Accurate reconstruction optimization method and device for active power distribution network

Abstract

The invention provides an active power distribution network accurate reconstruction optimization method and device, and the method comprises the steps: generating random sample data corresponding to the output of a distributed power supply and the load characteristics of a power grid according to the operation parameters of an active power distribution network, and generating a probabilistic power flow input variable set of the active power distribution network according to the random sample data; performing probabilistic load flow calculation based on the input variable set to obtain probabilistic load flow index information of each node voltage and line load flow; generating an electric energy quality comprehensive evaluation index of the active power distribution network according to the probabilistic power flow index information; establishing a multi-target optimization model for accurate transformation of the active power distribution network based on the comprehensive evaluation index of the power quality by taking the highest and minimum transformation cost of the power quality as a target; and solving the multi-objective optimization model by adopting an improved particle swarm algorithm to obtain a precise reconstruction scheme of the active power distribution network. According to the technical scheme provided by the invention, refined and efficient transformation decision of the active power distribution network under high uncertainty is realized.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatch automation technology, specifically relating to a method and device for precise transformation and optimization of active distribution networks. Background Technology

[0002] With the continuous advancement of my country's "dual-carbon" strategy, the penetration rate of distributed generation (DG) in the distribution network is steadily increasing. However, the intermittency and volatility of DG output can affect power quality, and even lead to active power backflow, increased network losses, and voltage exceeding limits. To adapt to the high proportion of DG integration, it is urgent to promote the transformation and upgrading of traditional distribution networks, improve their power quality and economic efficiency, and contribute to the high-quality development of new distribution systems.

[0003] Currently, numerous scholars have conducted in-depth research on distribution network transformation, but few studies have incorporated probabilistic power flow results into power quality evaluation indicators, failing to effectively assess the power quality of active distribution networks with high proportions of distributed photovoltaic (PV) integration. Furthermore, existing literature on distribution network transformation lacks precision, achieving some results only in multi-objective optimization, multi-timescale collaborative planning, and consideration of various uncertainties, but these are insufficient to meet the demands of precise distribution network transformation for high-precision modeling and refined decision-making. Simultaneously, solving precise power grid transformation models involves high complexity, encompassing numerous discrete decision-making problems, thus requiring sophisticated solution algorithms.

[0004] Therefore, there is an urgent need for a method that can fully consider the impact of the uncertainty of distributed photovoltaic output on power quality and improve the accuracy of distribution network transformation, so as to enhance the operational reliability and economy of the new energy distribution network. Summary of the Invention

[0005] This invention provides a method for precise transformation and optimization of active distribution networks. The solution of this invention quantifies the impact of high-proportion distributed photovoltaic access on power quality by constructing an evaluation index system for active distribution network operation that considers probabilistic power flow; it constructs a precise transformation model for active distribution networks that considers transformation costs, absorption rate, and power quality, which can effectively solve power quality problems such as voltage fluctuations while strictly controlling costs; and it uses an adaptive and feedback-enhanced multi-strategy particle swarm optimization algorithm to solve the model, thereby improving the solution efficiency and accuracy of complex models.

[0006] In a first aspect, the present invention provides a method for precise transformation and optimization of an active power distribution network, the method comprising: Based on the operating parameters of the active distribution network, random sample data corresponding to the output of distributed generation and the characteristics of grid load are generated, and based on the random sample data, a set of probabilistic power flow input variables for the active distribution network is generated. Based on the set of probabilistic power flow input variables, probabilistic power flow calculations are performed to obtain probabilistic power flow index information of voltage at each node and power flow on the line. Based on the probabilistic power flow index information, a comprehensive power quality evaluation index for the active distribution network is generated; With the goals of maximizing power quality and minimizing retrofit costs, a multi-objective optimization model for precise retrofitting of active distribution networks is established based on the aforementioned comprehensive power quality evaluation index, and the constraints of the multi-objective optimization model are determined. Based on the aforementioned constraints, an improved particle swarm optimization algorithm is used to solve the multi-objective optimization model, thereby obtaining a precise transformation scheme for the active power distribution network.

[0007] By adopting the above scheme, the present invention provides a precise transformation and optimization method for active distribution networks. This method fully considers the impact of the uncertainty of distributed photovoltaic output on power quality and the accuracy of the transformation model. It constructs a comprehensive power quality evaluation index for active distribution networks that considers probabilistic power flow and a precise transformation model for active distribution networks that considers transformation costs, absorption rate, and power quality. This approach effectively solves power quality problems such as voltage fluctuations and improves the accuracy of distribution network transformation while strictly controlling costs. An adaptive and feedback-enhanced multi-strategy particle swarm optimization algorithm is used to optimize the distribution network transformation model that considers probabilistic power flow, thereby improving solution accuracy and convergence speed.

[0008] In some embodiments of the present invention, the output and load characteristics of the distributed power source are obtained based on the Beta distribution of the output power of the photovoltaic power plant and the normal distribution of the grid load.

