5G base station standby energy storage feasible region aggregation method based on constraint sino polyhedron

Abstract

The invention relates to a 5G base station standby energy storage feasible region aggregation method based on a constraint sino polyhedron, and belongs to the field of distributed energy storage optimization scheduling. The method comprises the following steps: acquiring the upper and lower limits of power and the upper and lower limits of charge state of standby energy storage of a base station, and evaluating the schedulable capacity of the base station; according to the obtained upper and lower limits of the power, the upper and lower limits of the state of charge and the schedulable capacity, converting the feasible region description model of the standby energy storage of the to-be-aggregated base station in a half-plane representation form into a feasible region description model in a constraint sino polyhedron representation form; and Minkowski summation is carried out on the feasible region description model of the standby energy storage of the base station to obtain a feasible region aggregation model of the standby energy storage of the 5G base station. The 5G base station standby energy storage feasible region is efficiently aggregated by using the constraint sino polyhedron, and the base station standby energy storage in different distributions is aggregated into a whole to participate in the power market transaction, so that the operation cost of the 5G base station is reduced, and the mutual benefit and win-win situation of a power system and a communication system is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of distributed energy storage optimization scheduling, and specifically relates to a 5G base station backup energy storage feasible domain aggregation method based on constrained Chino polyhedron. Background Art

[0002] With the continuous development of 5G technology and the continuous expansion of its application fields, the deployment of 5G networks continues to advance. At the same time, the advantages of 5G communication such as high speed, high capacity, and high-density connection bring about the problem of increased energy consumption of base stations. At present, the full-load power consumption of a single 5G base station is about 4kW, which is about 3 times that of a 4G base station. The power consumption and construction quantity of 5G base stations have increased exponentially compared with 4G base stations, and the electricity cost has increased significantly. Therefore, for 5G base stations, problems such as saving electricity costs need to be solved urgently. With the continuous improvement of the reliability of the city power supply, the energy storage of 5G base stations has been in a floating charge state for a long time, resulting in a waste of resources. On the basis of ensuring the reliability of base station power supply, using idle base station energy storage to participate in grid interaction can obtain certain economic benefits to reduce electricity costs, and has little impact on the quality of user communication. At the same time, it can also reduce the investment pressure of the grid to build new energy storage power stations, maximize resource utilization, and promote mutual benefit and win-win results between the power system and the communication system. Since the energy storage capacity of a single base station is too small, if it directly participates in grid dispatching, it will lead to too many optimization variables that are difficult to clear, and most electricity markets have set access conditions. A single base station cannot participate in electricity market transactions as an independent individual. Therefore, it is necessary to aggregate the feasible domain of a single base station to obtain the feasible domain of the base station cluster, so as to provide a reference for the declared power and declared capacity for the base station cluster to participate in grid dispatching.

[0003] The feasible domain of a single base station backup energy storage geometrically corresponds to a high-dimensional convex polyhedron. The aggregation problem of the feasible domains of multiple base station backup energy storage is essentially to find the Minkowski sum of several convex polyhedrons. If the feasible domain of the base station backup energy storage is represented by vertices and the Minkowski sum is found, vertex enumeration is required. If the number of vertices in the feasible domain is large, the amount of calculation is too large and the aggregation time is long, which makes it meaningless. If the feasible domain of 5G base station backup energy storage is aggregated using Zono polyhedrons, the generator matrix of each base station backup energy storage needs to be preset. However, as the base station operating time changes, the generator matrix of the base station backup energy storage will also change accordingly, which has a great impact on the aggregation accuracy. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for aggregating the feasible domain of 5G base station backup energy storage based on constrained Zono polyhedrons. The feasible domain of 5G base station backup energy storage is aggregated using constrained Zono polyhedrons. According to the state of the 5G base station backup energy storage, a generator matrix of the base station backup energy storage is constructed. There is no need to preset the generator matrix, and the scaling coefficients corresponding to the generating vectors in the generator matrix are constrained. The feasible domain of 5G base station backup energy storage is accurately characterized, the aggregation accuracy is improved, the base station backup energy storage distributed in different places is aggregated as a whole to participate in the power market transaction, the operating cost of the base station is reduced, and the mutual benefit and win-win situation between the power system and the communication system is promoted.

