Method and device for optimizing operation of regional distribution network under the influence of impact load

Abstract

The present application relates to the technical field of distribution network optimization, and discloses a method and device for optimizing the operation of a regional distribution network under the influence of impact loads, including obtaining impact load characteristics and generating impact load random variables; randomly generating an initial population based on the current operating state of the distribution network control equipment, and determining the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variables, and screening out the population individuals that meet the preset constraints; iterating the population individuals using a genetic algorithm to obtain the optimal solution; generating the state adjustment strategy of the distribution network control equipment according to the optimal solution to optimize the operation of the regional distribution network. Its beneficial effect is to reduce the negative impact of the impact load of some high-power equipment on the power grid, and effectively improve the power quality of the enterprise distribution network under high-power, nonlinear, and high-impact loads.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network optimization, and in particular to a method and device for optimizing the operation of a regional distribution network under the influence of impact loads. Background Art

[0002] At present, the distribution networks of some large industrial production enterprises are usually very large in scale, and there are usually some nonlinear and highly load-impact equipment in their distribution networks, such as steel rolling production lines, grinding and crushing production lines, steelmaking furnaces and other industrial equipment. These impact loads often seriously affect the stable operation of the distribution network, resulting in inevitable negative problems in the power quality of the distribution network when facing large-capacity impact loads.

[0003] The optimization of the distribution network operating status is mainly aimed at ensuring that the daily voltage and load rate do not exceed the operating red lines. However, related technologies often ignore the impact load operation caused by some special equipment in the distribution network, which has great power fluctuations. This will directly lead to the power quality of the production park distribution network being difficult to meet the standards of normal and stable operation under special operating conditions, thus causing certain impacts on production. Summary of the invention

[0004] The present application provides a method and device for optimizing the operation of a regional distribution network under the influence of impact loads, which solves the technical problem that the current relevant distribution network operation optimization and control schemes are unable to cope with the adverse effects of impact loads on the power quality of the distribution network, and achieves the technical effect of effectively improving the power quality of the distribution network of a large industrial production park under high-power, nonlinear, and high-impact loads.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing the operation of a regional distribution network under the influence of an impact load, comprising:

[0007] Acquiring shock load characteristics, and generating shock load random variables according to the shock load characteristics;

[0008] Randomly generate an initial population based on the current operating state of the distribution network control equipment, determine the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable, and select population individuals that meet preset constraints from the initial population according to the flow state quantity;

[0009] Iterating the population individuals using a genetic algorithm to obtain an optimal solution, wherein in each iteration process, the population individuals that meet the preset constraint conditions are selected according to the current population state quantity corresponding to the population individuals to participate in the next iteration;

[0010] A state adjustment strategy for the distribution network control device is generated according to the optimal solution to optimize the operation of the regional distribution network.

[0011] The regional distribution network operation optimization method proposed in the embodiment of the present application can be used in various industrial production enterprises and industrial parks with large distribution network scales, and can achieve the purpose of simulating the impact of impact loads on the overall load fluctuations of the distribution network by generating shock load random variables that can reflect the characteristics of shock loads. Then, the shock load random variables are substituted into the flow calculation and genetic algorithm iteration, so that the consideration of shock load random fluctuations can be added in each population iteration, providing operation management personnel with an optimization strategy for the distribution network operation status under the influence of shock loads for reference.

[0012] Compared with the related technologies that take long-term operation safety as the optimization goal, the distribution network operation optimization method proposed in the embodiment of the present application is changed to be oriented to the overall and real-time operation quality. From the perspective of distribution network operation, on the premise of ensuring the safety and stability of the electrical operation indicators of each node of the enterprise distribution network, the goal of optimizing the power quality of the distribution network under impact load is achieved, which effectively improves the operation quality of the distribution network and ensures the normal production of the enterprise. Thus, while helping enterprises to achieve high-quality and stable production, it reduces the negative impact of the impact load of some high-power equipment on the power grid, and effectively improves the power quality of the enterprise distribution network under high-power, nonlinear, and high-impact loads.

[0013] Optionally, acquiring the shock load characteristics and generating the shock load random variable according to the shock load characteristics includes:

[0014] Obtain the historical load data of each impact load device in the distribution network at several sampling points within a single operating day;

[0015] For each impact load device, respectively obtain the difference of the historical load data between every two adjacent sampling points, and select the maximum value of the difference as the impact load feature of the corresponding impact load device;

[0016] The impact load characteristic corresponding to each impact load device is multiplied by a random number respectively, and the multiplication results are added together as the impact load random variable, wherein the random number randomly takes a value between 0 and 1.

[0017] The embodiment of the present invention calculates the difference change of the historical load data of each impact load device, and extracts the impact load characteristics by selecting the maximum value therein, which can effectively capture the maximum load fluctuation of the impact device during operation. In addition, the introduction of random numbers makes the generated impact load random variables random and dynamic, which can achieve the effect of simulating the random start and random impact of multiple impact load devices in the actual distribution network, thereby providing reliable input data for the subsequent solution of the state adjustment strategy of the distribution network control equipment, which helps to achieve the accuracy, reliability and efficiency of distribution network control.

[0018] Optionally, determining the power flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable includes:

[0019] Obtain the historical total load data of all equipment in the distribution network on several operating days, and perform fitting analysis and aggregation processing on the historical total load data to obtain the distribution network forecast load on a single operating day;

[0020] Adding the distribution network predicted load to the impact load random variable to obtain the active power in the power flow state quantity corresponding to each node in the distribution network;

[0021] The product of the active power and the preset power factor is used as the reactive power in the power flow state quantity corresponding to each node in the distribution network.

