Line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method

Abstract

The invention discloses a line loss optimal distributed power supply layout configuration method based on a Monte Carlo simulation method. The method comprises the following steps: constructing a regional topological graph based on a regional geographical wiring diagram; constructing a load characteristic curve based on historical load data, constructing an output characteristic curve based on historical distributed power supply output data, and constructing a typical characteristic curve library by using the load characteristic curve and the output characteristic curve; obtaining regional power grid system parameters, and constructing a line loss simulation model by using the regional power grid system parameters, the regional topological graph and the typical characteristic curve library; and obtaining a distributed power supply layout configuration set corresponding to each distributed power supply permeability based on a Monte Carlo simulation method, and outputting an optimal distributed power supply layout configuration based on the minimum line loss rate according to the distributed power supply layout configuration set and the line loss simulation model. The method has the beneficial effects that the rationality of distributed power supply layout configuration is improved, and meanwhile, the efficiency of simulation calculation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of distributed power supply layout, and in particular to a method for configuring a distributed power supply layout with optimal line loss based on a Monte Carlo simulation method. Background Art

[0002] Compared with the traditional power system, the scale, physical form and operating characteristics of the new power system will change significantly. These changes are mainly reflected in the fact that the power system will be more complex, including a large number of distributed power sources, energy storage equipment, electric vehicles and various intelligent control devices, forming a highly interconnected and highly intelligent network. In such a system context, the existing traditional technologies, such as PSASP (power system analysis comprehensive program) and BPA (power flow calculation program), although they have played an important role in the planning and operation of power systems in the past, face the complexity and dynamics of the new power system. These technologies have been difficult to meet the needs of the optimal method for calculating line losses.

[0003] At the same time, in the new power system where distributed power sources are widely connected, due to the intermittent and uncertain output of distributed power sources, how to adaptively and reasonably layout and configure the distributed power sources according to their update status to ensure optimal line loss while ensuring the efficiency of adaptive updates has become an important challenge.

[0004] The patent "Distributed Photovoltaic Power Site Selection Optimization Method Based on Distribution Network Probabilistic Power Flow Calculation", publication number: CN112994017A, publication date: June 18, 2021, specifically discloses Latin hypercube sampling of actual historical operating data of multi-temporal and spatial distribution networks containing photovoltaic power sources, using Monte Carlo simulation to calculate probabilistic power flows, using probabilistic power flow calculation results to score different access plans, and combining vector evaluation genetic algorithms to obtain distributed photovoltaic power site selection optimization plans that consider the dynamic characteristics of distribution network topology lines. Although Latin hypercube sampling can improve the efficiency of Monte Carlo simulation and reduce the sample size compared to traditional random sampling methods, the amount of calculation is still very large when processing large-scale, multi-temporal and spatial distribution network data containing photovoltaic power sources, resulting in poor calculation efficiency.

[0005] The patent "A method for risk assessment of distribution network under high penetration of distributed power sources", publication number: CN108062633A, publication date: May 22, 2018, specifically discloses the following steps: 1) Considering the volatility and intermittency of the output of distributed power sources, a distributed power output model is established; 2) A line outage model is established based on the historical fault information of the line; 3) Considering the probability and consequences of the operation risk of the distribution network under high penetration of distributed power sources, a risk assessment index system considering the reliability and adequacy of the operation of the distribution network is established; 4) Using the Monte Carlo simulation-particle swarm-breadth search comprehensive algorithm to solve the distribution network risk assessment model under high penetration of distributed power sources, determine the risk assessment index value, and complete the risk assessment of the distribution network under high penetration of distributed power sources. This scheme adopts the comprehensive application of Monte Carlo simulation, particle swarm algorithm and breadth search, with high computational complexity. When dealing with large-scale distribution networks and a large number of distributed power sources, the computational efficiency is low. Summary of the invention

