Distribution network grid division method and device based on graph theory and energy balance

Abstract

The present invention discloses a distribution network grid division method and device based on graph theory and energy balance, wherein the method comprises: determining an initial distribution network grid division scheme based on a graph theory search according to a distribution network architecture topology structure; determining an optimization target for grid division, and establishing an objective function of a distribution network grid division mathematical model based on the optimization target; linearly combining the objective function to establish a multi-objective optimization function; determining the constraints of the multi-objective optimization function; solving the multi-objective optimization function using an improved particle swarm algorithm according to the initial distribution network grid division scheme, and obtaining an optimized distribution network grid division scheme, wherein the optimized distribution network grid division scheme is used for subsequent corresponding processing. The present invention can consider the distribution Internet of Things optimization configuration and cloud-edge collaborative operation mode in the planning and design stage, and establish a typical distribution Internet of Things edge system scheme for distribution grids.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network grid planning, and in particular to a distribution network grid division method and device based on graph theory and energy balance. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] With the construction of the energy Internet, the operation of the distribution network will face the pressure of massive data collection, processing and calculation. The data generated by the edge devices of the distribution network is increasing day by day, and the business demand and complexity of the distribution network are also increasing rapidly, generating a large amount of computing demand, which greatly increases the pressure on the main station. The progress of cloud computing and edge technology provides a way to process power big data. However, due to the complex structure of the distribution network system and the large functional differences, for the distribution network, the distribution area range and terminal volume contained in the edge need to be clarified. First, the size of the edge has different requirements for its processing capacity and the processing capacity of the main station. If the edge is defined too small, its computing capacity cannot be fully utilized, and it will not significantly reduce the pressure on the main station; if the edge is defined too large, the edge node will have great computing pressure, which is easy to cause regional information blockage, and may even cause the edge volume to be at the same level as the main station, which runs counter to the concept of edge computing. Therefore, when realizing the cloud-edge collaboration of the power distribution Internet of Things, it is necessary to divide the distribution network into grids. The gridding of the distribution network is the basis for the planning, operation and analysis of the distribution network. The gridding of the distribution network is to divide the distribution network into multiple areas according to the functional analysis requirements based on the city's control plan, forming the edge of the distribution network grid. The power supply range within the grid is relatively independent and does not cross each other, breaking the previous "top-down" approach, and taking user needs as the goal orientation, gradually forming a "bottom-up" planning concept, and arranging medium-voltage grid structures suitable for different power supply areas. In the existing technology, the distribution grid is divided through manual planning based on people's subjective experience, which makes it difficult to ensure the rationality of the plan. Summary of the invention

[0004] An embodiment of the present invention provides a distribution network grid division method based on graph theory and energy balance to ensure the rationality of the distribution network grid division scheme. The method includes:

[0005] Based on graph theory search, the initial distribution network grid division scheme is determined according to the distribution network architecture topology structure;

[0006] Determine an optimization target for grid division, and establish an objective function of a distribution network grid division mathematical model based on the optimization target;

[0007] Linearly combine the objective functions to establish a multi-objective optimization function;

[0008] Determine the constraints of the multi-objective optimization function;

[0009] According to the initial distribution network grid division scheme, the multi-objective optimization function is solved using the improved particle swarm algorithm to obtain an optimized distribution network grid division scheme, which is used for subsequent corresponding processing.

[0010] The embodiment of the present invention further provides a distribution network grid division device based on graph theory and energy balance, which is used to ensure the rationality of the distribution network grid division scheme. The device includes:

[0011] An initial partitioning module is used to determine the initial distribution network grid partitioning scheme based on graph theory search and the topological structure of the distribution network architecture;

[0012] An objective function determination module is used to determine the optimization target of grid division and establish the objective function of the distribution network grid division mathematical model based on the optimization target;

[0013] The objective function optimization module is used to linearly combine the objective functions and establish a multi-objective optimization function;

[0014] A constraint determination module, used to determine the constraint conditions of the multi-objective optimization function;

[0015] The optimization module is used to solve the multi-objective optimization function according to the initial distribution network grid division scheme by using the improved particle swarm algorithm to obtain the optimized distribution network grid division scheme, and the optimized distribution network grid division scheme is used for subsequent corresponding processing.

