Virtual unit model construction method considering space-time coupling relation of power supply type distributed energy cluster

Abstract

δThe invention discloses a virtual unit model construction method considering a power supply type distributed energy cluster space-time coupling relation, and relates to the field of electrical engineering, and the method comprises the following steps: carrying out the modeling of a virtual unit based on the dispatching power y of a superior power grid, obtaining a virtual unit parameter u, and enabling the constraint Uy to be smaller than or equal to u; the method comprises the following steps: modeling a variable x in a power supply type distributed energy cluster and a superior power grid dispatching power correction value delta based on a power distribution network framework to obtain a constraint psi x < = psi, setting target functions as the sum of all time periods and the weighted sum of unit parameters u, and decomposing the problem into a two-stage adaptive robust optimization problem; the sub-problem is set to solve the maximum power correction under the current virtual unit parameters, and the main problem is set to solve the current unit parameter weighted sum according to the maximum power error; and iteratively solving the main problem and the sub-problems until the solving result of the sub-problems is 0, and obtaining parameters of the virtual unit model. Experimental results prove that the method has the advantages of being high in solving speed, good in solving effect and suitable for participating in power system dispatching.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering technology, and more specifically, relates to a method for constructing a virtual machine group model that considers the spatiotemporal coupling relationship of power source-type distributed energy clusters. This method is applicable to microgrids. Background Technology

[0002] With the deepening of the "dual carbon" goals, building a low-carbon, intelligent, and sustainable new power system has become a key focus. In this new power system, the penetration rate of distributed energy resources has increased significantly. Distributed photovoltaic, distributed wind power, micro gas turbines, and small pumped storage power stations, among other distributed energy sources, are collectively referred to as "power-type distributed energy" because they all have the characteristic of outputting power as a source. These energy sources are geographically dispersed, have small individual capacities, and a large total number, posing severe challenges to the communication reliability and computational efficiency of traditional centralized grid dispatching models. This is because the centralized dispatching of massive distributed energy resources requires handling a large number of variables and constraints, resulting in exponentially increasing computational complexity, making it difficult to meet real-time requirements. Therefore, aggregating power-type distributed energy clusters before they participate in regulation is essential.

[0003] Aggregated regulation of power-generating distributed energy clusters presents several challenges: First, all power-generating distributed energy sources exhibit power output characteristics, necessitating the use of a virtual generator set to characterize the aggregated output characteristics of the cluster. Second, power-generating distributed energy sources face temporal coupling constraints, such as power ramping constraints for micro gas turbines and small pumped storage power stations, which need to be reflected in the aggregated virtual machine group model. Third, power-generating distributed energy clusters exhibit spatial coupling constraints. Distributed energy resources are mostly located within the distribution network and are relatively dispersed spatially; traditional aggregation methods cannot account for the network constraints of interconnections between power-generating distributed energy sources, requiring modifications to the scheduling scheme for successful execution by the cluster. The power boundary of the virtual machine group model must consider the impact of spatial coupling constraints within the distributed energy cluster. Summary of the Invention

[0004] The purpose of this invention is to provide a virtual machine group model construction method that considers the spatiotemporal coupling relationship of power-type distributed energy clusters, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for constructing a virtual machine group model that considers the spatiotemporal coupling relationship of power-type distributed energy clusters includes:

[0007] S1. Based on the power dispatched by the upper-level power grid y, model the virtual machine group to obtain the virtual machine group parameters u and the constraint Uy≤u.

[0008] S2. Based on the distribution network framework, the variable x in the power generation distributed energy cluster is compared with the upper-level grid dispatch power correction value. δ By modeling, the constraint Ψx≤ψ is obtained.

[0009] S3, Set the objective function for each time period. The problem is decomposed into a two-stage adaptive robust optimization problem by summing the sums of the parameters and the weighted sum of the unit parameters u.

[0010] S4. Set the subproblem to solve for the maximum power correction under the current virtual machine group parameters, and set the main problem to solve for the weighted sum of the current group parameters based on the maximum power error.

[0011] S5. Iteratively solve the main problem and subproblems until the subproblem solution result is 0, and obtain the parameters of the virtual machine group model.

[0012] Preferably, the power source-type distributed energy cluster interacts with the upstream power grid at the PCC with bidirectional power y; the bidirectional power y corresponds to the dispatching instructions from the upstream power grid to the power source-type distributed energy cluster.