[0009] In some embodiments of the present invention, the probabilistic power flow input variable set includes: the output power of distributed generation sources within a specific time period and the grid load power generated based on historical electricity consumption data; the probabilistic power flow calculation based on the input variable set includes: calculating based on historical operation information data of the distribution network and a preset number of Monte Carlo simulation calculations to obtain deterministic power flow calculation results of historical distribution network data.

[0010] In some embodiments of the present invention, generating a comprehensive power quality evaluation index for an active distribution network based on the probabilistic power flow index information includes: generating multiple power quality indices for the active distribution network based on the probabilistic power flow index information; and classifying the multiple power quality indices into cost-type indices and benefit-type indices. The weights of each power quality indicator are obtained by solving the weights of the cost-type and benefit-type indicators based on the normalized sequential weighting method. The cost-type and benefit-type indicators are weighted using the weights of the various power quality indicators to generate the comprehensive power quality evaluation index.

[0011] In some embodiments of the present invention, the objective function of the multi-objective optimization model includes a power grid transformation cost objective function, an absorption rate objective function, and a power quality evaluation index objective function.

[0012] In some embodiments of the present invention, the constraints of the multi-objective optimization model include: network structure constraints and line operation constraints of the active power distribution network.

[0013] In some embodiments of the present invention, the improved particle swarm algorithm includes: constructing the particle structure in the improved particle swarm algorithm based on the line modification state and the switch switching state.

[0014] In some embodiments of the present invention, the line structure or switch operation mode of the distribution network is adjusted according to the precise transformation scheme of the active distribution network.

[0015] In some embodiments of the present invention, the improved particle swarm optimization algorithm further includes: constructing a corresponding active power distribution network topology based on the encoding result of each particle; generating the power quality comprehensive evaluation index according to the plurality of power quality indicators in the active power distribution network topology; and generating the fitness evaluation of the particle based on the power quality comprehensive evaluation index.

[0016] Compared with existing technologies, the advantages of this invention lie in its construction of a comprehensive evaluation index for the power quality of active distribution networks that considers probabilistic power flow, and a precise transformation model for active distribution networks that considers transformation costs, absorption rate, and power quality. This allows for strict cost control while effectively addressing power quality issues such as voltage fluctuations and improving the accuracy of distribution network transformation. An adaptive and feedback-enhanced multi-strategy particle swarm optimization algorithm is used to optimize the distribution network transformation model considering probabilistic power flow, thereby improving solution accuracy and convergence speed.

[0017] In a second aspect, the present invention provides an active power distribution network precision transformation and optimization device, characterized in that the device includes a computer device, the computer device includes a processor and a memory, the processor stores computer instructions, and when the computer instructions are executed, the device implements the active power distribution network precision transformation and optimization method.

[0018] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention will become apparent from the description and the accompanying drawings.

[0019] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] In the attached diagram: Figure 1 This is a flowchart illustrating a method for precise transformation and optimization of an active power distribution network, as provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of an active power distribution network radial network provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the network topology of the precisely modified active power distribution network provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the voltage distribution before modification provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the modified voltage distribution provided in an embodiment of the present invention; Figure 6 This is a schematic diagram showing the comparison results of power quality evaluation indicators provided in an embodiment of the present invention; Figure 7 This is a schematic diagram showing the comparison and analysis of the power quality of each node before and after the modification, provided in an embodiment of the present invention. Figure 8 A schematic diagram illustrating the comparison and analysis of the AFM-PSO algorithm provided in this embodiment of the invention with PSO, GA, and SSA; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0023] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0024] Figure 1This is a flowchart illustrating a method for precise transformation and optimization of an active power distribution network according to an embodiment of the present invention.

[0025] Example 1, as Figure 1 As shown, this invention provides a method for precise transformation and optimization of an active power distribution network, the method comprising the following steps: S1: Generate random sample data corresponding to the output of distributed generation and the characteristics of grid load based on the operating parameters of the active distribution network, and generate a set of probabilistic power flow input variables for the active distribution network based on the random sample data. S2: Perform probabilistic power flow calculations based on the set of probabilistic power flow input variables to obtain probabilistic power flow index information of voltage at each node and power flow on the line; S3: Generate a comprehensive power quality evaluation index for the active distribution network based on the probabilistic power flow index information; S4: With the goal of maximizing power quality and minimizing retrofit costs, establish a multi-objective optimization model for precise retrofitting of active distribution networks based on the aforementioned comprehensive power quality evaluation index, and determine the constraints of the multi-objective optimization model; S5: Based on the constraints, the improved particle swarm optimization algorithm is used to solve the multi-objective optimization model to obtain a precise transformation scheme for the active power distribution network.

