A distribution network maintenance method, device, electronic device and storage medium considering distributed power supply

Abstract

The present invention discloses a distribution network maintenance method, device, electronic device, and storage medium that considers distributed power sources. The method includes: obtaining candidate maintenance line data, a topological structure diagram, several typical daily data, operating cost parameters, and hardware parameters of wind, solar, and storage devices of the distribution network; constructing a power generation and load uncertainty model based on the several typical daily data of the distribution network and a preset prediction model; solving the power generation and load uncertainty model based on the hardware parameters of the wind, solar, and storage devices and the preset prediction model to generate power generation and load data for the maintenance day; constructing a distribution network maintenance model and constraints based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar, and storage devices, and the power generation and load data for the maintenance day; solving the distribution network maintenance model under the constraints to generate a distribution network maintenance plan; and maintaining the distribution network according to the distribution network maintenance plan. The implementation of the present invention can improve the reliability of distribution network maintenance.

Description

Technical Field

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

[0002] In modern power systems, distribution networks, as a critical link in power transmission and distribution, are crucial for their maintenance. With the continued growth of electricity demand and the gradual aging of power components, distribution network reliability faces severe challenges. Failure to perform regular maintenance can lead to increased frequency of power system failures, and in severe cases, even large-scale power outages, with severe economic and social impacts. Therefore, implementing effective maintenance strategies is crucial to ensuring the safe and stable operation of power systems.

[0003] Preventive maintenance in distribution networks involves preventing failures before they occur through the design of rational scheduling and maintenance measures. Current preventive maintenance strategies typically consider the impact of factors such as short-circuit current and overload current on system reliability. However, existing methods often fail to fully account for the uncertainty in power generation capacity introduced by distributed generation (DGs) when performing preventive maintenance. This can lead to significant risks in the formulation and implementation of maintenance plans. For example, during preventive maintenance, the support capabilities provided by DGs during normal operation cannot be fully utilized, potentially increasing load loss costs and the operating costs of the entire power system. Furthermore, the lack of responsiveness to different scenarios leaves the power system without a response strategy when encountering emergencies, thereby increasing the probability of failures. These issues not only affect the optimization of maintenance costs but also weaken the overall reliability of the system. Summary of the Invention

[0004] Embodiments of the present invention provide a distribution network maintenance method, apparatus, electronic device, and storage medium that consider distributed power sources. Implementing this invention enables preventive maintenance of distribution networks, effectively alleviating the problem that traditional preventive maintenance strategies fail to fully utilize the support capabilities provided by distributed power sources. This reduces load loss and operating costs, and improves the scientific nature and reliability of maintenance plans.

[0005] An embodiment of the present invention provides a distribution network maintenance method considering distributed power sources, including:

[0006] Obtain candidate maintenance line data for the distribution network, a topological diagram of the distribution network, several typical daily data for the distribution network, operating cost parameters for the distribution network, and hardware parameters for wind, solar, and storage devices. Typical daily data for the distribution network includes typical daily load data for the distribution network, typical daily wind speed data for wind power stations, and typical daily solar radiation data for photovoltaic power stations.

[0007] Based on several typical daily data of the distribution network and the preset forecasting models, a power generation and load uncertainty model is constructed; wherein the preset forecasting models include a wind speed forecasting model, a solar radiation forecasting model, and a load forecasting model;

[0008] Solve the power generation and load uncertainty model based on the hardware parameters of the wind, solar, and storage equipment and the preset prediction model to generate power generation and load data for the maintenance day; wherein the power generation and load data for the maintenance day include the predicted power generation of the wind power station on the maintenance day, the predicted power generation of the photovoltaic power station on the maintenance day, and the predicted load of the distribution network on the maintenance day;

[0009] Based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, a distribution network maintenance model and constraints are constructed with the goal of minimizing the total cost of the power system. The constraints include power balance constraints, power output constraints, power upper and lower limit constraints, maintenance cost constraints, maintenance duration constraints, maintenance operation continuity constraints, battery status constraints, battery capacity constraints, and topology connectivity constraints.

[0010] Under the constraints, the distribution network maintenance model is solved to generate the distribution network maintenance plan;

[0011] The distribution network is maintained according to the distribution network maintenance plan.

[0012] Furthermore, the generation and load uncertainty model is constructed based on several typical daily data of the distribution network and a preset prediction model, including:

[0013] Performing statistical analysis on wind power data of several typical days of the wind power station to determine a first average value and a first standard deviation; generating a first random Gaussian variable based on the first average value and the first standard deviation;

[0014] Statistically analyzing solar radiation data of several typical days of the photovoltaic power station to determine a second mean value and a second standard deviation; generating a second random Gaussian variable based on the second mean value and the second standard deviation;

[0015] Statistically analyzing several typical daily load data of the distribution network to determine a third mean value and a third standard deviation; generating a third random Gaussian variable based on the third mean value and the third standard deviation;

[0016] Generate predicted wind speed data for several typical days of the wind power station based on the preset wind speed prediction model;

[0017] Generate predicted solar radiation data for several typical days of the photovoltaic power station based on the preset solar radiation prediction model;

[0018] Generate several typical daily load data of the distribution network according to the preset load forecast model;

[0019] Calculating the difference between the wind power data of a typical day of the wind power station and the predicted wind power data of the same typical day of the wind power station to generate a first prediction error of the wind power data of the typical day of the wind power station;

[0020] Calculating the difference between the typical day solar radiation data of the photovoltaic power station and the predicted solar radiation data of the same typical day of the photovoltaic power station to generate a second prediction error of the typical day solar radiation data of the photovoltaic power station;

[0021] Calculating the difference between the typical daily load data of the distribution network and the predicted load data of the same typical day of the distribution network to generate a third prediction error of the typical daily load data of the distribution network;

[0022] A power generation and load uncertainty model is constructed according to the first prediction error, the first random Gaussian variable, the second prediction error, the second random Gaussian variable, the third prediction error and the third random Gaussian variable.