[0005] To achieve the above object, the technical solution of the present invention is: a method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron, comprising: Obtain the upper and lower limits of power and state of charge of the base station backup energy storage, and evaluate the dispatchable capacity of the base station backup energy storage; According to the obtained upper and lower limits of power, upper and lower limits of charge state, and dispatchable capacity of the base station backup energy storage, the feasible domain of a single base station backup energy storage is characterized using a half-plane representation, and a feasible domain characterization model of a single base station backup energy storage in a half-plane representation is obtained; Convert the feasible domain characterization model of all base station backup energy storage in the form of a half-plane into the feasible domain characterization model in the form of a constrained Chino polyhedron; The Minkowski summation is performed on the feasible domain characterization model of all base station backup energy storage to be aggregated in the form of constrained Chino polyhedron representation to obtain the feasible domain aggregation model of 5G base station backup energy storage.

[0006] In one embodiment of the present invention, the implementation method of evaluating the dispatchable capacity of the base station backup energy storage is as follows: Base station load factor It is expressed as: in, L is the number of users accessing the base station, The maximum number of users that the base station can handle. k is the correlation coefficient, is the minimum value of the base station backup energy storage charge state obtained according to the base station load, is the upper limit of the charge state of the base station backup energy storage, is the lower limit of the charge state of the base station backup energy storage, The dispatchable capacity of the base station backup energy storage.

[0007] In one embodiment of the present invention, the feasible domain characterization model of a single base station backup energy storage in the form of a half-plane representation is expressed as follows: in, H The coefficient matrix of inequality constraints when the base station backup energy storage is running; x is the constraint vector, including power and state of charge; f It is the boundary vector of the base station backup energy storage operation, including the upper and lower limits of the power of the base station backup energy storage, the upper and lower limits of the charge state, and the minimum value of the charge state of the base station backup energy storage obtained according to the base station load.

[0008] In one embodiment of the present invention, the feasible domain characterization model of a single base station backup energy storage in the form of a constrained Chino polyhedron is expressed as follows: in, G is the generator matrix, Scaling factor, C is the geometric center of the feasible region of the single base station backup energy storage in the constrained Qino polyhedron representation, A is the scaling factor The constraint coefficient matrix, b is the scaling factor Constraint offset vector, For the j The generated vectors, ng is the number of generated vectors.

[0009] In one embodiment of the present invention, the feasible domain characterization model of all base station backup energy storage in the form of half-plane representation is converted into the feasible domain characterization model in the form of constrained Qino polyhedron as follows: for the feasible domain characterization model of a single base station backup energy storage in the form of half-plane representation, a Qino polyhedron is constructed by: P The intersection is calculated with the feasible domain of a single base station backup energy storage in the form of a half-plane representation, and the feasible domain characterization model of a single base station backup energy storage in the form of a constrained Qino polyhedron is obtained. The same operation is performed on all base station backup energy storages, and the feasible domain characterization model of all base station backup energy storages in the form of a constrained Qino polyhedron is obtained.