[0006] This application aims at the problem that the layout configuration of distributed power sources considering the line loss rate in the prior art cannot take into account both the rationality of configuration and the efficiency of planning. It provides a line loss optimal distributed power source layout configuration method based on the Monte Carlo simulation method. The line loss simulation model reflects the grid structure, load characteristics and distributed power output characteristics of the corresponding area, and the Monte Carlo simulation method is used to simulate and calculate the distributed power source layout configuration under different distributed power source penetration rates, and obtain the possible combinations of distributed power source layout configurations corresponding to the distributed power source penetration rate. Each combination of the possible combinations of distributed power source layout configurations is input into the line loss simulation model to obtain the corresponding line loss rate, and the distributed power source layout configuration corresponding to the minimum line loss rate is used as the optimal distributed power source layout configuration. The Monte Carlo simulation method is used to generate a variety of random scenarios to show the behavioral characteristics of the power grid in different situations, improve the rationality of the distributed power source layout configuration, and at the same time, the parallel computing characteristics of the Monte Carlo simulation method are used to improve the efficiency of the simulation calculation.

[0007] To achieve the above-mentioned technical objectives, a technical solution provided by the present application is a method for configuring the layout of distributed power sources with optimal line loss based on the Monte Carlo simulation method, comprising the following steps: constructing a regional topology map based on a regional geographic wiring diagram; constructing a load characteristic curve based on historical load data, constructing an output characteristic curve based on historical distributed power output data, and constructing a typical characteristic curve library with the load characteristic curve and the output characteristic curve; obtaining regional power grid system parameters, and constructing a line loss simulation model with the regional power grid system parameters, the regional topology map and the typical characteristic curve library; obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on the Monte Carlo simulation method, and outputting the optimal distributed power layout configuration based on the distributed power layout configuration set and the line loss simulation model based on the minimum line loss rate.

[0008] Furthermore, constructing a load characteristic curve based on historical load data includes: acquiring historical load data, and constructing a corresponding load characteristic curve according to user type and historical load data.

[0009] Furthermore, the construction of a typical characteristic curve library using load characteristic curves and output characteristic curves includes: performing data cleaning on the load characteristic curves and the output characteristic curves; performing normalization processing on the load characteristic curves and the output characteristic curves after data cleaning; and integrating the normalized load characteristic curves and the output characteristic curves based on a statistical algorithm to obtain a typical characteristic curve library.

[0010] Furthermore, the method of integrating the normalized load characteristic curves and output characteristic curves based on a statistical algorithm to obtain a typical characteristic curve library includes: averaging the load characteristic curves and output characteristic curves of the same type to obtain typical characteristic curves of the corresponding type, and constructing a curve library with the typical characteristic curves.

[0011] Furthermore, the construction of a line loss simulation model based on regional power grid system parameters, regional topology map and typical characteristic curve library includes: creating a main network model based on OpenDSS, adding regional power grid system parameters to the main network model according to the regional topology map, and entering the typical characteristic curve library into the main network model to construct a line loss simulation model.

[0012] Furthermore, the method of obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on the Monte Carlo simulation method includes: generating a random allocation combination based on the number and location of distributed power sources according to each distributed power penetration rate in combination with the Monte Carlo simulation method, so as to obtain a distributed power layout configuration set based on the random allocation combination based on the number and location of distributed power sources.

[0013] Furthermore, the method of obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on the Monte Carlo simulation method, and outputting the optimal distributed power layout configuration based on the minimum line loss rate according to the distributed power layout configuration set and the line loss simulation model also includes: obtaining the initial line loss rate based on the initial distributed power penetration rate and the line loss simulation model; obtaining the distributed power penetration rate to be simulated based on the preset adjustment range and the initial distributed power penetration rate; obtaining the distributed power layout configuration set corresponding to the distributed power penetration rate to be simulated based on the Monte Carlo simulation method; using the initial line loss rate as a constraint condition, and outputting the optimal distributed power layout configuration based on the distributed power layout configuration set and the line loss simulation model based on the minimum line loss rate.