[0023] Furthermore, solving the power generation and load uncertainty model based on the hardware parameters of the wind, solar and storage equipment and the preset prediction model to generate the power generation and load data for the maintenance day includes:

[0024] Input the maintenance date into the power generation and load uncertainty model to generate the wind data prediction error of the wind power station on the maintenance day, the solar radiation data prediction error of the photovoltaic power station on the maintenance day, and the load data prediction error of the distribution network on the maintenance day;

[0025] Input the maintenance date into a preset forecasting model to generate forecasted wind power data for the wind power station on the maintenance day, forecasted solar radiation data for the photovoltaic power station on the maintenance day, and forecasted load data for the distribution network on the maintenance day;

[0026] Correcting the predicted wind data of the wind power station on the maintenance day according to the wind power data prediction error of the wind power station on the maintenance day, and updating the predicted wind data of the wind power station on the maintenance day;

[0027] Correct the predicted solar radiation data of the photovoltaic power station on the maintenance day according to the prediction error of the solar radiation data of the photovoltaic power station on the maintenance day, and update the predicted solar radiation data of the photovoltaic power station on the maintenance day;

[0028] Correcting the predicted load data of the distribution network on the maintenance day according to the load data prediction error of the distribution network on the maintenance day, and updating the predicted load data of the distribution network on the maintenance day;

[0029] Based on the hardware parameters of the wind, solar and storage equipment and the predicted wind power data of the wind power station on the maintenance day, the predicted power generation power of the wind power station on the maintenance day is generated by the following formula:

[0030]

[0031] in, To maintain the predicted power generation of the wind power station on a daily basis; is the wind speed; is the starting wind speed of the wind turbine; is the rated wind speed of the wind turbine; is the wind speed at which the wind turbine stops; is the air density; is the area swept by the wind turbine blades; is the power coefficient;

[0032] Based on the hardware parameters of the wind, solar and storage equipment and the predicted solar radiation data of the photovoltaic power station on the maintenance day, the predicted power generation power of the photovoltaic power station on the maintenance day is generated by the following formula:

[0033]

[0034] in, To maintain the predicted power generation of the daily photovoltaic power station; is light intensity; is the effective area of ​​the photovoltaic panel; is the conversion efficiency of the photovoltaic panel; is the temperature coefficient of the photovoltaic panel; is the actual operating temperature of the photovoltaic panel; is the reference temperature of the photovoltaic panel;

[0035] The predicted load data of the distribution network on the maintenance day, the predicted power generation of the wind power station on the maintenance day, and the predicted power generation of the photovoltaic power station on the maintenance day are used as the power generation and load data on the maintenance day.

[0036] Furthermore, the distribution network maintenance model is constructed based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, with the goal of minimizing the total cost of the power system, including:

[0037] Divide the maintenance day into several scenarios according to preset time intervals;

[0038] Based on the operating cost parameters of the distribution network under several scenarios, the hardware parameters of wind, solar and storage equipment, and the power generation and load data on the maintenance day, the following distribution network maintenance model is constructed:

[0039]

[0040] in, For the region and region exist Time Path Whether maintenance operations are performed; and It is the regional identifier; Total cost for maintenance scheduling; A collection of scenes; It is the expected value operation; is the expected cost of all scenarios; For the scene Load loss costs under For maintenance costs; is the degradation cost; For the scene Battery maintenance cost under

[0041] The loss costs are specifically:

[0042]

[0043] in, is the upper limit of the time range; is the total number of distribution network areas; For the region In the scene Down Predicted electric power at the moment; For the region In the scene Down Planned electric power at the time; For the region exist Load loss cost coefficient at the moment;

[0044] The maintenance costs are specifically:

[0045]

[0046] in, For the region and region All paths between; For the region and region On the path Maintenance duration on For the region and region On the path Maintenance costs during the day; For the region and region On the path Maintenance costs during the night; Gather for the daytime time period; For night time period collection;

[0047] The degradation cost is specifically:

[0048]

[0049] in, is the weight factor of degradation cost; For the region and region path degenerate state;

[0050] The battery maintenance cost is specifically:

[0051]

[0052] in, is the weight factor; is the number of batteries; For batteries In the scene Down The working status of planned energy storage at all times; For batteries Rated capacity; is the target coefficient of battery capacity.

[0053] Furthermore, the topological connectivity constraint is constructed in the following manner:

[0054] Based on the topological structure diagram of the distribution network and the candidate maintenance line data of the distribution network, the distribution network is divided into a plurality of connected areas with the purpose of maintaining the connectivity of each node within the area unchanged;

[0055] A topological connectivity constraint is constructed based on the connected area and the topological structure diagram of the distribution network.

[0056] Furthermore, the distribution network is divided according to the topological structure diagram of the distribution network and the candidate maintenance line data of the distribution network, with the purpose of maintaining the connectivity of each node within the region unchanged, to generate several connected areas, including:

[0057] Starting from the nodes at both ends of the candidate maintenance line of the distribution network, a recursive search is performed to traverse all unvisited nodes until all reachable nodes have been visited. During the search process, if at any point the current node is found to have been visited, the search along the path leading to the visited node is immediately stopped, and the search is backtracked to the previous node to continue exploring other possible paths.

[0058] All nodes visited from a starting point and their connecting lines are regarded as a connected area, thereby generating multiple connected areas.

[0059] Furthermore, the power balance constraint is:

[0060]

[0061] Where, For the region and region exist connectivity variables at the moment;

[0062] The power output constraint is:

[0063]

[0064] in, For the region the number of power generation units; For the region In the scene Down Moment The power generated or consumed by each power generation, power consumption or energy storage component;

[0065] The power upper and lower limits are:

[0066]

[0067] in, For the The minimum power of each power generation, power consumption or energy storage component; For the The maximum power of each power generation, power consumption or energy storage component;

[0068] The maintenance cost constraint is:

[0069]

[0070] in, For The maximum maintenance cost during the daytime period set at the time; For The maximum maintenance fee for the night time period set at the time;

[0071] The maintenance duration constraint is:

[0072]

[0073] The maintenance operation continuity constraint is:

[0074]

[0075] in, For the region and region exist Time Path Whether maintenance operations are performed;

[0076] The battery status constraints are:

[0077]

[0078] in, is the charging power; is the discharge power; is the charge amount;

[0079] The battery capacity constraint is:

[0080]

[0081] in, is the minimum allowed battery capacity; is the maximum allowed battery capacity;

[0082] The topological connectivity constraints are:

[0083]

[0084] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0085] An embodiment of the present invention provides a distribution network maintenance device taking distributed power sources into consideration, comprising: a data acquisition module, a power generation and load uncertainty model construction module, a power generation and load uncertainty solution module, a distribution network maintenance model construction module, a distribution network maintenance plan generation module and a distribution network maintenance module.

