Distributed energy storage aggregation scheduling method and device based on homologous polyhedral theory

Abstract

The application discloses a kind of distributed energy storage aggregation scheduling method and device based on the theory of same family polyhedron, method includes: obtaining the preselected operating parameter of each distributed energy storage;According to preselected operating parameter, the feasible region polyhedron of each distributed energy storage is constructed;The average value of preselected operating parameter of each distributed energy storage is calculated, and reference parameter is obtained;According to reference parameter, the reference feasible region polyhedron is constructed;According to the absolute value of constraint vector in feasible region polyhedron, weight adaptation coefficient vector is obtained;According to the weight adaptation coefficient vector of each distributed energy storage and reference feasible region polyhedron, the weight adaptation feasible region polyhedron of each distributed energy storage is obtained;According to the feasible region polyhedron and weight adaptation feasible region polyhedron of each distributed energy storage, boundary optimization is solved, and distribution parameter is obtained;Distribution parameter includes translation vector and scaling factor;According to the distribution parameter and feasible region polyhedron of each distributed energy storage, power instruction distribution is carried out.

Description

Technical Field

[0001] This invention relates to the field of electrical automation technology, and in particular to a distributed energy storage aggregation and scheduling method and device based on the theory of homogeneous polyhedra. Background Technology

[0002] With the large-scale integration of flexible resources such as distributed photovoltaics, energy storage systems, and electric vehicles into the distribution network, power system operation and control face new challenges. Although the capacity of a single unit is limited, the aggregation of massive distributed resources contains considerable flexible adjustment capabilities, and their efficient utilization has significant engineering value for maintaining real-time power balance in the system and improving the efficient absorption of new energy sources.

[0003] On the one hand, existing research typically employs polyhedral theory to mathematically characterize the flexibility of energy storage resources. By constructing a power-energy feasible region, the operational constraints of individual energy storage units are expressed as polyhedral geometry in a multidimensional space. Traditional methods are mainly based on the homogeneous polyhedral assumption, approximating the operational characteristics of heterogeneous resources through affine transformations of a benchmark virtual battery model. However, when dealing with the parameter heterogeneity present in practical engineering, such methods often lead to overly conservative boundaries of the aggregation feasible region, making it difficult to accurately characterize the overall adjustment capability of the cluster. On the other hand, with the increase in the number of resources, direct computational methods for distributed resource aggregation face an exponential increase in computational complexity. In particular, when the cluster size reaches a certain threshold, existing algorithms exhibit a significant contradiction between computational efficiency and accuracy. Existing methods such as Zonotope and homogeneous polyhedrals cannot simultaneously improve computational efficiency and maintain aggregation accuracy.

[0004] Therefore, a new technical solution is urgently needed to address the technical problem of how to improve the aggregation accuracy and efficiency of distributed energy storage in power systems. Summary of the Invention

[0005] This invention provides a distributed energy storage aggregation and scheduling method and device based on the theory of homogeneous polyhedra, which is used to solve the technical problem of how to improve the aggregation accuracy and efficiency of distributed energy storage in power systems.

[0006] To achieve the above objectives, this invention provides a distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra, comprising: Obtain the pre-selected operating parameters for each distributed energy storage system; construct the feasible domain polyhedron for each distributed energy storage system based on the pre-selected operating parameters; calculate the average value of the pre-selected operating parameters for each distributed energy storage system to obtain the benchmark parameters; construct the benchmark feasible domain polyhedron based on the benchmark parameters; obtain the weight adaptation coefficient vector based on the absolute value of the constraint vectors in the feasible domain polyhedron. The weight-adapted feasible region polyhedron for each distributed energy storage is obtained based on the weight adaptation coefficient vector of each distributed energy storage and the baseline feasible region polyhedron. Boundary optimization is performed based on the feasible region polyhedron and the weight-adapted feasible region polyhedron for each distributed energy storage to obtain the allocation parameters. The allocation parameters include translation vectors and scaling factors. Power command allocation is performed based on the allocation parameters and feasible region polyhedron for each distributed energy storage.

[0007] Preferably, the pre-selected operating parameters for each distributed energy storage system include: Obtain the initial energy state of each distributed energy storage unit. Energy operating range Power adjustment range Self-discharge rate and charge / discharge efficiency The system filters and interpolates abnormal data to obtain pre-selected operating parameters.

