Distributed power supply planning method and device, electronic equipment and storage medium

Abstract

The invention provides a distributed power supply planning method and device, electronic equipment and a storage medium, and belongs to the field of power distribution network planning, and the method comprises the steps: carrying out the important node screening of selectable nodes of a to-be-planned distributed power supply based on a small world theory, obtaining alternative nodes, and determining a solving space according to the alternative nodes; constructing an optimization objective function and constraint conditions, initializing a to-be-planned variable of a to-be-planned distributed power supply to obtain an initial dung beetle population, and performing adaptive weight and Levy flight strategy improved dung beetle algorithm optimization on the initial dung beetle population to obtain a filial generation population; and determining the population fitness of the offspring population according to the optimization objective function, performing screening to obtain a new round of dung beetle population, and performing iterative optimization to obtain an optimal planning variable. According to the method, important nodes are screened through the small world theory, the solution dimension can be effectively reduced, the algorithm convergence speed and the global search capability are improved through the adaptive weight and the Levy flight strategy, and rapid and accurate solution of the distributed power supply planning problem is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a distributed power supply planning method, device, electronic equipment and storage medium. Background Art

[0002] With the development of low-carbon energy, distributed power sources such as photovoltaic and wind power are becoming more and more important green energy sources in the power grid. Distributed power sources can effectively supplement the traditional centralized power supply mode and achieve optimal allocation of resources. Distributed power source planning is a crucial preliminary work in the development planning of the power system, including the access location planning and capacity planning of distributed power sources. Reasonable distributed power source planning methods can make full use of existing resources, technologies and facilities to improve the power quality and stability of the distribution network.

[0003] In distributed power planning, the existing technology usually uses heuristic algorithms such as multi-objective genetic algorithms to solve after establishing a site selection and sizing optimization model. However, due to the large number of optional locations for distributed power, the solution space of distributed power planning is large. Especially as the scale of distributed power expands, the number of nodes in distributed power planning increases, and the network structure becomes more complicated, the excessive solution space will lead to a significant increase in the difficulty of solving, which in turn leads to poor performance of existing algorithms in both solution speed and solution accuracy.

[0004] Therefore, the existing technology has the technical problem that the solution space for distributed power generation planning is too large and the existing optimization algorithm is difficult to solve quickly and accurately, which needs to be improved. Summary of the invention

[0005] In view of this, it is necessary to provide a distributed power planning method, device, electronic device and storage medium to solve the technical problems in the prior art that the solution space of distributed power planning is too large and the existing optimization algorithms are difficult to solve quickly and accurately.

[0006] In order to solve the above technical problems, on the one hand, the present invention provides a distributed power planning method, comprising: Based on the small-world theory, the optional nodes of the planned distributed power generation are screened for important nodes to obtain candidate nodes, and the solution space is determined according to the candidate nodes; Construct the optimization objective function and constraints of the distributed power generation to be planned, initialize the variables to be planned of the distributed power generation to get the initial dung beetle population, perform adaptive weighting and Levy flight strategy on the initial dung beetle population in the solution space to improve the dung beetle algorithm to get the offspring population, determine the population fitness of the offspring population according to the optimization objective function, screen the offspring population according to the population fitness to get a new round of dung beetle population, and iterate the optimization to get the optimal planning variables.

[0007] In a possible implementation, based on the small-world theory, the optional nodes of the planned distributed power generation are screened for important nodes to obtain candidate nodes, including: The power loss improvement rate, betweenness centrality and closeness centrality of each optional node of the planned distributed power generation are calculated in turn; Filter the optional nodes according to the power loss improvement rate, betweenness centrality, closeness centrality and preset filtering ratio to obtain the filtered nodes; Adjacent nodes are filtered and branch nodes are supplemented for the screening nodes to obtain candidate nodes.

