents the time variable;

[0201] T represents the time scale;

[0202] The superscript VB indicates the relevant parameters of the VB model;

[0203] The subscript ess indicates the energy storage system;

[0204] They represent the charging power and discharging power of the energy storage VB model respectively;

[0205] Indicates the equivalent electric quantity of VB model;

[0206] They represent the lower and upper bounds of the energy state of the VB model respectively;

[0207] Represents the energy state of the VB model at time t.

[0208] In more preferred embodiments, the module M2 eliminates the Defining dummy variables replace Operational domain for time-coupling constraint decoupling

[0209] Among them, x t represents variables without time coupling constraints;

[0210] x t represents variables with time-coupled constraints;

[0211] p A represents the power of aggregated DERs;

[0212] represents a constraint set without time coupling constraints;

[0213] A, b, E, F and g all represent the coefficient matrices of Ω1;

[0214] Г represents the time domain set, Γ=1,...,T;

[0215] t represents the time variable;

[0216] T represents the time scale;

[0217] represents the energy state of the VB model at time t;

[0218] Indicates the charging power of the VB model;

[0219] represents the discharge power of the VB model;

[0220] Indicates the equivalent electrical quantity of the VB model.

[0221] In more preferred embodiments, the module M3 reconstructs the time coupling constraint in Ω3, extracting the source load variable and the time coupling variable as the key projection variable The PVE algorithm is used to project the aggregate power and key variables to obtain Change the source load variable from p A (t) separation, p F (t) = p A (t)-p RL (t), calculate the regulation domain of the original operating domain After projecting once, the source-load scene is input and combined to obtain a general aggregation model

[0222] Among them, p A represents the power of aggregated DERs;

[0223] α2 and β2 represent the matrix coefficients in Ω4;

[0224] represents the energy state of the VB model at time t;

[0225] Ω3 represents the operation domain of the decoupling of time coupling constraints;

[0226] Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables;

[0227] Represents the virtual energy storage charge state variable;

[0228] x key Represents the key variables of the projection;

[0229] p F (t), pRL (t) represent the regulated power and net load respectively;

[0230] β 3,t represents the matrix coefficients at time t;

[0231] Ω 4,t Represents the operating domain Ω4 at time t.

[0232] In more preferred embodiments, the module M4 inputs the source load parameters into the general aggregation model The time coupling constraint is reconstructed and reapplied to obtain

[0233] Among them, p A represents the power of aggregated DERs;

[0234] α2 and β2 represent the matrix coefficients in Ω4;

[0235] represents the energy state of the VB model at time t;

[0236] Ω3 represents the operation domain of the decoupling of time coupling constraints;

[0237] Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables;

[0238] Represents the virtual energy storage charge state variable;

[0239] x key Represents the key variables of the projection;

[0240] Г represents the time domain set, Γ=1,...,T;

[0241] t represents the time variable;

[0242] T represents the time scale;

[0243] p F (t), p RL (t) represent the regulated power and net load respectively;

[0244] α 3,t and β 3,t represents the matrix coefficients at time t;

[0245] Ω 4,t Represents the operating domain Ω4 at time t.

[0246] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0247] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A PVE-based DERs full-time aggregated operation domain calculation method, characterized in that: include: Step S1: construct the VB model and calculate the time coupling constraints; Step S2: decouple the time coupling constraints to obtain a low-dimensional operating domain; Step S3: extract key projection variables in the low-dimensional operation domain, construct a general aggregation model and solve it; Step S4: Reconstruct the time coupling constraints in the general aggregation model to obtain the equivalent aggregation operation domain.

2. The PVE-based DERs full-time aggregated operation domain calculation method according to claim 1 is characterized in that: In step S1, the VB model is used to perform preliminary aggregation of DERs based on the Minkowski sum, and the Minkowski sum is used to solve the VB model parameters. Ω2={p A |α1 Τ p A ≤β1}, The aggregated VB model is: in, x t They represent variables without time coupling constraints and variables with time coupling constraints respectively; p A represents the power of aggregated DERs; Cx t ≤d represents the constraint set without time coupling constraints and the constraint set with time coupling constraints respectively; A, b, C, d, E, F and g all represent the coefficient matrices of Ω1; α1 and β1 both represent the coefficient matrix of Ω2; Г represents the time domain set, Γ=1,...,T; t represents the time variable; T represents the time scale; The superscript VB indicates the relevant parameters of the VB model; The subscript ess indicates the energy storage system; They represent the charging power and discharging power of the energy storage VB model respectively; Indicates the equivalent electric quantity of VB model; They represent the lower and upper bounds of the energy state of the VB model respectively; Represents the energy state of the VB model at time t.

3. The PVE-based DERs full-time aggregated operation domain calculation method according to claim 1 is characterized in that: In step S2, the VB model is eliminated Defining dummy variables replace Operational domain for time-coupling constraint decoupling Among them, x t represents variables without time coupling constraints; x t represents variables with time-coupled constraints; p A represents the power of aggregated DERs; represents a constraint set without time coupling constraints; A, b, E, F and g all represent the coefficient matrices of Ω1; Г represents the time domain set, Γ=1,...,T; t represents the time variable; T represents the time scale; represents the energy state of the VB model at time t; Indicates the charging power of the VB model; represents the discharge power of the VB model; Indicates the equivalent electrical quantity of the VB model.