[0022] Optionally, performing fitting analysis and aggregation processing on the historical total load data to obtain a distribution network forecast load for a distribution network in a single operation day includes:

[0023] Perform linear regression fitting on the historical total load data, and process the results of the linear regression fitting into a load forecast sequence based on a preset time interval ,in, Represents the load forecast data at the nth time on the dth operating day;

[0024] The root mean square value of the load forecast sequence of each operation day is calculated to realize the aggregation of the load forecast sequence into the distribution network forecast load of a single operation day, wherein the formula for calculating the root mean square value is:

[0025] ;

[0026] In the formula, is the distribution network forecast load at the nth moment in a single operating day, and D is the total number of operating days.

[0027] The embodiment of the present application performs fitting analysis on the historical total load data of the distribution network, aggregates the load conditions of multiple days to achieve accurate load prediction that is closer to the actual distribution network operation conditions, and can comprehensively consider the overall load change conditions and accurately reflect the load change trend of the distribution network within a single operation day. On this basis, the random simulation of the impact load is added to obtain the corresponding flow state quantity to provide reliable data support for the determination of the distribution network control strategy.

[0028] Optionally, the power flow state quantity further includes a voltage amplitude and a line impedance, and the voltage amplitude and the line impedance are obtained by performing power flow calculation on a distribution network topology corresponding to the initial population.

[0029] Optionally, the preset constraint condition includes a power flow constraint;

[0030] The power flow constraint includes a power flow constraint, and the formula of the power flow constraint is:

[0031] ;

[0032] In the formula, is the set of all nodes in the distribution network. is the connectivity state variable between distribution network node i and distribution network node j, is the conductance of the equivalent line between distribution network node i and distribution network node j, is the susceptance of the equivalent line between distribution network node i and distribution network node j, is the voltage amplitude of distribution network node i, is the voltage amplitude of the distribution network node j, is the phase angle difference of the voltage between distribution network node i and distribution network node j, is the active power from other nodes flowing into node i, is the active power from other nodes flowing into node i, is the active power at node i, where , and All are determined according to the power flow state quantity.

[0033] Because the power flow state quantity is determined on the basis of considering the random impact load, the power flow constraint in the optimization solution includes the consideration of the impact load, so that the solved state adjustment strategy has higher adaptability to the randomly started impact load, and can achieve the goal of optimizing the comprehensive power quality of the distribution network under the impact load while ensuring the safety and stability of the electrical operation indicators of each node in the distribution network, effectively improving the operation quality of the distribution network and ensuring the normal production of the enterprise.

[0034] Optionally, the preset constraint conditions also include branch state constraints and switch action constraints;

[0035] Wherein, the branch state constraint is used to constrain each node in the distribution network topology to have only one parent node;

[0036] The switch action constraint is used to constrain the upper limit of the number of adjustments to the operating state of the distribution network control device.

[0037] By setting the above branch state constraints, it can be ensured that the distribution network avoids isolated point operation during the reconstruction process. By setting the above switch action constraints, it can be avoided that too frequent state adjustment actions affect the service life of the distribution network control equipment. In summary, the above constraint settings can make the optimal solution obtained not only meet the power supply requirements of the distribution network, but also improve the power supply reliability and optimize the economy.

[0038] Optionally, the iterating the population individuals using a genetic algorithm to obtain an optimal solution, wherein in each iteration process, the population individuals satisfying the preset constraint conditions are selected according to the current flow state quantity corresponding to the population individuals to participate in the next iteration, including:

[0039] In each iteration, the voltage deviation corresponding to each individual in the population is calculated respectively, and the best individual with the smallest voltage deviation is selected from the individuals in the population;

[0040] If the optimal individual meets the preset convergence condition, the optimal individual is output as the optimal solution and the iteration is stopped; otherwise, a sub-individual is selected from the current population individuals based on the roulette method, and the sub-individual is cross-mutated according to the preset rules to generate the next generation of population individuals;

[0041] The flow state quantity of the next generation of population individuals is determined according to the distribution network predicted load and the impact load random variable, and the population individuals whose flow state quantity meets the preset constraint condition are screened out to enter the next iteration.

[0042] The embodiment of the present application, through the optimization iteration of the genetic algorithm, combined with the flow constraints including random impact loads, can achieve more effective distribution network operation optimization based on the impact of impact loads, so as to improve the power quality of the distribution network when facing high-power load impacts, and ensure the reliability of the distribution network in various complex and changeable environments.

[0043] Optionally, the preset rule is that individuals in the population achieve the crossover variation only through a vertical crossover operation.

[0044] The embodiment of the present application limits the population individuals to only vertical crossover, which can introduce appropriate changes to support diversity in the optimization process, and can less damage the genetic combination of the population individuals, thereby generating infeasible solutions that do not conform to the actual distribution network operation, thereby helping to find the optimal solution more quickly in the optimization solution process.

[0045] In a second aspect, an embodiment of the present application provides a regional distribution network operation optimization device under the influence of an impact load, comprising:

[0046] An impact load generation module, used for acquiring impact load characteristics and generating impact load random variables according to the impact load characteristics;

[0047] An initialization module, used to randomly generate an initial population based on the current operating state of the distribution network control equipment, determine the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable, and screen out population individuals that meet preset constraints in the initial population according to the flow state quantity;

[0048] An iterative solution module, used to iterate the population individuals using a genetic algorithm to obtain an optimal solution, wherein in each iteration process, the population individuals that meet the preset constraint conditions are selected according to the current population state quantity corresponding to the population individuals to participate in the next iteration;

[0049] The optimization and adjustment module is used to generate a state adjustment strategy for the distribution network control equipment according to the optimal solution to optimize the operation of the regional distribution network.