[0008] Preferably, the feasible domain polyhedrons for each distributed energy storage system, constructed based on pre-selected operating parameters, include: Define power vector and energy state vector satisfy: ; ; ; in, and They represent the first The distributed energy storage unit in the first Power and energy over a period of time To optimize the time range The last time period; Based on the state transition matrix get and Dynamic relationship model: ; ; Among them, intermediate quantity ; express 3D identity matrix; intermediate quantity , This indicates the transpose; According to the power adjustment range Power constraints are obtained: ; in, , , and They represent the first The first distributed energy storage The upper and lower limits of the power adjustment range for each time period; According to the energy operating range The energy state constraints are obtained from the dynamic relationship model: ; Among them, intermediate quantity intermediate quantity , for A dimensional vector of all 1s; Based on power constraints and energy state constraints, the feasible domain polyhedra of each distributed energy storage system are obtained, the first... The feasible region polyhedron of a distributed energy storage system is represented as follows: ; ; in, The direction matrix, For constraint vectors, , , This represents a matrix, with the superscript indicating the dimension.

[0009] Preferably, the average value of the pre-selected operating parameters for each distributed energy storage system is calculated to obtain the baseline parameters; the baseline feasible region polyhedron is constructed based on the baseline parameters, including: Calculate the average of the pre-selected operating parameters for each distributed energy storage system to obtain the baseline parameters. : ; in, , , , , and They are respectively , , , , and The average value; This represents the total number of distributed energy storage units. Based on the method of constructing feasible domain polyhedra for each distributed energy storage system, and according to the baseline parameters... The baseline feasible region polyhedron was constructed. : ; ; in, The orientation matrix of the reference feasible region polyhedron. Let be the constraint vector of the baseline feasible region polyhedron, and be the state transition matrix. intermediate quantity intermediate quantity .

[0010] Preferably, the weight adaptation coefficient vector obtained from the absolute values ​​of the constraint vectors in the feasible region polyhedron includes: The absolute value of the constraint vector of the feasible region polyhedron is taken to construct the weight adaptation coefficient vector. : ; in, Represents the constraint vector The Each element.

[0011] Preferably, the weight-adapted feasible region polyhedron for each distributed energy storage system is obtained based on the weight adaptation coefficient vector of each distributed energy storage system and the baseline feasible region polyhedron, including: The weight adaptation coefficient vectors of each distributed energy storage system are configured into the constraint vectors of the baseline feasible region polyhedron to generate the weight-adapted feasible region polyhedron for each distributed energy storage system: ; Among them, the weighted constraint vector , .

[0012] Preferably, boundary optimization is performed based on the feasible region polyhedron and the weighted adapted feasible region polyhedron of each distributed energy storage system to obtain the allocation parameters, including: Boundary optimization problems include: ; in, Represents the translation vector. This is the scaling factor; Solving the boundary optimization problem includes: make The boundary optimization problem is transformed into a linear programming problem: ; in, , sum matrix These are all intermediate quantities for ease of calculation; Solve the linear programming problem to obtain the optimal solution. Based on the optimal solution of the linear programming problem Performing the inverse transformation yields the optimal solution to the boundary optimization problem. That is, the allocation parameters: ; Preferably, power command allocation based on the allocation parameters of each distributed energy storage system and the feasible domain polyhedron includes: Calculate the weighted sum of the allocation parameters for each distributed energy storage system: ; in, This represents the weighted sum of scaling factors. This represents the weighted sum of the translation vectors; Based on the weighted sum of the allocation parameters and the power command issued by the dispatch center Calculation benchmark instructions : ; Power commands are allocated based on the weight adaptation coefficient vector of each distributed energy storage system and the baseline command: ; in, Indicates the first The power command allocated to each distributed energy storage system.

[0013] Preferably, it also includes calculating the maximum power handling capacity of the polymerization result: Based on the optimal solution of the boundary optimization problem The approximate flexibility parameters of each distributed energy storage feasible region polyhedron are obtained by weight-adapting the feasible region polyhedrons of each distributed energy storage system. ; The parameters of the aggregation result are obtained by weighting the allocation parameters and the baseline parameters: ; in, , , , and These represent the aggregation results and the baseline parameters, respectively. , , , and The corresponding parameters; The Minkowski sum is calculated based on the parameters of the polymerization result and the flexibility approximation parameters to obtain the maximum power handling capacity of the polymerization result. : ; ; in, for The direction matrix, for constraint vector; intermediate quantity intermediate quantity ; If the power command issued by the dispatch center If the power exceeds the maximum capacity of the aggregated result, distributed energy storage will be added to the grid according to the preset demand, and the allocation parameters will be recalculated before issuing the power command. The allocation.