[0010] In one embodiment of the present invention, the specific implementation steps of converting the feasible domain characterization model of all base station backup energy storage in the form of a half-plane representation into the feasible domain characterization model in the form of a constrained Chino polyhedron representation are as follows: Step 1: Find the feasible domain of the base station backup energy storage in the form of a half-plane representation. nThe maximum value in each direction and minimum value , the maximum value and minimum value difference As the diagonal elements, we get the generator matrix, expressed as: Step 2: Set the maximum value With minimum The sum of C , expressed as: Step 3: Substitute the generated matrices obtained in steps 1 and 2 G and center point C , construct a Chino polyhedron, expressed as: Step 4: Calculate the Chino polyhedron P The hyperplane where the feasible region of the single base station backup energy storage in the half-plane representation is located is No. n The farthest point in the direction The orthogonal distance between Specifically, the center point of the Chino polyhedron C and the generator matrix G Each generated vector in is projected onto the hyperplane where the feasible domain of a single base station backup energy storage in the half-plane representation is located , the boundary vector of the base station backup energy storage operation f Subtract the center point of the Kinos polyhedron C The hyperplane where the feasible domain of the backup energy storage of a single base station in the half-plane representation lies Projection of the center point of the Chino polyhedron C The hyperplane where the feasible region of a single base station backup energy storage in the half-plane representation lies No. n The orthogonal distances in each direction, plus the center point of the Chino polyhedron C The farthest point The orthogonal distance between them is expressed as: in, represents the infinite norm; Step 5: According to step 4 Construct a diagonal matrix to project each generated vector onto the hyperplane where the feasible domain of the single base station backup energy storage in the half-plane representation is located. with by Construct a diagonal matrix and take the union to get the constraint coefficient matrixA , expressed as: The center point of the Kinos polyhedron C The hyperplane where the feasible region of a single base station backup energy storage in the half-plane representation lies No. n The orthogonal distance in each direction is the hyperplane where the feasible domain of the backup energy storage of a single base station is located in the half-plane representation form. No. n The farthest point in the direction The orthogonal distance between Take the difference to get the scaling factor Constraint offset vector b , expressed as: . In one embodiment of the present invention, the feasible domain characterization model of all the backup energy storages of the base stations to be aggregated in the form of constrained Chino polyhedron is subjected to Minkowski summation, and the implementation method of the feasible domain aggregation model of the backup energy storage of the 5G base stations is obtained as follows: Convert the feasible domain characterization model of all the backup energy storages of the base stations to be aggregated in the form of a semi-plane into a feasible domain characterization model in the form of a constrained Chino polyhedron; The feasible domain model of the first two base station backup energy storage in the form of constrained Qino polyhedron is described , , perform Minkowski summation to obtain a new feasible domain model; The obtained new feasible domain model is used as the initial value and Minkowski summed with the feasible domain characterization model of the next base station backup energy storage in the form of constrained Chino polyhedron representation to obtain a new feasible domain model again. The feasible domains of all base station backup energy storage to be aggregated are repeatedly equated as a whole to construct a 5G base station backup energy storage feasible domain aggregation model.

[0011] In one embodiment of the present invention, the feasible domain characterization model of the first two base station backup energy storage in the form of constrained Qino polyhedron representation , , perform Minkowski summation, expressed as follows: in, The new feasible domain model obtained by Minkowski summation of the feasible domain characterization model of the first and second base station backup energy storage in the form of constrained Chino polyhedron representation is represented. are the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the first base station backup energy storage in the form of constrained Qino polyhedron, respectively. They are respectively the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the second base station backup energy storage in the constrained Chino polyhedron representation; The Minkowski sum is performed cyclically to establish the 5G base station feasible domain aggregation model. When the number of cycles is N-2, the aggregation is completed and the 5G base station feasible domain aggregation model is output, which is expressed as follows: in, represents the feasible domain characterization model of the spare energy storage of the i+2th base station in the form of constrained Chino polyhedron, They represent the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the spare energy storage of the i+2th base station in the form of constrained Chino polyhedron, respectively. They respectively represent the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the 5G base station backup energy storage feasible domain aggregation model obtained after one cycle, i=1,2,3,…,N-2, and N is the number of base station backup energy storage to be aggregated.