[0050] The regional distribution network operation optimization device proposed in the embodiment of the present application can be used in various large industrial production enterprises and industrial parks with large distribution network scales. In terms of improving power quality, an impact load random variable that can reflect the characteristics of the impact load is proposed, so as to achieve the purpose of simulating the impact of the impact load on the overall load fluctuation of the distribution network. Then, the impact load random variable is substituted into the flow calculation and genetic algorithm iteration, so that the consideration of the random fluctuation of the impact load can be added in each population iteration, and the optimization strategy of the distribution network operation status under the influence of the impact load is provided to the operation management personnel for reference. It can help enterprises achieve high-quality and stable production from the perspective of distribution network operation, while reducing the negative impact of the impact load of some high-power equipment on the power grid, and effectively improve the power quality of the enterprise distribution network under high-power, nonlinear, and high-impact loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A schematic diagram of a flow chart of a method for optimizing the operation of a regional distribution network under the influence of an impact load proposed in an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of the feature extraction and generation process of impact load in an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of the iterative process of the legacy algorithm in an embodiment of the present invention;

[0055] Figure 4 A structural block diagram of a regional distribution network operation optimization device under the influence of an impact load proposed in an embodiment of the present invention;

[0056] Figure 5 The present invention is a schematic diagram of the structure of a computer device for executing a method for optimizing the operation of a regional distribution network under the influence of an impact load, as proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0058] In some application scenarios, the impact of impact load on the distribution network is often ignored when optimizing the operation of the distribution network. Impact load is a load that changes suddenly and greatly. It is usually a large-power electrical equipment in large industrial production enterprises, such as steel rolling production lines, grinding and crushing production lines, and steelmaking furnaces. Generally, the maximum load lasts for a short time, but its peak value may be several times or dozens of times its average load. Therefore, this type of load has a greater impact on the power system. When its change amplitude is large relative to the distribution network capacity, it is very likely to cause negative problems such as voltage sag and voltage deviation in the distribution network.

[0059] Therefore, for enterprises with the above-mentioned high-power electrical equipment, it is difficult to avoid the power quality problems caused by the commissioning of high-power, nonlinear, and highly impactful loads when optimizing the operation of their distribution networks. If the impact of impact loads is ignored, it will directly lead to the power quality being difficult to meet normal standards under special operating conditions when impact loads are started, which will in turn affect production. At the same time, the distribution networks of such enterprises generally have more complex nodes, and the item-by-item optimization of a single node often cannot achieve the goal of optimizing the global voltage index. Most of the enterprise's own operation and maintenance teams lack support for the theory, technology, and methods in the field of distribution network operation optimization, making it difficult to carry out power quality management work for large-scale distribution networks under impact loads.

[0060] In response to the situation where the above-mentioned impact load affects the optimization effect of distribution network operation, the embodiment of the present application proposes a regional distribution network operation optimization method suitable for impact loads, which can be applied to scenarios of large-scale industrial production enterprises with large distribution networks such as steel, non-ferrous metals, and cement. The corresponding power quality management system in the distribution network of the industrial production enterprise completes the execution of the regional distribution network operation optimization method proposed in the embodiment of the present application.

[0061] According to an embodiment of the present application, an embodiment of a method for optimizing the operation of a regional distribution network under the influence of an impact load is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0062] In this embodiment, a method for optimizing the operation of a regional distribution network under the influence of an impact load is provided, which can be used in the power quality management system of the above-mentioned large industrial production enterprises. Figure 1 is a flow chart of a method for optimizing regional distribution network operation according to an embodiment of the present application. Figure 1 As shown, the process includes the following steps:

[0063] Step S1, obtaining shock load characteristics, and generating shock load random variables according to the shock load characteristics.

[0064] In this step, by extracting the impact load characteristics, the operating status of the distribution network under different load impacts can be accurately reflected, and on this basis, impact load random variables are generated to achieve the purpose of simulating the random start of impact loads.

[0065] Step S3, randomly generating an initial population based on the current operating status of the distribution network control equipment, determining the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable, and selecting population individuals that meet preset constraints in the initial population according to the flow state quantity.

[0066] In this step, the genetic algorithm is initialized to prepare for the subsequent genetic iteration to obtain the optimal solution for the distribution network operation. At the same time, the initial population that meets the preset constraints is selected during the initialization process to participate in the genetic iteration. Since the flow state quantity is determined according to the distribution network predicted load impact load random variable, the flow state quantity in this step can accurately characterize the dynamic change of the load while simulating the impact of the impact load, so that the distribution network operation mode solved by the genetic algorithm can be safely implemented under the constraints of each node of the distribution network.

[0067] Step S5, iterating the population individuals using a genetic algorithm to obtain an optimal solution, wherein in each iteration process, the population individuals that meet the preset constraint conditions are selected according to the current population state quantity corresponding to the population individuals to participate in the next iteration.

[0068] In this step, the population is iterated by genetic algorithm to achieve the optimal solution of distribution network operation. In each iteration, the current population individuals are calculated with power flow, and the shock load random variable is added on the basis of the power flow calculation, so as to add the attribute of random start of simulated shock load to the population individuals, and realize the optimization solution of distribution network operation state under the influence of shock load.

[0069] Step S7, generating a state adjustment strategy for the distribution network control device according to the optimal solution to optimize the operation of the regional distribution network.

[0070] In this step, the optimal solution will correspond to the operating state of the distribution network control equipment, and generate a state adjustment strategy to adjust the current operating state of the distribution network control equipment to the operating state corresponding to the optimal solution, thereby providing a reference for distribution network operation optimization.

[0071] The regional distribution network operation optimization method provided in this embodiment achieves the purpose of simulating the impact of impact load on the overall load fluctuation of the distribution network by generating impact load random variables that can reflect the characteristics of impact load. Then, the impact load random variables are substituted into the power flow calculation and genetic algorithm iteration, so that the consideration of random fluctuation of impact load can be added in each population iteration, and the optimization strategy of the distribution network operation status under the influence of impact load is provided for reference by the operation management personnel.

[0072] Compared with the related technologies that take long-term operation safety as the optimization goal, the distribution network operation optimization method proposed in the embodiment of the present application is instead oriented towards global and real-time operation quality. From the perspective of distribution network operation, while helping enterprises to achieve high-quality and stable production, it reduces the negative impact of the impact load of some high-power equipment on the power grid, and effectively improves the power quality of the enterprise distribution network under high-power, nonlinear, and high-impact loads.