[0008] In a possible implementation, the optimization objective function includes a total active power loss objective function, a voltage stability objective function and a total capacity objective function; and the constraint conditions include a power balance constraint, a node voltage constraint, a power supply capacity constraint and a total installed capacity constraint.

[0009] In a possible implementation, initializing the variables to be planned of the distributed power source to be planned to obtain an initial dung beetle population includes: Initializing the variables to be planned of the distributed power source to be planned based on the chaotic map to obtain the initial dung beetle population; Among them, the variables to be planned include site selection and capacity determination.

[0010] In a possible implementation, the adaptive weight and Levy flight strategy of the initial dung beetle population are used to improve the dung beetle algorithm in the solution space to obtain the offspring population, including: In the solution space, the initial dung beetle population is updated in sequence using adaptive weighted rolling ball behavior simulation, reproduction behavior simulation and Levy flight strategy stealing behavior simulation to obtain the offspring population.

[0011] In a possible implementation, the adaptive weighted rolling ball behavior simulates population update, including: Determine a position update weight based on a current number of iterations and a preset maximum number of iterations; The weighted rolling ball behavior simulation is performed on the initial dung beetle population according to the position update weight to update the individual positions of the initial dung beetle population.

[0012] In one possible implementation, Levy flight strategy stealing behavior simulates population update, including: Generate a first random direction vector and a second random direction vector within a preset parameter range, and determine a Levy perturbation step length according to the first random direction vector and the second random direction vector; The initial dung beetle population is simulated for Levy disturbance stealing behavior according to the disturbance step length, and the individual positions of the initial dung beetle population are updated.

[0013] On the other hand, the present invention also provides a distributed power planning device, comprising: A node screening unit is used to screen the optional nodes of the planned distributed power source based on the small-world theory to obtain candidate nodes, and determine the solution space according to the candidate nodes; The dung beetle algorithm optimization unit is used to construct the optimization objective function and constraint conditions of the distributed power source to be planned, initialize the variables to be planned of the distributed power source to be planned to obtain the initial dung beetle population, perform adaptive weighting and Levy flight strategy on the initial dung beetle population in the solution space to improve the dung beetle algorithm to obtain the offspring population, determine the population fitness of the offspring population according to the optimization objective function, screen the offspring population according to the population fitness to obtain a new round of dung beetle population, and iterate the optimization to obtain the optimal planning variables.

[0014] On the other hand, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned distributed power supply planning method is implemented.

[0015] On the other hand, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned distributed power supply planning method is implemented.

[0016] The beneficial effects of the present invention are as follows: in the distributed power planning method provided by the present invention, firstly, based on the small world theory, the optional nodes of the planned distributed power are screened for important nodes to obtain candidate nodes, and the solution space is determined according to the candidate nodes; then, the optimization objective function and constraint conditions of the planned distributed power are constructed, the planned variables of the planned distributed power are initialized to obtain the initial dung beetle population, the initial dung beetle population is optimized by adaptive weight and Levy flight strategy to improve the dung beetle algorithm in the solution space to obtain the offspring population, the population fitness of the offspring population is determined according to the optimization objective function, the offspring population is screened according to the population fitness to obtain a new round of dung beetle population, and the optimal planning variables are obtained by iterative optimization. The present invention screens the optional nodes for important nodes through the small world theory, which can effectively reduce the solution dimension, improve the algorithm convergence speed and global search capability through adaptive weight and Levy flight strategy, and realize the rapid and accurate solution of the distributed power planning problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic diagram of a flow chart of an embodiment of a distributed power planning method provided by the present invention; Figure 2 A schematic diagram of a process for selecting important nodes of the small world theory according to an embodiment of the present invention; Figure 3 A schematic diagram of a flow chart of updating a population of a simulated adaptively weighted rolling ball according to an embodiment of the present invention; Figure 4 A schematic diagram of a flow chart of simulating population update of Levy flight strategy stealing behavior according to an embodiment of the present invention; Figure 5 It is the voltage distribution diagram of the distribution network nodes; Figure 6 This is the improvement result diagram of active power loss in distribution network; Figure 7 A schematic diagram of the structure of an embodiment of a distributed power planning device provided by the present invention; Figure 8 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0021] The descriptions of "first" and "second" in the embodiments of the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] The present invention provides a distributed power planning method, device, electronic device and storage medium, which are described below respectively.

