Power distribution network control method, power distribution network control device and electronic equipment

Abstract

The embodiment of the invention provides a power distribution network control method, a power distribution network control device and electronic equipment. The method comprises the following steps: acquiring ecological data in an ecological region and user data of various types of users in the ecological region; then, based on the ecological data, a region type of the ecological region is determined. And based on the user data and the region type, a composite control strategy is configured for the users in the ecological region. And finally, according to the composite control strategy, controlling operation of electric power facilities in the power distribution network for supplying power to the users and / or electric power resources distributed to the users. The method improves the control accuracy of the power distribution network.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a distribution network control method, distribution network control device, and electronic equipment. Background Technology

[0002] In the operation of a power system, the control strategy of the distribution network is a key means to ensure its safe and stable operation. Through a scientific and reasonable control strategy, dynamic regulation of power load can be achieved, thereby improving power supply reliability and power quality.

[0003] In existing technologies, by acquiring users' electricity consumption behavior and analyzing information such as users' electricity consumption time distribution, power characteristics, and equipment usage frequency, it is possible to identify the electricity consumption habits of different users and formulate differentiated distribution network control strategies accordingly.

[0004] However, existing technologies suffer from low accuracy in formulating distribution network control strategies, resulting in low control accuracy of the distribution network. Summary of the Invention

[0005] This application provides a power distribution network control method, a power distribution network control device, and electronic equipment to improve the control accuracy of the power distribution network.

[0006] In a first aspect, embodiments of this application provide a power distribution network control method, including:

[0007] Acquire ecological data within the ecological area, as well as user data of various types of users within the ecological area. Ecological data includes at least one of meteorological data, environmental quality data, ecological physical characteristic data, and biodiversity data. User data includes at least one of basic electricity consumption information data, electrical equipment data, electricity consumption behavior data, and geographic location data.

[0008] Based on ecological data, determine the regional type of the ecological area;

[0009] Based on user data and region type, configure composite control strategies for users within the ecosystem region;

[0010] According to the composite control strategy, the operation of power facilities in the distribution network supplying power to users and / or the power resources allocated to users are controlled.

[0011] In one possible implementation, a comprehensive evaluation value for user data is determined based on user data; the user's influence level is determined based on the comprehensive evaluation value; a first control strategy for the user is determined based on the influence level; a second control strategy for the user is determined based on the region type; and a composite control strategy is determined based on the first and second control strategies.

[0012] In one possible implementation, the formula is: Calculate the overall evaluation value of user data;

[0013] Where U is the comprehensive evaluation value of user data; n is the number of types of user electrical equipment; P i T represents the rated power of the i-th type of electrical equipment; i E represents the total usage time of the i-th type of device within the statistical period; i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j β is the feature value related to the j-th user behavior or geographic location; j I represents the importance coefficient of the j-th feature; p represents the number of indicators related to the operating parameters of the distribution network; k γ is the average value of the k-th distribution network operation parameter index within the statistical period; k The associated weight of the k-th distribution network operation parameter index.

[0014] In one possible implementation, if a user is at a high impact level, the user's high-energy-consuming equipment is restricted from accessing the distribution network; if a user is at a medium impact level, the price of the electricity resources allocated to the user is reduced; if a user is at a low impact level, it is determined whether the user's electricity consumption behavior matches a preset personalized electricity consumption plan, and if the electricity consumption behavior matches the preset personalized electricity consumption plan, the price of the electricity resources allocated to the user is reduced.

[0015] In one possible implementation, real-time monitoring is conducted on user electricity consumption behavior, distribution network operating parameters, and changes in ecological and environmental indicators within the ecological area. Electricity consumption behavior includes electricity usage time distribution, power characteristics, frequency of electrical equipment use, and participation in demand response. Distribution network operating parameters include voltage, current, power factor, frequency, and load factor. Ecological and environmental indicators include meteorological indicators, environmental quality indicators, ecophysical characteristic indicators, and biodiversity indicators. Real-time monitoring results are obtained based on these changes in electricity consumption behavior, distribution network operating parameters, and ecological and environmental indicators. Based on these real-time monitoring results, the composite control strategy is optimized and adjusted.