4. The PVE-based DERs full-time aggregated operation domain calculation method according to claim 1 is characterized in that: In step S3, the time coupling constraint in Ω3 is reconstructed, and the source load variable and the time coupling variable are extracted as key projection variables. The PVE algorithm is used to project the aggregate power and key variables to obtain Change the source load variable from p A (t) separation, p F (t) = p A (t)-p RL (t), calculate the regulation domain of the original operating domain After projecting once, the source-load scene is input and combined to obtain a general aggregation model Among them, p A represents the power of aggregated DERs; α2 and β2 represent the matrix coefficients in Ω4; represents the energy state of the VB model at time t; Ω3 represents the operation domain of the decoupling of time coupling constraints; Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables; Represents the virtual energy storage charge state variable; x key Represents the key variables of the projection; p F (t), p RL (t) represent the regulated power and net load respectively; β 3,t represents the matrix coefficients at time t; Ω 4,t Represents the operating domain Ω4 at time t.

5. The PVE-based DERs full-time aggregated operation domain calculation method according to claim 1 is characterized in that: In step S4, the source load parameters are input into the general aggregation model. The time coupling constraint is reconstructed and reapplied to obtain Among them, p A represents the power of aggregated DERs; α2 and β2 represent the matrix coefficients in Ω4; represents the energy state of the VB model at time t; Ω3 represents the operation domain of the decoupling of time coupling constraints; Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables; Represents the virtual energy storage charge state variable; x key Represents the key variables of the projection; Г represents the time domain set, Γ=1,...,T; t represents the time variable; T represents the time scale; p F (t), p RL (t) represent the regulated power and net load respectively; α 3,t and β 3,t represents the matrix coefficients at time t; Ω 4,t Represents the operating domain Ω4 at time t.

6. A PVE-based DERs full-time domain aggregation operation domain computing system, characterized in that: include: Module M1: Build VB model and calculate time coupling constraints; Module M2: decouple time coupling constraints to obtain a low-dimensional operating domain; Module M3: Extract key projection variables in the low-dimensional operating domain, construct a general aggregation model and solve it; Module M4: Reconstruct the time coupling constraints in the general aggregation model to obtain the equivalent aggregation operation domain.

7. The PVE-based DERs full-time domain aggregation operation domain computing system according to claim 6 is characterized in that: In the module M1, the VB model is used to perform preliminary aggregation of DERs based on the Minkowski sum, and the Minkowski sum is used to solve the VB model parameters. Ω2={p A |α1 Τ p A ≤β1}, The aggregated VB model is: in, x t They represent variables without time coupling constraints and variables with time coupling constraints respectively; p A represents the power of aggregated DERs; Cx t ≤d represents the constraint set without time coupling constraints and the constraint set with time coupling constraints respectively; A, b, C, d, E, F and g all represent the coefficient matrices of Ω1; α1 and β1 both represent the coefficient matrix of Ω2; Г represents the time domain set, Γ=1,...,T; t represents the time variable; T represents the time scale; The superscript VB indicates the relevant parameters of the VB model; The subscript ess indicates the energy storage system; They represent the charging power and discharging power of the energy storage VB model respectively; Indicates the equivalent electric quantity of VB model; They represent the lower and upper bounds of the energy state of the VB model respectively; Represents the energy state of the VB model at time t.

8. The PVE-based DERs full-time domain aggregation operation domain computing system according to claim 6 is characterized in that: The module M2 eliminates the VB model Defining dummy variables replace Operational domain for time-coupling constraint decoupling Among them, x l represents variables without time coupling constraints; x t represents variables with time-coupled constraints; p A represents the power of aggregated DERs; represents a constraint set without time coupling constraints; A, b, E, F and g all represent the coefficient matrices of Ω1; Г represents the time domain set, Γ=1,...,T; t represents the time variable; T represents the time scale; represents the energy state of the VB model at time t; Indicates the charging power of the VB model; represents the discharge power of the VB model; Indicates the equivalent electrical quantity of the VB model.

9. The PVE-based DERs full-time domain aggregation operation domain computing system according to claim 6 is characterized in that: The module M3 reconstructs the time coupling constraints in Ω3 and extracts the source-load variables and the time coupling variables as key projection variables. The PVE algorithm is used to project the aggregate power and key variables to obtain Change the source load variable from p A (t) separation, p F (t) = p A (t)-p RL (t), calculate the regulation domain of the original operating domain After projecting once, the source-load scene is input and combined to obtain a general aggregation model Among them, p A represents the power of aggregated DERs; α2 and β2 represent the matrix coefficients in Ω4; represents the energy state of the VB model at time t; Ω3 represents the operation domain of the decoupling of time coupling constraints; Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables; Represents the virtual energy storage charge state variable; x key Represents the key variables of the projection; p F (t), p RL (t) represent the regulated power and net load respectively; β 3,t represents the matrix coefficients at time t; Ω 4,t Represents the operating domain Ω at time t 4 .

10. The PVE-based DERs full-time domain aggregation operation domain computing system according to claim 6 is characterized in that: The module M4 inputs the source and load parameters into the general aggregation model The time coupling constraint is reconstructed and reapplied to obtain Among them, p A represents the power of aggregated DERs; α2 and β2 represent the matrix coefficients in Ω4; represents the energy state of the VB model at time t; Ω3 represents the operation domain of the decoupling of time coupling constraints; Ω4 represents the decoupled operation domain considering the time coupling constraints of key variables; Represents the virtual energy storage charge state variable; x key Represents the key variables of the projection; Г represents the time domain set, Γ=1,...,T; t represents the time variable; T represents the time scale; p F (t), p RL (t) represent the regulated power and net load respectively; α 3,t and β 3,t represents the matrix coefficients at time t; Ω 4,t Represents the operating domain Ω4 at time t.

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