[0016] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned distribution network grid division method based on graph theory and energy balance when executing the computer program.

[0017] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the above-mentioned distribution network grid division method based on graph theory and energy balance.

[0018] In an embodiment of the present invention, based on graph theory search, an initial distribution network grid division scheme is determined according to the topological structure of the distribution network architecture; the optimization target of grid division is determined, and the objective function of the distribution network grid division mathematical model is established based on the optimization target; the objective function is linearly combined to establish a multi-objective optimization function; the constraint conditions of the multi-objective optimization function are determined; according to the initial distribution network grid division scheme, the multi-objective optimization function is solved using an improved particle swarm algorithm to obtain an optimized distribution network grid division scheme, and the optimized distribution network grid division scheme is used for subsequent corresponding processing. Compared with the technical solution in the prior art that relies on subjective experience of manual planning and is difficult to ensure the rationality of the solution, by establishing a division model and an optimization algorithm to solve the solution, the feasibility and effectiveness of the solution obtained by the grid division model can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 The flow chart of the distribution network grid division method based on graph theory and energy balance in the embodiment of the present invention is as follows: Figure 1 ;

[0021] Figure 2 The flow chart of the distribution network grid division method based on graph theory and energy balance in the embodiment of the present invention is as follows: Figure 2 ;

[0022] Figure 3 This is a flow chart of a method for solving an objective function using an improved particle swarm algorithm in an embodiment of the present invention;

[0023] Figure 4 It is a structural block diagram of a distribution network grid division device based on graph theory and energy balance in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0025] Figure 1 The flow chart of the distribution network grid division method based on graph theory and energy balance in the embodiment of the present invention is as follows: Figure 1 ,like Figure 1 As shown, the method includes:

[0026] Step 102: Based on graph theory search, determine an initial distribution network grid division scheme according to the distribution network architecture topology structure;

[0027] Step 104: determining an optimization target for grid division, and establishing an objective function of a distribution network grid division mathematical model based on the optimization target;

[0028] Step 106: linearly combine the objective functions to establish a multi-objective optimization function;

[0029] Step 108: Determine the constraints of the multi-objective optimization function;

[0030] Step 110: according to the initial distribution network grid division scheme, the multi-objective optimization function is solved by using the improved particle swarm algorithm to obtain an optimized distribution network grid division scheme, and the optimized distribution network grid division scheme is used for subsequent corresponding processing.

[0031] In the embodiment of the present invention, step 104 and step 108 may be parallel steps, such as Figure 2 As shown in Figure 2, when constructing a mathematical model for distribution network grid division, it is necessary to determine both the objective function and the constraints.

[0032] In an embodiment of the present invention, the method divides the grid based on the graph theory search principle of the distribution network and the balance of power flow and information flow during the operation of the distribution network. The method divides the network edge nodes according to the geographical characteristics, functional characteristics and energy interaction characteristics of the distribution network.

[0033] The topological structure of the distribution network architecture is the basis for the division of the distribution grid. The distribution network consists of distribution substations, distribution lines, and power equipment. It is the terminal network for the final distribution of electric energy. It is a link in the power system that is directly connected to customers and has a huge impact on the reliability of the entire power supply. A robust, economical, and adaptable grid structure can ensure the safe, reliable, and economical operation of the power grid. It can not only provide users with high-quality and economical services, but also save a lot of construction and transformation investment and operation and maintenance costs for the power sector. Using the graph search principle, different devices in the distribution network are represented by the vertices of the graph, and the connections between them can be represented by the lines between the vertices. This method is vivid and intuitive. The relationship between power points, loads, and switches is represented in the form of a matrix, and the connection matrix and reachability matrix are used as the basis for the constraints.

