Multi-element aggregation regulation and control method suitable for user-side flexible resources

Abstract

The invention discloses a multi-element aggregation regulation and control method suitable for user-side flexible resources, and belongs to the field of power system operation and control. The multi-aggregation regulation and control method is designed for aggregating the large-scale user side flexibility resource regulation capability to participate in centralized management and control and coordinated scheduling, meanwhile, the system flexibility requirement is considered, and in order to meet the flexible supply and demand balance and weaken the conservative property of inner approximation feasible region calculation under the condition of avoiding an infeasible solution, the method has a good application prospect. According to the method, the flexible resource aggregation feasible region can be accurately described, the optimization effect is not affected while the solving time is remarkably shortened, and the method can be used for solving the flexible resource aggregation feasible region under the constraints of three aspects of the disposable computing power, the storage space and the computing time. And centralized control and coordinated scheduling of large-scale flexible resources are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and planning, and relates to a multi-element aggregation control method suitable for user-side flexibility resources. Background Art

[0002] With the advancement of the construction of new power systems in my country's power system, the proportion of new energy sources has continued to rise, and the speed of electric energy substitution on the energy consumption side has significantly accelerated. This change has led to a decrease in the proportion of traditional power sources, while the demand for electricity has increased, which has exacerbated the challenges of the randomness and volatility of new energy sources to the balance of supply and demand in the power system. Therefore, the system's demand for flexible regulation capabilities has increased, and it has become a key issue to ensure that the power system has sufficient flexible regulation capabilities in the face of new energy and load fluctuations. At present, the increase in emerging flexibility resources has enhanced the regulation potential of the power system. However, due to the dispersion and heterogeneity of user-side flexibility resources, their huge scale causes the system to face the "curse of dimensionality" problem during scheduling. This problem limits the effective use of the regulation potential in terms of available computing power, storage space, and computing time.

[0003] To improve the aggregation efficiency of flexibility resources, researchers have proposed aggregation methods based on convex polyhedron interior approximation, based on Minkowski and technology, to characterize the regulation capacity of individual flexibility resources. However, as the number of resources increases, these methods face high-dimensional nonlinear problems and are difficult to solve accurately. To simplify the problem, scholars have proposed approximate linear summation methods, such as vertex method, half-plane method and zonotope method, which have high accuracy but low efficiency. In addition, overall approximation methods based on virtual generator and virtual battery models have been proposed, which improve the solution efficiency but sacrifice a certain degree of accuracy. In order to balance the solution accuracy and efficiency, researchers have also explored internal approximation and external approximation methods. The internal approximation ensures that only feasible solutions are involved, while the external approximation may contain infeasible solutions. The maximum volume approximation is performed through convex optimization technology, which improves the solution efficiency and effectively solves the computational difficulties in the aggregation of large-scale flexibility resources. Therefore, how to balance accuracy and efficiency while reducing computational complexity remains a challenge to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-element aggregation and control method suitable for user-side flexibility resources, so as to cope with the dual challenges of aggregation speed and accuracy faced by current user-side resources due to the dispersion and heterogeneity of user-side flexibility resources, so as to make full use of the regulation potential of user-side flexibility resources and realize centralized control and coordinated scheduling of large-scale flexibility resources.

[0005] A multi-aggregation control method applicable to user-side flexibility resources, the method comprising the following steps:

[0006] Step S1: construct a similarity index between the power generation of new energy and the load power, and further construct an ideal new energy power generation scenario model that meets the similarity index assuming that the ideal new energy power generation and the predicted new energy power generation are equivalent;

[0007] Step S2: construct a model based on flexibility resources, where the flexibility resources include user-side energy storage, electric vehicles, and temperature control loads. The model is used to characterize operating characteristics and regulatory response capabilities.

[0008] Step S3: quantifying the aggregate response capability of the model in step S2 by Minkowski's aggregation method based on approximation within a convex polyhedron;

[0009] Step S4: Introduce the operating cost into the aggregation method in step S3 and further construct a multivariate aggregation control model.