[0086] The data acquisition module is used to obtain candidate maintenance line data of the distribution network, a topological structure diagram of the distribution network, several typical daily data of the distribution network, operating cost parameters of the distribution network, and hardware parameters of wind, solar and storage devices; wherein the typical daily data of the distribution network includes typical daily load data of the distribution network, typical daily wind data of the wind power station, and typical daily solar radiation data of the photovoltaic power station;

[0087] The power generation and load uncertainty model construction module is used to construct a power generation and load uncertainty model based on a number of typical daily data of the distribution network and a preset prediction model; wherein the preset prediction model includes a wind speed prediction model, a solar radiation prediction model and a load prediction model;

[0088] The power generation and load uncertainty solving module is used to solve the power generation and load uncertainty model based on the hardware parameters of the wind, solar and storage equipment and the preset prediction model, and generate the power generation and load data for the maintenance day; wherein the power generation and load data for the maintenance day include the predicted power generation power of the wind power station on the maintenance day, the predicted power generation power of the photovoltaic power station on the maintenance day, and the predicted load of the distribution network on the maintenance day;

[0089] The distribution network maintenance model construction module is used to construct a distribution network maintenance model and constraints based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, with the goal of minimizing the total cost of the power system; wherein the constraints include power balance constraints, power output constraints, power upper and lower limit constraints, maintenance cost constraints, maintenance duration constraints, maintenance operation continuity constraints, battery status constraints, battery capacity constraints, and topology connectivity constraints;

[0090] The distribution network maintenance plan generation module is used to solve the distribution network maintenance model under the constraint conditions and generate a distribution network maintenance plan;

[0091] The distribution network maintenance module is used to maintain the distribution network according to the distribution network maintenance plan.

[0092] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0093] An embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it can implement the distribution network maintenance method considering distributed power sources as described in any one of the above-mentioned method embodiments.

[0094] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0095] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the distribution network maintenance method considering distributed power sources as described in any one of the above method embodiments can be implemented.

[0096] Compared with the prior art, the present invention has the following beneficial effects:

[0097] Embodiments of the present invention provide a distribution network maintenance method, apparatus, electronic device, and storage medium that consider distributed power sources. The method constructs a power generation and load uncertainty model based on typical daily distribution network data and generates power generation and load data for the maintenance day. Finally, based on the distribution network's operating cost parameters, power generation and load data for the maintenance day, topological connectivity constraints, and hardware parameters of wind, solar, and storage devices, a distribution network maintenance model and constraints related to the distribution network maintenance model are constructed with the goal of minimizing the total cost of the power system. Under the constraints, the distribution network maintenance model is solved to generate a distribution network maintenance plan. The distribution network is then maintained according to the distribution network maintenance plan.

[0098] By constructing a power generation and load uncertainty model, the present invention comprehensively considers the uncertainty of the power generation capacity of distributed power sources such as wind power and photovoltaic power and the fluctuation of the distribution network load. It can accurately predict the power generation and load data on the maintenance day, effectively alleviating the problem that traditional preventive maintenance strategies fail to fully utilize the support capabilities provided by distributed power sources, thereby reducing load loss costs and operating costs, and improving the scientific nature and reliability of maintenance plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 This is a flow chart of a distribution network maintenance method considering distributed power sources provided by one embodiment of the present invention.

[0100] Figure 2 (a) is a schematic diagram of a maintenance case 1 based on an IEEE-34 node network provided by an embodiment of the present invention; Figure 2 (b) is a schematic diagram of maintenance case 2 based on an IEEE-34 node network provided by an embodiment of the present invention; Figure 2 (c) is a schematic diagram of maintenance case 3 based on an IEEE-34 node network provided by an embodiment of the present invention; Figure 2 (d) is a schematic diagram of maintenance case 4 based on an IEEE-34 node network provided by an embodiment of the present invention; Figure 2 (e) is a schematic diagram of maintenance case 5 based on the IEEE-34 node network provided by an embodiment of the present invention.

[0101] Figure 3 The diagram is a structural diagram of a distribution network maintenance device taking distributed power sources into consideration, provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0102] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0103] like Figure 1 As shown, an embodiment of the present invention provides a distribution network maintenance method considering distributed power sources, which includes at least the following steps:

[0104] Step S1: Obtain candidate maintenance line data of the distribution network, a topological structure diagram of the distribution network, several typical daily data of the distribution network, operating cost parameters of the distribution network, and hardware parameters of wind, solar, and storage devices;

[0105] It should be noted here that the typical daily data of the distribution network includes the typical daily load data of the distribution network, the typical daily wind data of the wind power station, and the typical daily solar radiation data of the photovoltaic power station;

[0106] The sampling interval and sampling frequency can be flexibly set based on actual conditions. In one embodiment, data is collected with a daily sampling interval and a one-hour sampling frequency. For example, on a typical day, distribution network load data, wind power data from wind turbines, and solar radiation data from photovoltaic power plants are collected at 0:00, 1:00, 2:00, and so on. This detailed data collection method facilitates a comprehensive analysis of the distribution network's operation during a typical day and its impact on wind, solar, and storage equipment, providing a scientific basis for subsequent maintenance decisions.