[0073] In the embodiment of the present application, the step of obtaining the shock load characteristics and generating the shock load random variable according to the shock load characteristics may include the following steps:

[0074] Step S110, obtaining historical load data of a plurality of sampling points of each impact load device in the distribution network within a single operation day;

[0075] Step S120, for each impact load device, respectively obtain the difference of the historical load data between every two adjacent sampling points, and select the maximum value of the difference as the impact load feature of the corresponding impact load device;

[0076] Step S130: multiply the impact load characteristic corresponding to each impact load device by a random number respectively, and add the multiplication results as the impact load random variable, wherein the random number randomly takes a value between 0 and 1.

[0077] like Figure 2 As shown, based on the actual situation of the industrial production enterprise, the impact load equipment on each production line is determined, such as steel rolling production lines, grinding, crushing production lines, steelmaking furnaces and other high-power electrical equipment, and the all-day power load curve of the impact load equipment on each production line is obtained. In some examples of the embodiments of the present application, the 24-point historical load data of the impact load equipment is obtained with a sampling time interval of one hour.

[0078] Then, for each impact load device on the production line, calculate the difference between each adjacent point in the 24 points of its historical load data, where the load at the moment before startup is recorded as 0, and select the maximum value of all differences, thereby obtaining the impact load characteristics corresponding to the impact load device:

[0079] ;

[0080] ;

[0081] In the formula, Indicates the impact load characteristics corresponding to the nth impact load device, represents the historical load data of the impact load equipment at the jth moment, The historical load data of the impact load device at time j-1.

[0082] On this basis, the total impact load of all impact load equipment on the production line is obtained:

[0083] ;

[0084] Represents the entire shock load sequence, each sequence value in the sequence is the shock load characteristic of each shock load device, and N is the total number of shock load devices.

[0085] At the same time, a randomly generated 0 / 1 value is used as a coefficient to simulate the fluctuations when multiple loads are randomly started under extreme conditions, that is, to generate a random number , the random sequence of impact load is obtained as

[0086]

[0087] Finally, the sum of all sequence values ​​in the above shock load random sequence is taken as the shock load random variable.

[0088] The embodiment of the present invention calculates the difference change of the historical load data of each impact load device, and extracts the impact load characteristics by selecting the maximum value therein, which can effectively capture the maximum load fluctuation of the impact device during operation. In addition, the introduction of random numbers makes the generated impact load random variables random and dynamic, which can achieve the effect of simulating the random start and random impact of multiple impact load devices in the actual distribution network, thereby providing reliable input data for the subsequent solution of the state adjustment strategy of the distribution network control equipment, which helps to achieve the accuracy, reliability and efficiency of distribution network control.

[0089] In the stage of determining the state quantity and the set of optimized quantities, the step of determining the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable may include the following steps:

[0090] Step S210, obtaining the historical total load data of all devices in the distribution network on several operating days, and performing fitting analysis and aggregation processing on the historical total load data to obtain the distribution network predicted load on a single operating day;

[0091] Step S220, adding the distribution network predicted load and the impact load random variable to obtain the active power in the power flow state quantity corresponding to each node in the distribution network;

[0092] Step S230: taking the product of the active power and the preset power factor as the reactive power in the power flow state quantity corresponding to each node in the distribution network.

[0093] In this embodiment of the present application, step S210 may further include the following steps:

[0094] Step S211, performing linear regression fitting on the historical total load data, and processing the results of the linear regression fitting into a load forecast sequence based on a preset time interval ,in, Represents the load forecast data at the nth time on the dth operating day;

[0095] Step S212, calculating the root mean square value of the load forecast sequence of each operation day to realize the aggregation of the load forecast sequence into the distribution network forecast load of a single operation day, wherein the formula for calculating the root mean square value is:

[0096] ;

[0097] In the formula, is the distribution network forecast load at the nth moment in a single operating day, and D is the total number of operating days.

[0098] The main goal of load forecasting is to determine the load fluctuation on the operating day. The load forecasting accuracy is not required to be high. To simplify the solution, in some examples of the embodiments of the present application, a generalized linear fitting model such as linear regression can be used according to the actual production characteristics of the enterprise, and other methods such as decision trees can also be used to achieve load forecasting, thereby obtaining load forecasting data from regression analysis:

[0099] ;

[0100] In the formula, is the regression coefficient, is the historical total load data, Load forecast data.

[0101] In the process of regression analysis, the above regression coefficients are obtained by the gradient descent method, and the mean-square error (MSE) is set accordingly as the corresponding error function of the regression analysis.

[0102] On this basis, the load forecasting sequence can be obtained , D is the total number of operating days.

[0103] To simplify the calculation, subsequent optimizations are based on a 24-hour cycle. Therefore, after obtaining the 24-hour load forecast data for multiple operating days, the multiple-day load data is equivalent to the load data for a single day. Considering that the network loss value and the load rate are approximately in a square relationship, in some examples of the embodiments of the present application, the root mean square value method is used to aggregate the multiple-day load data, that is, the distribution network forecast load at the nth moment in a single operating day is obtained as:

[0104] .

[0105] The embodiment of the present application performs fitting analysis on the historical total load data of the distribution network, aggregates the load conditions of multiple days to achieve accurate load prediction that is closer to the actual distribution network operation conditions, and can comprehensively consider the overall load change conditions and accurately reflect the load change trend of the distribution network within a single operation day. On this basis, the random simulation of the impact load is added to obtain the corresponding flow state quantity to provide reliable data support for the determination of the distribution network control strategy.

[0106] In the embodiment of the present application, the initial population is randomly generated based on the current operating state of the distribution network control equipment, which means that the initial code of the population individuals is randomly generated according to the set population size, and the operating state represented by the initial population adopts the current actual operating state. In some examples of the embodiments of the present application, binary segmented coding is adopted, wherein each transformer voltage regulation range occupies 4 bits, each interconnecting switch occupies 1 bit, and the operating state of each SVG and other reactive output equipment adopts 5-bit coding. Taking a double-bus single radial network as an example, assuming that the initial code of the population individuals is [00000000|0|0000100002], it means that distribution transformers 1 and 2 are both connected at the rated gear, the interconnecting switch is disconnected, and the reactive compensation on the first and second bus sections operates at 3.125% and 6.25% of the rated power.