[0014] The present invention also provides a distributed energy storage aggregation and scheduling device based on the theory of homogeneous polyhedra, for implementing the method of the present invention.

[0015] The present invention has the following beneficial effects: This invention presents a distributed energy storage aggregation and scheduling method based on the theory of family polyhedra. By collecting operational data from each energy storage unit and constructing a weighted, personalized polyhedral model, it more accurately characterizes the operational feasible region of heterogeneous energy storage. Utilizing the parallel geometric properties of family polyhedral boundaries, it transforms the complex feasible region aggregation problem into efficient algebraic operations, effectively resolving the contradiction between computational complexity and aggregation accuracy in traditional methods. By establishing a scalable aggregation-deaggregation architecture, it achieves closed-loop control from cluster-level scheduling commands to unit-level power allocation, providing accurate and reliable flexibility support for ancillary services such as power system peak shaving and frequency regulation, and significantly improving the grid's ability to coordinate and control massive distributed energy storage resources. This invention achieves more accurate flexibility characterization while ensuring computational efficiency, providing effective technical support for the participation of massive distributed resources in grid regulation.

[0016] The distributed energy storage aggregation and scheduling device based on the theory of homogeneous polyhedra of the present invention, when used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0017] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the method flow of a preferred embodiment of the present invention. Detailed Implementation

[0019] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0020] See Figure 1 In a preferred embodiment of the present invention, a distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra is provided, comprising: S1. Obtain the pre-selected operating parameters for each distributed energy storage system; construct the feasible domain polyhedron for each distributed energy storage system based on the pre-selected operating parameters.

[0021] In a preferred embodiment of the present invention, obtaining the pre-selected operating parameters for each distributed energy storage system includes: Obtain the initial energy state of each distributed energy storage unit. Energy operating range Power adjustment range Self-discharge rate and charge / discharge efficiency The system filters and interpolates abnormal data to obtain pre-selected operating parameters. By filtering and interpolating abnormal data, the accuracy and reliability of the parameters can be ensured.

[0022] Specifically, Indicates the first The initial energy state of each distributed energy storage system is directly obtained by the battery management system; Indicates the first The energy operating range of each distributed energy storage system is dynamically adjusted based on the battery's rated capacity, current health status, and aging characteristics. Indicates the first The distributed energy storage in the first The power adjustment range for a given time period is determined by combining the converter's rated power and the current operating status. Indicates the first The self-discharge rate of distributed energy storage is identified based on historical operating data and reflects the static loss characteristics of the energy storage unit. The charging and discharging efficiency is represented by the efficiency curve provided by the equipment manufacturer and verified by piecewise linear fitting using measured data.

[0023] In a preferred embodiment of the present invention, constructing the feasible domain polyhedron for each distributed energy storage system based on pre-selected operating parameters includes: Define power vector and energy state vector satisfy: ; ; ; in, and They represent the first The distributed energy storage unit in the first Power and energy over a period of time To optimize the time range The last time period is determined by the actual dispatching needs, and the length of each time period is set according to the actual dispatching needs, usually consistent with the power market clearing cycle.

[0024] Based on the state transition matrix get and Dynamic relationship model: ; ; Among them, intermediate quantity ; express 3D identity matrix; intermediate quantity , This indicates the transpose; According to the power adjustment range Power constraints are obtained: ; in, , , and They represent the first The first distributed energy storage The upper and lower limits of the power adjustment range for each time period; According to the energy operating range The energy state constraints are obtained from the dynamic relationship model: ; Among them, intermediate quantity intermediate quantity , for A dimensional vector of all 1s; Based on power constraints and energy state constraints, the feasible domain polyhedra of each distributed energy storage system are obtained, the first... The feasible region polyhedron of a distributed energy storage system is represented as follows: ; ; in, The direction matrix, For constraint vectors, , , This represents a matrix, with the superscript indicating the dimension.