[0013] Specifically, this invention uses a virtual machine group model to represent a power source-type distributed energy cluster, and the upper-level power grid only needs to make scheduling instructions y based on the parameters of the virtual machine group.

[0014] Preferably, the model parameters and operational constraints of the virtual machine group are as follows:

[0015] Uy≤u (29)

[0016] U = [I τ ,-I τ [,W1,-W1] (30)

[0017]

[0018] Specifically, τ represents the total number of time periods, U is a constant coefficient matrix, and I... τ W1 is an identity matrix of dimension τ×τ, and W1 is a parameter matrix; the parameters in the unit parameter u This corresponds to the maximum output capacity of the virtual machine group. P V This corresponds to the lower limit of the output of the virtual machine group. This corresponds to the upper limit of the output of the virtual machine group. R V This corresponds to the upper limit of the output of the virtual machine group.

[0019] Preferably, the power-type distributed energy cluster based on the distribution network includes micro gas turbines, distributed wind power, distributed photovoltaics, and small pumped storage power stations; and i,j are node numbers, and t is the time period.

[0020] Specifically, This represents the active power / reactive power output of the micro gas turbine. E represents the active power / reactive power output of a small pumped storage power station. i,t This represents the stored energy value. This represents the active power / reactive power output of distributed photovoltaic systems. This represents the active power / reactive power output of distributed wind power. V represents the active / reactive power flowing on the transmission line between nodes. i 2 It is the square of the node voltage.

[0021] Preferably, equations (33)-(35) are the operating constraints of the micro gas turbine; equations (36)-(40) are the operating constraints of the small pumped storage power station; equations (41)-(44) are the output constraints of distributed wind power and distributed photovoltaic power; and equations (45)-(50) are the distribution network constraints.

[0022]

[0023]

[0024] Specifically, P i mt , These represent the upper and lower limits of the power output of the micro gas turbine. The upper and lower limits of power output for the gas turbine are defined by WeChat; P i dis ,P i ch These are the upper limits for the power generation and energy storage capacity of small pumped storage power stations. For small pumped storage power stations, E is the upper limit of power ramp-up and the upper limit of power ramp-down. i , These are the upper and lower limits for energy storage; This refers to the power generation from distributed photovoltaic and distributed wind power. This represents the upper limit of the curtailment rate for distributed wind and distributed solar power. Active / reactive load, These are the upper and lower limits of the active power of the transmission line. V represents the upper and lower limits of the reactive power of the transmission line. i 2 , r represents the upper and lower limits of the square of the node voltage. ij,x ij For the resistance and inductance of the transmission line; These are the power angles of micro gas turbines, small pumped storage power stations, distributed wind power, and distributed photovoltaic power, respectively.

[0025] Preferably, the model of the virtual machine group can be written in the following compact form:

[0026] Ψx≤ψ (51)

[0027]

[0028] Specifically, vector x is the vector composed of all decision variables in equations (33)-(50); the parameter matrices Ψ,Γ and the coefficient vectors ψ,ζ are all determined by equations (33)-(50); δ is the amount that needs to be adjusted upwards or downwards to ensure that the dispatch scheme y can be executed by the power-type distributed energy cluster based on the distribution network.

[0029] Preferably, the problem of power-type distributed energy clusters participating in grid dispatch is divided into two parts: solving equations (54)-(55) for the weighted sum of the largest virtual machine group parameters, and solving equation (56) for the minimum power correction value that enables the power-type distributed energy cluster to operate normally; the goal of equations (54)-(57) is to iteratively solve the largest virtual machine group parameters that enable the power correction value to be 0.

[0030]

[0031] β=[w1;w2;w3;w4] (55)

[0032]

[0033] Specifically, β is the weighting vector, 1 T Let be a τ-dimensional vector with all elements equal to 1; Equation (54) is the main problem; Equation (56) is the subproblem.

[0034] Preferably, the initial values ​​of the virtual machine group parameters are determined as follows:

[0035]

[0036] Preferably, in the k-th iteration, after dualizing the inner-level min problem of the subproblem, the following subproblem is obtained:

[0037]

[0038] Specifically, θ, ω, and π are dual variables, bigM is a sufficiently large number, and z is a Boolean variable.

[0039] Specifically, by solving problems (60)-(61), the solution values ​​θ of the dual variables are obtained.k ,ω k ,π k and the constraint (ζ-Γy) of the kth iteration k ) T θ k +ψ T ω k ≤0.