[0107] Step S2: constructing a power generation and load uncertainty model based on several typical daily data of the distribution network and a preset prediction model;

[0108] Specifically, the pre-set forecasting models include wind speed, solar radiation, and load forecasting models. These models are derived through a combination of historical data analysis and machine learning algorithms to ensure they effectively reflect actual operating conditions. The wind speed forecasting model is primarily constructed based on historical wind speed and meteorological data. Time series analysis methods (such as the ARIMA model) or machine learning algorithms (such as random forests and support vector machines) can be used to model wind speed variations. Furthermore, meteorological factors (such as temperature, air pressure, and humidity) are incorporated to improve the accuracy of wind speed forecasts. Solar radiation forecasting models are typically based on historical solar radiation data, meteorological conditions, and geographic information. Regression analysis or deep learning models (such as LSTM) are used to capture temporal variations in solar radiation. Furthermore, considering the impact of cloud cover and meteorological changes on solar radiation, meteorological forecast data is incorporated to enhance model robustness. The load forecasting model is constructed based on historical load data, using time series methods or machine learning methods (such as neural networks and regression analysis) to identify periodicity and trends in load variations.

[0109] By constructing the above three models, the uncertainty of power generation and load can be effectively reflected, providing reliable data support for the optimized operation and maintenance decisions of the distribution network.

[0110] In a preferred embodiment, the generation and load uncertainty model is constructed based on several typical daily data of the distribution network and a preset prediction model, including:

[0111] Performing statistical analysis on wind power data of several typical days of the wind power station to determine a first average value and a first standard deviation; generating a first random Gaussian variable based on the first average value and the first standard deviation;

[0112] Statistically analyzing solar radiation data of several typical days of the photovoltaic power station to determine a second mean value and a second standard deviation; generating a second random Gaussian variable based on the second mean value and the second standard deviation;

[0113] Statistically analyzing several typical daily load data of the distribution network to determine a third mean value and a third standard deviation; generating a third random Gaussian variable based on the third mean value and the third standard deviation;

[0114] Generate predicted wind speed data for several typical days of the wind power station based on the preset wind speed prediction model;

[0115] Generate predicted solar radiation data for several typical days of the photovoltaic power station based on the preset solar radiation prediction model;

[0116] Generate several typical daily load data of the distribution network according to the preset load forecast model;

[0117] Calculating the difference between the wind power data of a typical day of the wind power station and the predicted wind power data of the same typical day of the wind power station to generate a first prediction error of the wind power data of the typical day of the wind power station;

[0118] Calculating the difference between the typical day solar radiation data of the photovoltaic power station and the predicted solar radiation data of the same typical day of the photovoltaic power station to generate a second prediction error of the typical day solar radiation data of the photovoltaic power station;

[0119] Calculating the difference between the typical daily load data of the distribution network and the predicted load data of the same typical day of the distribution network to generate a third prediction error of the typical daily load data of the distribution network;

[0120] A power generation and load uncertainty model is constructed according to the first prediction error, the first random Gaussian variable, the second prediction error, the second random Gaussian variable, the third prediction error and the third random Gaussian variable.

[0121] In a preferred embodiment, the power generation and load uncertainty model is constructed by an ARMA model, specifically:

[0122]

[0123] in, For load demand, wind speed and solar radiation in stage The prediction error, that is, in the stage The set of prediction errors; For load demand, wind speed and solar radiation in stage A random Gaussian variable; is the number of autoregressive terms in the model; is the number of moving average terms; is the coefficient of the autoregressive term; is the coefficient of the moving average term; It is the stage identifier; is a constant term;

[0124] The load demand, wind speed and solar radiation in the stage The prediction error , specifically:

[0125]

[0126] in, For the stage The prediction error of wind speed, i.e. the first prediction error; For the stage The prediction error of solar radiation, i.e. the second prediction error; For the stage The forecast error of load demand is also called the third forecast error.

[0127] The load demand, wind speed and solar radiation in the stage A random Gaussian variable , specifically:

[0128]

[0129] in, For the stage Unpredictable random fluctuations in wind speed, i.e., the first random Gaussian variable; For the stage The unpredictable random volatility of solar radiation, i.e. the second random Gaussian variable; For the stage Unpredictable random volatility of load demand, i.e. the third random Gaussian variable.

[0130] The ARMA model is fitted by using several typical daily data of the distribution network and a preset forecasting model. Finally, multiple possible scenarios can be generated using the ARMA model. Each scenario reflects the load demand, wind speed, and solar radiation conditions that may occur at a certain moment in the future.

[0131] Step S3: Solve the power generation and load uncertainty model based on the hardware parameters of the wind, solar and storage equipment and the preset prediction model to generate power generation and load data for the maintenance day;

[0132] It should be explained here that the power generation and load data on the maintenance day include the predicted power generation of the wind power station on the maintenance day, the predicted power generation of the photovoltaic power station on the maintenance day, and the predicted load of the distribution network on the maintenance day;

[0133] In an optional embodiment, solving the power generation and load uncertainty model based on the hardware parameters of the wind, solar and storage equipment and a preset prediction model to generate power generation and load data for the maintenance day includes:

[0134] The date of the maintenance day is input into the power generation and load uncertainty model to generate the wind data prediction error of the wind power station on the maintenance day, the solar radiation data prediction error of the photovoltaic power station on the maintenance day, and the load data prediction error of the distribution network on the maintenance day; these errors reflect the possible gap between the actual situation and the prediction, providing a basis for subsequent correction of the prediction data.

[0135] Input the maintenance date into a preset forecasting model to generate forecasted wind power data for the wind power station on the maintenance day, forecasted solar radiation data for the photovoltaic power station on the maintenance day, and forecasted load data for the distribution network on the maintenance day;

[0136] Correcting the predicted wind data of the wind power station on the maintenance day according to the wind power data prediction error of the wind power station on the maintenance day, and updating the predicted wind data of the wind power station on the maintenance day;

[0137] Correct the predicted solar radiation data of the photovoltaic power station on the maintenance day according to the prediction error of the solar radiation data of the photovoltaic power station on the maintenance day, and update the predicted solar radiation data of the photovoltaic power station on the maintenance day;

[0138] According to the load data prediction error of the distribution network on the maintenance day, the predicted load data of the distribution network on the maintenance day is corrected and updated; by introducing the random volatility of the data, the prediction result is ensured to be closer to the actual situation.