[0107] After the shock load random variables and distribution network predicted loads are obtained in the above process, the optimal power flow state quantity and the set of optimized quantities will be determined in combination with the optimal power flow (OPF). OPF means that when the structural parameters and load conditions of the distribution network are given, the available control variables, such as generator output power, adjustable transformer taps, etc., are adjusted to find the power flow distribution that can meet all operating constraints and make a certain performance indicator of the system reach the optimal value. In the embodiment of the present application, under the corresponding structural parameters in the initial population, three types of monitoring points of distribution network power quality A, B, and C are set, among which:

[0108] Class A monitoring points are 110kV or 10kV busbars of the distribution network, and the highest voltage level after transformation by the main transformer in the park is selected;

[0109] Class B monitoring points are 0.4kV busbars of the distribution network or 0.4kV incoming lines of the main cabinet;

[0110] Class C monitoring points are power supply nodes for key energy-consuming equipment at each voltage level.

[0111] The voltage deviation rate is taken as the objective function, specifically:

[0112] ;

[0113] In the formula, is the comprehensive voltage deviation rate, the ideal value is 0, is the voltage average value of multiple monitoring points among various monitoring points, is the average voltage deviation rate of various monitoring points A, B, and C, is the average voltage deviation rate of Class A monitoring points, is the average voltage deviation rate of Class B monitoring points, is the average voltage deviation rate of Class C monitoring points, It is the standard voltage reference value for various monitoring points.

[0114] Since all the electricity in the enterprise's distribution network is purchased from outside, including its own photovoltaic power generation, which is also regarded as purchased electricity, there is no need to consider the cost of electricity and fuel here. Therefore, when optimizing, only the network loss cost represented by the voltage deviation rate needs to be considered.

[0115] On this basis, the state quantity of the optimal power flow under the distribution network topology corresponding to different population individuals is obtained. And the optimization amount The collection is:

[0116] ;

[0117] in, is the active power flowing between node i and node j, which is obtained through the above steps , is the reactive power flowing between node i and node j, that is, The result is obtained by multiplying the power factor by the preset power factor. is the active power flowing between node i and node j, which is obtained through the above steps .

[0118] In the embodiment of the present application, the power flow state quantity also includes voltage amplitude and line impedance, and the voltage amplitude and the line impedance are obtained by performing power flow calculation on the distribution network topology corresponding to the initial population. Specifically, is the voltage amplitude between node i and node j, is the line impedance between node i and node j, both of which are obtained through conventional power flow calculation.

[0119] is the main tap position of each level branch transformer, is the bus tie switch status between node i and node j, is the rated power of SVG at node i.

[0120] In order to ensure that the optimal solution obtained by the genetic algorithm can meet the safe operation conditions of the distribution network, the following preset constraints are set in the embodiment of the present application, including power flow constraints, branch state constraints and switch action constraints.

[0121] The power flow constraint includes a power flow constraint, and the formula of the power flow constraint is:

[0122] ;

[0123] In the formula, is the set of all nodes in the distribution network. is the connectivity state variable between distribution network node i and distribution network node j, is the conductance of the equivalent line between distribution network node i and distribution network node j, is the susceptance of the equivalent line between distribution network node i and distribution network node j, is the voltage amplitude of distribution network node i, is the voltage amplitude of the distribution network node j, is the phase angle difference of the voltage between distribution network node i and distribution network node j, is the active power from other nodes flowing into node i, is the active power from other nodes flowing into node i, is the active power at node i, where , and All are determined according to the power flow state quantity.

[0124] It should be noted that, and It is based on u Determined, in the formula and According to the variables in the above power flow state Sure, It is through u Calculated. and According to u Sure, It is through u If there is no photovoltaic or other generator equipment at node i, then Recorded as 0.

[0125] Because the power flow state quantity is determined on the basis of considering the random impact load, the power flow constraint in the optimization solution includes the consideration of the impact load, so that the solved state adjustment strategy has higher adaptability to the randomly started impact load. On the premise of ensuring the safety and stability of the electrical operation indicators of each node in the distribution network, the goal of optimizing the comprehensive power quality of the distribution network under the impact load is achieved, which effectively improves the operation quality of the distribution network and ensures the normal production of the enterprise.

[0126] The power flow constraints also include the following constraints:

[0127] Node voltage constraint, its constraint formula is:

[0128] ;

[0129] In the formula, is the voltage of distribution network node i, and They are the preset lower voltage limit and the preset upper voltage limit of the distribution network node i, respectively, which can be configured according to the requirements of the manufacturer;

[0130] Branch current safety constraint, its constraint formula is:

[0131] ;

[0132] In the formula, is the branch current between node i and node j, which is given by Sure, is the upper limit of the branch current between node i and node j;

[0133] Continuous reactive power compensation constraint, its constraint formula is:

[0134] ;

[0135] is the reactive power generated by the SVG of node i, is the maximum rated power limit of the SVG at node i.

[0136] In addition to the above-mentioned power flow constraints, the preset constraint conditions in the embodiment of the present application also set branch state constraints and switch action constraints.

[0137] The branch state constraint is used to constrain each node in the distribution network topology to have only one parent node. In order to ensure that the distribution network still operates in a radial structure during the reconstruction process and avoid the existence of isolated point operation due to the ring network, the embodiment of the present application is based on the spanning tree constraint. The branch state constraint formula for ensuring the radial operation of the distribution network is established as follows:

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142]

[0143]

[0144] ;

[0145] ;

[0146] In the formula, is the branch state variable, Represents branch connectivity, represents the branch disconnection; n is the total number of nodes, is the set of all nodes connected to node i; and is a binary variable used to give a branch The connection status of the above formula group mainly indicates the line In the spanning tree, When Indicates that node i is the parent node of node j. Conversely, if Indicates that node j is the parent node of node i. The above formula group restricts each node to have only one parent node.