[0024] Figure 1 A flow chart of an embodiment of a distributed power planning method provided by the present invention is shown as follows: Figure 1 As shown, the distributed power planning method includes: S101, based on the small-world theory, important nodes are screened for optional nodes of the planned distributed power source to obtain candidate nodes, and a solution space is determined according to the candidate nodes; S102. Construct the optimization objective function and constraint conditions of the distributed power source to be planned, initialize the variables to be planned of the distributed power source to be planned to obtain the initial dung beetle population, optimize the initial dung beetle population by adaptive weight and Levy flight strategy to improve the dung beetle algorithm to obtain the offspring population, determine the population fitness of the offspring population according to the optimization objective function, screen the offspring population according to the population fitness to obtain a new round of dung beetle population, and iterate the optimization to obtain the optimal planning variables.

[0025] Compared with the prior art, in the distributed power planning method provided by the embodiment of the present invention, firstly, based on the small world theory, the optional nodes of the distributed power to be planned are screened for important nodes to obtain candidate nodes, and the solution space is determined according to the candidate nodes; then, the optimization objective function and constraint conditions of the distributed power to be planned are constructed, the variables to be planned of the distributed power to be planned are initialized to obtain the initial dung beetle population, the initial dung beetle population is optimized by adaptive weight and Levy flight strategy to improve the dung beetle algorithm in the solution space to obtain the offspring population, the population fitness of the offspring population is determined according to the optimization objective function, the offspring population is screened according to the population fitness to obtain a new round of dung beetle population, and the optimal planning variables are obtained by iterative optimization. The present invention screens the optional nodes for important nodes through the small world theory, which can effectively reduce the solution dimension, improve the algorithm convergence speed and global search capability through adaptive weight and Levy flight strategy, and realize the rapid and accurate solution of the distributed power planning problem.

[0026] In some embodiments of the present invention, Figure 2 Schematic diagram of the process of selecting important nodes of the small world theory according to an embodiment of the present invention, such as Figure 2 As shown, based on the small-world theory, the optional nodes of the planned distributed power generation are screened for important nodes to obtain candidate nodes, including: S201, sequentially calculating the power loss improvement rate, betweenness centrality and closeness centrality of each optional node of the distributed power source to be planned; S202, screening the optional nodes according to the power loss improvement rate, betweenness centrality, closeness centrality and a preset screening ratio to obtain screening nodes; S203: Filter the adjacent nodes and supplement the branch nodes of the screening node to obtain candidate nodes.

[0027] Specifically, considering that there are many optional locations for distributed power sources in the distributed power source planning problem, the solution space for distributed power source planning is large and the solution difficulty is high, the embodiment first screens the optional access nodes from two perspectives: the improvement effect of the active network loss of the distribution network and the importance of the nodes in the distribution network, and determines the locations of the candidate nodes, so as to reduce the solution dimension.

[0028] The active power loss improvement rate formula is expressed as:

[0029] in, Indicates the original active network loss of the distribution network, Indicates The active network loss after each node is connected to the distributed power source is: The smaller the value is, the better the improvement effect of connecting distributed power generation at this node is.

[0030] According to the concept of small world theory, there is a correlation between the clustering coefficient and the average path length of the network, and the distribution network has both a higher clustering coefficient and a shorter average path length. Therefore, the embodiment uses the betweenness centrality and closeness centrality in the small world theory to evaluate the importance of nodes in the distribution network. Among them, the betweenness centrality measures the frequency of a node as the intermediate node of the shortest path between other node pairs. Nodes with high betweenness centrality play a bridging role in the distribution network, improving connectivity and energy transfer efficiency; closeness centrality measures the average shortest path length from a node to all other nodes in the network. Nodes with high closeness centrality are located in the center of the distribution network, making it easier to connect to other nodes and promote rapid energy transfer.