[0139] Based on the hardware parameters of the wind, solar and storage equipment and the predicted wind power data of the wind power station on the maintenance day, the predicted power generation power of the wind power station on the maintenance day is generated by the following formula:

[0140]

[0141] in, To maintain the predicted power generation of the wind power station on a daily basis; is the wind speed; is the starting wind speed of the wind turbine; is the rated wind speed of the wind turbine; is the wind speed at which the wind turbine stops; is the air density; is the area swept by the wind turbine blades; is the power coefficient;

[0142] Based on the hardware parameters of the wind, solar and storage equipment and the predicted solar radiation data of the photovoltaic power station on the maintenance day, the predicted power generation power of the photovoltaic power station on the maintenance day is generated by the following formula:

[0143]

[0144] in, To maintain the predicted power generation of the daily photovoltaic power station; is light intensity; is the effective area of ​​the photovoltaic panel; is the conversion efficiency of the photovoltaic panel; is the temperature coefficient of the photovoltaic panel; is the actual operating temperature of the photovoltaic panel; is the reference temperature of the photovoltaic panel;

[0145] The predicted load data of the distribution network on the maintenance day, the predicted power generation of the wind power station on the maintenance day, and the predicted power generation of the photovoltaic power station on the maintenance day are used as the power generation and load data on the maintenance day.

[0146] Step S4: Based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, a distribution network maintenance model and constraints are constructed with the goal of minimizing the total cost of the power system;

[0147] Specifically, the constraints include power balance constraints, power output constraints, power upper and lower limit constraints, maintenance cost constraints, maintenance duration constraints, maintenance operation continuity constraints, battery status constraints, battery capacity constraints, and topology connectivity constraints;

[0148] Optionally, the distribution network maintenance model is constructed based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, with the purpose of minimizing the total cost of the power system, including:

[0149] Divide the maintenance day into several scenarios according to preset time intervals;

[0150] Based on the operating cost parameters of the distribution network under several scenarios, the hardware parameters of wind, solar and storage equipment, and the power generation and load data on the maintenance day, the following distribution network maintenance model is constructed:

[0151]

[0152] in, For the region and region exist Time Path Whether maintenance operations are performed; Total cost for maintenance scheduling; A collection of scenes; It is the expected value operation; is the expected cost of all scenarios; For the scene Load loss costs under For maintenance costs; is the degradation cost; For the scene The battery maintenance cost under the condition of known candidate maintenance line data of the distribution network; the distribution network maintenance model aims to take into account the uncertainty of the distributed power supply, use optimization solution, and take the lowest cost as the goal to develop the optimal maintenance plan, such as At what time, which maintenance operations need to be performed, how long they need to be performed, where they need to be performed, etc. At each moment, what maintenance operations need to be performed? By dynamically adjusting the load power, maintenance cycle, equipment degradation status and battery status in different time periods, an optimized maintenance plan is formulated.

[0153] The loss costs are specifically:

[0154]

[0155] in, For the region In the scene Down Predicted electric power at the moment; For the region In the scene Down Planned electric power at the time; For the region exist Load loss cost coefficient at the moment; is the upper limit of the time range; is the total number of distribution network areas;

[0156] The maintenance costs are specifically:

[0157]

[0158] in, For the region and region On the path Maintenance duration on For the region and region On the path Maintenance costs during the day; For the region and region On the path Maintenance costs during the night; Gather for the daytime time period; For night time period collection; and It is the regional identifier; For the region and region All paths between.

[0159] The degradation cost is specifically:

[0160]

[0161] in, is the weight factor of degradation cost; For the region and region path degenerate state;

[0162] The degradation state is estimated through on-site monitoring data (such as temperature, vibration, etc.), the age of the equipment, and the load conditions.

[0163] The battery maintenance cost is specifically:

[0164]

[0165] in, is the weight factor; is the number of batteries; For batteries In the scene Down The working status of planned energy storage at all times; For batteries Rated capacity; is the target coefficient of battery capacity.

[0166] In a preferred embodiment, the topological connectivity constraint is constructed in the following manner:

[0167] Based on the topological structure diagram of the distribution network and the candidate maintenance line data of the distribution network, the distribution network is divided into a plurality of connected areas with the purpose of maintaining the connectivity of each node within the area unchanged;

[0168] A topological connectivity constraint is constructed based on the connected area and the topological structure diagram of the distribution network.

[0169] Specifically, the distribution network is divided according to the topological structure diagram of the distribution network and the candidate maintenance line data of the distribution network, with the purpose of maintaining the connectivity of each node within the region unchanged, to generate several connected areas, including:

[0170] Starting from the nodes at both ends of the candidate maintenance line of the distribution network, a recursive search is performed to traverse all unvisited nodes until all reachable nodes have been visited. During the search process, if at any point the current node is found to have been visited, the search along the path leading to the visited node is immediately stopped, and the search is backtracked to the previous node to continue exploring other possible paths.

[0171] All nodes visited from a starting point and their connecting lines are considered as a connected region, thereby generating multiple connected regions. Here, the recursive search can be performed using the depth-first search method.

[0172] In a preferred embodiment, the power balance constraint is:

[0173]

[0174] Where, For the region and region exist connectivity variables at the moment;

[0175] The power output constraint is:

[0176]

[0177] The power upper and lower limits are:

[0178]

[0179] The maintenance cost constraint is:

[0180]

[0181] The maintenance duration constraint is:

[0182]

[0183] The maintenance operation continuity constraint is:

[0184]

[0185] The battery status constraints are:

[0186]

[0187] The battery capacity constraint is:

[0188]

[0189] The topological connectivity constraints are:

[0190]

[0191] in, For the region the number of power generation units; For the region In the scene Down Moment The power generated or consumed by each power generation, power consumption or energy storage component; For the The minimum power of each power generation, power consumption or energy storage component; For the The maximum power of each power generation, power consumption or energy storage component; For The maximum maintenance cost during the daytime period set at the time; For The maximum maintenance fee for the night time period set at the time; For the region and region exist Time Path Whether maintenance operations are performed; is the charging power; is the discharge power; is the charge amount; is the minimum allowed battery capacity; is the maximum allowed battery capacity; For the region and region exist connectivity variables at the moment;

[0192] Said in The maximum maintenance cost during the daytime period set at the time ,exist The maximum maintenance cost during the daytime period set at the time ,area and region On the path Maintenance costs during the day ,area and region On the path Nighttime maintenance costs ,area and region On the path Maintenance duration on These are all known quantities set by the power company or operator.