[0014] Furthermore, the method of obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on the Monte Carlo simulation method, and outputting the optimal distributed power layout configuration based on the minimum line loss rate according to the distributed power layout configuration set and the line loss simulation model also includes: obtaining the current distributed power penetration rate based on a preset adjustment range and the previous distributed power penetration rate; obtaining a distributed power layout configuration set corresponding to the current distributed power penetration rate based on the Monte Carlo simulation method; outputting the current minimum line loss rate according to the distributed power layout configuration set and the line loss simulation model; obtaining the previous minimum line loss rate corresponding to the previous distributed power penetration rate, and performing iterative judgment according to the changing trend of the current minimum line loss rate compared to the previous minimum line loss rate and the changing trend threshold; if the iterative judgment is that no iteration is required, outputting the optimal distributed power layout configuration with the distributed power layout configuration corresponding to the current minimum line loss rate.

[0015] Furthermore, performing iterative judgment based on the changing trend of the current minimum line loss rate compared to the previous minimum line loss rate and the changing trend threshold includes: judging whether the changing trend of the current minimum line loss rate compared to the previous minimum line loss rate reaches the changing trend threshold, if so, it is considered that no iteration is required, if not, it is considered that iteration is required.

[0016] Furthermore, it also includes: constructing a distributed power layout planning timing table based on the optimal distributed power layout configuration under each distributed power penetration rate; obtaining the growth status of distributed power, and outputting the current optimal distributed power layout configuration according to the distributed power growth status and the distributed power layout planning timing table.

[0017] The beneficial effects of the present application are as follows: a distributed power layout planning time sequence table is constructed by utilizing the optimal distributed power layout configuration under each distributed power penetration rate. When the distributed power access increases, the corresponding distributed power penetration rate changes, and the corresponding optimal distributed power layout configuration can be directly obtained according to the distributed power planning time sequence table. While ensuring the rationality of the distributed power layout configuration, the efficiency of the distributed power layout configuration planning is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for configuring a distributed power supply layout with optimal line loss based on the Monte Carlo simulation method of this application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the drawings and examples. It should be understood that the specific implementation method described here is only an optimal embodiment of the present application, which is only used to explain the present application and does not limit the scope of protection of the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0020] like Figure 1 As shown in the figure, as the first embodiment of the present application, a method for configuring a distributed power supply layout with optimal line loss based on a Monte Carlo simulation method comprises the following steps: Construct regional topology map based on regional geographic wiring diagram; Build a load characteristic curve based on historical load data, build an output characteristic curve based on historical distributed power output data, and build a typical characteristic curve library based on load characteristic curves and output characteristic curves; Obtain regional power grid system parameters, and build a line loss simulation model based on regional power grid system parameters, regional topology map, and typical characteristic curve library; Based on the Monte Carlo simulation method, a distributed power layout configuration set corresponding to each distributed power penetration rate is obtained, and the optimal distributed power layout configuration is output according to the distributed power layout configuration set and the line loss simulation model based on the minimum line loss rate.

[0021] In this embodiment, the line loss simulation model is used to reflect the grid structure, load characteristics and distributed power output characteristics of the corresponding area, and the Monte Carlo simulation method is used to simulate the distributed power layout configuration under different distributed power penetration rates, and the possible combinations of distributed power layout configurations corresponding to the distributed power penetration rates are obtained. Each combination of the possible combinations of distributed power layout configurations is input into the line loss simulation model to obtain the corresponding line loss rate, and the distributed power layout configuration corresponding to the minimum line loss rate is used as the optimal distributed power layout configuration. The Monte Carlo simulation method is used to generate a variety of random scenarios to show the behavioral characteristics of the power grid under different circumstances, improve the rationality of the distributed power layout configuration, and at the same time, the parallel computing characteristics of the Monte Carlo simulation method are used to improve the efficiency of the simulation calculation.