[0031] In the screening process, the embodiment first sorts all nodes in the distribution network according to the active loss improvement rate Arrange in ascending order, and set the screening ratio according to the scale of the distribution network. Select certain nodes according to the screening ratio, and take the first 50% in the embodiment. Then calculate the betweenness centerline and closeness centrality among these selected nodes, and arrange them in descending order respectively, and then screen them according to the screening ratio. In the embodiment, take the first 50% each and then take the union of the two. Then, on this basis, the embodiment adds nodes whose load accounts for a higher proportion of the total load of the distribution network. At the same time, considering the impact of distributed power access on adjacent load nodes, the distributed power layout should not be overly concentrated. Therefore, among the adjacent nodes of the candidate nodes, the embodiment only selects Finally, check whether all branches in the distribution network have alternative nodes. For branches without alternative nodes for distributed power generation, calculate the proportion of the branch load to the total load. If the proportion exceeds the inverse of the number of distributed power generation to be connected, add a node in the branch. The node with the smallest value.

[0032] In some embodiments of the present invention, the optimization objective function includes a total active power loss objective function, a voltage stability objective function and a total capacity objective function; the constraints include a power balance constraint, a node voltage constraint, a power supply capacity constraint and a total installed capacity constraint.

[0033] Specifically, in the planning of distributed power sources, it is necessary to comprehensively consider the total active power loss, voltage stability and total capacity of the distribution network. The embodiment takes these three indicators as optimization targets and constructs an optimization objective function, where: The total active power loss objective function of the distribution network The formula is:

[0034] in, Indicates the total number of branches in the distribution network, Indicates The current in the branch, Indicates The resistance of the branch.

[0035] Power flow calculation is a basic and important calculation in power system analysis. Its principle is to solve the steady-state operating parameters of the power system through mathematical methods based on the given power grid structure and operating conditions. In power flow calculation, the Newton-Raphson method is one of the most commonly used methods. This method is reflected in the solution of power imbalance and Jacobi matrix, and its expression is:

[0036] in, and They represent the partial derivatives of active power with respect to voltage phase angle and voltage amplitude, respectively. and They represent the partial derivatives of reactive power with respect to voltage phase angle and voltage amplitude, respectively. and Represent the phase angle change and voltage change respectively, and They represent the active and reactive unbalance of the node respectively.

[0037] In order to eliminate the phase angle variation and only study the relationship between voltage stability and node power, the Jacobian matrix is ​​simplified. for:

[0038] Eigenvalue analysis can evaluate the stability of the system. A large eigenvalue derivative indicates that the system is close to an unstable state. Therefore, the embodiment constructs a voltage stability objective function that combines eigenvalue information and node power changes. , which can reflect the voltage stability of the distribution network under different distributed power access conditions. The smaller the value, the higher the voltage stability of the distribution network, and the voltage stability objective function The formula is:

[0039] in, To simplify the Jacobian matrix The characteristic value of is the absolute value of the reactive unbalance at the node.

[0040] Total capacity objective function of distributed generation The formula is:

[0041] in, represents the total number of nodes in the distribution network, Representation Node Distributed power capacity.

[0042] Then, the constraints of the distributed power planning problem are established, including power balance constraints, node voltage constraints, power capacity constraints and total installed capacity constraints, where: The power balance constraint formula is expressed as:

[0043] in, and Respectively represent nodes The active injection power and reactive injection power of Representation Node The voltage amplitude, Representation and Node The number of directly connected nodes, Representation and Node Directly connected nodes, , and Representation Node and nodes The conductance, susceptance and phase angle difference between them.