[0026] By employing the above scheme, the present invention provides a precise transformation and optimization method for active distribution networks. This method constructs a comprehensive power quality evaluation index for active distribution networks that considers probabilistic power flow and a precise transformation model for active distribution networks that considers transformation costs, absorption rate, and power quality. While strictly controlling costs, it effectively solves power quality problems such as voltage fluctuations and improves the precision of distribution network transformation. An adaptive and feedback-enhanced multi-strategy particle swarm optimization algorithm is used to optimize the distribution network transformation model considering probabilistic power flow, thereby improving solution accuracy and convergence speed.

[0027] In some embodiments of the present invention, the output and load characteristics of the distributed power source are obtained based on the Beta distribution of the output power of the photovoltaic power plant and the normal distribution of the grid load.

[0028] The output power of a photovoltaic power station adopts a Beta distribution, and its expression is: (1) In the formula: , These represent the actual and maximum photovoltaic output during this period, respectively, in W / m. α , β This represents the shape parameter of the Beta distribution. This represents the Gamma function.

[0029] The load follows a normal distribution, and its expression is: (2) (3) In the formula, and These are the expected values ​​of active power and reactive power, respectively. and These are the standard deviations of active power and reactive power, respectively. P Indicates active power. Q This indicates reactive power.

[0030] In some embodiments of the present invention, the probabilistic power flow input variable set includes: the output power of distributed generation sources within a specific time period and the grid load power generated based on historical electricity consumption data; the probabilistic power flow calculation based on the input variable set includes: calculating based on historical operation information data of the distribution network and a preset number of Monte Carlo simulation calculations to obtain deterministic power flow calculation results of historical distribution network data.

[0031] The Newton-Raphson method Cartesian coordinate system power flow equations for deterministic power flow calculations are as follows: (4) (5) (6) (7) In the formula, and It is the active and reactive power injected into the network by node i. and It is a line ij The trend of meritorious and ineffective actions on the surface and It is a node i and j voltage amplitude, It is a node i and j The phase angle difference between voltages and It is the corresponding admittance matrix The real and imaginary parts, It is a line ij Near the node i The capacity to accommodate.

[0032] The steps for calculating probabilistic power flow based on Monte Carlo simulation are as follows: Step 1: Input deterministic data such as the network topology, node parameters, and line parameters of the distribution network, as well as the number of Monte Carlo simulations. n .

[0033] Step 2: Based on the probability model of photovoltaic power and load, generate one set of corresponding random number samples of photovoltaic power output and load.

[0034] Step 3: Use the obtained samples as input variables for deterministic power flow. Based on the power flow equations (4), (5), (6), and (7), perform deterministic power flow calculation using the Newton-Raphson method and save the power flow calculation results.

[0035] Step 4: Repeat steps 2 and 3 until completed. n Sub-independent Monte Carlo simulation probabilistic power flow calculation.

[0036] Step 5: [Regarding...] n The results of the subdeterministic power flow calculations were statistically analyzed to obtain the node voltages. Branch Road Trend Mean, standard deviation and cumulative distribution function , .

[0037] In some embodiments of the present invention, generating a comprehensive power quality evaluation index for an active distribution network based on the probabilistic power flow index information includes: generating multiple power quality indices for the active distribution network based on the probabilistic power flow index information; dividing the multiple power quality indices into cost-type indices and benefit-type indices; solving for the weights of the cost-type indices and benefit-type indices using a normalized sequential weighting method to obtain the weights of each power quality index; and weighting the cost-type indices and benefit-type indices using the weights of each power quality index to generate the comprehensive power quality evaluation index.

[0038] The comprehensive power quality evaluation index for active distribution networks considering probabilistic power flow consists of five key sub-indicators (voltage deviation index, voltage qualification rate, maximum voltage deviation rate, average voltage fluctuation rate, and node voltage over-limit risk index), which quantify the power quality of active distribution networks from different aspects. The multiple power quality indicators include:

[0039] (1) Voltage deviation index (8) In the formula, for t time i Voltage deviation at the node represent t time i The average value of node voltages; Represents the nominal voltage; N is the total number of system nodes.

[0040] (2) Voltage qualification rate (9) In the formula, for t Time Node i Voltage qualification rate; This refers to the number of times the voltage exceeded the upper limit. This refers to the number of times the voltage exceeded the lower limit. Given the total number of Monte Carlo samples, the allowable range for voltage deviation is: .

[0041] (3) Maximum voltage deviation rate (10) In the formula, U DR,i ( t )for t Time Node i Maximum voltage offset, Δ U k (t) for t Time of the first k The difference between the actual voltage and the nominal voltage of each sample.

[0042] (4) Average voltage fluctuation rate (11) In the formula, for t time i Average voltage fluctuation of the node for t time i The total number of samples for each node.