[0147] The switch action constraint is used to constrain the upper limit of the number of adjustments to the operating state of the distribution network control device. The above-mentioned spanning tree constraint ensures that the distribution network is always in an open-loop operation state during the reconstruction process to meet the power supply requirements. However, in the actual operation of the distribution network, it is necessary to weigh the number of switch switching operations and the degree of comprehensive voltage compliance. Since too frequent switching actions will affect the service life of the switch, the embodiment of the present application proposes that the switch action constraint formula that can minimize the loss under a given allowable number of switch actions is:

[0148] ;

[0149] In the formula, is the total number of lines, is the current branch status, is the branch status after reconstruction; This indicates that the branch status has changed. It is the limit of the number of times the branch status is allowed to occur.

[0150] Since this formula is difficult to process directly, the embodiment of the present application is based on , the absolute value sign can be eliminated, and the above formula can be expressed as:

[0151] .

[0152] By setting the above branch state constraints, it can be ensured that the distribution network avoids isolated point operation during the reconstruction process. By setting the above switch action constraints, it can be avoided that too frequent state adjustment actions affect the service life of the distribution network control equipment. In summary, the above constraint settings can make the optimal solution obtained not only meet the power supply requirements of the distribution network, but also improve the power supply reliability and optimize the economy.

[0153] In the embodiment of the present application, the genetic algorithm is used to iterate the population individuals to obtain the optimal solution, and in each iteration, the population individuals that meet the preset constraint conditions are selected according to the current population state quantity corresponding to the population individuals to participate in the next iteration, including the following steps:

[0154] Step S510, calculating the voltage deviation corresponding to each population individual in each iteration, and selecting the best individual with the minimum voltage deviation among the population individuals;

[0155] Step S520, when the optimal individual satisfies the preset convergence condition, output the optimal individual as the optimal solution and stop iteration; otherwise, select a sub-individual from the current population individuals based on the roulette method, and perform crossover mutation on the sub-individual according to the preset rules to generate the next generation of population individuals;

[0156] Step S530, determining the power flow state quantity of the next generation population individuals according to the distribution network predicted load and the impact load random variable, and selecting the population individuals whose power flow state quantity meets the preset constraint condition to enter the next iteration.

[0157] Figure 3 A flowchart of a genetic algorithm based on the above steps is shown, and the algorithm steps shown in the flowchart are described in detail below:

[0158] Step S5001, at the beginning of the algorithm, encode the individuals into chromosomes to generate an initial population. Generate the initial population according to the existing operating mode. In some examples of the embodiments of the present application, the initial coding of the population individuals is randomly generated, and the operating state represented by the initial population adopts the current actual operating state, using binary segmented coding, wherein each transformer voltage regulation range occupies 4 bits, each interconnecting switch occupies 1 bit, and each SVG and other reactive output device operating state is coded in 5 bits. Taking a double-bus single radial network as an example, assuming that the initial coding of the population individuals is [00000000|0|0000100002], it means that distribution transformers 1 and 2 are both connected at rated gears, the interconnecting switches are disconnected, and the reactive compensation on the first and second busbars operates at 3.125% and 6.25% of the rated power.

[0159] Step S5002, calculation of constraints of individuals in the population, specifically includes executing the above steps S1 and S2 to complete the process of carrying out power flow calculation based on impact load prediction and network data under the distribution network topology corresponding to each individual in the population.

[0160] Step S5003, determine whether the individual meets the constraints. By determining whether the above calculation results meet the above constraints including the power flow constraints, branch state constraints and switch action constraints, the population individuals are screened. If all the constraints are met, step S5006 is executed, otherwise step S5004 is executed.

[0161] Step S5004, eliminate individuals that do not meet the constraints, and continue to calculate the indicators of the next individual until all individuals are calculated.

[0162] Step S5005, calculate the fitness of individuals in the population. If all constraints are met, calculate the comprehensive voltage deviation for the individual to calculate the fitness of individuals in the population. It should be noted that the comprehensive voltage deviation mentioned here is the calculated comprehensive voltage deviation rate given in the above power flow calculation. Then, step S5006 is executed.

[0163] Step S5006, determine whether the optimal individual has converged. In the stage of determining the convergence of the optimal individual, the voltage deviation rate The optimal individual is determined based on the minimum principle, and the convergence value is set to the comprehensive voltage deviation threshold, that is, the convergence of the comprehensive voltage deviation rate is used as the end condition of the algorithm iteration. If the comprehensive voltage deviation rates corresponding to the optimal individuals obtained by multiple consecutive iterations are all below the convergence value, and the number of consecutive iterations reaches a certain preset number, it indicates that the difference in the maximum power supply capacity of the distribution network corresponding to the calculation results of the most recent multiple consecutive iterations is within a certain error range. Therefore, it is judged that the optimal individual converges, and steps S5011 to S5012 are executed, otherwise steps S5007 to S5010 are executed.

[0164] Step S5007, elite retention operation. The elite retention strategy is proposed for genetic algorithms. In order to prevent the best individuals of the current group from being lost in the next generation, which causes the genetic algorithm to fail to converge to the global optimal solution, the idea of ​​this strategy is to call the best individuals that have appeared in the group so far in the evolution process elite individuals, and the operating individuals are directly copied to the next generation without pairing and crossover. This selection operation is also called replication.

[0165] Step S5008, roulette wheel method is used to select sub-individuals and make up the number of individuals. Since the number of sub-individuals generated by step 5008 may be smaller than the initial population size, the number of individuals is made up to ensure that individuals with high fitness have a higher probability of inheritance.

[0166] Step S5009, crossover operation.