[0044] The node voltage constraint formula is expressed as:

[0045] in, represents the total number of nodes in the distribution network, and Representation Node The voltage upper and lower limits at the .

[0046] The power capacity constraint formula of a single distributed power source is expressed as:

[0047] in, Representation Node The distributed power capacity, Indicates the maximum capacity of distributed power generation.

[0048] The total installed capacity constraint means that the total installed capacity of distributed generation should not exceed the pre-designed value of the distribution network. The formula is:

[0049] in, The renewable energy penetration rate set for the distribution network, Represents the total active load in the distribution network.

[0050] In some embodiments of the present invention, initializing the variables to be planned of the distributed power source to be planned to obtain an initial dung beetle population includes: Initializing the variables to be planned of the distributed power source to be planned based on the chaotic map to obtain the initial dung beetle population; Among them, the variables to be planned include site selection and capacity determination.

[0051] Specifically, to further improve the speed and accuracy of distributed power planning solution, the embodiment uses the improved dung beetle optimization algorithm for solution. To improve the diversity of the initial population, the embodiment introduces chaos mapping to initialize the dung beetle population, and the chaos mapping formula is expressed as:

[0052] in, is the population number, is the expansion coefficient of the chaotic mapping, and its value range is , the selected value in the embodiment is 0.49.

[0053] Based on chaotic mapping, population The expression is:

[0054] in, and Respectively represent the upper and lower boundaries of the variables to be optimized. In the embodiment, the variables to be optimized include the site selection and capacity determination of the distributed power source.

[0055] In some embodiments of the present invention, the initial dung beetle population is optimized by adaptively weighting and improving the dung beetle algorithm using the Levy flight strategy in the solution space to obtain a progeny population, including: In the solution space, the initial dung beetle population is updated in sequence using adaptive weighted rolling ball behavior simulation, reproduction behavior simulation and Levy flight strategy stealing behavior simulation to obtain the offspring population.

[0056] Specifically, in the dung beetle optimization algorithm, the positions of the dung beetle population individuals are updated through rolling behavior simulation, breeding behavior simulation and stealing behavior simulation. At the same time, in order to make the algorithm have a faster solution speed and higher accuracy, the embodiment improves the rolling behavior simulation through adaptive weights and improves the stealing behavior simulation through the Levy flight strategy, thereby improving the convergence speed and global search ability of the algorithm.

[0057] In the dung beetle optimization algorithm before the improvement, the rolling ball dung beetle will randomly select a direction in the global search space and move forward in a straight line according to external environmental factors. In the absence of obstacles, the expression for the dung beetle position update is:

[0058] in, is the number of iterations, For the Dung beetle The position at the iteration, and is the weight, and , . It is a state variable. When the value is 1, it means there is no position offset. When the value is -1, it means there is a position offset. represents the global worst position, Used to simulate external environment changes.

[0059] In the presence of obstacles, the expression for updating the dung beetle position is:

[0060] in, is the turning angle of the dung beetle and ,when is 0, and The dung beetle's position remains unchanged.

[0061] In the simulation of reproductive behavior, dung beetles will roll their dung balls to a safe area during reproduction to provide a better birth environment for their offspring. The boundary update formula for the breeding area is:

[0062] in, represents the current local optimal position, and represents the upper and lower bounds of the variable to be optimized, is an inertia weight variable, which is related to the number of iterations. Indicates the maximum number of iterations set.

[0063] In the breeding area, the female dung beetle will generate a new egg in each iteration. The breeding area will also be dynamically updated in each iteration. The expression for updating the position of the breeding ball is:

[0064] in, Indicates The position of the breeding ball after iterations, and For two dimensional row vector, is the number of decision variables in the model to be solved.

[0065] After hatching, the young dung beetles will start to forage on their own. The boundary conditions of the foraging area are:

[0066] in, and represent the upper and lower boundaries of the foraging area, respectively. represents the global optimal position.