[0022] Specifically, a regional geographic wiring diagram of a target configuration area is obtained, the regional geographic wiring diagram is simplified, and main topological structures such as nodes, lines, transformers, etc. are drawn to construct a regional topological diagram.

[0023] Among them, constructing a load characteristic curve based on historical load data includes: Obtain historical load data and build corresponding load characteristic curves based on user type and historical load data.

[0024] User types include at least residential users, commercial users and industrial users. Corresponding load characteristic curves are constructed for different user types, so that the load characteristic curves are more targeted, so that the load data predicted according to the load characteristic curves are more accurate, thereby improving the accuracy of line loss rate calculation.

[0025] The output characteristic curve constructed based on historical distributed power output data includes: Obtain historical distributed power output data, and construct corresponding output characteristic curves based on the distributed power type and historical distributed power output data.

[0026] Distributed power sources include at least photovoltaic power sources, wind power sources, hydropower sources and other renewable energy sources. Output characteristic curves for different types of distributed power sources are constructed based on the differences in the environmental impacts of different distributed power sources. For example, the output characteristics of renewable energy sources such as solar energy and wind energy are greatly affected by weather conditions and have significant seasonal and diurnal changes, so the output characteristic curves fluctuate with seasonal and diurnal changes.

[0027] The typical characteristic curve library constructed with load characteristic curve and output characteristic curve includes: Perform data cleaning on load characteristic curve and output characteristic curve; Perform normalization processing on the load characteristic curve and output characteristic curve after data cleaning; Based on the statistical algorithm, the normalized load characteristic curve and output characteristic curve are integrated to obtain a typical characteristic curve library.

[0028] In this embodiment, the typical characteristic curve is obtained by integrating the load characteristic curve and the output characteristic curve, so that the typical characteristic curve in the typical characteristic curve library represents the average behavior of most users and distributed power sources, thereby ensuring that the subsequent calculation of the line loss rate takes into account the load conditions caused by user behavior and the output conditions of distributed power sources, thereby ensuring the accuracy of the line loss rate calculation.

[0029] Data cleaning of load characteristic curve and output characteristic curve includes: Perform abnormal value and error data identification on the load characteristic curve and the output characteristic curve respectively; Abnormal values ​​and erroneous data are processed and corrected, and the load characteristic curve and output characteristic curve are sorted.

[0030] Performing abnormal value and error data identification on the load characteristic curve and the output characteristic curve respectively includes: Use the 3σ principle to identify abnormal values ​​in load characteristic curves and output characteristic curves; The continuity of the load characteristic curve and the distributed power output characteristic curve is used to identify the erroneous data in the load characteristic curve and the distributed power output characteristic curve.

[0031] Using the 3σ principle to identify outliers in the load characteristic curve and the output characteristic curve means that the data points in the load characteristic curve and the output characteristic curve that exceed the range of the mean plus or minus three times the standard deviation are regarded as outliers. In other cases, the box plot method can also be used to identify outliers in the load characteristic curve and the output characteristic curve. The box plot method is to regard data points outside the upper and lower quartiles 1.5 times the IQR as outliers. At the same time, based on the consistency of the data, the continuity characteristics of the load characteristic curve and the distributed power output characteristic curve are used to identify erroneous data, such as determining the location of erroneous data based on whether the timestamp is continuous or whether the data point is missing.

[0032] Then, the abnormal values ​​and erroneous data are processed and corrected. For example, for abnormal values, they can be replaced with the average value of adjacent data points, processed by moving average method or exponential smoothing method, and for erroneous data, the missing data points can be filled by interpolation method, or the erroneous data points can be corrected by the average value of adjacent data points. After the abnormal values ​​and erroneous data are processed and corrected, the data is resampled at a uniform time interval (such as every hour) to ensure that the time resolution of the load characteristic curve and the distributed power output characteristic curve is consistent.

[0033] In this embodiment, the Min-Max normalization method is used to perform normalization processing on the load characteristic curve and the output characteristic curve after data cleaning. In other cases, normalization processing can also be performed using normalization methods such as Z-score normalization method, maximum value normalization, decimal calibration normalization, etc.