[0167] Step S5010, mutation operation. Considering that the sub-individuals after the crossover operation and the mutation operation may be repeated, in the embodiment of the present application, the codes corresponding to the voltage regulation range of the distribution transformer, the state of the interconnecting switch, the reactive compensation capacity, etc. do not participate in the horizontal crossover, and only crossover and mutation operations are performed with the state of the distribution transformer through the vertical crossover, thereby ensuring the diversity of the population. Subsequently, return to execute step S5002 and perform loop iteration until the stop iteration condition is met, and execute steps S5011 and S5012.

[0168] Step S5011, optimal individual decoding.

[0169] Step S5012: Output the optimal solution. Decode the optimal individual of the last iteration and end the genetic algorithm solution process.

[0170] It can be seen from this that in each iteration, the individuals in the population will be screened to meet the constraints, so that the random start-up of the impact load, a factor affecting the power quality, is introduced through this screening process, and the process of obtaining the optimal solution for the distribution network operation considering the impact of the impact load is completed.

[0171] In addition, in addition to setting the algorithm iteration end condition for whether the optimal individual converges, the embodiment of the present application also sets the maximum number of iterations and the target voltage deviation rate to avoid program dead loops. When the number of iterations reaches the upper limit or reaches the optimal target voltage deviation rate, the algorithm iterative calculation is settled.

[0172] The embodiment of the present application, through the optimization iteration of the genetic algorithm, combined with the flow constraints including random impact loads, can achieve more effective distribution network operation optimization based on the impact of impact loads, so as to improve the power quality of the distribution network when facing high-power load impacts, and ensure the reliability of the distribution network in various complex and changeable environments.

[0173] It should be noted that in the actual application of the above step S7 in the embodiment of the present application, the binary code corresponding to the optimal solution is translated and sorted according to the difference from the existing operation mode, and the top 80% of the results similar to the existing operation mode are taken and submitted to experts for evaluation. The experts select the solution with the lowest labor cost and submit it to the relevant operation and maintenance department, so that the operation and maintenance department can perform the state adjustment of the distribution network control equipment. In addition, the embodiment of the present application uses the monthly and quarterly time dimensions as the execution cycle of the control strategy, realizes low-cost daily operation, provides operation and maintenance personnel with a global power quality optimization strategy under impact load, and further reduces the manual control cost of each device.

[0174] It should also be noted that the embodiment of the present application manually selects the distribution network control equipment with adjustable operation mode, and these distribution network control equipment preferably include transformer on-load voltage regulating taps and steplessly adjustable SVG equipment, and the remaining distribution network control equipment include transformer off-excitation voltage regulating taps, switchable capacitors, tie switches, etc. In this way, in terms of operation management, it is possible to directly provide operation instruction references for equipment such as static var generators (SVG), tie switches, and voltage regulating switches, filling the management gap that was previously based on manual experience and lacked unified quantitative guidance, and achieving the high-quality operation goal of quantitative control.

[0175] It can be seen from the above description that the regional distribution network operation optimization method proposed in the embodiment of the present application can be used in various industrial production enterprises and industrial parks with large distribution network scales. By generating shock load random variables that can reflect the characteristics of shock loads, the purpose of simulating the impact of shock loads on the overall load fluctuations of the distribution network is achieved. Then, the shock load random variables are substituted into the flow calculation and genetic algorithm iteration, so that the consideration of the random fluctuations of the shock load can be added in each population iteration, and the optimization strategy of the distribution network operation state under the influence of shock load is provided for reference by the operation management personnel. Compared with the related technology that takes long-term operation safety as the optimization goal, the distribution network operation optimization method proposed in the embodiment of the present application is changed to global and real-time operation quality orientation. From the perspective of distribution network operation, while helping enterprises to achieve high-quality and stable production, it reduces the negative impact of the shock load of some high-power equipment on the power grid, and effectively improves the power quality of the enterprise distribution network under high-power, nonlinear, and high-impact loads.

[0176] Accordingly, please refer to Figure 4 The embodiment of the present application provides a distribution network operation optimization device under the influence of impact load, including:

[0177] The shock load generating module 810 is used to obtain shock load characteristics and generate shock load random variables according to the shock load characteristics;

[0178] Initialization module 820, used to randomly generate an initial population based on the operating status of the distribution network control equipment, obtain the distribution network predicted load, and combine the impact load random variable to determine the flow state quantity of the initial population, and then screen out population individuals that meet preset constraints in the initial population according to the flow state quantity;

[0179] Iterative solution module 830, used to iterate the population individuals using genetic algorithm to obtain the optimal solution, and in each iteration, select the population individuals that meet the preset constraint conditions according to the current population state quantity corresponding to the population individuals to participate in the next iteration;

[0180] The optimization and adjustment module 840 is used to adjust the operating state of the distribution network control device according to the optimal solution.

[0181] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0182] The distribution network operation optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0183] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0184] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0185] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0186] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0187] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0188] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0189] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0190] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0191] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0192] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0193] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0194] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0195] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0196] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0197] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0198] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0199] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0200] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0201] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing the operation of a regional distribution network under the influence of impact loads, characterized in that: The regional distribution network operation optimization method comprises: Acquiring shock load characteristics, and generating shock load random variables according to the shock load characteristics; Randomly generate an initial population based on the current operating state of the distribution network control equipment, determine the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable, and select population individuals that meet preset constraints from the initial population according to the flow state quantity; Iterating the population individuals using a genetic algorithm to obtain an optimal solution, wherein in each iteration process, the population individuals that meet the preset constraint conditions are selected according to the current population state quantity corresponding to the population individuals to participate in the next iteration; Generating a state adjustment strategy for the distribution network control device according to the optimal solution to optimize the operation of the regional distribution network; The step of obtaining the shock load characteristics and generating shock load random variables according to the shock load characteristics includes: Obtain the historical load data of each impact load device in the distribution network at several sampling points within a single operating day; For each impact load device, respectively obtain the difference of the historical load data between every two adjacent sampling points, and select the maximum value of the difference as the impact load feature of the corresponding impact load device; Multiplying the impact load characteristic corresponding to each impact load device by a random number respectively, and adding the multiplication results as the impact load random variable, wherein the random number randomly takes a value between 0 and 1; The step of determining the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable comprises: Obtaining the historical total load data of all equipment in the distribution network on several operating days, and performing fitting analysis and aggregation processing on the historical total load data to obtain the distribution network forecast load on a single operating day; Adding the distribution network predicted load to the impact load random variable to obtain the active power in the power flow state quantity corresponding to each node in the distribution network; The product of the active power and the preset power factor is used as the reactive power in the power flow state quantity corresponding to each node in the distribution network; The fitting analysis and aggregation processing of the historical total load data to obtain the distribution network forecast load of the distribution network in a single operation day includes: Perform linear regression fitting on the historical total load data, and process the results of the linear regression fitting into a load forecast sequence based on a preset time interval ,in, Represents the load forecast data at the nth time on the dth operating day; The root mean square value of the load forecast sequence of each operation day is calculated to realize the aggregation of the load forecast sequence into the distribution network forecast load of a single operation day, wherein the formula for calculating the root mean square value is: ； In the formula, is the distribution network forecast load at the nth moment in a single operating day, and D is the total number of operating days.