[0025] S2. Calculate the average value of the pre-selected operating parameters for each distributed energy storage system to obtain the baseline parameters; construct the baseline feasible region polyhedron based on the baseline parameters. S2 specifically includes: Calculate the average of the pre-selected operating parameters for each distributed energy storage system to obtain the baseline parameters. : ; in, , , , , and They are respectively , , , , and The average value; This represents the total number of distributed energy storage units. Based on the method of constructing feasible domain polyhedra for each distributed energy storage system, and according to the baseline parameters... The baseline feasible region polyhedron was constructed. : ; ; in, The orientation matrix of the reference feasible region polyhedron. Let be the constraint vector of the baseline feasible region polyhedron, and be the state transition matrix. intermediate quantity intermediate quantity .

[0026] S3. Obtain the weight adaptation coefficient vector based on the absolute value of the constraint vector in the feasible region polyhedron; obtain the weight-adapted feasible region polyhedron for each distributed energy storage based on the weight adaptation coefficient vector of each distributed energy storage and the baseline feasible region polyhedron.

[0027] In a preferred embodiment of the present invention, the weight adaptation coefficient vector is obtained based on the absolute value of the constraint vector in the feasible region polyhedron, including: The absolute value of the constraint vector of the feasible region polyhedron is taken to construct the weight adaptation coefficient vector. : ; in, Represents the constraint vector The Each element.

[0028] In a preferred embodiment of the present invention, obtaining the weight-adapted feasible region polyhedron for each distributed energy storage system based on the weight adaptation coefficient vector of each distributed energy storage system and the baseline feasible region polyhedron includes: The weight adaptation coefficient vectors of each distributed energy storage system are configured into the constraint vectors of the baseline feasible region polyhedron to generate the weight-adapted feasible region polyhedron for each distributed energy storage system: ; Among them, the weighted constraint vector , .

[0029] The weight-adapted feasible region polyhedra of each distributed energy storage system constitute a family of weight-adapted feasible region polyhedra.

[0030] In a preferred embodiment of the present invention, personalized adaptation of the baseline feasible region polyhedron is achieved through weight adjustment, quantifying the differences in the adjustment capabilities of each unit across different constraint dimensions: when Time: This corresponds to the expansion of the constraint boundary, adapting to a stronger adjustment capability in this dimension.

[0031] when Time: This corresponds to the constraint boundary shrinking, indicating a weaker matching capability in that dimension.

[0032] when Time: Maintaining the baseline boundary indicates that the individual and cluster average characteristics are similar.

[0033] express The Each element.

[0034] In a preferred embodiment of the present invention, for The weight calculation of each distributed energy storage system adopts parallel computing, which can improve the processing efficiency of large-scale clusters.

[0035] The method of this invention introduces a weight adjustment mechanism to reconstruct the baseline feasible region polyhedral model in a personalized manner, which significantly improves the fitting accuracy of heterogeneous units while maintaining the uniformity of geometric structure.

[0036] S4. Boundary optimization is performed based on the feasible region polyhedrons and weighted adapted feasible region polyhedrons of each distributed energy storage system to obtain the allocation parameters; the allocation parameters include the translation vector and scaling factor. S4 specifically includes: Boundary optimization problems include: ; in, This represents a translation vector, enabling coordination and cooperation between units; This is a scaling factor that reflects the adjustability of the feasible region for distributed energy storage.

[0037] Solving the boundary optimization problem includes: make The boundary optimization problem is transformed into a linear programming problem: ; in, , sum matrix All of these are intermediate quantities that are easy to calculate; by applying non-negativity constraints to matrix G, physical realizability can be ensured; the CPLEX solver is used to solve the above linear programming problem, and convergence tolerance is set to ensure calculation accuracy.

[0038] The optimal solution to the linear programming problem can be obtained by solving the linear programming problem. Based on the optimal solution of the linear programming problem Performing the inverse transformation yields the optimal solution to the boundary optimization problem. That is, the allocation parameters: ; S5. Power command allocation is performed based on the allocation parameters of each distributed energy storage system and the feasible domain polyhedron. S5 specifically includes: Calculate the weighted sum of the allocation parameters for each distributed energy storage system: ; in, This represents the weighted sum of scaling factors. This represents the weighted sum of the translation vectors; Based on the weighted sum of the allocation parameters and the power command issued by the dispatch center Calculation benchmark instructions : ; Power commands are allocated based on the weight adaptation coefficient vector of each distributed energy storage system and the baseline command: ; in, Indicates the first The power command allocated to each distributed energy storage system.