[0034] Based on the statistical algorithm, the load characteristic curve and output characteristic curve after normalization are integrated to obtain the typical characteristic curve library, including: The load characteristic curves and output characteristic curves of the same type are averaged to obtain the typical characteristic curves of the corresponding type, and a curve library is constructed with the typical characteristic curves.

[0035] In some cases, load characteristic curves of the same user type are averaged to obtain a typical characteristic curve corresponding to the user type, and output characteristic curves of the same distributed power source type are averaged to obtain a typical characteristic curve corresponding to the distributed power source type.

[0036] In other cases, the typical characteristic curves corresponding to the user type and the distributed power type are obtained by correlating and averaging the load characteristic curves of the same user type and the output characteristic curves of the same distributed power type. Since the output is usually related to the load, based on the correlation between the output and the load, the output characteristic curves of the same distributed power type corresponding to the load characteristic curves of the same user type are obtained, and the average is calculated to obtain the typical characteristic curves of the corresponding user type and the distributed power type.

[0037] The regional power grid system parameters include at least the main network core parameters, reactor core parameters, two-winding transformer core parameters, three-winding transformer core parameters, transformer tap core parameters, linear core parameters, line core parameters, capacitor bank core parameters, load core parameters, generator core parameters, observer core parameters and meter core parameters.

[0038] Constructing a line loss simulation model based on regional power grid system parameters, regional topology map and typical characteristic curve library includes: creating a main network model based on OpenDSS, adding regional power grid system parameters to the main network model according to the regional topology map, and entering the typical characteristic curve library into the main network model to construct a line loss simulation model.

[0039] Create a main network model in OpenDSS, including busbars, lines, and transformers, add the regional power grid system components to the main network model based on the regional topology map, set the core parameters of the regional power grid system components according to the regional power grid system parameters, and enter the typical characteristic curve library into the main network model to obtain a line loss simulation model. In this embodiment, the line loss simulation model is an OpenDSS model. OpenDSS can simulate distribution networks of any node scale, including simulations of different time scales such as days, months, and years. By building simulations with the OpenDSS model, we can understand the performance of the distribution network under different operating conditions.

[0040] The distributed power layout configuration set corresponding to each distributed power penetration rate obtained based on the Monte Carlo simulation method includes: According to the penetration rate of each distributed power source combined with the Monte Carlo simulation method, a random allocation combination based on the number and location of distributed power sources is generated, and a distributed power source layout configuration set is obtained by randomly allocating combinations based on the number and location of distributed power sources.

[0041] Monte Carlo simulation, also known as statistical simulation, statistical test or random sampling, is a numerical calculation method based on the principle of probability statistics. Monte Carlo simulation uses random numbers or pseudo-random numbers to simulate actual physical processes or probabilistic phenomena, thereby obtaining an approximate solution to the problem. In this embodiment, the Monte Carlo simulation method is used to simulate the distributed power layout configuration, and a random distribution combination of the number and location of distributed power sources corresponding to the distributed power penetration rate is randomly generated, and then a line loss simulation model is used to obtain possible scenario combinations, and the line loss rate under the corresponding scenario is calculated, so as to determine the optimal distributed power layout configuration under different distributed power penetration rates.

[0042] The penetration rate of distributed power generation refers to the ratio of the total amount of electricity provided by distributed power generation throughout the year to the total amount of electricity consumed by the load in its power supply area throughout the year. When the penetration rate of distributed power generation reaches a high level, it is called high penetration. At this time, the duration of the reverse flow of the system increases significantly, the distribution network flow changes significantly, and the load is mainly supplied by the nearest distributed power generation, forming multiple power supply areas centered on distributed power generation. The distributed power generation access node situation under the corresponding distributed power generation access degree is obtained through the distributed power generation penetration rate, and the proportion of power loss caused by power conversion and transmission in the transmission process of each distributed power generation access node is calculated. The distributed power generation access node situation with the smallest proportion is used as the optimal distributed power generation layout configuration that meets the minimum line loss rate. In this way, the optimal access position is adaptively updated according to the distributed power generation penetration rate, and the rationality of the distributed power generation access node is improved.