[0040] Preferably, based on the results of solving the subproblems, the main problem for the k-th iteration is solved as follows:

[0041]

[0042] Specifically, i = 0, 1, ..., k-1, k, and constraint (63) considers all the solution results from the initial iteration to the kth iteration and the constraints in the iteration.

[0043] Preferably, the virtual machine group parameters for the k-th iteration are obtained by solving. Then, let k→k+1 and

[0044] Preferably, the steps described in formulas (60)-(63) are repeated until f' = 0 is solved to obtain the virtual machine group parameters.

[0045] Specifically, the constraint Uy≤u is submitted to the upper-level power grid dispatch center, which can then issue the corresponding dispatch instruction for y.

[0046] The virtual machine group model of the present invention, which considers the spatiotemporal coupling relationship of power-type distributed energy clusters, has the following beneficial effects:

[0047] 1. This invention provides a method for aggregating power-generating distributed energy sources, which aggregates power-generating distributed energy sources based on a distribution network framework into a virtual machine group to represent all distributed energy sources.

[0048] 2. This invention provides an adjustable range for the virtual machine group model. After the adjustable range is provided to the upper-level power grid, the upper-level power grid can conveniently schedule the entire power-type distributed energy cluster through the adjustable range. Attached Figure Description

[0049] Figure 1 A flowchart of a virtual machine group model construction method that considers the spatiotemporal coupling relationship of power-type distributed energy clusters;

[0050] Figure 2 A conceptual diagram illustrating the interactive power operation between a power-generating distributed energy cluster and its upstream power grid.

[0051] Figure 3This is a topology diagram of a power-type distributed energy cluster based on the IEEE-33 node system.

[0052] Figure 4 This is the daily power generation curve of distributed photovoltaic power in a power-type distributed energy cluster.

[0053] Figure 5 This is the daily power generation curve of distributed wind power in a power-type distributed energy cluster;

[0054] Figure 6 The curve represents the total daily load in a power-type distributed energy cluster.

[0055] Figure 7 The dispatch instructions for power-type distributed energy clusters are obtained by adopting the centralized dispatch of the superior power grid.

[0056] Figure 8 The scheduling curve for the power-type distributed energy cluster is obtained by using the virtual machine group model scheduling proposed in this invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0058] This invention discloses a method for constructing a virtual machine group model considering the spatiotemporal coupling relationship of a power-source distributed energy cluster, relating to the field of electrical engineering. The method includes the following steps: modeling the virtual machine group based on the dispatched power y from the upper-level power grid to obtain the virtual machine group parameters u and the constraint Uy≤u; and adjusting the variable x and the dispatched power from the upper-level power grid within the power-source distributed energy cluster based on the distribution network framework. δ By modeling, the constraint Ψx≤ψ is obtained. The objective functions are set for each time period. The problem is decomposed into a two-stage adaptive robust optimization problem by summing the sum of the parameters and the weighted sum of the unit parameters u. The subproblem is defined as solving for the maximum power correction under the current virtual machine group parameters, while the main problem is defined as solving for the weighted sum of the current unit parameters based on the maximum power error. The main problem and subproblems are iteratively solved until the subproblem solution result is 0, thus obtaining the parameters of the virtual machine group model. Experimental results demonstrate that the method described in this invention has the characteristics of fast solution speed, good solution effect, and suitability for power system dispatching.

[0059] To achieve the above objectives, this invention provides a virtual machine group model that considers the spatiotemporal coupling relationship of power-type distributed energy clusters, comprising:

[0060] S1. Based on the power dispatched by the upper-level power grid y, model the virtual machine group to obtain the virtual machine group parameters u and the constraint Uy≤u.

[0061] S2. Based on the distribution network framework, the variable x and the upper-level grid dispatch power correction value in the power generation-type distributed energy cluster are analyzed. δ By modeling, the constraint Ψx≤ψ is obtained.

[0062] S3, Set the objective function for each time period. The problem is decomposed into a two-stage adaptive robust optimization problem by summing the sums of the parameters and the weighted sum of the unit parameters u.

[0063] S4. Set the subproblem to solve for the maximum power correction under the current virtual machine group parameters, and set the main problem to solve for the weighted sum of the current group parameters based on the maximum power error.

[0064] S5. Iteratively solve the main problem and subproblems until the subproblem solution result is 0, and obtain the parameters of the virtual machine group model.

[0065] Preferably, the power source-type distributed energy cluster interacts with the upstream power grid at the PCC with bidirectional power y; the bidirectional power y corresponds to the dispatching instructions from the upstream power grid to the power source-type distributed energy cluster.