[0010] Furthermore, step S1 is specifically as follows:

[0011] Step S11: Calculate the time series similarity and shape similarity between the renewable energy power generation and the load demand, and construct a similarity index between the renewable energy power generation and the load power, as shown below:

[0012]

[0013] Where P N is the power generated by renewable energy, R(P N ,P L ) is the similarity index between the power generation of renewable energy and the power consumption of loads, and its value range is [-1,1]. If R(P N ,P L ) is closer to 1, indicating that the curve fluctuations of renewable energy power generation and load power consumption are more similar; if it is closer to 0, it means that the two are completely dissimilar; N Represents the power generated by renewable energy; P L Indicates load power; cov(P N ,P L ) represents the covariance between the power generated by renewable energy and the power consumed by loads, Represents the variance of renewable energy power generation and load power consumption;

[0014] Step S12: Consider ideal new energy power generation And load power P L The similarity index R(P N ,P L ), and assuming that the ideal new energy power generation and the predicted new energy power generation are equivalent, an ideal new energy power generation scenario model that meets the similarity index is constructed, which is specifically expressed as follows:

[0015]

[0016] in, It is expressed as the flexible adjustment power required for the new energy-load in period t, and r is the target value of the similarity between the new energy power generation power and load fluctuation under ideal conditions. The closer it is to 1, the more similar the curve fluctuations of the new energy power generation power and load demand are; if it is closer to 0, it means that the two are completely dissimilar.

[0017] Furthermore, step S2 is specifically as follows:

[0018] Step S21: Based on the conditional constraints of the flexible resources including user-side energy storage, electric vehicles, and temperature control loads during operation, combined with the operating characteristics and regulation capabilities, a general compact expression is established as follows:

[0019]

[0020] In the formula, is the constraint space of the user-side energy storage i, which is essentially a polyhedron constrained by a set of linear inequality equations. S,i The matrix is ​​the decision variable of the user-side energy storage i, W S,i 、M S,i is the parameter matrix and vector of the user-side energy storage i; is the constraint space of the i-th electric vehicle in cluster k, W EVk,i 、M EVk,i denote the coefficient matrix and vector of the i-th electric vehicle in cluster k, Z EVk,i The matrix is ​​the decision variable; is the constraint space of the temperature control load of the i-th unit in cluster k, Z HVAC,k,i The matrix is ​​the decision variable matrix of the temperature control load of the i-th unit in cluster k, W HVAC,k,i 、M HVAC,k,i are the parameter matrix and vector of the temperature control load respectively;

[0021] Step S22: convert formula (3) into a user-side flexibility resource individual flexibility model:

[0022]

[0023] In the formula, is the flexible feasible domain of the i-th flexible resource in any cluster k, the intersection of the bounded sets consisting of 4T closed half-space polyhedra, and are the upper and lower limits of the decision variables, Z k,i represents the decision variable; Represents the real number space.

[0024] Furthermore, step S3 is specifically as follows:

[0025] Step S31: The total flexibility set of the limited flexibility resource group can be expressed as the Minkowski sum of the individual flexibility sets given by equation (4), as shown below:

[0026]

[0027] In the formula, is a Full-dimensional polyhedron,Since individual flexibility sets are usually viewed as H-polyhedrons, computing the Minkowski sum of two H-polyhedrons is an NP-hard problem;

[0028] Step S32: To reduce the complexity of the model, the polyhedron is calculated using a method with high computational efficiency. The set of inner approximations, a polyhedron satisfy Polyhedron Benchmark After the radiation transformation, it can be expressed as formula (6):

[0029]

[0030] In the formula, and are the translation and scaling factors to ensure that the polyhedron Closely approximate the original polyhedron

[0031] Step S33: Considering that the averaging method is simple and easy to implement and has a good approximation effect, it is used to perform the benchmark setting as follows:

[0032]

[0033] Among them, M k,0 is the mean value matrix of individual parameters of flexibility resources, N k is the number of flexible resources in cluster k;

[0034] Step S34: Calculate the polyhedrons included in the aggregate flexibility set by solving the following optimal polyhedron inclusion problem: The maximum volume within can be expressed as:

[0035]

[0036] Wherein, Vol() represents the volume of the polyhedron P;

[0037] The feasible solution of the above equation is called the inner approximation of the set flexibility set, and an easily solvable goal is pursued by seeking a conservative approximation with a tractable convex programming;

[0038] Step S35: In this case, first construct an internal approximation of each individual flexibility set, given by the following formula:

[0039]

[0040] in, and are translation and scaling factors to ensure that each polyhedron Closely approximate its corresponding constraint space

[0041] Step S36: Obtained heterogeneous The Minkowski sum of a polyhedron is an aggregate flexibility set The internal approximation of is:

[0042]

[0043] Avoid computing the Minkowski sum by summing the elements of each transformation As shown below:

[0044]

[0045] Can be set and

[0046] In the polyhedron inclusion problem, in order to maximize the inner approximation volume, the volume of the inner approximation subset polyhedron should be the largest, and its volume is determined by the following formula:

[0047]

[0048] Where det(P) represents the absolute value of the determinant of the matrix;

[0049] therefore, The maximum volume is equivalent to maximizing |det(P)|. To further simplify the model, we use the identity matrix

[0050] The order Taylor expansion linearizes |det(P)|:

[0051]

[0052] The meaning of Tr() represents the trace of the matrix;

[0053] Where P is a non-singular matrix, constant is a constant term;

[0054] According to Farkas theorem, there exists a matrix Gi such that G i W=WΓ i , G iM0≤Μ i -Wγ i , so the volume of the objective function in the formula can be linearly approximated to obtain the maximum approximation of the optimal inner approximation polyhedron inclusion problem:

[0055]

[0056] The above formula represents the decision variables A, B, γ i , Γ i and G i Linear programming conditions of ;

[0057] Aggregate feasible domain using Minkowski and computational flexibility resources The feasible domain of user-side energy storage, electric vehicles and temperature control loads is aggregated as follows:

[0058]

[0059] Represents the feasible domain of the aggregate of multiple flexibility resources; represents the feasible region of energy storage; represents the feasible domain of electric vehicles; represents the feasible domain of temperature control load; Represents the Minkowski summation symbol.

[0060] Furthermore, step S4 is specifically as follows:

[0061] Step S41: Based on the aggregation result of the user-side flexibility resources, a multivariate aggregation control model suitable for the user-side flexibility resources is constructed. In order to ensure that the system flexibility requirements are maximized while keeping the operating cost as low as possible, the multivariate aggregation control model is constructed as shown in the formula:

[0062]

[0063] In the formula, g(x)≥α, h(x)=β are the inequality constraint and equality constraint on the decision variable x respectively;

[0064] F1 represents the minimum system flexibility requirement, and F2 represents the minimum cost;

[0065] Step S42: according to the importance of objectives F1 and F2, weights are set for linear weighting, thereby converting into a single-objective optimization problem; in this paper, λ1 and λ2 are respectively set to 0.5, and the specific weight setting can be adjusted according to the needs of decision makers; F1 and F2 can be expressed as:

[0066] F1=sum(abs(F t flex -F t up -Ft down ));

[0067] F2=C g +C N +C S +C EV +C HVAC +C FL ;

[0068]

[0069] In the formula, F1 represents the minimum difference between the system flexibility deficit and the adjustable capacity of the flexibility resources, that is, the system reaches the optimal state, and the flexibility deficit and redundancy are minimized; F t flex 、F t up 、F t down They are flexibility demand, flexibility upward adjustment ability and flexibility downward adjustment ability, F t S,up 、F t EV,up 、F t HVAC,up and F t S,down 、F t EV,down 、F t HVAC,down Corresponding to the upward and downward adjustment capabilities of energy storage, electric vehicles and temperature control loads, C g , C N , C S , C EV , C HVAC , C FL They are respectively the cost of purchasing electricity for surplus electricity, the operating cost of new energy, the operating cost of energy storage, the operating cost of electric vehicles and temperature control loads, and their compensation costs.

[0070] The huge scale of the scattered heterogeneous flexibility resources on the user side causes the system to face the "curse of dimensionality" problem during scheduling. In order to avoid limitations in computing power, storage space and computing time, the regulation potential of user-side flexibility resources has not been fully utilized. The present invention uses the inner approximate Minkowski method to aggregate user-side flexibility resources, reducing the conservatism in the inner approximate feasible domain calculation, thereby comprehensively improving the efficiency and accuracy of the flexibility resource aggregation algorithm. In addition, a comprehensive optimization scheduling model is constructed, which integrates the energy storage system, electric vehicles (electric vehicles) and HVAC systems (temperature control loads) on the user side, while ensuring that the system flexibility requirements are maximized, maintaining the lowest operating cost, and significantly improving the solution speed of the scheduling algorithm.