2. The method for optimizing regional distribution network operation under the influence of impact load according to claim 1, characterized in that: The power flow state quantity also includes voltage amplitude and line impedance, and the voltage amplitude and the line impedance are obtained by performing power flow calculation on the distribution network topology corresponding to the initial population.

3. The method for optimizing regional distribution network operation under impact load according to claim 1, characterized in that: The preset constraint conditions include power flow constraints; The power flow constraint includes a power flow constraint, and the formula of the power flow constraint is: ； In the formula, is the set of all nodes in the distribution network. is the connectivity state variable between distribution network node i and distribution network node j, is the conductance of the equivalent line between distribution network node i and distribution network node j, is the susceptance of the equivalent line between distribution network node i and distribution network node j, is the voltage amplitude of distribution network node i, is the voltage amplitude of the distribution network node j, is the phase angle difference of the voltage between distribution network node i and distribution network node j, is the active power from other nodes flowing into node i, is the active power from other nodes flowing into node i, is the active power at node i, where , and All are determined according to the power flow state quantity.

4. The method for optimizing regional distribution network operation under the influence of impact load according to claim 3 is characterized in that: The preset constraint conditions also include branch state constraints and switch action constraints; Wherein, the branch state constraint is used to constrain each node in the distribution network topology to have only one parent node; The switch action constraint is used to constrain the upper limit of the number of adjustments to the operating state of the distribution network control device.

5. The method for optimizing regional distribution network operation under impact load according to claim 1, characterized in that: The method of iterating the population individuals using a genetic algorithm to obtain an optimal solution, wherein in each iteration process, the population individuals satisfying the preset constraint conditions are selected according to the current flow state quantity corresponding to the population individuals to participate in the next iteration, including: In each iteration, the voltage deviation corresponding to each individual in the population is calculated respectively, and the best individual with the smallest voltage deviation is selected from the individuals in the population; If the optimal individual meets the preset convergence condition, the optimal individual is output as the optimal solution and the iteration is stopped; otherwise, a sub-individual is selected from the current population individuals based on the roulette method, and the sub-individual is cross-mutated according to the preset rules to generate the next generation of population individuals; The flow state quantity of the next generation of population individuals is determined according to the distribution network predicted load and the impact load random variable, and the population individuals whose flow state quantity meets the preset constraint condition are screened out to enter the next iteration.

6. The method for optimizing regional distribution network operation under impact load according to claim 5, characterized in that: The preset rule is that the individuals in the population achieve the crossover variation only through the vertical crossover operation.

7. A regional distribution network operation optimization device under the influence of impact load, characterized in that: The regional distribution network operation optimization device comprises: An impact load generation module, used for acquiring impact load characteristics and generating impact load random variables according to the impact load characteristics; An initialization module, used to randomly generate an initial population based on the current operating state of the distribution network control equipment, determine the flow state quantity of the initial population according to the distribution network predicted load and the impact load random variable, and screen out population individuals that meet preset constraints in the initial population according to the flow state quantity; An iterative solution module, used to iterate the population individuals using a genetic algorithm to obtain an optimal solution, wherein in each iteration process, the population individuals that meet the preset constraint conditions are selected according to the current population state quantity corresponding to the population individuals to participate in the next iteration; An optimization and adjustment module, used to generate a state adjustment strategy for the distribution network control equipment according to the optimal solution, so as to optimize the operation of the regional distribution network; The impact load generation module is specifically used to: obtain historical load data of several sampling points of each impact load device in the distribution network within a single operation day; For each impact load device, respectively obtain the difference of the historical load data between every two adjacent sampling points, and select the maximum value of the difference as the impact load feature of the corresponding impact load device; Multiplying the impact load characteristic corresponding to each impact load device by a random number respectively, and adding the multiplication results as the impact load random variable, wherein the random number randomly takes a value between 0 and 1; The initialization module is specifically used for: Obtain the historical total load data of all equipment in the distribution network on several operating days, and perform fitting analysis and aggregation processing on the historical total load data to obtain the distribution network forecast load on a single operating day; Adding the distribution network predicted load to the impact load random variable to obtain the active power in the power flow state quantity corresponding to each node in the distribution network; The product of the active power and the preset power factor is used as the reactive power in the power flow state quantity corresponding to each node in the distribution network; The fitting analysis and aggregation processing of the historical total load data to obtain the distribution network forecast load of the distribution network in a single operation day includes: Perform linear regression fitting on the historical total load data, and process the results of the linear regression fitting into a load forecast sequence based on a preset time interval ,in, Represents the load forecast data at the nth time on the dth operating day; The root mean square value of the load forecast sequence of each operation day is calculated to realize the aggregation of the load forecast sequence into the distribution network forecast load of a single operation day, wherein the formula for calculating the root mean square value is: ； In the formula, is the distribution network forecast load at the nth moment in a single operating day, and D is the total number of operating days.

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