[0066] Specifically, this invention uses a virtual machine group model to represent a power source-type distributed energy cluster, and the upper-level power grid only needs to make scheduling instructions y based on the parameters of the virtual machine group.

[0067] Preferably, the model parameters and operational constraints of the virtual machine group are as follows:

[0068] Uy≤u (64)

[0069] U = [I τ ,-I τ ,W1,-W1] (65)

[0070]

[0071] Specifically, τ represents the total number of time periods, U is a constant coefficient matrix, and I... τ W1 is an identity matrix of dimension τ×τ, and W1 is a parameter matrix; the parameters in the unit parameter u This corresponds to the maximum output capacity of the virtual machine group, P. V This corresponds to the lower limit of the output of the virtual machine group. This corresponds to the upper limit of the output of the virtual machine group.

[0072] Preferably, the power-type distributed energy cluster based on the distribution network includes micro gas turbines, distributed wind power, distributed photovoltaics, and small pumped storage power stations; and i,j are node numbers, and t is the time period.

[0073] Specifically, This represents the active power / reactive power output of the micro gas turbine. E represents the active power / reactive power output of a small pumped storage power station. i,t This represents the stored energy value. This represents the active power / reactive power output of distributed photovoltaic systems. This represents the active power / reactive power output of distributed wind power. V represents the active / reactive power flowing on the transmission line between nodes. i 2 It is the square of the node voltage.

[0074] Preferably, equations (68)-(70) are the operating constraints of the micro gas turbine; equations (71)-(75) are the operating constraints of the small pumped storage power station; equations (76)-(79) are the output constraints of distributed wind power and distributed photovoltaic power; and equations (80)-(85) are the distribution network constraints.

[0075]

[0076]

[0077] Specifically, P i mt , These represent the upper and lower limits of the power output of the micro gas turbine. The upper and lower limits of power output for the gas turbine are defined by WeChat; P i dis ,P i ch These are the upper limits for the power generation and energy storage capacity of small pumped storage power stations. For small pumped storage power stations, E is the upper limit of power ramp-up and the upper limit of power ramp-down. i , These are the upper and lower limits for energy storage; This refers to the power generation from distributed photovoltaic and distributed wind power. This represents the upper limit of the curtailment rate for distributed wind and distributed solar power. Active / reactive load, These are the upper and lower limits of the active power of the transmission line. V represents the upper and lower limits of the reactive power of the transmission line. i 2 , r represents the upper and lower limits of the square of the node voltage. ij ,x ij For the resistance and inductance of the transmission line; These are the power angles of micro gas turbines, small pumped storage power stations, distributed wind power, and distributed photovoltaic power, respectively.

[0078] Preferably, the model of the virtual machine group can be written in the following compact form:

[0079] Ψx≤ψ (86)

[0080]

[0081] Specifically, vector x is the vector composed of all decision variables in equations (68)-(85); the parameter matrices Ψ,Γ and the coefficient vectors ψ,ζ are all determined by equations (68)-(85); δ is the amount that needs to be adjusted upwards or downwards to ensure that the dispatch scheme y can be executed by the power-type distributed energy cluster based on the distribution network.

[0082] Preferably, the problem of power-type distributed energy clusters participating in grid dispatch is divided into two parts: solving equation (89)-(90) for the weighted sum of the largest virtual machine group parameters, and solving equation (89) for the minimum power correction value that enables the power-type distributed energy cluster to operate normally; the goal of equation (89)-(92) is to iteratively solve the largest virtual machine group parameter that makes the power correction value 0.

[0083]

[0084] β=[w1;w2;w3;w4] (90)

[0085]

[0086] Specifically, β is the weighting vector, 1 T Let be a τ-dimensional vector with all elements equal to 1; Equation (89) is the main problem; Equation (91) is the subproblem.

[0087] Preferably, the initial values ​​of the virtual machine group parameters are determined as follows:

[0088]

[0089] Preferably, in the k-th iteration, after dualizing the inner-level min problem of the subproblem, the following subproblem is obtained:

[0090]

[0091] Specifically, θ, ω, and π are dual variables, bigM is a sufficiently large number, and z is a Boolean variable.

[0092] Specifically, by solving equations (95)-(96), the solution values ​​θ of the dual variables are obtained. k ,ω k ,π k and the constraint (ζ-Γy) of the kth iteration k ) T θ k +ψ T ω k ≤0.