[0071] The present invention realizes a method for regulating and controlling the multi-aggregation participation of user-side flexibility resources, which can accurately describe the feasible domain of flexibility resource aggregation and significantly shorten the solution time without affecting the optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 The present invention is a flowchart of the steps of a multi-aggregation control method applicable to user-side flexibility resources. DETAILED DESCRIPTION

[0075] Reference will now be made in detail to exemplary embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings.

[0076] Reference Figure 1 , a multi-aggregation control method applicable to user-side flexibility resources, the method comprising the following steps:

[0077] Step S1: construct a similarity index between the power generation of new energy and the load power, and further construct an ideal new energy power generation scenario model that meets the similarity index assuming that the ideal new energy power generation and the predicted new energy power generation are equivalent;

[0078] Step S2: construct a model based on flexibility resources, where the flexibility resources include user-side energy storage, electric vehicles, and temperature control loads. The model is used to characterize operating characteristics and regulatory response capabilities.

[0079] Step S3: quantifying the aggregate response capability of the model in step S2 by Minkowski's aggregation method based on approximation within a convex polyhedron;

[0080] Step S4: Introduce the operating cost into the aggregation method in step S3 and further construct a multivariate aggregation control model.

[0081] Furthermore, step S1 is specifically as follows:

[0082] Step S11: Calculate the time series similarity and shape similarity between the renewable energy power generation and the load demand, and construct a similarity index between the renewable energy power generation and the load power, as shown below:

[0083]

[0084] Where P N is the power generated by renewable energy, R(P N ,P L ) is the similarity index between the power generation of renewable energy and the power consumption of loads, and its value range is [-1,1]. If R(P N ,P L) is closer to 1, indicating that the curve fluctuations of renewable energy power generation and load power consumption are more similar; if it is closer to 0, it means that the two are completely dissimilar; N Represents the power generated by renewable energy; P L Indicates load power; cov(P N ,P L ) represents the covariance between the power generated by renewable energy and the power consumed by loads, Represents the variance of renewable energy power generation and load power consumption;

[0085] Step S12: Consider ideal new energy power generation And load power P L The similarity index R(P N ,P L ), and assuming that the ideal new energy power generation and the predicted new energy power generation are equivalent, an ideal new energy power generation scenario model that meets the similarity index is constructed, which is specifically expressed as follows:

[0086]

[0087] in, It is expressed as the flexible adjustment power required for the new energy-load in period t, and r is the target value of the similarity between the new energy power generation power and load fluctuation under ideal conditions. The closer it is to 1, the more similar the curve fluctuations of the new energy power generation power and load demand are; if it is closer to 0, it means that the two are completely dissimilar.

[0088] Furthermore, step S2 is specifically as follows:

[0089] Step S21: Based on the conditional constraints of the flexible resources including user-side energy storage, electric vehicles, and temperature control loads during operation, combined with the operating characteristics and regulation capabilities, a general compact expression is established as follows:

[0090]

[0091] In the formula, is the constraint space of the user-side energy storage i, which is essentially a polyhedron constrained by a set of linear inequality equations. S,i The matrix is ​​the decision variable of the user-side energy storage i, W S,i 、M S,i is the parameter matrix and vector of the user-side energy storage i; is the constraint space of the i-th electric vehicle in cluster k, W EVk,i 、M EVk,i denote the coefficient matrix and vector of the i-th electric vehicle in cluster k, Z EVk,i The matrix is ​​the decision variable; is the constraint space of the temperature control load of the i-th unit in cluster k, Z HVAC,k,i The matrix is ​​the decision variable matrix of the temperature control load of the i-th unit in cluster k, W HVAC,k,i 、M HVAC,k,i are the parameter matrix and vector of the temperature control load respectively;

[0092] Step S22: convert formula (3) into a user-side flexibility resource individual flexibility model:

[0093]

[0094] In the formula, is the flexible feasible domain of the i-th flexible resource in any cluster k, the intersection of the bounded sets consisting of 4T closed half-space polyhedra, and are the upper and lower limits of the decision variables, Z k,i represents the decision variable; Represents the real number space.