[0012] The present invention also provides a 5G base station backup energy storage feasible domain aggregation system based on constrained Chino polyhedron, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0013] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for aggregating the feasible domain of 5G base station backup energy storage based on constrained Chino polyhedron, converts the feasible domain characterization model of the base station backup energy storage in the semi-plane representation into the feasible domain characterization model of the constrained Chino polyhedron, and performs Minkowski summation by the intersection method, without the need to preset the size of the generator matrix and the elements of each generator vector, thereby reducing the complexity of calculation and improving the accuracy of aggregation; the present invention uses constrained Chino polyhedron to aggregate base stations distributed in different places into a whole to participate in power market transactions, obtain certain economic benefits, thereby reducing the operating cost of 5G base stations and promoting mutual benefit and win-win results between power systems and communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedrons of the present invention; Figure 2 This is a flow chart of the 5G base station feasible domain aggregation model obtained by cyclic Minkowski summation in the present invention; Figure 3 It is a schematic diagram of the feasible domain of the base station 1 obtained based on the constrained Chino polyhedron of the present invention; Figure 4 It is a schematic diagram of the feasible domain of the base station 2 obtained based on the constrained Chino polyhedron of the present invention; Figure 5 It is a schematic diagram of a feasible domain obtained after aggregating the feasible domains of base station 1 and base station 2 according to the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0017] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0018] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0019] The present invention provides a method for aggregating a feasible domain of a 5G base station backup energy storage based on a constrained Chino polyhedron, comprising: Obtain the upper and lower limits of power and state of charge of the base station backup energy storage, and evaluate the dispatchable capacity of the base station backup energy storage; According to the obtained upper and lower limits of power, upper and lower limits of charge state, and dispatchable capacity of the base station backup energy storage, the feasible domain of a single base station backup energy storage is characterized using a half-plane representation, and a feasible domain characterization model of a single base station backup energy storage in a half-plane representation is obtained; Convert the feasible domain characterization model of all base station backup energy storage in the form of a half-plane into the feasible domain characterization model in the form of a constrained Chino polyhedron; The Minkowski summation is performed on the feasible domain characterization model of all base station backup energy storage to be aggregated in the form of constrained Chino polyhedron representation to obtain the feasible domain aggregation model of 5G base station backup energy storage.

[0020] The following are specific implementation examples of the present invention.

[0021] like Figure 1As shown, this embodiment provides a method for aggregating 5G base station backup energy storage based on constrained Chino polyhedron, comprising the following steps: Step S1, obtaining the upper and lower limits of power and the upper and lower limits of the state of charge of the base station backup energy storage, and evaluating the dispatchable capacity of the base station backup energy storage; Step S2, according to the upper and lower limits of power, upper and lower limits of charge state, and dispatchable capacity obtained in step S1, the feasible domain of the backup energy storage of a single base station is characterized using a half-plane representation to obtain a feasible domain characterization model of the backup energy storage of a single base station in the half-plane representation; Step S3, converting the feasible domain characterization model of all the backup energy storages of the base stations to be aggregated in the form of a half plane into a feasible domain characterization model in the form of a constrained Chino polyhedron; Step S4: In the constrained Chino polyhedron representation, a cyclic Minkowski summation is performed on the feasible domain characterization model of the base station backup energy storage to obtain a feasible domain aggregation model of the 5G base station backup energy storage.

[0022] In step S1, the dispatchable capacity of the base station backup energy storage is evaluated as follows: Base station load factor It is expressed as: in, L is the number of users accessing the base station, The maximum number of users that the base station can handle. k is the correlation coefficient, is the minimum value of the base station backup energy storage charge state obtained according to the base station load, is the upper limit of the charge state of the base station backup energy storage, is the lower limit of the charge state of the base station backup energy storage, The dispatchable capacity of the base station backup energy storage.

[0023] In step S2, the feasible domain characterization model of a single base station backup energy storage in the form of a half-plane representation is expressed as follows: in, H The coefficient matrix of inequality constraints when the base station backup energy storage is running; x is the constraint vector, including power and state of charge; f It is the boundary vector of the base station backup energy storage operation, including the upper and lower limits of the power of the base station backup energy storage, the upper and lower limits of the charge state, and the minimum value of the charge state of the base station backup energy storage obtained according to the base station load.

[0024] In step S3, the feasible domain characterization model of a single base station backup energy storage in the form of constrained Chino polyhedron is expressed as follows: in, G is the generator matrix, Scaling factor, C is the geometric center of the feasible region of the single base station backup energy storage in the constrained Qino polyhedron representation, A is the scaling factor The constraint coefficient matrix, b is the scaling factor Constraint offset vector, For the j The generated vectors, ng is the number of generated vectors.