[0093] Preferably, based on the results of solving the subproblems, the main problem for the k-th iteration is solved as follows:

[0094]

[0095]

[0096] Specifically, i = 0, 1, ..., k-1, k, and the constraint formula considers all solution results from the initial iteration to the k-th iteration, as well as the constraints in the iteration.

[0097] Preferably, the virtual machine group parameters for the k-th iteration are obtained by solving. Then, let k→k+1 and

[0098] Preferably, the steps described in formulas (95)-(98) are repeated until f' = 0 is solved to obtain the virtual machine group parameters.

[0099] Specifically, the constraint Uy≤u is submitted to the upper-level power grid dispatch center, which can then issue the corresponding dispatch instruction for y.

[0100] The following description, using a specific application scenario, further illustrates the beneficial effects achievable in this embodiment. For a power-type distributed energy cluster based on an IEEE-33 node distribution network system, the topology is as follows: Figure 3 As shown, the daily output value of distributed wind power is as follows: Figure 4 As shown, the daily output value of distributed photovoltaic power. Figure 5 As shown, the maximum curtailment rate for both distributed wind power and distributed photovoltaic power is 60%; the load in the power generation-type distributed energy cluster is as follows: Figure 6 As shown:

[0101] Table 1 Parameters of the Micro Gas Turbine

[0102] serial number upper limit of output power Lower limit of power output Power climbing upper limit Power Downward Climbing Limit 1 0.5MW 0.3MW 0.15MW / h 0.15MW / h 2 0.4MW 0.2MW 0.15MW / h 0.15MW / h 3 0.8MW 0.2MW 0.4MW / h 0.4MW / h 4 0.6MW 0.2MW 0.3MW / h 0.3MW / h

[0103] Table 2 Parameters of Small Pumped Storage Power Stations

[0104]

[0105] A program was written in the computational software MATLAB R2024a to call the Yalmip solver equipped with Gurobi for solving the optimization problem. The computing device used to solve the optimization problem was a Legion laptop with an Intel Core i7-10510U processor, 32GB of RAM, and running Windows 11 Professional operating system.

[0106] Let the weighting coefficient of the virtual machine group parameters be β = [1; 1; 1; 1]. Then the aggregated virtual machine group parameters are shown in Table 3.

[0107] Table 3 Virtual Machine Group Parameters

[0108] upper limit of power generation Lower limit of power generation Power climbing upper limit Power Downward Climbing Limit 2.314MW -2.002MW 3.417MW / h 3.423MW / h

[0109] Using this virtual machine group model to participate in the upper-level power grid dispatching, the dispatching curve is as follows: Figure 6 As shown in the figure. Simultaneously, using the centralized dispatching of power source-type distributed energy sources from the upper-level power grid, the number of dispatching variables, number of constraints, solution time, and total solution cost of the two methods are compared. The solution results are shown in Table 4. The dispatch curves obtained by the centralized dispatching method and the dispatch curves obtained using the virtual machine group model are shown in the figure. Figure 7 , Figure 8 As shown:

[0110] Table 4 Scheduling Time

[0111] method Number of variables Number of constraints Solution time Total operating costs Virtual machine group model 432 3048 0.463s 18732.85￥ Centralized dispatch 1608 6370 1.207s 18734.32￥

[0112] As can be seen, compared with the centralized control of all power source-type distributed energy by the upper-level power grid, the virtual machine group model provided by this invention can significantly improve the speed of scheduling solution and reduce the variables and constraints that need to be solved while ensuring the quality of the upper-level power grid scheduling operation solution.

[0113] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0114] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for constructing a virtual machine group model that considers the spatiotemporal coupling relationship of power-type distributed energy clusters, characterized by the following steps: S1. Based on the dispatch power y of the upper-level power grid, model the virtual machine group to obtain the virtual machine group parameters u and the constraint Uy≤u; S2. Based on the distribution network framework, the variable x and the upper-level grid dispatch power correction value in the power generation-type distributed energy cluster are analyzed. δ By modeling, the constraint Ψx≤ψ is obtained. in, Ψ,Γ are parameter matrices, and ψ,ζ are coefficient vectors; δ These are the amounts that need to be adjusted upwards and downwards to ensure that the power dispatched by the upper-level power grid y can be executed by the power source-type distributed energy cluster based on the distribution network; S3, Set the objective function for each time period. The sum of the sums and the weighted sum of the unit parameters u decompose the problem into a two-stage adaptive robust optimization problem. S4. Set the subproblem to solve for the maximum power correction under the current virtual machine group parameters, and set the main problem to solve for the weighted sum of the current group parameters based on the maximum power error; S5. Iteratively solve the main problem and subproblems until the subproblem solution result is 0, and obtain the parameters of the virtual machine group model.