[0095] Furthermore, step S3 is specifically as follows:

[0096] Step S31: The total flexibility set of the limited flexibility resource group can be expressed as the Minkowski sum of the individual flexibility sets given by equation (4), as shown below:

[0097]

[0098] In the formula, is a Full-dimensional polyhedron,Since individual flexibility sets are usually viewed as H-polyhedrons, computing the Minkowski sum of two H-polyhedrons is an NP-hard problem;

[0099] Step S32: To reduce the complexity of the model, the polyhedron is calculated using a method with high computational efficiency. The set of inner approximations, a polyhedron satisfy Polyhedron Benchmark After the radiation transformation, it can be expressed as formula (6):

[0100]

[0101] In the formula, and are the translation and scaling factors to ensure that the polyhedron Closely approximate the original polyhedron

[0102] Step S33: Considering that the averaging method is simple and easy to implement and has a good approximation effect, it is used to perform the benchmark setting as follows:

[0103]

[0104] Among them, M k,0 is the mean value matrix of individual parameters of flexibility resources, N k is the number of flexible resources in cluster k;

[0105] Step S34: Calculate the polyhedrons included in the aggregate flexibility set by solving the following optimal polyhedron inclusion problem: The maximum volume within can be expressed as:

[0106]

[0107] Wherein, Vol() represents the volume of the polyhedron P;

[0108] The feasible solution of the above equation is called the inner approximation of the set flexibility set, and an easily solvable goal is pursued by seeking a conservative approximation with a tractable convex programming;

[0109] Step S35: In this case, first construct an internal approximation of each individual flexibility set, given by the following formula:

[0110]

[0111] in, and are translation and scaling factors to ensure that each polyhedron Closely approximate its corresponding constraint space

[0112] Step S36: Obtained heterogeneous The Minkowski sum of a polyhedron is an aggregate flexibility set The internal approximation of is:

[0113]

[0114] Avoid computing the Minkowski sum by summing the elements of each transformation As shown below:

[0115]

[0116] Can be set and

[0117] In the polyhedron inclusion problem, in order to maximize the inner approximation volume, the volume of the inner approximation subset polyhedron should be the largest, and its volume is determined by the following formula:

[0118]

[0119] Where det(P) represents the absolute value of the determinant of the matrix;

[0120] therefore, The maximum volume is equivalent to maximizing |det(P)|. In order to further simplify the model, the first-order Taylor expansion of the identity matrix is ​​used to linearize |det(P)|:

[0121]

[0122] The meaning of Tr() represents the trace of the matrix;

[0123] Where P is a non-singular matrix, constant is a constant term;

[0124] According to Farkas theorem, there exists a matrix Gi such that G i W=WΓ i , G i M0≤Μ i -Wγ i , so the volume of the objective function in the formula can be linearly approximated to obtain the maximum approximation of the optimal inner approximation polyhedron inclusion problem:

[0125]

[0126] The above formula represents the decision variables A, B, γ i , Γ i and G i Linear programming conditions of ;

[0127] Aggregate feasible domain using Minkowski and computational flexibility resources The feasible domain of user-side energy storage, electric vehicles and temperature control loads is aggregated as follows:

[0128]

[0129] Represents the feasible domain of the aggregate of multiple flexibility resources; represents the feasible region of energy storage; represents the feasible domain of electric vehicles; represents the feasible domain of temperature control load; Represents the Minkowski summation symbol.

[0130] Furthermore, step S4 is specifically as follows:

[0131] Step S41: Based on the aggregation result of the user-side flexibility resources, a multivariate aggregation control model suitable for the user-side flexibility resources is constructed. In order to ensure that the system flexibility requirements are maximized while keeping the operating cost as low as possible, the multivariate aggregation control model is constructed as shown in the formula:

[0132]

[0133] In the formula, g(x)≥α, h(x)=β are the inequality constraint and equality constraint on the decision variable x respectively;

[0134] F1 represents the minimum system flexibility requirement, and F2 represents the minimum cost;

[0135] Step S42: according to the importance of objectives F1 and F2, weights are set for linear weighting, thereby converting into a single-objective optimization problem; in this paper, λ1 and λ2 are respectively set to 0.5, and the specific weight setting can be adjusted according to the needs of decision makers; F1 and F2 can be expressed as:

[0136] F1=sum(abs(F t flex -F t up -F t down ));

[0137] F2=C g +C N +C S +C EV +C HVAC +C FL ;

[0138]

[0139] In the formula, F1 represents the minimum difference between the system flexibility deficit and the adjustable capacity of the flexibility resources, that is, the system reaches the optimal state, and the flexibility deficit and redundancy are minimized; F t flex 、F t up 、F t down They are flexibility demand, flexibility upward adjustment ability and flexibility downward adjustment ability, F t S,up 、F t EV,up 、F t HVAC,up and F t S,down 、F t EV,down 、F t HVAC,down Corresponding to the upward and downward adjustment capabilities of energy storage, electric vehicles and temperature control loads, C g , C N , C S , C EV , C HVAC , CFL They are respectively the cost of purchasing electricity for surplus electricity, the operating cost of new energy, the operating cost of energy storage, the operating cost of electric vehicles and temperature control loads, and their compensation costs.