[0043] (5) Risk indicators for node voltage exceeding limits 1) Node voltage over-limit probability t Time Node i The voltage over-limit probability is: (12) In the formula, for t Time Node i The probability of the voltage exceeding the upper limit, for t Time Node i The probability of the voltage falling below the lower limit. and These are the upper and lower limits of the allowable offset of the node voltage, respectively, which are 1.05. and 0.95 , For nodes i Voltage at t The cumulative distribution function at time step 1.

[0044] 2) Severity of node voltage exceeding limits t Time Node i The severity of the voltage exceeding the limit is: (13) (14) In the formula, for t time i The lower the voltage limit of the node, the more severe the situation. for t Time Node i The lower the voltage limit, the more severe the condition. For nodes i Voltage at t The average value over time.

[0045] 3) Voltage over-limit risk indicators t time i The voltage over-limit risk indicator for nodes is: (15) The five power quality evaluation indicators mentioned above are weighted and integrated using the normalized sequential weighting method to form a comprehensive power quality evaluation index. The steps are as follows: 1) The min-max standardization method is used to eliminate the dimensions of the index. The calculation formula is as follows: (16) 2) The weights of each power quality index are determined using the ordinal relation analysis method. The specific steps are as follows: Step 1: According to the experts, the power quality indicators are ranked from highest to lowest according to their importance.

[0046] Step 2: Compare the importance of each indicator in turn, and determine the corresponding scale based on the importance ratio scale of adjacent indicators. .

[0047] Step 3: After determining the scale values ​​in the previous step, calculate the values ​​of other elements according to the transitivity of the importance of each indicator and establish the following judgment matrix. R : (17) matrix R middle, m The number of indicators to be evaluated, and the number of elements. Indicates the first i The first indicator and the first j The scale value compared between the indicators.

[0048] Step 4: From the matrix R The weighting coefficients for each indicator are calculated using the following formula: (18) In the formula, It is the first i The weight of each indicator, It is the first in matrix R i The product of all elements in a row.

[0049] 3) Based on the weight coefficients obtained by the order relation analysis method, the five sub-indicators after normalization are weighted and fused to calculate the result. t Time Node i Comprehensive power quality evaluation indicators: (19) In the formula, , , , , These are the weighting coefficients for the corresponding indicators.

[0050] In some embodiments of the present invention, the objective function of the multi-objective optimization model includes a power grid transformation cost objective function, an absorption rate objective function, and a power quality evaluation index objective function.

[0051] By combining the above three objective functions using a linear weighted combination method, the objective function F of the multi-objective optimization model is expressed as follows: (20) In the formula, , and The weights of the sub-objectives. , and These are the objective functions for power grid transformation cost, power absorption rate, and power quality evaluation indicators, respectively.

[0052] (1) Objective function of transformation cost (twenty one) In the formula, The renovation cost includes the cost of the line investment and the cost of the tie switch investment. and This is a binary variable; its value is 1 when selected and 0 otherwise. For the first a The unit cost of upgrading each line; For the first a The length of the modified line; For the firstb The unit renovation cost for a proposed new interconnection switch.

[0053] (2) Objective function of absorption rate (twenty two) (twenty three) In the formula, To increase the local consumption rate of new energy; Power output for new energy sources that are consumed by loads in real time in the distribution network; for t The charging and discharging power of energy storage during a specific time period, and the charging power of energy storage during charging. >0; Real-time power of load after electricity price optimization; It contributes to the day-ahead forecasting of photovoltaics.

[0054] (3) Objective function of power quality evaluation index (twenty four) In some embodiments of the present invention, the constraints of the multi-objective optimization model include: network structure constraints and line operation constraints of the active power distribution network.

[0055] The specific constraints for solving the multi-objective optimization model of active distribution networks are as follows: (1) Network structure constraints 1) Constraints on the number of newly built lines (25) In the formula, The actual number of lines to be upgraded. This represents the maximum number of lines to be upgraded.

[0056] 2) Constraints on the number of newly built tie switches (26) In the formula, To determine the actual number of interconnecting switches to be upgraded, This represents the maximum number of interconnecting switches to be modified.

[0057] 3) Radial structural constraint (27) In the formula, T For system structure, To satisfy the radial system structure set.

[0058] (2) Operational constraints 1) Node voltage constraints (28) In the formula, and They are nodes i Minimum and maximum voltage values.

[0059] 2) Power balance constraints (29) In the formula, , , N and Branch roads ij The conductivity, susceptance, total number of nodes and nodes i , j The phase angle difference; and They are nodes i , j Voltage at the point; and They are nodes i The active and reactive power at the location.

[0060] 3) Line voltage drop constraint (30) In the formula, and They are nodes i and nodes j voltage, Let M be the line current, and M be a large constant. This indicates the construction status of the line.

[0061] 4) Photovoltaic output constraints (31) In the formula, To achieve the maximum light rejection rate, The photovoltaic construction status is as follows. Contribute to photovoltaic forecasting.

[0062] (3) Second-order cone constraint (32) In the formula, , Branch nodes i Flow to Node j Active and reactive power; , It represents the square of the branch current and the node voltage.