2. The virtual machine group model construction method considering the spatiotemporal coupling relationship of power-type distributed energy clusters as described in claim 1, characterized in that, The virtual power plant interacts with the upstream power grid bidirectionally through a common coupling point; the model parameters and operational constraints of the virtual machine group are as follows: Uy≤u (1) U=[I τ ,-I τ ,W1,-W1] (2) Where τ is the total number of time periods, U is a constant coefficient matrix, and I τ W1 is an identity matrix of dimension τ×τ, and W1 is a parameter matrix; the parameters in the unit parameter u This corresponds to the maximum output capacity of the virtual machine group. P V This corresponds to the lower limit of the output of the virtual machine group. This corresponds to the upper limit of the output of the virtual machine group. R V This corresponds to the upper limit of the output of the virtual machine group.

3. The virtual machine group model construction method considering the spatiotemporal coupling relationship of power-type distributed energy clusters as described in claim 2, characterized in that, Power-type distributed energy sources include micro gas turbines, distributed wind power, distributed photovoltaic power, and small hydropower stations. This represents the active power / reactive power output of the micro gas turbine. E represents the active power / reactive power output of a small pumped storage power station. i,t This represents the stored energy value. This represents the active power / reactive power output of distributed photovoltaic systems. This represents the active power / reactive power output of distributed wind power. The active power / reactive power flowing on the transmission line between nodes. Let be the square of the node voltage, and with the following constraints: Among them, equations (5)-(7) are the operating constraints of the micro gas turbine; equations (8)-(11) are the operating constraints of the small pumped storage power station; equations (12)-(15) are the output constraints of distributed wind power and distributed photovoltaic power; equations (16)-(21) are the distribution network constraints; specifically, These represent the upper and lower limits of the power output of the micro gas turbine. The upper and lower limits of power output for WeChat gas turbines; These are the upper limits for the power generation and energy storage capacity of small pumped storage power stations. For small pumped storage power stations, E is the upper limit of power ramp-up and the upper limit of power ramp-down. i , These are the upper and lower limits for energy storage; This refers to the power generation from distributed photovoltaic and distributed wind power. This represents the upper limit of the curtailment rate for distributed wind and distributed solar power. Active / reactive load, These are the upper and lower limits of the active power of the transmission line. These are the upper and lower limits of the reactive power of the transmission line. r represents the upper and lower limits of the square of the node voltage. ij ,x ij For the resistance and inductance of the transmission line; These are the power angles of micro gas turbines, small pumped storage power stations, distributed wind power, and distributed photovoltaic power, respectively.

4. The virtual machine group model construction method considering the spatiotemporal coupling relationship of power-type distributed energy clusters as described in claim 3, characterized in that, The problem of power-source distributed energy clusters participating in grid dispatch is a max-min two-level problem: Let x be the vector composed of all decision variables in equations (5)-(21), and the parameter matrix Ψ,Γ and the coefficient vector ψ,ζ are all determined by equations (5)-(21); δ represents the amount that needs to be adjusted upwards or downwards to ensure that the dispatch scheme y can be executed by the power-type distributed energy cluster based on the distribution network; β is the weighting vector, 1 T Let be a τ-dimensional vector with all elements equal to 1; Equation (22) is the main problem, which solves for the virtual machine group model parameters; Equation (23) is the sub-problem, which solves for the minimum power correction value that enables the power-type distributed energy cluster to operate normally.

5. The virtual machine group model construction method considering the spatiotemporal coupling relationship of power-type distributed energy clusters as described in claim 4, characterized in that, In the k-th iteration, the inner min problem of the subproblem is dualized as follows: Where θ, ω, and π are dual variables, bigM is a sufficiently large positive number, and z is a Boolean variable.

6. The virtual machine group model construction method considering the spatiotemporal coupling relationship of power-type distributed energy clusters as described in claim 5, characterized in that, The problem of solving virtual machine group parameters is as follows: Where i = 0, 1, ..., k-1, k.

7. The virtual machine group model construction method considering the spatiotemporal coupling relationship of power-type distributed energy clusters as described in claim 6, characterized in that, Solving for the virtual machine group parameters in the k-th iteration yields the results. Then, let k→k+1 and Repeat equations (25)-(28) until f' = 0 to obtain the virtual machine group parameters.

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