[0034] In the embodiment of the present invention, the optimization objectives are to minimize the number of cross-grid lines within the distribution network grid, minimize the electric power flow between distribution grids, and minimize the information interaction between grids.

[0035] Wherein, the objective function is:

[0036]

[0037] Among them, F1 is the membership value of optimization goal 1, which is to minimize the number of cross-grid lines in the distribution grid; i is the feeder number, l i is the number of grids spanned by feeder i, and m is the number of feeders;

[0038]

[0039] Among them, F2 is the power flow between distribution grids of optimization target 2, and optimization target 2 is to minimize the power flow between distribution grids; P_flow j is the interaction power between distribution grid j and the outside world; Ω Area represents a collection of distribution grids;

[0040]

[0041] Among them, F3 is the information interaction value between distribution grids of optimization target 3, and optimization target 3 is to minimize the information interaction between distribution grids; Info_flow j It is the exchange information between the distribution grid j and the outside world.

[0042] In the embodiment of the present invention, since the properties of various optimization targets are different and the values ​​thereof are different, each optimization target is normalized and converted into the interval [0, 1].

[0043] In the embodiment of the present invention, a linear weighted summation method is used to obtain the final objective function, and the distribution network grid division problem can also be regarded as a multi-objective optimization problem.

[0044] Among them, the objective functions are linearly combined according to the following formula to establish a multi-objective optimization function:

[0045] F = min (ω1F1+ω2F2+ω3F3);

[0046] Among them, F represents the multi-objective optimization function; ω1, ω2, and ω3 are the weight values ​​of the objective functions F1, F2, and F3 respectively. From the formula, it can be seen that the distribution grid division objective function is a multi-objective optimization problem, which is solved using graph theory and optimization methods.

[0047] In the embodiment of the present invention, basic attributes of feeders, distribution transformers, etc. in the power supply area of ​​the distribution network are counted. Constraints include electrical constraints in the grid during actual operation of the distribution grid, capacity constraints of equipment and lines in the grid, and regional connection constraints.

[0048] Among them, (1) the electrical constraints within the distribution grid during actual operation include power flow, load self-sufficiency rate, equipment capacity, and total number of feeders.

[0049] Power flow constraints:

[0050]

[0051] in, They are the energy storage system, distributed power source and load active power, are the reactive powers of energy storage system, distributed generation and load respectively; U is the node voltage in the grid.

[0052] The equipment and line capacity constraints within the grid include distributed generation capacity and output power, line current, node voltage, and energy storage system SOC constraints.

[0053]

[0054] (2) The regional connection constraint includes that any region contained in the grid must be adjacent to at least one other region in the grid. That is, there is no region in the grid that exists independently from other regions in the grid, forming a regional connection constraint, which is a necessary constraint condition for grid division. The basic constraint condition is to determine whether the region contained in the grid in the grid division scheme satisfies the adjacent connection constraint, which can be obtained by combining the adjacency matrix and the reachability matrix of the graph. The specific process is as follows:

[0055] Based on the regional topological connection, establish the adjacency matrix A=a of all plots in the grid pq :

[0056]

[0057] p and q are the area numbers respectively;

[0058] Define the adjacency matrix A to the power of u: A u =A×A u-1 , in

[0059]

[0060] Where: w is the region number, t is the total number of regions included in the network, if a pw a qw =1 means that region p can connect to region q through region w; if This means that areas p and q can be connected. Construct the network reachability matrix Y = B + B 2 +B 3 +…+B t , based on the reachability analysis, it is determined whether each area in the network satisfies the adjacent connection constraint. If the area in the grid satisfies the adjacent connection constraint, the reachability matrix Y is an all-1 matrix. By obtaining the reachability matrix for each grid, it is possible to determine whether the current grid division scheme satisfies the adjacent connection constraint.