[0063] In some embodiments of the present invention, the line structure or switch operation mode of the distribution network is adjusted according to the precise transformation scheme of the active distribution network.

[0064] In some embodiments of the present invention, the improved particle swarm algorithm includes: constructing the particle structure in the improved particle swarm algorithm based on the line modification state and the switch switching state.

[0065] In some embodiments of the present invention, the improved particle swarm optimization algorithm further includes: constructing a corresponding active power distribution network topology based on the encoding result of each particle; generating the power quality comprehensive evaluation index according to the plurality of power quality indicators in the active power distribution network topology; and generating the fitness evaluation of the particle based on the power quality comprehensive evaluation index.

[0066] The multi-strategy particle swarm optimization algorithm for AFM-PSO proposed in this invention is: The AFM-PSO algorithm implements an adaptive search and feedback-linked optimization mechanism through nine key steps. The algorithm implementation steps are as follows: (1) Initialize algorithm parameters: Before optimization begins, the basic control parameters of the algorithm are first set, including: population size. =50, current iteration number t=1, maximum iteration number =500, initial inertia weight =0.9, minimum / maximum weight =0.4, =0.9, learning factor =1.5, =1.7, Stagnation Disturbance Threshold =30, structural entropy interval threshold =0.05, =0.15.

[0067] (2) Constructing a unified structured particle coding system: To adapt to the characteristics of various discrete and continuous variables such as line selection and switch states in power distribution network renovation problems, this paper designs a unified particle coding structure. It uses a combination of Boolean and binary variables to represent line renovation states and switch switching states, thus adapting to the mixed variable space and improving the feasibility and adaptability of the algorithm. The particle structure is defined as follows: (33) in: This is a Boolean variable indicating whether the corresponding branch should be modified. This is an integer variable representing the selected line model; This is a continuous variable, representing the actual length of the line, in km; This is a binary variable indicating whether the tie switch is installed and its on / off status (1 indicates installed, 0 indicates not in operation).

[0068] (3) Embed the power flow model and construct the upper and lower layer collaborative fitness function: Based on the encoding results of each particle, the corresponding distribution network topology is constructed, and the probabilistic power flow analysis module is called to evaluate key indicators such as node voltage, branch power, voltage qualification rate, and voltage deviation. Then, the fitness of the particles is evaluated according to the multi-objective comprehensive function to realize closed-loop feedback between upper-level optimization and lower-level simulation.

[0069] (4) Update individual and global optimal solutions: Traverse the current population and compare the current fitness of each particle. Best in history If it is better than the historical record, then update it to: the individual's best position. ,like Then update the globally optimal particle to .

[0070] (5) To understand the degree of diversity of the population during the search process, the structural entropy index is introduced: (34) in, K The number of particle feature partitions, For the first j Particle number, Population size.

[0071] Set the entropy change Divide the current evolutionary state: If If it is determined to be in the "convergence phase", enhance convergence; if This is the "diversity phase," promoting exploration; if This is the "stagnation phase," which triggers the disturbance mechanism.

[0072] (6) Dynamically adjust the inertia weight based on the entropy feedback state. With learning factors , This enables the transition from extensive search to intensive tuning: (35) in: , These are adjustment coefficients used to sense evolutionary trends and adjust the search strategy. The updated parameters directly affect the particle's velocity and position updates, controlling the search direction and range.

[0073] (7) Execute position and speed updates: (36) (37) in: This represents the optimal position in the particle's history. This represents the current globally optimal particle position. , These are uniformly random numbers.

[0074] The updated particles will undergo variable type conversion and boundary checks through the type mapper and feasibility correction module to ensure that the position is valid, the type matches, and the constraints are met. (38) in: This represents a variable boundary correction function, used to project out-of-bounds variables back into the domain; The table represents a variable type mapping function, used to convert integers, Boolean variables, etc. in particles into feasible variables that meet the constraints.

[0075] (8) Set up a perturbation mechanism to break through local optima: To avoid getting trapped in local optima, the algorithm constructs a perturbation triggering mechanism based on information entropy and stagnation algebra, with the perturbation probability being: (39) in: This represents the current structural entropy value. The upper limit of entropy during algorithm initialization; This is the global disturbance sensitivity coefficient; Indicates the first i The position of the historical best solution of each particle in the global ranking; Population size.

[0076] To simplify the triggering criteria, a perturbation threshold θ = 0.3 is introduced. Perform the following perturbation: (40) in: Indicates zero mean and variance The normal distribution disturbance term, The disturbance amplitude can be controlled and dynamically adjusted with iterations, and can be set as follows: (41) (9) Termination of iteration: Output the optimal particle. The algorithm terminates when any termination condition is met, including reaching the maximum number of iterations or the global optimal solution showing no significant improvement over multiple consecutive iterations and the structural entropy essentially converging. The final output is the current globally optimal particle, whose encoding can represent decision information such as the modification status, model selection, and capacity configuration of power distribution lines and tie switches.