[0140] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-aggregation control method for user-side flexibility resources, characterized in that: The method comprises the following steps: Step S1: constructing a similarity index between the power generation of new energy and the load power, and further constructing an ideal new energy power generation scenario model that meets the similarity index assuming that the ideal new energy power generation and the predicted new energy power generation are equivalent; Step S2: construct a model based on flexibility resources, where the flexibility resources include user-side energy storage, electric vehicles, and temperature control loads. The model is used to characterize operating characteristics and regulatory response capabilities. Step S3: quantifying the aggregate response capability of the model in step S2 by Minkowski's aggregation method based on approximation within a convex polyhedron; Step S4: Introduce the operating cost into the aggregation method in step S3 and further construct a multivariate aggregation control model.

2. The multi-aggregation control method for user-side flexibility resources according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: Calculate the time series similarity and shape similarity between the renewable energy power generation and the load demand, and construct a similarity index between the renewable energy power generation and the load power, as shown below: Where P N is the power generated by renewable energy, R(P N ,P L ) is the similarity index between the power generation of renewable energy and the power consumption of loads, and its value range is [-1,1]. If R(P N ,P L ) is closer to 1, indicating that the curve fluctuations of renewable energy power generation and load power consumption are more similar; if it is closer to 0, it means that the two are completely dissimilar; N Represents the power generated by renewable energy; P L Indicates load power; cov(P N ,P L ) represents the covariance between the power generated by renewable energy and the power consumed by loads, Represents the variance of renewable energy power generation and load power consumption; Step S12: Consider ideal new energy power generation And load power P L The similarity index R(P N ,P L ), and assuming that the ideal new energy power generation and the predicted new energy power generation are equivalent, an ideal new energy power generation scenario model that meets the similarity index is constructed, which is specifically expressed as follows: Among them, P t flex It is expressed as the flexible adjustment power required by the new energy-load in period t, and r is the power generation of new energy under ideal conditions. The closer the target value of the similarity between electric power and load fluctuation is to 1, the more similar the curve fluctuations of renewable energy power generation and load demand are; if it is closer to 0, it means that the two are completely dissimilar.

3. A multi-aggregation control method for user-side flexibility resources according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: Based on the conditional constraints of the flexible resources including user-side energy storage, electric vehicles, and temperature control loads during operation, combined with the operating characteristics and regulation capabilities, a general compact expression is established as follows: In the formula, is the constraint space of the user-side energy storage i, which is essentially a polyhedron constrained by a set of linear inequality equations. S,i The matrix is ​​the decision variable of the user-side energy storage i, W S,i 、M S,i is the parameter matrix and vector of the user-side energy storage i; is the constraint space of the i-th electric vehicle in cluster k, W EVk,i 、M EVk,i denote the coefficient matrix and vector of the i-th electric vehicle in cluster k, Z EVk,i The matrix is ​​the decision variable; is the constraint space of the temperature control load of the i-th unit in cluster k, Z HVAC,k,i The matrix is ​​the decision variable matrix of the temperature control load of the i-th unit in cluster k, W HVAC,k,i 、M HVAC,k,i are the parameter matrix and vector of the temperature control load respectively; Step S22: convert formula (3) into a user-side flexibility resource individual flexibility model: In the formula, is the flexible feasible domain of the i-th flexible resource in any cluster k, the intersection of the bounded sets consisting of 4T closed half-space polyhedra, and are the upper and lower limits of the decision variables, Z k,i represents the decision variable; Represents the real number space.