[0061] In the embodiment of the present invention, an improved particle swarm algorithm is used to solve the objective function. Figure 3 As shown, although the basic particle swarm algorithm (PSO) can have a good solving ability when solving the objective function, it can quickly find the optimal solution through iterative calculation. Each particle in the subgroup represents a possible solution to a problem. Through the simple behavior of individual particles and the information interaction within the group, the intelligence of problem solving is realized. Due to the simple operation and fast convergence speed of PSO, it has been widely used in many fields. However, the basic PSO can easily fall into the local optimum, resulting in large errors in the results. When this method is implemented, the improvement measures of the particle swarm algorithm include one or more of 1) to 3):

[0062] 1) In the process of speed and position iteration, the iteration of individual average position is increased to improve the information communication between particles, thereby avoiding falling into the local optimal solution.

[0063]

[0064] Among them, x j (t) is the position of the jth particle in the tth generation, v j (t) is the velocity of the jth particle of the tth generation, is the optimal solution for individual j, p best_avg is the middle position of the optimal solution of the individual in generation t, g best is the global optimal solution, C1, C2 and C3 are learning factors, rand1, rand2 and rand3 are random numbers between (0,1), ω is the inertia weight of the particle swarm algorithm, and λ is the compression factor.

[0065] 2) The inertia weight of the particle swarm algorithm is adjusted nonlinearly with the evolution process, and the sigmoid function is used for mapping iteration.

[0066]

[0067] Among them, F pbest_avg is the average fitness value of the individual optimal solution, F gbest The fitness value of the global optimal solution.

[0068] 3) Nonlinear adjustment of the acceleration factor of the particle swarm algorithm, using the normal form equation to define the acceleration factor and calculate the compression factor.

[0069]

[0070] C=C1+C2+C3;

[0071]

[0072] The embodiment of the present invention also provides a distribution network grid division device based on graph theory and energy balance, as described in the following embodiment. Since the principle of solving the problem by the device is similar to the distribution network grid division method based on graph theory and energy balance, the implementation of the device can refer to the implementation of the distribution network grid division method based on graph theory and energy balance, and the repeated parts will not be repeated.

[0073] Figure 4 FIG. 1 is a structural block diagram of a distribution network grid division device based on graph theory and energy balance in an embodiment of the present invention. Figure 4 As shown, the device comprises:

[0074] Initial partitioning module 02, used to determine the initial distribution network grid partitioning scheme according to the distribution network architecture topology structure based on graph theory search;

[0075] The objective function determination module 04 is used to determine the optimization target of grid division and establish the objective function of the distribution network grid division mathematical model based on the optimization target;

[0076] The objective function optimization module 06 is used to linearly combine the objective functions to establish a multi-objective optimization function;

[0077] The constraint condition determination module 08 is used to determine the constraint conditions of the multi-objective optimization function;

[0078] The optimization module 10 is used to solve the multi-objective optimization function according to the initial distribution network grid division scheme by using the improved particle swarm algorithm to obtain an optimized distribution network grid division scheme, and the optimized distribution network grid division scheme is used for subsequent corresponding processing.

[0079] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned graph theory and energy balance distribution network grid division method when executing the computer program.

[0080] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the above-mentioned graph theory and energy balance distribution network grid division method.

[0081] In an embodiment of the present invention, based on graph theory search, an initial distribution network grid division scheme is determined according to the topological structure of the distribution network architecture; the optimization target of grid division is determined, and the objective function of the distribution network grid division mathematical model is established based on the optimization target; the objective function is linearly combined to establish a multi-objective optimization function; the constraint conditions of the multi-objective optimization function are determined; according to the initial distribution network grid division scheme, the multi-objective optimization function is solved using an improved particle swarm algorithm to obtain an optimized distribution network grid division scheme, and the optimized distribution network grid division scheme is used for subsequent corresponding processing. Compared with the technical solution in the prior art that relies on subjective experience of manual planning and is difficult to ensure the rationality of the solution, by establishing a division model and an optimization algorithm to solve the solution, the feasibility and effectiveness of the solution obtained by the grid division model can be achieved.

[0082] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take 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 code.

[0083] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0084] 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 including 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.