[0016] In one possible implementation, based on real-time detection results, the following are acquired: the number of types of user electrical equipment, the user's comprehensive evaluation value of user data, the average energy efficiency of the electrical equipment, the standard energy efficiency of the electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operating parameters, and the average value of distribution network operating parameter indicators within the statistical period. Based on the number of types of user electrical equipment, the user's comprehensive evaluation value of user data, the average energy efficiency of the electrical equipment, the standard energy efficiency of the electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operating parameters, and the average value of distribution network operating parameter indicators within the statistical period, the adjustment probability of the composite control strategy is determined. Based on the adjustment probability, it is determined whether to optimize and adjust the composite control strategy.

[0017] In one possible implementation, the formula is: Calculate the adjustment probability of the composite control strategy;

[0018] Where P is the adjustment probability of the composite control strategy; n is the number of types of user electrical equipment; U i E represents the comprehensive evaluation value of user data for the i-th type of user; i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j Let be the feature value related to the j-th user behavior or geographical location; p is the number of indicators related to the power distribution network operation parameters; I k This represents the average value of the k-th distribution network operation parameter within the statistical period.

[0019] In one possible implementation, optimizing the composite control strategy includes at least one of the following methods: increasing the dynamic electricity price fluctuation range; reclassifying electricity consumption periods; and dynamically adjusting the power threshold of electrical equipment that has a high impact on users.

[0020] Secondly, embodiments of this application provide a power distribution network control device, comprising:

[0021] The data acquisition module is used to acquire ecological data within the ecological area, as well as user data of various types of users within the ecological area. The ecological data includes at least one of meteorological data, environmental quality data, ecological physical characteristic data, and biodiversity data. The user data includes at least one of basic electricity consumption information data, electrical equipment data, electricity consumption behavior data, and geographic location data.

[0022] The type determination module is used to determine the regional type of an ecological region based on ecological data. The ecological region includes at least one of the following: natural ecological protection zone, ecological agriculture zone, ecotourism zone, and urban ecological zone.

[0023] The strategy configuration module is used to configure composite control strategies for users within the ecosystem based on user data and region type.

[0024] The distribution network control module is used to control the operation of power facilities in the distribution network supplying power to users and / or the power resources allocated to users, according to a composite control strategy.

[0025] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0026] The memory stores instructions that the computer executes;

[0027] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0029] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0030] The power distribution network control method, device, and electronic equipment provided in this application acquire ecological data and various types of user data within an ecological area, using these as the basis for formulating a composite control strategy. Then, based on the ecological data, the region type of the ecological area is determined, allowing for different control strategies to be developed for different ecological areas. Simultaneously, based on user data and region type, a composite control strategy is configured for users within the ecological area, thus formulating a control strategy that considers the ecological region and combines user data. Finally, according to the composite control strategy, the operation of power facilities in the power distribution network supplying power to users and / or the power resources allocated to users are controlled, improving the accuracy of power distribution network control strategy formulation and thus enhancing the control accuracy of the power distribution network. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0032] Figure 1 A flowchart illustrating the power distribution network control method provided in the embodiments of this application. Figure 1 ;

[0033] Figure 2 A flowchart illustrating the power distribution network control method provided in the embodiments of this application. Figure 2 ;

[0034] Figure 3 A flowchart illustrating the power distribution network control method provided in the embodiments of this application. Figure 3 ;

[0035] Figure 4 This is a schematic diagram of the structure of the power distribution network control device provided in the embodiments of this application;

[0036] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0037] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0039] In existing technologies, distribution network control strategies are generally based on user usage patterns and habits, allowing for targeted adjustments to control related power facilities or personalized allocation of power resources. However, different users may reside in different ecological zones. Allocating the same power resources or controlling related power equipment in the same way to users in different ecological zones could lead to resource waste and disrupt the stability of that ecological zone. Therefore, existing technologies suffer from low accuracy in formulating distribution network control strategies, resulting in low overall control accuracy of the distribution network.