[0193] Step S5: Solve the distribution network maintenance model under the constraint conditions and generate a distribution network maintenance plan;

[0194] In a preferred embodiment, the above model is solved using a particle swarm optimization algorithm to generate a distribution network maintenance plan, including: In the scene Down The working status of planned energy storage at all times ,area and region exist Time Path Whether to perform maintenance operations ,area In the scene Down Planned electric power at the time .

[0195] Step S6: Maintain the distribution network according to the distribution network maintenance plan.

[0196] In a preferred embodiment, based on the IEEE-34 node network, five different maintenance operations are selected as research cases. In each case, the uncertainty of the distributed power generation and the scenarios of different load demands are considered. The network structure and maintenance operations of these five cases are as follows: Figure 2 Specific parameters related to maintenance operations are shown in Appendix 1. The rated power of DGs 1, 2, 3, and 4 is 100 kW, 150 kW, 200 kW, and 150 kW, respectively. The comparison method employed in this invention does not consider the support capabilities of the DGs and batteries. The load loss costs derived from the experiment are detailed in Appendix 2.

[0197] Among them, Appendix 1 is:

[0198]

[0199] Among them, Appendix 2 is:

[0200]

[0201] As can be seen from Appendix 1 and Appendix 2, although the maintenance operations in Case 4 and Case 5 last for the same duration, the load loss costs caused by the differences in the locations and paths where these operations are performed are different. For example, when performing maintenance work, although the maintenance operations in Case 4 and Case 5 last for the same duration, due to the differences in the paths where the operations are performed, the maintenance operations in Case 4 affect a larger load area than the maintenance operations in Case 5, resulting in higher load losses. In addition, the analysis also shows that in various scenarios, the load loss costs of the proposed method are reduced by 35%, 17.5%, 7.3% and 8.4% respectively compared to the comparative method. The results verify the effectiveness and efficiency of the present invention in reducing the load loss costs during maintenance after taking into account the support capabilities of distributed power sources in preventive maintenance.

[0202] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0203] like Figure 3 As shown, one embodiment of the present invention provides a distribution network maintenance device taking into account distributed power sources, including: a data acquisition module, a power generation and load uncertainty model construction module, a power generation and load uncertainty solution module, a distribution network maintenance model construction module, a distribution network maintenance plan generation module and a distribution network maintenance module.

[0204] The data acquisition module is used to obtain candidate maintenance line data of the distribution network, a topological structure diagram of the distribution network, several typical daily data of the distribution network, operating cost parameters of the distribution network, and hardware parameters of wind, solar and storage devices; wherein the typical daily data of the distribution network includes typical daily load data of the distribution network, typical daily wind data of the wind power station, and typical daily solar radiation data of the photovoltaic power station;

[0205] The power generation and load uncertainty model construction module is used to construct a power generation and load uncertainty model based on a number of typical daily data of the distribution network and a preset prediction model; wherein the preset prediction model includes a wind speed prediction model, a solar radiation prediction model and a load prediction model;

[0206] The power generation and load uncertainty solving module is used to solve the power generation and load uncertainty model based on the hardware parameters of the wind, solar and storage equipment and the preset prediction model, and generate the power generation and load data for the maintenance day; wherein the power generation and load data for the maintenance day include the predicted power generation power of the wind power station on the maintenance day, the predicted power generation power of the photovoltaic power station on the maintenance day, and the predicted load of the distribution network on the maintenance day;

[0207] The distribution network maintenance model construction module is used to construct a distribution network maintenance model and constraints based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, with the goal of minimizing the total cost of the power system; wherein the constraints include power balance constraints, power output constraints, power upper and lower limit constraints, maintenance cost constraints, maintenance duration constraints, maintenance operation continuity constraints, battery status constraints, battery capacity constraints, and topology connectivity constraints;

[0208] The distribution network maintenance plan generation module is used to solve the distribution network maintenance model under the constraint conditions and generate a distribution network maintenance plan;

[0209] The distribution network maintenance module is used to maintain the distribution network according to the distribution network maintenance plan.

[0210] It should be noted that the embodiments of the device described above correspond to the above-mentioned embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the embodiment of the device provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0211] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0212] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the distribution network maintenance method considering distributed power sources described in any one of the present invention is implemented, or when the processor executes the computer program, the functions of each module in the above-mentioned device embodiments are implemented.

[0213] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0214] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0215] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device and connects various parts of the entire terminal device using various interfaces and lines.

[0216] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0217] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment;

[0218] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program runs, the device where the storage medium is located is controlled to execute any of the above-mentioned distribution network maintenance methods considering distributed power sources of the present invention.

[0219] The aforementioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, an executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunications signal, and a software distribution medium. It should be noted that the content of the computer-readable medium may be appropriately increased or decreased based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunications signals.