[0085] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0086] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distribution network grid division method based on graph theory and energy balance, characterized in that: include: Based on graph theory search, the initial distribution network grid division scheme is determined according to the distribution network architecture topology structure; Determine an optimization target for grid division, and establish an objective function of a distribution network grid division mathematical model based on the optimization target; Linearly combine the objective functions to establish a multi-objective optimization function; Determine the constraints of the multi-objective optimization function; According to the initial distribution network grid division scheme, the improved particle swarm algorithm is used to solve the multi-objective optimization function to obtain the optimized distribution network grid division scheme; The optimization objectives are to minimize the number of cross-grid lines within the distribution network grid, minimize the electric power flow between distribution grids, and minimize the information interaction between grids; The constraints include electrical constraints within the grid during actual operation of the distribution grid, capacity constraints of equipment and lines within the grid, and regional connection constraints; The electrical constraints within the distribution grid during actual operation include power flow, load self-sufficiency rate, equipment capacity and total number of feeders; The equipment and line capacity constraints within the grid include distributed generation capacity and output power, line current, node voltage, and energy storage system SOC constraints; The region connectivity constraints include that any region contained in the grid must be adjacent to at least one other region in the grid; The improved particle swarm algorithm comprises one or more of increasing the iteration of individual average position, performing nonlinear adjustment on inertia weight and performing nonlinear adjustment on acceleration factor in the process of velocity and position iteration.

2. The distribution network grid division method based on graph theory and energy balance according to claim 1, characterized in that: The objective function is: Among them, F1 is the membership value of optimization goal 1, which is to minimize the number of cross-grid lines in the distribution grid; i is the feeder number, l i is the number of grids spanned by feeder i, and m is the number of feeders; Among them, F2 is the power flow between distribution grids of optimization target 2, and optimization target 2 is to minimize the power flow between distribution grids; P_flow j is the interaction power between distribution grid j and the outside world; Ω Area represents a collection of distribution grids; Among them, F3 is the information interaction value between distribution grids of optimization target 3, and optimization target 3 is to minimize the information interaction between distribution grids; Info_flow j It is the exchange information between the distribution grid j and the outside world.

3. The distribution network grid division method based on graph theory and energy balance according to claim 2, characterized in that: The objective functions are linearly combined according to the following formula to establish a multi-objective optimization function: F = min (ω1F1+ω2F2+ω3F3); Wherein, F represents the multi-objective optimization function; ω1, ω2, and ω3 are the weight values ​​of objective functions F1, F2, and F3, respectively.

4. A distribution network grid division device based on graph theory and energy balance, characterized in that: include: An initial partitioning module is used to determine the initial distribution network grid partitioning scheme based on graph theory search and the topological structure of the distribution network architecture; An objective function determination module is used to determine the optimization target of grid division and establish the objective function of the distribution network grid division mathematical model based on the optimization target; The objective function optimization module is used to linearly combine the objective functions and establish a multi-objective optimization function; A constraint determination module, used to determine the constraint conditions of the multi-objective optimization function; An optimization module is used to solve the multi-objective optimization function according to the initial distribution network grid division scheme by using the improved particle swarm algorithm to obtain an optimized distribution network grid division scheme; The optimization objectives are to minimize the number of cross-grid lines within the distribution network grid, minimize the electric power flow between distribution grids, and minimize the information interaction between grids; The constraints include electrical constraints within the grid during actual operation of the distribution grid, capacity constraints of equipment and lines within the grid, and regional connection constraints; The electrical constraints within the distribution grid during actual operation include power flow, load self-sufficiency rate, equipment capacity and total number of feeders; The equipment and line capacity constraints within the grid include distributed generation capacity and output power, line current, node voltage, and energy storage system SOC constraints; The region connectivity constraints include that any region contained in the grid must be adjacent to at least one other region in the grid; The improved particle swarm algorithm comprises one or more of increasing the iteration of individual average position, performing nonlinear adjustment on inertia weight and performing nonlinear adjustment on acceleration factor in the process of velocity and position iteration.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 3.

Images