[0067] The position update expression of the dung beetle is:

[0068] in, Indicates A little dung beetle is in the The position at the iteration, is a normally distributed random number, is a random vector between 0 and 1.

[0069] The stealing behavior simulation means that there are some thieves in the dung beetle group, and they will steal the food of the rest of the dung beetles. The expression of their position update is:

[0070] in, Indicates The thief is in The position at the iteration, is a normal distribution. Random vectors; is a fixed value. Since the thief's goal is the best food, its position is constantly updated and it will eventually get the optimal position .

[0071] In some embodiments of the present invention, Figure 3 FIG. 1 is a flow chart of updating a population of a simulated adaptive weighted rolling ball according to an embodiment of the present invention. Figure 3 As shown, the adaptive weighted rolling ball behavior simulates population updates, including: S301, determining a location update weight based on a current number of iterations and a preset maximum number of iterations; S302: Perform weighted rolling ball behavior simulation on the initial dung beetle population according to the position update weight, and update the individual positions of the initial dung beetle population.

[0072] Specifically, in the dung beetle optimization algorithm, the update of the dung beetle position is subject to the weight The value of has a great influence on Modified to a variable that changes with the number of iterations, the formula is expressed as:

[0073] in, and express The maximum and minimum values ​​of the embodiment are set to be 0.9 and 0.1, is the current iteration number, The preset maximum number of iterations.

[0074] Will Substituting into the dung beetle position update formula, we can get the new position formula:

[0075] Embodiment By setting adaptive weight , which can make the step size of the rolling dung beetle position update larger in the early stage of iteration, and gradually reduce the step size as the number of iterations increases, so as to balance the global and local search capabilities and improve the speed and accuracy of model solution.

[0076] In some embodiments of the present invention, Figure 4 FIG. 1 is a flow chart of simulating population update of Levy flight strategy stealing behavior according to an embodiment of the present invention, such as Figure 4 As shown, Levy's flight strategy stealing behavior simulates population updates, including: S401, generating a first random direction vector and a second random direction vector within a preset parameter range, and determining a Levy perturbation step length according to the first random direction vector and the second random direction vector; S402, simulating the Levy disturbance stealing behavior of the initial dung beetle population according to the disturbance step length, and updating the individual positions of the initial dung beetle population.

[0077] Specifically, in the dung beetle algorithm, the stealer will However, if the algorithm has fallen into a local optimal solution, it will be stagnant. In this embodiment, the Levy flight strategy is introduced to update the position of the thief, thereby expanding the search space of some thieves and jumping out of the local optimal solution. The Levy perturbation step length calculation formula is expressed as:

[0078] in, , is the first random direction vector, subject to distributed, is the second random direction vector, subject to distributed. It is obtained by the following formula:

[0079] in, .

[0080] The position expression of the stealer that finally introduced the Levy flight strategy is:

[0081] Finally, in order to verify the effectiveness of the present invention, the embodiment compares the IDBO (Improved Dung Beetle Optimizer) of the present invention with the DBO (Dung Beetle Optimizer) and the GA (Genetic Algorithms), and obtains Figure 5 and Figure 6 The results, among which Figure 5 The voltage distribution diagram of the distribution network nodes under the three algorithms is shown in Figure 2. Figure 6 The following are the improvement results of active power loss of distribution network under three algorithms. Figure 5 and Figure 6 It can be seen that the solution of the present invention is significantly better than the prior art in terms of voltage distribution and improvement of active power loss.