[0025] In step S3, the feasible domain characterization model of all base station backup energy storage in the form of half-plane representation is converted into the feasible domain characterization model in the form of constrained Chino polyhedron representation as follows: for the feasible domain characterization model of a single base station backup energy storage in the form of half-plane representation, a Chino polyhedron is constructed by: P The intersection is calculated with the feasible domain of a single base station backup energy storage in the form of a half-plane representation, and the feasible domain characterization model of a single base station backup energy storage in the form of a constrained Qino polyhedron is obtained. The same operation is performed on all base station backup energy storages, and the feasible domain characterization model of all base station backup energy storages in the form of a constrained Qino polyhedron is obtained. The specific implementation steps are as follows: Step S31, find the feasible domain of the base station backup energy storage in the form of a half plane. n The maximum value in each direction and minimum value , the maximum value and minimum value difference As the diagonal elements, we get the generator matrix, expressed as: Step S32: The maximum value With minimum The sum of C , expressed as: Step S33: The generator matrix obtained in step S31 and step S32 is G and center point C , construct a Chino polyhedron, expressed as: Step S34: Calculate the Chino polyhedron P The hyperplane where the feasible region of the single base station backup energy storage in the half-plane representation is located is No. n The farthest point in the direction The orthogonal distance between Specifically, the center point of the Chino polyhedron C and the generator matrix G Each generated vector in is projected onto the hyperplane where the feasible domain of a single base station backup energy storage in the half-plane representation is located , the boundary vector of the base station backup energy storage operation f Subtract the center point of the Kinos polyhedron C The hyperplane where the feasible domain of the backup energy storage of a single base station in the half-plane representation lies Projection of the center point of the Chino polyhedron C The hyperplane where the feasible region of a single base station backup energy storage in the half-plane representation lies No. n The orthogonal distances in each direction, plus the center point of the Chino polyhedron C The farthest point The orthogonal distance between them is expressed as: in, represents the infinite norm; Step S35: According to the step S34 Construct a diagonal matrix to project each generated vector onto the hyperplane where the feasible domain of the single base station backup energy storage in the half-plane representation is located. with by Construct a diagonal matrix and take the union to get the constraint coefficient matrix A , expressed as: The center point of the Kinos polyhedron C The hyperplane where the feasible region of a single base station backup energy storage in the half-plane representation lies No. n The orthogonal distance in each direction is the hyperplane where the feasible domain of the backup energy storage of a single base station is located in the half-plane representation form. No. n The farthest point in the direction The orthogonal distance between Take the difference to get the scaling factor Constraint offset vector b , expressed as: . like Figure 2As shown, in step S4, the feasible domain characterization model of all the backup energy storages of the base stations to be aggregated in the form of constrained Chino polyhedron is subjected to Minkowski summation, and the implementation method of the feasible domain aggregation model of the backup energy storage of the 5G base stations is obtained as follows: Convert the feasible domain characterization model of all the backup energy storages of the base stations to be aggregated in the form of a semi-plane into a feasible domain characterization model in the form of a constrained Chino polyhedron; The feasible domain model of the first two base station backup energy storage in the form of constrained Qino polyhedron representation , , perform Minkowski summation to obtain a new feasible domain model; The obtained new feasible domain model is used as the initial value and Minkowski summed with the feasible domain characterization model of the next base station backup energy storage in the form of constrained Chino polyhedron representation to obtain a new feasible domain model again. The feasible domains of all base station backup energy storage to be aggregated are repeatedly equated as a whole to construct a 5G base station backup energy storage feasible domain aggregation model.

[0026] Specifically, the feasible domain characterization model of the first two base station backup energy storage in the form of constrained Qino polyhedron representation is , , perform Minkowski summation, expressed as follows: in, The new feasible domain model obtained by Minkowski summation of the feasible domain characterization model of the first and second base station backup energy storage in the form of constrained Chino polyhedron representation is represented. are the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the first base station backup energy storage in the form of constrained Qino polyhedron, respectively. They are respectively the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the second base station backup energy storage in the constrained Chino polyhedron representation; The Minkowski sum is performed cyclically to establish the 5G base station feasible domain aggregation model. When the number of cycles is N-2, the aggregation is completed and the 5G base station feasible domain aggregation model is output, which is expressed as follows: in, represents the feasible domain characterization model of the spare energy storage of the i+2th base station in the form of constrained Chino polyhedron, They represent the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the spare energy storage of the i+2th base station in the form of constrained Chino polyhedron, respectively. They respectively represent the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the 5G base station backup energy storage feasible domain aggregation model obtained after one cycle, i=1,2,3,…,N-2, and N is the number of base station backup energy storage to be aggregated.