[0220] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0221] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distribution network maintenance method considering distributed power sources, characterized in that: include: Obtain candidate maintenance line data for the distribution network, a topological diagram of the distribution network, several typical daily data for the distribution network, operating cost parameters for the distribution network, and hardware parameters for wind, solar, and storage devices. Typical daily data for the distribution network includes typical daily load data for the distribution network, typical daily wind speed data for wind power stations, and typical daily solar radiation data for photovoltaic power stations. Based on several typical daily data of the distribution network and the preset forecasting models, a power generation and load uncertainty model is constructed; wherein the preset forecasting models include a wind speed forecasting model, a solar radiation forecasting model, and a load forecasting model; Solve the power generation and load uncertainty model based on the hardware parameters of the wind, solar, and storage equipment and the preset prediction model to generate power generation and load data for the maintenance day; wherein the power generation and load data for the maintenance day include the predicted power generation of the wind power station on the maintenance day, the predicted power generation of the photovoltaic power station on the maintenance day, and the predicted load of the distribution network on the maintenance day; Based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, a distribution network maintenance model and constraints are constructed with the goal of minimizing the total cost of the power system. The constraints include power balance constraints, power output constraints, power upper and lower limit constraints, maintenance cost constraints, maintenance duration constraints, maintenance operation continuity constraints, battery status constraints, battery capacity constraints, and topology connectivity constraints. Under the constraints, the distribution network maintenance model is solved to generate the distribution network maintenance plan; Maintaining the distribution network according to the distribution network maintenance plan; The generation and load uncertainty model is constructed based on several typical daily data of the distribution network and a preset prediction model, including: Performing statistical analysis on wind power data of several typical days of the wind power station to determine a first average value and a first standard deviation; generating a first random Gaussian variable based on the first average value and the first standard deviation; Statistically analyzing solar radiation data of several typical days of the photovoltaic power station to determine a second mean value and a second standard deviation; generating a second random Gaussian variable based on the second mean value and the second standard deviation; Statistically analyzing several typical daily load data of the distribution network to determine a third mean value and a third standard deviation; generating a third random Gaussian variable based on the third mean value and the third standard deviation; Generate predicted wind speed data for several typical days of the wind power station based on the preset wind speed prediction model; Generate predicted solar radiation data for several typical days of the photovoltaic power station based on the preset solar radiation prediction model; Generate several typical daily load data of the distribution network according to the preset load forecast model; Calculating the difference between the wind power data of a typical day of the wind power station and the predicted wind power data of the same typical day of the wind power station to generate a first prediction error of the wind power data of the typical day of the wind power station; Calculating the difference between the typical day solar radiation data of the photovoltaic power station and the predicted solar radiation data of the same typical day of the photovoltaic power station to generate a second prediction error of the typical day solar radiation data of the photovoltaic power station; Calculating the difference between the typical daily load data of the distribution network and the predicted load data of the same typical day of the distribution network to generate a third prediction error of the typical daily load data of the distribution network; A power generation and load uncertainty model is constructed according to the first prediction error, the first random Gaussian variable, the second prediction error, the second random Gaussian variable, the third prediction error and the third random Gaussian variable.

2. The method for maintaining a distribution network taking distributed power sources into consideration according to claim 1, wherein: Solving the power generation and load uncertainty model based on the hardware parameters of the wind, solar and storage equipment and the preset prediction model to generate power generation and load data for the maintenance day includes: Input the maintenance date into the power generation and load uncertainty model to generate the wind data prediction error of the wind power station on the maintenance day, the solar radiation data prediction error of the photovoltaic power station on the maintenance day, and the load data prediction error of the distribution network on the maintenance day; Input the maintenance date into a preset forecasting model to generate forecasted wind power data for the wind power station on the maintenance day, forecasted solar radiation data for the photovoltaic power station on the maintenance day, and forecasted load data for the distribution network on the maintenance day; Correcting the predicted wind data of the wind power station on the maintenance day according to the wind power data prediction error of the wind power station on the maintenance day, and updating the predicted wind data of the wind power station on the maintenance day; Correct the predicted solar radiation data of the photovoltaic power station on the maintenance day according to the prediction error of the solar radiation data of the photovoltaic power station on the maintenance day, and update the predicted solar radiation data of the photovoltaic power station on the maintenance day; Correcting the predicted load data of the distribution network on the maintenance day according to the load data prediction error of the distribution network on the maintenance day, and updating the predicted load data of the distribution network on the maintenance day; Based on the hardware parameters of the wind, solar and storage equipment and the predicted wind power data of the wind power station on the maintenance day, the predicted power generation power of the wind power station on the maintenance day is generated by the following formula: in, To maintain the predicted power generation of the wind power station on a daily basis; is the wind speed; is the starting wind speed of the wind turbine; is the rated wind speed of the wind turbine; is the wind speed at which the wind turbine stops; is the air density; is the area swept by the wind turbine blades; is the power coefficient; Based on the hardware parameters of the wind, solar and storage equipment and the predicted solar radiation data of the photovoltaic power station on the maintenance day, the predicted power generation power of the photovoltaic power station on the maintenance day is generated by the following formula: in, To maintain the predicted power generation of the daily photovoltaic power station; is light intensity; is the effective area of ​​the photovoltaic panel; is the conversion efficiency of the photovoltaic panel; is the temperature coefficient of the photovoltaic panel; is the actual operating temperature of the photovoltaic panel; is the reference temperature of the photovoltaic panel; The predicted load data of the distribution network on the maintenance day, the predicted power generation of the wind power station on the maintenance day, and the predicted power generation of the photovoltaic power station on the maintenance day are used as the power generation and load data on the maintenance day.

3. The method for maintaining a distribution network taking distributed power sources into consideration according to claim 2, wherein: The distribution network maintenance model is constructed based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, with the goal of minimizing the total cost of the power system. It includes: Divide the maintenance day into several scenarios according to preset time intervals; Based on the operating cost parameters of the distribution network under several scenarios, the hardware parameters of wind, solar and storage equipment, and the power generation and load data on the maintenance day, the following distribution network maintenance model is constructed: in, For the region and region exist Time Path Whether maintenance operations are performed; and It is the regional identifier; Total cost for maintenance scheduling; A collection of scenes; It is the expected value operation; is the expected cost of all scenarios; For the scene Load loss costs under For maintenance costs; is the degradation cost; For the scene Battery maintenance cost under The loss costs are specifically: in, is the upper limit of the time range; is the total number of distribution network areas; For the region In the scene Down Predicted electric power at the moment; For the region In the scene Down Planned electric power at the time; For the region exist Load loss cost coefficient at the moment; The maintenance costs are specifically: in, For the region and region All paths between; For the region and region On the path Maintenance duration on For the region and region On the path Maintenance costs during the day; For the region and region On the path Maintenance costs during the night; Gather for the daytime time period; For night time period collection; The degradation cost is specifically: in, is the weight factor of degradation cost; For the region and region path degenerate state; The battery maintenance cost is specifically: in, is the weight factor; is the number of batteries; For batteries In the scene Down The working status of planned energy storage at all times; For batteries Rated capacity; is the target coefficient of battery capacity.