[0039] In a preferred embodiment of the present invention, the method further includes calculating the maximum power handling capacity of the polymerization result: Based on the optimal solution of the boundary optimization problem The approximate flexibility parameters of each distributed energy storage feasible region polyhedron are obtained by weight-adapting the feasible region polyhedrons of each distributed energy storage system. ; The parameters of the aggregation result are obtained by weighting the allocation parameters and the baseline parameters: ; in, , , , and These represent the aggregation results and the baseline parameters, respectively. , , , and The corresponding parameters; The Minkowski sum is calculated based on the parameters of the polymerization result and the flexibility approximation parameters to obtain the maximum power handling capacity of the polymerization result. : ; ; in, for The direction matrix, for constraint vector; intermediate quantity intermediate quantity ; If the power command issued by the dispatch center If the power exceeds the maximum capacity of the aggregated result, distributed energy storage will be added to the grid according to the preset demand, and the allocation parameters will be recalculated before issuing the power command. The allocation.

[0040] This invention presents a distributed energy storage aggregation and scheduling method based on the theory of family polyhedra. By collecting operational data from each energy storage unit and constructing a weighted, personalized polyhedral model, it more accurately characterizes the operational feasible region of heterogeneous energy storage. Utilizing the parallel geometric properties of family polyhedral boundaries, it transforms the complex feasible region aggregation problem into efficient algebraic operations, effectively resolving the contradiction between computational complexity and aggregation accuracy in traditional methods. By establishing a scalable aggregation-deaggregation architecture, it achieves closed-loop control from cluster-level scheduling commands to unit-level power allocation, providing accurate and reliable flexibility support for ancillary services such as power system peak shaving and frequency regulation, and significantly improving the grid's ability to coordinate and control massive distributed energy storage resources. This invention achieves more accurate flexibility characterization while ensuring computational efficiency, providing effective technical support for the participation of massive distributed resources in grid regulation.

[0041] In a preferred embodiment of the present invention, a distributed energy storage aggregation and scheduling device based on the theory of homogeneous polyhedra is also provided to implement the method of the present invention.

[0042] The distributed energy storage aggregation and scheduling device based on the theory of homogeneous polyhedra of the present invention, when used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra, characterized in that, include: Obtain the pre-selected operating parameters for each distributed energy storage; construct the feasible domain polyhedron for each distributed energy storage based on the pre-selected operating parameters; Calculate the average value of the pre-selected operating parameters for each distributed energy storage to obtain the benchmark parameters; construct a benchmark feasible region polyhedron based on the benchmark parameters; obtain the weight adaptation coefficient vector based on the absolute value of the constraint vectors in the feasible region polyhedron. The weight-adapted feasible region polyhedron for each distributed energy storage is obtained based on the weight adaptation coefficient vector of each distributed energy storage and the baseline feasible region polyhedron; boundary optimization is performed based on the feasible region polyhedron and the weight-adapted feasible region polyhedron for each distributed energy storage to obtain the allocation parameters; the allocation parameters include translation vector and scaling factor. Power commands are allocated based on the allocation parameters of each distributed energy storage system and the feasible domain polyhedron. Obtaining the pre-selected operating parameters for each distributed energy storage system includes: Obtain the initial energy state of each distributed energy storage unit. Energy operating range Power adjustment range Self-discharge rate and charge / discharge efficiency The abnormal data is then filtered and interpolated to obtain the pre-selected operating parameters. Constructing the feasible domain polyhedron for each distributed energy storage system based on the pre-selected operating parameters includes: Define power vector and energy state vector satisfy: ； ； ； in, and They represent the first The distributed energy storage unit in the first Power and energy over a period of time To optimize the time range The last time period; Based on the state transition matrix get and Dynamic relationship model: ； ； Among them, intermediate quantity ; express 3D identity matrix; intermediate quantity , This indicates the transpose; According to the power adjustment range Power constraints are obtained: ； in, , , and They represent the first The first distributed energy storage The upper and lower limits of the power adjustment range for each time period; According to the energy operating range The energy state constraints are obtained from the dynamic relationship model: ； Among them, intermediate quantity intermediate quantity , for A dimensional vector of all 1s; Based on the power constraints and the energy state constraints, the feasible domain polyhedra of each distributed energy storage are obtained, the first... The feasible region polyhedron of a distributed energy storage system is represented as follows: ； ； in, The direction matrix, For constraint vectors, , , This represents a matrix, with the superscript indicating the dimension.