[0027] Reference Figures 3 to 5 Since different base stations are located in different locations, the traffic demands of mobile users are different at different times and locations, which causes the load rate of the base station to change over time, and thus causes the dispatchable capacity of the base station to be different. Therefore, the feasible domain of the backup energy storage of each base station is heterogeneous. The feasible domains of the backup energy storage of these base stations can be aggregated into a whole through the Minkowski summation, and the Minkowski summation under the constrained Chino polyhedron representation is closed, that is, the feasible domain of the backup energy storage of a single base station is a convex set. After aggregating the feasible domains of the backup energy storage of all base stations to be aggregated into a whole, the aggregated feasible domain is still a convex set. This property provides great convenience for base station clusters to participate in power market transactions.

[0028] The present invention also provides a 5G base station backup energy storage feasible domain aggregation system based on constrained Chino polyhedron, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0029] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0030] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0031] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0032] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0033] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0034] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron, characterized in that: include: Obtain the upper and lower limits of power and state of charge of the base station backup energy storage, and evaluate the dispatchable capacity of the base station backup energy storage; According to the obtained upper and lower limits of power, upper and lower limits of charge state, and dispatchable capacity of the base station backup energy storage, the feasible domain of a single base station backup energy storage is characterized using a half-plane representation, and a feasible domain characterization model of a single base station backup energy storage in a half-plane representation is obtained; Convert the feasible domain characterization model of all base station backup energy storage in the form of a half-plane into the feasible domain characterization model in the form of a constrained Chino polyhedron; The Minkowski summation is performed on the feasible domain characterization model of all base station backup energy storage to be aggregated in the form of constrained Chino polyhedron representation to obtain the feasible domain aggregation model of 5G base station backup energy storage.

2. The method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron according to claim 1 is characterized in that: The implementation method of evaluating the dispatchable capacity of the base station backup energy storage is as follows: Base station load factor It is expressed as: in, L is the number of users accessing the base station, The maximum number of users that the base station can handle. k is the correlation coefficient, is the minimum value of the base station backup energy storage charge state obtained according to the base station load, is the upper limit of the charge state of the base station backup energy storage, is the lower limit of the charge state of the base station backup energy storage, The dispatchable capacity of the base station backup energy storage.

3. The method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron according to claim 1 is characterized in that: The feasible domain characterization model of a single base station backup energy storage in the form of a semi-plane representation is expressed as follows: in, H The coefficient matrix of inequality constraints when the base station backup energy storage is running; x is the constraint vector, including power and state of charge; f It is the boundary vector of the base station backup energy storage operation, including the upper and lower limits of the power of the base station backup energy storage, the upper and lower limits of the charge state, and the minimum value of the charge state of the base station backup energy storage obtained according to the base station load.

4. The method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron according to claim 3 is characterized in that: The feasible domain characterization model of a single base station backup energy storage in the form of constrained Chino polyhedron is expressed as follows: in, G is the generator matrix, Scaling factor, C is the geometric center of the feasible region of the single base station backup energy storage in the constrained Qino polyhedron representation, A is the scaling factor The constraint coefficient matrix, b is the scaling factor Constraint offset vector, For the j The generated vectors, ng is the number of generated vectors.

5. The method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron according to claim 4 is characterized in that: The feasible domain characterization model of all base station backup energy storage in the form of half-plane representation is converted into the feasible domain characterization model in the form of constrained Qino polyhedron representation as follows: for the feasible domain characterization model of a single base station backup energy storage in the form of half-plane representation, a Qino polyhedron is constructed. P The intersection is calculated with the feasible domain of a single base station backup energy storage in the form of a half-plane representation, and the feasible domain characterization model of a single base station backup energy storage in the form of a constrained Qino polyhedron is obtained. The same operation is performed on all base station backup energy storages, and the feasible domain characterization model of all base station backup energy storages in the form of a constrained Qino polyhedron is obtained.