4. The method for maintaining a distribution network taking distributed power sources into consideration according to claim 3, wherein: The topological connectivity constraints are constructed in the following way: Based on the topological structure diagram of the distribution network and the candidate maintenance line data of the distribution network, the distribution network is divided into a plurality of connected areas with the purpose of maintaining the connectivity of each node within the area unchanged; A topological connectivity constraint is constructed based on the connected area and the topological structure diagram of the distribution network.

5. The method for maintaining a distribution network with distributed power sources as claimed in claim 4, wherein the distribution network is divided into several connected areas based on the topological structure diagram of the distribution network and the candidate maintenance line data of the distribution network, with the purpose of maintaining the connectivity of each node within the area unchanged, and the generated areas include: Starting from the nodes at both ends of the candidate maintenance line of the distribution network, a recursive search is performed to traverse all unvisited nodes until all reachable nodes have been visited. During the search process, if at any point the current node is found to have been visited, the search along the path leading to the visited node is immediately stopped, and the search is backtracked to the previous node to continue exploring other possible paths. All nodes visited from a starting point and their connecting lines are regarded as a connected area, thereby generating multiple connected areas.

6. The method for maintaining a distribution network taking distributed power sources into consideration according to claim 5, wherein: The power balance constraint is: Where, For the region and region exist connectivity variables at the moment; The power output constraint is: in, For the region the number of power generation units; For the region In the scene Down Moment The power generated or consumed by each power generation, power consumption or energy storage component; The power upper and lower limits are: in, For the The minimum power of each power generation, power consumption or energy storage component; For the The maximum power of each power generation, power consumption or energy storage component; The maintenance cost constraint is: in, For The maximum maintenance cost during the daytime period set at the time; For The maximum maintenance fee for the night time period set at the time; The maintenance duration constraint is: The maintenance operation continuity constraint is: in, For the region and region exist Time Path Whether maintenance operations are performed; The battery status constraints are: in, is the charging power; is the discharge power; is the charge amount; The battery capacity constraint is: in, is the minimum allowed battery capacity; is the maximum allowed battery capacity; The topological connectivity constraints are: 。 7. A distribution network maintenance device considering distributed power sources, characterized in that: include: Data acquisition module, power generation and load uncertainty model construction module, power generation and load uncertainty solution module, distribution network maintenance model construction module, distribution network maintenance plan generation module and distribution network maintenance module; The data acquisition module is used to obtain candidate maintenance line data of the distribution network, a topological structure diagram of the distribution network, several typical daily data of the distribution network, operating cost parameters of the distribution network, and hardware parameters of wind, solar and storage devices; wherein the typical daily data of the distribution network includes typical daily load data of the distribution network, typical daily wind data of the wind power station, and typical daily solar radiation data of the photovoltaic power station; The power generation and load uncertainty model construction module is used to construct a power generation and load uncertainty model based on a number of typical daily data of the distribution network and a preset prediction model; wherein the preset prediction model includes a wind speed prediction model, a solar radiation prediction model and a load prediction model; The power generation and load uncertainty solving module is used to solve the power generation and load uncertainty model based on the hardware parameters of the wind, solar and storage equipment and the preset prediction model, and generate the power generation and load data for the maintenance day; wherein the power generation and load data for the maintenance day include the predicted power generation power of the wind power station on the maintenance day, the predicted power generation power of the photovoltaic power station on the maintenance day, and the predicted load of the distribution network on the maintenance day; The distribution network maintenance model construction module is used to construct a distribution network maintenance model and constraints based on the operating cost parameters of the distribution network, the hardware parameters of the wind, solar and storage equipment, and the power generation and load data on the maintenance day, with the goal of minimizing the total cost of the power system; wherein the constraints include power balance constraints, power output constraints, power upper and lower limit constraints, maintenance cost constraints, maintenance duration constraints, maintenance operation continuity constraints, battery status constraints, battery capacity constraints, and topology connectivity constraints; The distribution network maintenance plan generation module is used to solve the distribution network maintenance model under the constraint conditions and generate a distribution network maintenance plan; The distribution network maintenance module is used to maintain the distribution network according to the distribution network maintenance plan; The generation and load uncertainty model is constructed based on several typical daily data of the distribution network and a preset prediction model, including: Performing statistical analysis on wind power data of several typical days of the wind power station to determine a first average value and a first standard deviation; generating a first random Gaussian variable based on the first average value and the first standard deviation; Statistically analyzing solar radiation data of several typical days of the photovoltaic power station to determine a second mean value and a second standard deviation; generating a second random Gaussian variable based on the second mean value and the second standard deviation; Statistically analyzing several typical daily load data of the distribution network to determine a third mean value and a third standard deviation; generating a third random Gaussian variable based on the third mean value and the third standard deviation; Generate predicted wind speed data for several typical days of the wind power station based on the preset wind speed prediction model; Generate predicted solar radiation data for several typical days of the photovoltaic power station based on the preset solar radiation prediction model; Generate several typical daily load data of the distribution network according to the preset load forecast model; Calculating the difference between the wind power data of a typical day of the wind power station and the predicted wind power data of the same typical day of the wind power station to generate a first prediction error of the wind power data of the typical day of the wind power station; Calculating the difference between the typical day solar radiation data of the photovoltaic power station and the predicted solar radiation data of the same typical day of the photovoltaic power station to generate a second prediction error of the typical day solar radiation data of the photovoltaic power station; Calculating the difference between the typical daily load data of the distribution network and the predicted load data of the same typical day of the distribution network to generate a third prediction error of the typical daily load data of the distribution network; A power generation and load uncertainty model is constructed according to the first prediction error, the first random Gaussian variable, the second prediction error, the second random Gaussian variable, the third prediction error and the third random Gaussian variable.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the distribution network maintenance method considering distributed power sources as described in any one of claims 1 to 6 can be implemented.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the distribution network maintenance method considering distributed power sources as described in any one of claims 1 to 6.

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