[0082] In summary, in order to improve the speed and accuracy of distributed power planning solution. The present invention first screens the optional nodes of the planned distributed power source based on the small world theory to obtain the candidate nodes, and determines the solution space according to the candidate nodes; then constructs the optimization objective function and constraint conditions of the planned distributed power source, initializes the planned variables of the planned distributed power source to obtain the initial dung beetle population, and optimizes the initial dung beetle population in the solution space by adaptive weight and Levy flight strategy to improve the dung beetle algorithm to obtain the offspring population, determines the population fitness of the offspring population according to the optimization objective function, screens the offspring population according to the population fitness to obtain a new round of dung beetle population, and iterates and optimizes to obtain the optimal planning variables. The present invention uses the small world theory to screen the optional nodes for important nodes, which can effectively reduce the solution dimension, improve the algorithm convergence speed and global search capability through adaptive weight and Levy flight strategy, and realize the rapid and accurate solution of the distributed power planning problem.

[0083] In order to better implement the distributed power planning method in the embodiment of the present invention, based on the distributed power planning method, correspondingly, Figure 7 As shown, the present invention also provides a distributed power planning device, and the distributed power planning device 700 includes: A node screening unit 701 is used to screen the optional nodes of the planned distributed power source based on the small-world theory to obtain candidate nodes, and determine the solution space according to the candidate nodes; The dung beetle algorithm optimization unit 702 is used to construct the optimization objective function and constraint conditions of the distributed power source to be planned, initialize the variables to be planned of the distributed power source to be planned to obtain an initial dung beetle population, perform adaptive weighting and Levy flight strategy on the initial dung beetle population in the solution space to improve the dung beetle algorithm to obtain a descendant population, determine the population fitness of the descendant population according to the optimization objective function, screen the descendant population according to the population fitness to obtain a new round of dung beetle population, and iterate the optimization to obtain the optimal planning variables.

[0084] The distributed power supply planning device 700 provided in the above embodiment can implement the technical solution described in the above distributed power supply planning method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above distributed power supply planning method embodiment, which will not be repeated here.

[0085] like Figure 8 As shown, the present invention also provides an electronic device 800. The electronic device 800 includes a processor 801, a memory 802 and a display 803. Figure 8 Only some components of the electronic device 800 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0086] In some embodiments, the processor 801 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 802, such as the distributed power planning method of the present invention.

[0087] In some embodiments, the processor 801 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 801 may be local or remote. In some embodiments, the processor 801 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0088] In some embodiments, the memory 802 may be an internal storage unit of the electronic device 800, such as a hard disk or memory of the electronic device 800. In other embodiments, the memory 802 may also be an external storage device of the electronic device 800, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 800.

[0089] Furthermore, the memory 802 may include both an internal storage unit of the electronic device 800 and an external storage device. The memory 802 is used to store application software installed in the electronic device 800 and various data.

[0090] In some embodiments, the display 803 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 803 is used to display information of the electronic device 800 and to display a visual user interface. The components 801-803 of the electronic device 800 communicate with each other via a system bus.

[0091] In one embodiment, when the processor 801 executes the distributed power planning program in the memory 802, the following steps may be implemented: Based on the small-world theory, the optional nodes of the planned distributed power generation are screened for important nodes to obtain candidate nodes, and the solution space is determined according to the candidate nodes; Construct the optimization objective function and constraints of the distributed power generation to be planned, initialize the variables to be planned of the distributed power generation to get the initial dung beetle population, perform adaptive weighting and Levy flight strategy on the initial dung beetle population in the solution space to improve the dung beetle algorithm to get the offspring population, determine the population fitness of the offspring population according to the optimization objective function, screen the offspring population according to the population fitness to get a new round of dung beetle population, and iterate the optimization to get the optimal planning variables.

[0092] It should be understood that: when the processor 801 executes the distributed power planning program in the memory 802, in addition to the above functions, other functions may also be implemented, and details may refer to the description of the corresponding method embodiments above.

[0093] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 800 mentioned, and the electronic device 800 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 800 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0094] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions in the distributed power supply planning method provided in the above-mentioned method embodiments can be implemented.