6. The method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron according to claim 5 is characterized in that: The specific implementation steps of converting the feasible domain characterization model of all base station backup energy storage in the form of a half-plane into the feasible domain characterization model in the form of a constrained Qino polyhedron are as follows: Step 1: Find the feasible domain of the base station backup energy storage in the form of a half-plane representation. n The maximum value in each direction and minimum value , the maximum value and minimum value difference As the diagonal elements, we get the generator matrix, expressed as: Step 2: Set the maximum value With minimum The sum of C , expressed as: Step 3: Substitute the generated matrices obtained in steps 1 and 2 G and center point C , construct a Chino polyhedron, expressed as: Step 4: Calculate the Chino polyhedron P The hyperplane where the feasible region of the single base station backup energy storage in the half-plane representation is located is No. n The farthest point in the direction The orthogonal distance between Specifically, the center point of the Chino polyhedron C and the generator matrix G Each generated vector in is projected onto the hyperplane where the feasible domain of a single base station backup energy storage in the half-plane representation is located , the boundary vector of the base station backup energy storage operation f Subtract the center point of the Chino polyhedron C The hyperplane where the feasible domain of the backup energy storage of a single base station in the half-plane representation lies Projection of the center point of the Chino polyhedron C The hyperplane where the feasible region of a single base station backup energy storage in the half-plane representation lies No. n The orthogonal distances in each direction, plus the center point of the Chino polyhedron C The farthest point The orthogonal distance between them is expressed as: in, represents the infinite norm; Step 5: According to step 4 Construct a diagonal matrix to project each generated vector onto the hyperplane where the feasible domain of the single base station backup energy storage in the half-plane representation is located. with by Construct a diagonal matrix and take the union to get the constraint coefficient matrix A , expressed as: The center point of the Kinos polyhedron C The hyperplane where the feasible region of a single base station backup energy storage in the half-plane representation lies No. n The orthogonal distance in each direction is the hyperplane where the feasible domain of the backup energy storage of a single base station is located in the half-plane representation form. No. n The farthest point in the direction The orthogonal distance between Take the difference to get the scaling factor Constraint offset vector b , expressed as: 。 7. The method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron according to claim 6 is characterized in that: The Minkowski summation is performed on the feasible domain characterization model of all the backup energy storage of the base stations to be aggregated in the form of constrained Chino polyhedron representation, and the implementation method of the feasible domain aggregation model of the backup energy storage of 5G base stations is obtained as follows: Convert the feasible domain characterization model of all the backup energy storages of the base stations to be aggregated in the form of a semi-plane into a feasible domain characterization model in the form of a constrained Chino polyhedron; The feasible domain model of the first two base station backup energy storage in the form of constrained Qino polyhedron is described , , perform Minkowski summation to obtain a new feasible domain model; The obtained new feasible domain model is used as the initial value and Minkowski summed with the feasible domain characterization model of the next base station backup energy storage in the form of constrained Chino polyhedron representation to obtain a new feasible domain model again. The feasible domains of all base station backup energy storage to be aggregated are repeatedly equated as a whole to construct a 5G base station backup energy storage feasible domain aggregation model.

8. The method for aggregating feasible domains of 5G base station backup energy storage based on constrained Chino polyhedron according to claim 7 is characterized in that: The feasible domain characterization model of the first two base station backup energy storage in the form of constrained Qino polyhedron representation , , perform Minkowski summation, expressed as follows: in, The new feasible domain model obtained by Minkowski summation of the feasible domain characterization model of the first and second base station backup energy storage in the form of constrained Chino polyhedron representation is represented. are the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the first base station backup energy storage in the form of constrained Qino polyhedron, respectively. They are respectively the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the second base station backup energy storage in the constrained Chino polyhedron representation; The Minkowski sum is performed cyclically to establish the 5G base station feasible domain aggregation model. When the number of cycles is N-2, the aggregation is completed and the 5G base station feasible domain aggregation model is output, which is expressed as follows: in, represents the feasible domain characterization model of the spare energy storage of the i+2th base station in the form of constrained Chino polyhedron, They represent the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the spare energy storage of the i+2th base station in the form of constrained Chino polyhedron, respectively. They respectively represent the generator matrix, center point, constraint coefficient matrix, and constraint offset vector of the 5G base station backup energy storage feasible domain aggregation model obtained after one cycle, i=1,2,3,…,N-2, and N is the number of base station backup energy storage to be aggregated.

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