[0043] In this embodiment, a random allocation combination based on the number and location of distributed power sources is generated based on the randperm function of MATLAB. Taking photovoltaic distributed power sources as an example, 10kW is used as a benchmark to randomly generate photovoltaic distributed power source capacity to ensure that the sum of photovoltaic distributed power source capacity is consistent with the distributed power source penetration rate requirement. According to the line loss simulation model, the grid line is evenly divided into 10 nodes, and the randomly generated photovoltaic distributed power sources are randomly connected to the 10 nodes to calculate the line loss rate. The scene with the lowest line loss rate is selected as the optimal distributed power source layout configuration under the penetration rate.

[0044] As a second embodiment of the present application, a distributed power layout configuration set corresponding to each distributed power penetration rate is obtained based on the Monte Carlo simulation method, and the optimal distributed power layout configuration is output according to the distributed power layout configuration set and the line loss simulation model based on the minimum line loss rate, and the following further includes: Obtaining an initial line loss rate based on an initial distributed power generation penetration rate and a line loss simulation model; Obtaining the distributed power penetration rate to be simulated based on the preset adjustment range and the initial distributed power penetration rate; Obtaining a distributed power layout configuration set corresponding to the distributed power penetration rate to be simulated based on a Monte Carlo simulation method; Taking the initial line loss rate as the constraint condition, the optimal distributed power layout configuration is output based on the minimum line loss rate according to the distributed power layout configuration set and the line loss simulation model.

[0045] In this embodiment, the initial distributed power penetration rate is 0, and the line loss rate is simulated when the distributed power penetration rate is 0 through the line loss simulation model, and the initial line loss rate of the power grid is calculated. The distributed power penetration rate to be simulated is obtained based on the preset adjustment range, so that the output of the distributed power layout configuration set is performed based on the actual demand, avoiding the additional calculation of invalid penetration rate, and further ensuring the calculation efficiency. At the same time, since the initial line loss rate is obtained based on the distributed power penetration rate of 0, the initial line loss rate does not include the line loss rate of the distributed power source. With this as the baseline, when the calculated minimum line loss rate deviates greatly from the initial line loss rate, it can be intuitively judged that the minimum line loss rate is wrong, which can effectively avoid the influence of erroneous data. For example, after adding distributed power sources, the change in line loss rate should conform to the physical laws and the actual situation of power grid operation. If only the distributed power sources are added without changing the initial circuit flow state, if the line loss rate is greatly reduced, it does not conform to the physical laws. Obviously, the distributed power layout configuration corresponding to the line loss rate is not reasonable or the simulation result is wrong. At this time, the operation and maintenance personnel are prompted to pay attention to the debugging of the simulation model.

[0046] Specifically, the preset adjustment range is 5%, and each Monte Carlo simulation increases the distributed power penetration rate of the previous calculation scenario by 5%.

[0047] In other cases, obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on a Monte Carlo simulation method, and outputting an optimal distributed power layout configuration based on the distributed power layout configuration set and the line loss simulation model based on a minimum line loss rate also includes: Obtaining the current distributed power penetration rate based on a preset adjustment range and the previous distributed power penetration rate; Obtaining a set of distributed power generation layout configurations corresponding to the current distributed power generation penetration rate based on the Monte Carlo simulation method; Output the current minimum line loss rate according to the distributed power layout configuration set and the line loss simulation model; Obtain the previous minimum line loss rate corresponding to the previous distributed power penetration rate, and perform iterative judgment based on the change trend of the current minimum line loss rate compared with the previous minimum line loss rate and the change trend threshold; If the iteration determines that no iteration is required, the optimal distributed power layout configuration is output based on the distributed power layout configuration corresponding to the current minimum line loss rate.