[0095] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0096] The above is a detailed introduction to the distributed power planning method, device, electronic device and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A distributed power planning method, characterized in that: include: Based on the small-world theory, important nodes are screened for optional nodes of the planned distributed power source to obtain candidate nodes, and a solution space is determined according to the candidate nodes; Constructing the optimization objective function and constraint conditions of the distributed power source to be planned, initializing the variables to be planned of the distributed power source to be planned to obtain an initial dung beetle population, optimizing the initial dung beetle population by adaptive weight and Levy flight strategy improved dung beetle algorithm in the solution space to obtain a descendant population, determining the population fitness of the descendant population according to the optimization objective function, screening the descendant population according to the population fitness to obtain a new round of dung beetle population, and iteratively optimizing to obtain the optimal planning variables.

2. The distributed power planning method according to claim 1, characterized in that: The important node screening of the optional nodes of the planned distributed power source based on the small-world theory to obtain the candidate nodes includes: The power loss improvement rate, betweenness centrality and closeness centrality of each optional node of the planned distributed power generation are calculated in turn; Perform node screening on the optional nodes according to the power loss improvement rate, the betweenness centrality, the closeness centrality and a preset screening ratio to obtain screening nodes; Adjacent nodes are filtered and branch nodes are supplemented for the screening nodes to obtain candidate nodes.

3. The distributed power planning method according to claim 1, characterized in that: The optimization objective function includes a total active power loss objective function, a voltage stability objective function and a total capacity objective function; the constraint conditions include a power balance constraint, a node voltage constraint, a power supply capacity constraint and a total installed capacity constraint.

4. The distributed power planning method according to claim 1, characterized in that: Initializing the variables to be planned of the distributed power source to be planned to obtain an initial dung beetle population includes: Initializing the variables to be planned of the distributed power source to be planned based on the chaotic map to obtain an initial dung beetle population; The variables to be planned include site selection and capacity determination.

5. The distributed power planning method according to claim 1, characterized in that: The step of optimizing the initial dung beetle population by adaptively weighting and improving the dung beetle algorithm using the Levy flight strategy in the solution space to obtain a progeny population includes: In the solution space, the initial dung beetle population is sequentially updated by adaptive weighted rolling ball behavior simulation population update, reproduction behavior simulation population update and Levy flight strategy stealing behavior simulation population update to obtain a descendant population.

6. The distributed power planning method according to claim 5, characterized in that: The adaptive weighted rolling ball behavior simulates population updating, including: Determine a position update weight based on a current number of iterations and a preset maximum number of iterations; The weighted rolling ball behavior simulation is performed on the initial dung beetle population according to the position update weight, and the individual positions of the initial dung beetle population are updated.

7. The distributed power planning method according to claim 5, characterized in that: The Levy flight strategy stealing behavior simulates population update, including: Generate a first random direction vector and a second random direction vector within a preset parameter range, and determine a Levy perturbation step length according to the first random direction vector and the second random direction vector; The initial dung beetle population is subjected to a Levy disturbance stealing behavior simulation according to the disturbance step length, and the individual positions of the initial dung beetle population are updated.

8. A distributed power planning device, characterized in that: include: A node screening unit, used for screening important nodes of optional nodes of the planned distributed power source based on the small-world theory to obtain candidate nodes, and determining a solution space according to the candidate nodes; A dung beetle algorithm optimization unit is used to construct an optimization objective function and constraint conditions of the distributed power source to be planned, initialize the variables to be planned of the distributed power source to be planned to obtain an initial dung beetle population, optimize the initial dung beetle population in the solution space by using adaptive weights and Levy flight strategy to improve the dung beetle algorithm to obtain a progeny population, determine the population fitness of the progeny population according to the optimization objective function, screen the progeny population according to the population fitness to obtain a new round of dung beetle population, and iteratively optimize to obtain the optimal planning variables.

9. An electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the distributed power supply planning method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed power supply planning method according to any one of claims 1 to 7 is implemented.

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