[0077] To optimize and upgrade the existing distribution network structure, this study embeds a multi-strategy particle swarm optimization algorithm into the solution process of the precise transformation problem of active distribution networks. The overall implementation steps are as follows: (1) Input node load ( P i , Q i Branch parameters (resistance) R ij Reactance X ij Voltage upper and lower limit constraints , ), optional line models and their unit costs, etc., while setting optimization weighting coefficients ( w 1, w 2, w 3) Control parameters of the AFM-PSO algorithm.

[0078] (2) Calculate the voltage deviation at each node. DI Voltage qualification rate QR Maximum offset FR Average voltage fluctuation V var Voltage over-limit probability and risk indicators V limit Indicators such as [list of indicators]. A scoring function is calculated by weighting and combining all indicators.

[0079] (3) Select the route to be optimized Line model selection Line length , Connecting switch configuration variables A unified encoding is used to construct the search space and initialize the position and velocity of the particle swarm.

[0080] (4) The above evaluation indicators, power grid losses and renovation costs are combined. C cost Normalization is performed, and a comprehensive fitness function is constructed based on the weights.

[0081] (5) Based on the fitness function value, the AFM-PSO algorithm is used to dynamically update the particle position, perform a global search, and determine whether the convergence condition is met. (6) When the algorithm converges, output the modification scheme corresponding to the global optimal particle, including the line modification type, switch configuration, etc.

[0082] To verify the feasibility of the aforementioned method for precise transformation and optimization of active distribution networks, this paper uses the IEEE 33-bus standard distribution network system for simulation verification, such as... Figure 2As shown. The active distribution network is a radial network with a given reference voltage of 12.66 kV. The upper and lower limits of the node voltage range from 0.95 to 1.05 kV. The reference power is 100 MVA, the total active load power is 3715 kW, and the total reactive load power is 2300 kvar. The network is connected to external nodes through root node 1. The PV locations are nodes 28 to 33, with each node having a 1 MW connection. The basic data of the distribution network adopts the IEEE 33-node standard distribution system data. The parameters of the optional lines and tie switches for line modification are shown in Tables 1 and 2.

[0083] Table 2 Optional Line Parameters

[0084] Table 3 Optional Interconnection Switch Parameter Table

[0085] The simulation-derived upgrade plan is as follows: add 5 branch lines, upgrade 24 old lines, and add 10 tie switches. Detailed upgrade results are shown in Table 4 (addition of branch lines), Table 5 (line upgrades), and Table 6 (addition of tie switches).

[0086] Table 4 Results of Precision Upgrade of Active Distribution Network (New Branches)

[0087] Table 5 Results of Precision Upgrade of Active Distribution Network (Line Upgrade)

[0088] Table 6 Results of Precision Upgrade of Active Distribution Network (New Interconnection Switches)

[0089] Figure 3 This is a precise upgrade of the distribution network topology. The upgrade replaced old lines with new low-loss, high-current-carrying-capacity conductors (JKLYJ series), significantly optimizing power transmission efficiency. New lines were added to expand the network coverage, making the power supply layout more rational. Furthermore, all new feeders were configured with sectionalizing switches strictly according to the "two-section, two-tie" wiring pattern, constructing a flexible and reliable network topology.

[0090] Based on the calculation results, the hourly node voltage distribution of the IEEE 33-node system before and after the modification can be obtained within a single day, such as... Figure 4 and Figure 5 As shown.

[0091] contrast Figure 4 and Figure 5 It can be seen that precise transformation significantly optimizes the voltage distribution characteristics of the distribution network and improves voltage quality. Figure 4 Before the upgrade, the voltage distribution was quite dispersed and fluctuated significantly, with some node voltage values ​​approaching 0.94 pu, reflecting low voltage stability and quality. Figure 5 This indicates that the voltage distribution is more concentrated after the upgrade, the fluctuation amplitude is reduced, and the voltage value is mainly above 0.99 pu, approaching 1 p.u., showing a significant enhancement in the voltage stability and reliability of the distribution network. These results verify the significant effectiveness of precise upgrades in improving voltage characteristics, ensuring the safe operation of the power grid, optimizing voltage distribution, and improving power quality.

[0092] The power distribution networks before and after the upgrade were evaluated and analyzed according to the evaluation methods of this study. The comparison of power quality evaluation indicators and power quality before and after the upgrade are as follows: Figure 6 and Figure 7 As shown.