[0048] According to the change trend of the current minimum line loss rate compared with the previous minimum line loss rate and the change trend threshold, iterative judgment is performed including: It is determined whether the change trend of the current minimum line loss rate compared with the previous minimum line loss rate reaches the change trend threshold. If so, it is considered that no iteration is required; if not, it is considered that iteration is required.

[0049] If the iteration is judged to be necessary, the current distributed power penetration rate is used as the previous distributed power penetration rate, and the current minimum line loss rate calculation is repeatedly performed until the iteration is judged to be unnecessary, and the distributed power layout configuration corresponding to the minimum line loss rate is output as the optimal distributed power layout configuration.

[0050] In this embodiment, the inflection point of the line loss rate change trend is used as the change trend threshold. If the change trend of the current minimum line loss rate compared to the previous minimum line loss rate does not reach the change trend threshold, the optimal distributed power layout configuration under the distributed power penetration rate is not output, but the distributed power penetration rate is continued to be updated to continue to calculate the optimal distributed power layout configuration with a higher distributed power penetration rate until the change trend threshold is met. The operating environment and conditions of the power grid may change over time, such as load growth, changes in the proportion of new energy access, etc. By continuously updating the distributed power penetration rate and recalculating the optimal layout configuration, it can be ensured that the layout configuration can adapt to these dynamic changes, maintain the optimized operating state of the power grid, and adapt to the operation of the power grid at any time.

[0051] In other cases, the line loss optimal distributed power generation layout configuration method based on the Monte Carlo simulation method also includes: Based on the optimal distributed power layout configuration under each distributed power penetration rate, a distributed power layout planning time sequence table is constructed; the growth of distributed power is obtained, and the current optimal distributed power layout configuration is output according to the growth of distributed power and the distributed power layout planning time sequence table.

[0052] In this embodiment, taking into account the possible changes in the proportion and distribution of renewable energy in the power grid, in order to better cope with various emergencies and load fluctuations, the distributed power layout planning time table is constructed using the optimal distributed power layout configuration under each distributed power penetration rate. When the distributed power access increases, the corresponding distributed power penetration rate changes, and the corresponding optimal distributed power layout configuration can be directly obtained according to the distributed power planning time table. While ensuring the rationality of the distributed power layout configuration, the efficiency of the distributed power layout configuration planning is improved.

[0053] In this application, the operation cycle of the opendss model is realized according to the for function in the matlab software, and the distributed power layout configuration set is adjusted and re-imported into the line loss simulation model through the com interface between matlab and openDSS. Using the matlab software com interface to call the OpenDSS software can effectively improve planning efficiency and ensure the applicability of scenario generation.

[0054] The specific implementation described above is a preferred implementation of the line loss optimal distributed power supply layout configuration method based on the Monte Carlo simulation method of this application, and is not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation method. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method, characterized by: The method comprises the following steps: constructing a regional topology map based on a regional geographic wiring map; Build a load characteristic curve based on historical load data, build an output characteristic curve based on historical distributed power output data, and build a typical characteristic curve library based on load characteristic curves and output characteristic curves; Obtain regional power grid system parameters, and build a line loss simulation model based on regional power grid system parameters, regional topology map, and typical characteristic curve library; Based on the Monte Carlo simulation method, a distributed power layout configuration set corresponding to each distributed power penetration rate is obtained, and the optimal distributed power layout configuration is output according to the distributed power layout configuration set and the line loss simulation model based on the minimum line loss rate.

2. The line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method according to claim 1, characterized in that: The constructing of a load characteristic curve based on historical load data includes: Obtain historical load data and build corresponding load characteristic curves based on user type and historical load data.