[0040] The inventors believe that different ecological regions reflect different ecological data. For example, combining meteorological data, environmental quality data, ecophysical characteristic data, and biodiversity data can serve as a basis for classifying ecological regions. By dividing these ecological regions and combining different types of user data, user-specific control strategies can be developed for different ecological regions and user types, thereby improving the accuracy of power distribution network control strategy formulation.

[0041] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0042] Figure 1 A flowchart illustrating the power distribution network control method provided in the embodiments of this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0043] S101. Obtain ecological data within the ecological area, as well as user data of various types of users within the ecological area.

[0044] An ecological region refers to an ecosystem unit formed by the interaction of biological communities and their abiotic environment.

[0045] For example, ecological regions can be divided according to their geographical location, climate conditions, vegetation type, and animal species, such as nature reserves, ecological agriculture regions, ecotourism regions, and urban ecological regions.

[0046] Ecological data includes at least one of meteorological data, environmental quality data, ecophysical characteristic data, and biodiversity data.

[0047] This includes meteorological data such as temperature, humidity, precipitation, and wind speed; environmental quality data such as air quality index and water quality parameters; ecological and physical characteristic data such as topographic information, soil type, and water body distribution; and biodiversity data such as the number, types, and distribution of plant and animal species.

[0048] Users of various types within an ecological area refer to the population that operates within that ecological area and depends on its resources.

[0049] For example, user types include residential users, ecological agriculture users, ecotourism users, industrial enterprise users, and public service institution users.

[0050] It should be understood that users can be categorized into various types because their electricity consumption behaviors and needs differ. For example, residential users consume electricity for daily life and have relatively low demand, while industrial users primarily use electricity to power equipment on production lines and have higher demand.

[0051] User data includes at least one of the following: basic electricity consumption information data, electrical equipment data, electricity consumption behavior data, and geographic location data.

[0052] This includes basic electricity consumption information data such as total electricity consumption and electricity bills; electrical equipment data such as equipment type and quantity; electricity consumption behavior data such as peak-hour usage habits and energy-saving behaviors; and geographic location data such as user coordinates and the city where the user is located.

[0053] In practical applications, ecological and user data can be collected through sensors. For example, meteorological sensors collect meteorological data, environmental quality sensors collect environmental quality data, ecological characteristic sensors collect ecological physical characteristic data, smart meters collect user electricity consumption data, load detection sensors collect user electricity peak and off-peak data, and equipment status sensors collect equipment status and energy consumption data.

[0054] S102. Based on ecological data, determine the regional type of the ecological area.

[0055] Determining the regional type of an ecological region refers to using at least one of meteorological data, environmental quality data, ecophysical characteristic data, and biodiversity data as the basis for classifying the ecological region.

[0056] For example, ecological data shows that the area has high biodiversity, including a rich variety of flora and fauna, and the air and water quality data in the environmental quality data are very good. At the same time, the meteorological data is relatively stable and less affected by human interference. Therefore, the ecological data can be classified as a natural ecological protection area.

[0057] S103. Based on user data and region type, configure composite control strategies for users within the ecological region;

[0058] S104. According to the composite control strategy, control the operation of power facilities in the distribution network supplying power to users and / or the power resources allocated to users.

[0059] Among them, the composite control strategy refers to the strategy for controlling the power equipment in the distribution network, and also refers to the strategy for the distribution network to allocate power resources to users, in which the distribution network supplies power to the corresponding users.

[0060] For example, electrical equipment can be power generation equipment, such as water turbines in hydroelectric power stations and wind turbines in wind farms; electrical equipment can also be power transmission equipment, such as high-voltage transmission lines and transformers. Electrical resources can be energy sources that generate electricity, such as hydroelectric resources and wind power resources.