[0093] according to Figure 6 The comparison results of the power quality evaluation indicators shown indicate that the overall efficiency of the distribution network has significantly improved after precise upgrades. The maximum voltage deviation U after the upgrade is... DI Compared to before the renovation, it decreased by 82.8% ( Figure 6 a) Average volatility U FR Reduced by 66.7% ( Figure 6 b), the average probability of exceeding the limit P decreases to 0 ( Figure 6 c), indicating that the upgrade effectively reduced voltage fluctuations, improved voltage stability, and completely eliminated voltage exceedances; the overall power quality score Q ( Figure 6 d) The value increased from 0.578 before the upgrade to 0.917, a rise of 58.7%, exceeding the scoring threshold of 0.75, indicating that the distribution network no longer requires further upgrades. These reductions are mainly due to the upgrade optimizing the distribution network topology, enhancing network flexibility, adaptability, and stability through reasonable adjustments to line layout and switch configuration.

[0094] Through the Figure 7 A comparative analysis of power quality before and after the upgrade reveals the following conclusions: the upgraded distribution network shows significant improvements in voltage deviation and voltage fluctuation. Specifically, for example... Figure 7 As shown in (a), the modified voltage deviation U DI The curve is smoother, and the average value of its maximum voltage deviation has decreased by 72.2% compared to before the modification, which significantly enhances voltage stability. Figure 7 (b) Shows the modified voltage fluctuation rate U FR The average voltage level decreased by 72% compared to before the upgrade, effectively reducing voltage fluctuations and thus improving power quality. Furthermore, Figure 7(c) The average voltage over-limit probability P after the modification was reduced by 100%, which means that the modification measures completely eliminated voltage over-limit events and significantly improved the reliability of the distribution network. In summary, the proposed modification method has achieved significant results in reducing voltage deviation, reducing voltage fluctuation, and reducing voltage over-limit probability, thereby effectively improving the overall power quality of the distribution network.

[0095] To evaluate the performance of the proposed AFM-PSO algorithm, it is compared with PSO, GA, and SSA. The four algorithms are applied to solve a precise transformation model of the distribution network, and the change in fitness value with the number of iterations during the solution process is shown below. Figure 8 As shown.

[0096] Depend on Figure 8 Comparative analysis of experimental results shows that, compared with PSO, GA, and SSA, the AFM-PSO algorithm converges to a smaller objective function value, indicating that its solution is closer to the theoretical optimal solution and has higher accuracy. Furthermore, the AFM-PSO algorithm exhibits excellent convergence performance during iterative optimization, accurately locking the optimal value in only about 25 iterations, significantly reducing computation time. This efficient optimization capability effectively overcomes the bottlenecks of low efficiency and long computation time in traditional algorithms when dealing with complex optimization problems, greatly improving solution efficiency and providing more efficient and reliable algorithmic support for solving complex engineering optimization problems such as precise transformation of power distribution networks.

[0097] This invention addresses the multiple uncertainties arising from deep source-load interaction by probabilistic power flow in probabilistic power flow optimization and proposes a precise transformation and optimization method for active distribution networks. First, a comprehensive power quality evaluation index system for active distribution networks considering probabilistic power flow is established. Then, with power quality as the objective, a precise transformation and optimization model for active distribution networks considering probabilistic power flow is established. Based on this, an improved PSO algorithm adapted to the characteristics of active distribution network transformation is used to solve the optimization model. Finally, simulation analysis is conducted using an IEEE 33-node distribution system with a high proportion of photovoltaic integration, verifying the feasibility of the proposed model and method. The proposed evaluation index can effectively evaluate the effect of precise transformation of the distribution network. The precise transformation and optimization method can effectively improve the distribution network's ability to absorb new energy sources, significantly suppress voltage fluctuations, effectively solve power quality problems, and ensure the stable operation of the distribution network. The improved PSO algorithm possesses good convergence and global optimization capabilities. Under the same sample size, its optimization efficiency is significantly better than GA, SSA, and PSO. The proposed method is of great significance for guiding decision-making on precise transformation of active distribution networks.

[0098] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a comprehensive power quality evaluation index that considers probabilistic power flow, effectively quantifying the impact of high-proportion distributed photovoltaic (PV) grid integration on power quality and solving the power quality assessment challenges it presents. It also constructs a precise transformation model for active distribution networks that considers transformation costs, grid integration rate, and power quality, effectively addressing power quality issues such as voltage fluctuations while strictly controlling costs and improving the accuracy of distribution network transformation. Furthermore, it proposes an adaptive and feedback-enhanced multi-strategy particle swarm optimization algorithm to improve the solution efficiency and accuracy of complex models, supporting refined and efficient transformation decisions for distribution networks under high uncertainty. This invention aims to provide guidance and reference for planners in making decisions regarding the transformation of transformer substations, lines, and interconnection switches.

[0099] Example 2: The present invention also provides an active power distribution network precision transformation and optimization device. The device includes a computer device, which includes a processor and a memory. The processor stores computer instructions. When the computer instructions are executed, the device implements the active power distribution network precision transformation and optimization method.

[0100] Example 3, as Figure 9 As shown, the present invention also provides an electronic device 100 for realizing a method for precise transformation and optimization of active power distribution networks.