3. The line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method according to claim 1, characterized in that: The typical characteristic curve library constructed by using the load characteristic curve and the output characteristic curve includes: Perform data cleaning on load characteristic curve and output characteristic curve; Perform normalization processing on the load characteristic curve and output characteristic curve after data cleaning; Based on the statistical algorithm, the normalized load characteristic curve and output characteristic curve are integrated to obtain a typical characteristic curve library.

4. The line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method according to claim 3, characterized in that: The typical characteristic curve library obtained by integrating the normalized load characteristic curve and the output characteristic curve based on the statistical algorithm includes: The load characteristic curves and output characteristic curves of the same type are averaged to obtain the typical characteristic curves of the corresponding type, and a curve library is constructed with the typical characteristic curves.

5. The line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method according to claim 1, characterized in that: The line loss simulation model is constructed based on regional power grid system parameters, regional topology diagram and typical characteristic curve library, including: The main network model is created based on OpenDSS, the regional power grid system parameters are added to the main network model according to the regional topology map, and the typical characteristic curve library is entered into the main network model to build a line loss simulation model.

6. The line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method according to claim 1, characterized in that: The method of obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on the Monte Carlo simulation method includes: According to the penetration rate of each distributed power source combined with the Monte Carlo simulation method, a random allocation combination based on the number and location of distributed power sources is generated, and a distributed power source layout configuration set is obtained by randomly allocating combinations based on the number and location of distributed power sources.

7. The line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method according to claim 1, characterized in that: The method of obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on the Monte Carlo simulation method, and outputting an optimal distributed power layout configuration based on the distributed power layout configuration set and the line loss simulation model based on the minimum line loss rate also includes: Obtaining an initial line loss rate based on an initial distributed power generation penetration rate and a line loss simulation model; Obtaining the distributed power penetration rate to be simulated based on the preset adjustment range and the initial distributed power penetration rate; Obtaining a distributed power layout configuration set corresponding to the distributed power penetration rate to be simulated based on a Monte Carlo simulation method; Taking the initial line loss rate as the constraint condition, the optimal distributed power layout configuration is output based on the minimum line loss rate according to the distributed power layout configuration set and the line loss simulation model.

8. The line loss optimal distributed power supply layout configuration method based on Monte Carlo simulation method according to claim 1, characterized in that: The method of obtaining a distributed power layout configuration set corresponding to each distributed power penetration rate based on the Monte Carlo simulation method, and outputting an optimal distributed power layout configuration based on the distributed power layout configuration set and the line loss simulation model based on the minimum line loss rate also includes: Obtaining the current distributed power penetration rate based on a preset adjustment range and the previous distributed power penetration rate; Obtaining a set of distributed power generation layout configurations corresponding to the current distributed power generation penetration rate based on the Monte Carlo simulation method; Output the current minimum line loss rate according to the distributed power layout configuration set and the line loss simulation model; Obtain the previous minimum line loss rate corresponding to the previous distributed power penetration rate, and perform iterative judgment based on the change trend of the current minimum line loss rate compared with the previous minimum line loss rate and the change trend threshold; If the iteration determines that no iteration is required, the optimal distributed power layout configuration is output based on the distributed power layout configuration corresponding to the current minimum line loss rate.

9. The method for configuring the layout of distributed power sources with optimal line loss based on the Monte Carlo simulation method according to claim 8, characterized in that: The performing iterative judgment according to the change trend of the current minimum line loss rate compared with the previous minimum line loss rate and the change trend threshold comprises: It is determined whether the change trend of the current minimum line loss rate compared with the previous minimum line loss rate reaches the change trend threshold. If so, it is considered that no iteration is required; if not, it is considered that iteration is required.

10. The method for configuring the layout of distributed power sources with optimal line loss based on Monte Carlo simulation method according to claim 1, characterized in that: Also includes: Construct a distributed power layout planning time table based on the optimal distributed power layout configuration under each distributed power penetration rate; Obtain the growth status of distributed power sources, and output the current optimal distributed power source layout configuration based on the growth status of distributed power sources and the distributed power source layout planning timing table.

Images