[0061] It should be understood that different user types may have different electricity demands, thus requiring different control strategies. Similarly, users in different regional areas may have different electricity demands, again resulting in inconsistent control strategies. Therefore, in one possible implementation, the first step is to determine a comprehensive user data evaluation value based on the user data. Then, based on this comprehensive evaluation value, the user's impact level is determined, followed by a primary control strategy. Next, a secondary control strategy is determined based on the regional type. Finally, a composite control strategy is determined based on both the primary and secondary control strategies.

[0062] Among them, the comprehensive evaluation value of user data refers to the assessment value that measures the degree of impact of user electricity consumption on the power grid. This assessment value can reflect the user's electricity consumption habits and the amount of electricity consumed. At the same time, combined with the geographical location data in the user data, it can also reflect the electricity consumption behavior in the user's area.

[0063] In one possible implementation, via the formula: Calculate the overall evaluation value of user data;

[0064] Where U is the comprehensive evaluation value of user data; n is the number of types of user electrical equipment; P i T represents the rated power of the i-th type of electrical equipment; i E represents the total usage time of the i-th type of device within the statistical period; i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j β is the feature value related to the j-th user behavior or geographic location; j I represents the importance coefficient of the j-th feature; p represents the number of indicators related to the operating parameters of the distribution network; k γ is the average value of the k-th distribution network operation parameter index within the statistical period; k The associated weight of the k-th distribution network operation parameter index.

[0065] In practical applications, the rated power of electrical equipment, the average energy efficiency of user equipment, the standard energy efficiency, and the operating parameters of the power distribution network can be obtained from the electrical equipment data. The total usage time of the equipment within the statistical period can be obtained from the electricity consumption behavior data. The number of user behavior and geographical location-related features, and the user behavior or geographical location-related feature values ​​can be obtained from the geographical location data.

[0066] The user's impact level refers to the level that measures the magnitude of the comprehensive evaluation value of user data. For example, this impact level can be divided into low impact level, medium impact level, and high impact level, which respectively indicate that the user's electricity consumption has a small, medium, and large impact on the power grid, and thus different control strategies can be adopted for users of different levels.

[0067] The first control strategy is a control strategy used to adjust the impact of user electricity consumption on the power grid.

[0068] In one possible implementation, if a user is classified as high-impact, the user's high-energy-consuming equipment is restricted from accessing the distribution network; if a user is classified as medium-impact, the price of the electricity resources allocated to the user is reduced; if a user is classified as low-impact, it is determined whether the user's electricity consumption behavior matches the preset personalized electricity consumption plan, and if the electricity consumption behavior matches the personalized electricity consumption plan, the price of the electricity resources allocated to the user is reduced.

[0069] High-energy-consuming equipment refers to electrical equipment that consumes a large amount of electrical energy per unit time during operation and has a significant impact on the power grid load. It is generally determined whether an equipment is considered high-energy-consuming based on whether its rated power exceeds a power threshold or whether its power consumption per unit cycle exceeds a set standard.

[0070] The price of electricity resources for users refers to the unit price of electricity that users need to pay when obtaining electricity from the distribution network.

[0071] Preset personalized electricity plans refer to electricity plans customized for users based on factors such as their electricity usage history, lifestyle, equipment type, and ecological area characteristics.

[0072] In practical applications, high-impact users are subject to mandatory constraints, such as limiting the access time of high-energy-consuming devices, especially during peak hours; economic means are used to encourage users to change their electricity consumption habits, such as high electricity bills to serve as a warning to users.

[0073] For users with moderate impact, flexible incentive mechanisms are adopted to reduce the price of electricity resources allocated to them. For example, dynamic electricity pricing plans are used to encourage users to use electricity during off-peak hours when electricity prices are lower; energy efficiency subsidies are provided to incentivize users to replace their electrical appliances with more efficient ones.

[0074] For users with low impact levels, provide personalized electricity plans, encourage their participation in demand response programs, and incentivize further energy-saving behaviors by offering rewards, such as electricity bill discounts.