[0101] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0102] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the active power distribution network precision transformation and optimization method described in the first aspect of the present invention by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0103] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0104] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0105] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for precise transformation and optimization of an active power distribution network, and the processor 102 can execute multiple instructions to achieve the following: Based on the operating parameters of the active distribution network, random sample data corresponding to the output of distributed generation and the characteristics of grid load are generated, and based on the random sample data, a set of probabilistic power flow input variables for the active distribution network is generated. Based on the set of probabilistic power flow input variables, probabilistic power flow calculations are performed to obtain probabilistic power flow index information of voltage at each node and power flow on the line. Based on the probabilistic power flow index information, a comprehensive power quality evaluation index for the active distribution network is generated; With the goals of maximizing power quality and minimizing retrofit costs, a multi-objective optimization model for precise retrofitting of active distribution networks is established based on the aforementioned comprehensive power quality evaluation index, and the constraints of the multi-objective optimization model are determined. Based on the aforementioned constraints, an improved particle swarm optimization algorithm is used to solve the multi-objective optimization model, thereby obtaining a precise transformation scheme for the active power distribution network.

[0106] Example 4: If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0107] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0108] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for precise transformation and optimization of an active power distribution network, characterized in that, Includes the following steps: Based on the operating parameters of the active distribution network, random sample data corresponding to the output of distributed generation and the characteristics of grid load are generated, and based on the random sample data, a set of probabilistic power flow input variables for the active distribution network is generated. Based on the set of probabilistic power flow input variables, probabilistic power flow calculations are performed to obtain probabilistic power flow index information of voltage at each node and power flow on the line. Based on the probabilistic power flow index information, a comprehensive power quality evaluation index for the active distribution network is generated; With the goals of maximizing power quality and minimizing retrofit costs, a multi-objective optimization model for precise retrofitting of active distribution networks is established based on the aforementioned comprehensive power quality evaluation index, and the constraints of the multi-objective optimization model are determined. Based on the aforementioned constraints, an improved particle swarm optimization algorithm is used to solve the multi-objective optimization model, thereby obtaining a precise transformation scheme for the active power distribution network.

2. The method for precise transformation and optimization of active power distribution networks according to claim 1, characterized in that, The random sample data of the output and load characteristics of the distributed power source are obtained based on the Beta distribution of the output power of the photovoltaic power plant and the normal distribution of the grid load.

3. The method for precise transformation and optimization of active power distribution networks according to claim 1, characterized in that, The set of probabilistic power flow input variables includes: Distributed power generation output power and grid load power generated based on historical electricity consumption data during a specific time period; The probabilistic power flow calculation based on the set of input variables includes: The deterministic power flow calculation results of the historical power distribution network data are obtained by performing calculations based on the historical operation information data of the power distribution network and the preset number of Monte Carlo simulations.

4. The method for precise transformation and optimization of active power distribution networks according to claim 1, characterized in that, The generation of comprehensive power quality evaluation indicators for the active distribution network based on the probabilistic power flow index information includes: Multiple power quality indicators for the active distribution network are generated based on the probabilistic power flow index information. The aforementioned power quality indicators are categorized into cost-based indicators and benefit-based indicators; The weights of each power quality indicator are obtained by solving the weights of the cost-type and benefit-type indicators based on the normalized sequential weighting method. The cost-type and benefit-type indicators are weighted using the weights of the various power quality indicators to generate the comprehensive power quality evaluation index.

5. The method for precise transformation and optimization of active power distribution networks according to claim 1, characterized in that, The objective functions of the multi-objective optimization model include the power grid transformation cost objective function, the power absorption rate objective function, and the power quality evaluation index objective function.

6. The method for precise transformation and optimization of active power distribution networks according to claim 5, characterized in that, The constraints of the multi-objective optimization model include: network structure constraints and line operation constraints of the active distribution network.

7. The method for precise transformation and optimization of active power distribution networks according to claim 1, characterized in that, The improved particle swarm optimization algorithm includes: constructing the particle structure in the improved particle swarm optimization algorithm based on the line modification status and switch switching status.

8. The method for precise transformation and optimization of active power distribution networks according to claim 1, characterized in that, It also includes adjusting the line structure or switch operation mode of the distribution network according to the precise transformation plan of the active distribution network.

9. The method for precise transformation and optimization of active power distribution networks according to claim 7, characterized in that, The improved particle swarm optimization algorithm further includes: constructing a corresponding active power distribution network topology based on the encoding result of each particle, and generating the comprehensive power quality evaluation index according to the multiple power quality indicators in the active power distribution network topology; The fitness evaluation of the particles is generated based on the comprehensive power quality evaluation index.

10. An active power distribution network precision transformation and optimization device, characterized in that, The device includes a computer device, which includes a processor and a memory. The processor stores computer instructions. When the computer instructions are executed, the device implements the active power distribution network precision transformation and optimization method as described in any one of claims 1 to 9.

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