2. The distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra according to claim 1, characterized in that, Calculate the average value of the pre-selected operating parameters for each distributed energy storage system to obtain the baseline parameters; Constructing a baseline feasible region polyhedron based on the aforementioned baseline parameters includes: Calculate the average of the pre-selected operating parameters for each distributed energy storage system to obtain the baseline parameters. : ； in, , , , , and They are respectively , , , , and The average value; This represents the total number of distributed energy storage units. Based on the method for constructing feasible domain polyhedra of each distributed energy storage system, and according to the aforementioned benchmark parameters The baseline feasible region polyhedron was constructed. : ； ； in, The orientation matrix of the reference feasible region polyhedron. Let be the constraint vector of the baseline feasible region polyhedron, and be the state transition matrix. intermediate quantity intermediate quantity .

3. The distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra according to claim 2, characterized in that, The weight adaptation coefficient vector obtained from the absolute values ​​of the constraint vectors in the feasible region polyhedron includes: The absolute value of the constraint vector of the feasible region polyhedron is taken to construct the weight adaptation coefficient vector. : ； in, Represents the constraint vector The Each element.

4. The distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra according to claim 3, characterized in that, The weight-adapted feasible region polyhedrons for each distributed energy storage system are obtained based on the weight adaptation coefficient vectors of each distributed energy storage system and the baseline feasible region polyhedron, including: The weight adaptation coefficient vectors of each distributed energy storage system are configured into the constraint vectors of the baseline feasible region polyhedron to generate the weight-adapted feasible region polyhedron for each distributed energy storage system: ； Among them, the weighted constraint vector , .

5. The distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra according to claim 4, characterized in that, Boundary optimization is performed based on the feasible region polyhedrons and weight-adapted feasible region polyhedrons of each distributed energy storage system to obtain the allocation parameters, including: Boundary optimization problems include: ； in, Represents the translation vector. This is the scaling factor; Solving the boundary optimization problem includes: make The boundary optimization problem is then transformed into a linear programming problem: ； in, , sum matrix These are all intermediate quantities for ease of calculation; Solve the linear programming problem to obtain the optimal solution. Based on the optimal solution of the linear programming problem Perform an inverse transformation to obtain the optimal solution to the boundary optimization problem. That is, the allocation parameters: 。 6. The distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra according to claim 5, characterized in that, Power command allocation based on the allocation parameters of each distributed energy storage system and the feasible domain polyhedron includes: Calculate the weighted sum of the allocation parameters for each distributed energy storage system: ； in, This represents the weighted sum of scaling factors. This represents the weighted sum of the translation vectors; The weighted sum is based on the allocation parameters and the power command issued by the scheduling center. Calculation benchmark instructions : ； Power commands are allocated based on the weight adaptation coefficient vector of each distributed energy storage system and the baseline command: ； in, Indicates the first The power command allocated to each distributed energy storage system.

7. The distributed energy storage aggregation and scheduling method based on the theory of homogeneous polyhedra according to claim 6, characterized in that, It also includes calculating the maximum power handling capacity of the aggregation result: Based on the optimal solution of the boundary optimization problem The approximate flexibility parameters of each distributed energy storage feasible region polyhedron are obtained by weight-adapting feasible region polyhedra and combining them with the weights of each distributed energy storage system. ; The parameters for obtaining the aggregation result are obtained by weighting the allocation parameters and the baseline parameters: ； in, , , , and These represent the aggregation results and the baseline parameters, respectively. , , , and The corresponding parameters; Based on the parameters of the aggregation result and the flexibility approximation parameter, the Minkowski sum is calculated to obtain the maximum power handling capacity of the aggregation result. : ； ； in, for The direction matrix, for constraint vector; intermediate quantity intermediate quantity ; If the power command issued by the dispatch center If the power exceeds the maximum power capacity of the aggregation result, distributed energy storage will be added to the grid according to the preset requirements, and the allocation parameters will be recalculated before issuing the power command. The allocation.

8. A distributed energy storage aggregation and scheduling device based on the theory of homogeneous polyhedra, characterized in that, The apparatus is used to implement the method according to any one of claims 1 to 7.

Images