4. A multi-aggregation control method for user-side flexibility resources according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: The total flexibility set of the limited flexibility resource group can be expressed as the Minkowski sum of the individual flexibility sets given by equation (4), as shown below: In the formula, is a Full-dimensional polyhedron,Since individual flexibility sets are usually viewed as H-polyhedrons, computing the Minkowski sum of two H-polyhedrons is an NP-hard problem; Step S32: To reduce the complexity of the model, the polyhedron is calculated using a method with high computational efficiency. The set of inner approximations, a polyhedron satisfy Polyhedron Benchmark After the radiation transformation, it can be expressed as formula (6): In the formula, and are the translation and scaling factors to ensure that the polyhedron Closely approximate the original polyhedron Step S33: Considering that the averaging method is simple and easy to implement and has a good approximation effect, it is used to perform the benchmark setting as follows: Among them, M k,0 is the mean value matrix of individual parameters of flexibility resources, N k is the number of flexible resources in cluster k; Step S34: Calculate the polyhedrons included in the aggregate flexibility set by solving the following optimal polyhedron inclusion problem: The maximum volume within can be expressed as: Wherein, Vol() represents the volume of the polyhedron P; The feasible solution of the above equation is called the inner approximation of the set flexibility set, and an easily solvable goal is pursued by seeking a conservative approximation with a tractable convex programming; Step S35: In this case, first construct an internal approximation of each individual flexibility set, given by the following formula: in, and are translation and scaling factors to ensure that each polyhedron Closely approximate its corresponding constraint space Step S36: Obtained heterogeneous The Minkowski sum of a polyhedron is an aggregate flexibility set The internal approximation of is: Avoid computing the Minkowski sum by summing the elements of each transformation As shown below: Can be set and In the polyhedron inclusion problem, in order to maximize the inner approximation volume, the volume of the inner approximation subset polyhedron should be the largest, and its volume is determined by the following formula: Where det(P) represents the absolute value of the determinant of the matrix; therefore, The maximum volume is equivalent to maximizing |det(P)|. In order to further simplify the model, the first-order Taylor expansion of the identity matrix is ​​used to linearize |det(P)|: The meaning of Tr() represents the trace of the matrix; Where P is a non-singular matrix, constant is a constant term; According to Farkas theorem, there exists a matrix Gi such that G i W=WΓ i , G i M0≤Μ i -Wγ i , so the volume of the objective function in the formula can be linearly approximated to obtain the maximum approximation of the optimal inner approximation polyhedron inclusion problem: The above formula represents the decision variables A, B, γ i , Γ i and G i Linear programming conditions of Aggregate feasible domain using Minkowski and computational flexibility resources The feasible domain of user-side energy storage, electric vehicles and temperature control loads is aggregated as: Represents the feasible domain of the aggregate of multiple flexibility resources; represents the feasible region of energy storage; represents the feasible domain of electric vehicles; represents the feasible domain of temperature control load; Represents the Minkowski summation symbol.

5. A multi-aggregation control method for user-side flexibility resources according to claim 4, characterized in that: Step S4 is specifically as follows: Step S41: Based on the aggregation result of the user-side flexibility resources, a multivariate aggregation control model suitable for the user-side flexibility resources is constructed. In order to ensure that the system flexibility requirements are maximized while keeping the operating cost as low as possible, the multivariate aggregation control model is constructed as shown in the formula: In the formula, g(x)≥α, h(x)=β are the inequality constraint and equality constraint on the decision variable x respectively; F1 represents the minimum system flexibility requirement, and F2 represents the minimum cost; Step S42: according to the importance of objectives F1 and F2, weights are set for linear weighting, thereby converting into a single-objective optimization problem; in this paper, λ1 and λ2 are respectively set to 0.5, and the specific weight setting can be adjusted according to the needs of decision makers; F1 and F2 can be expressed as: F2=C g +C N +C S +C EV +C HVAC +C FL ; In the formula, F1 represents the minimum difference between the system flexibility deficit and the adjustable capacity of the flexibility resources, that is, the system reaches the optimal state, and the flexibility deficit and redundancy are minimized; F t flex 、F t up 、F t down They are flexibility demand, flexibility upward adjustment ability and flexibility downward adjustment ability, F t S,up 、F t EV,up 、F t HVAC,up and F t S,down 、F t EV,down 、F t HVAC,down Corresponding to the upward and downward adjustment capabilities of energy storage, electric vehicles and temperature control loads, C g , C N , C S , C EV , C HVAC , C FL They are respectively the cost of purchasing electricity for surplus electricity, the operating cost of new energy, the operating cost of energy storage, the operating cost of electric vehicles and temperature control loads, and their compensation costs.

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