[0075] The second control strategy is used to adjust the control strategies for the impact of ecological areas on the power grid. For example, for nature reserves, emphasis will be placed on ecological protection, restricting any form of pollution source within the area, prioritizing the use of clean energy technologies for power supply, and ensuring that any new infrastructure construction will not damage the local ecosystem.

[0076] The power distribution network control method provided in this application comprehensively collects ecological data and user electricity consumption data within an ecological area. Based on the regional type reflected in the ecological data and the degree of user impact reflected in the user data, it intelligently configures differentiated power control strategies, thereby dynamically regulating the operation of the power distribution network and the allocation of power resources. This improves the accuracy of power distribution network control strategy formulation and thus enhances the control accuracy of the power distribution network. Ultimately, this achieves efficient and stable power grid operation, optimized energy allocation, effective protection of the ecological environment, and positive incentives for users' energy-saving behaviors.

[0077] Figure 2 A flowchart illustrating the power distribution network control method provided in the embodiments of this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the examples, the power distribution network control method is described in detail, which includes:

[0078] S201. Real-time monitoring of user electricity consumption behavior, distribution network operation parameters, and changes in ecological environment indicators within the ecological area.

[0079] Electricity consumption behavior includes electricity consumption time distribution, electricity power characteristics, frequency of use of electrical equipment, and participation in demand response.

[0080] Electricity consumption time distribution refers to the distribution of a user's electricity consumption at different times within a preset period, such as electricity consumption at different times within a week. Electricity power characteristics refer to the power consumption characteristics of the electrical appliances used by the user, including starting current, power demand during operation, and power factor. Electrical equipment usage frequency refers to the number of times a user uses a device within a certain period, such as using a washing machine three times a week.

[0081] Demand response participation refers to whether users are willing or have already participated in demand response plans. Demand response is a mechanism by which power companies encourage users to reduce unnecessary electricity consumption or shift to off-peak hours when the power grid faces high load pressure, through economic incentives and other means.

[0082] The operating parameters of a power distribution network include voltage, current, power factor, frequency, and load factor.

[0083] Ecological and environmental indicators include meteorological indicators, environmental quality indicators, ecophysical characteristic indicators, and biodiversity indicators.

[0084] Among them, meteorological related indicators refer to the measured values ​​of meteorological data, environmental quality indicators refer to the measured values ​​of environmental quality data, ecological physical characteristic indicators refer to the measured values ​​of ecological physical characteristic data, and biodiversity indicators refer to the measured values ​​of biodiversity data.

[0085] It should be understood that changes in ecological and environmental indicators are the result of the combined effects of intervention by composite control strategies, natural environmental dynamics, and spontaneous user behavior. Composite control strategies directly or indirectly affect ecological indicators by precisely intervening in electricity consumption behavior and distribution network operation.

[0086] S202. Obtain real-time monitoring results based on changes in electricity consumption behavior, distribution network operating parameters, and ecological environment indicators;

[0087] S203. Optimize and adjust the composite control strategy based on real-time monitoring results.

[0088] Among them, real-time monitoring results refer to the changes in user data and ecosystem data after the control strategy is issued.

[0089] In one possible implementation, the following data is first obtained based on real-time detection results: the number of types of user electrical equipment, the comprehensive evaluation value of user data, the average energy efficiency of electrical equipment, the standard energy efficiency of electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operation parameters, and the average value of distribution network operation parameter indicators within the statistical period.

[0090] Next, based on the types and number of user electrical equipment, the comprehensive evaluation value of user data, the average energy efficiency of electrical equipment, the standard energy efficiency of electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operation parameters, and the average value of distribution network operation parameter indicators within the statistical period, the adjustment probability of the composite control strategy is determined.

[0091] Finally, based on the adjustment probability, it is determined whether to optimize the composite control strategy.

[0092] Among them, the adjustment probability refers to the probability of needing to adjust the composite control strategy.

[0093] In one possible implementation, via the formula: Calculate the adjustment probability of the composite control strategy;

[0094] Where P is the adjustment probability of the composite control strategy; n is the number of types of user electrical equipment; U i E represents the comprehensive evaluation value of user data for the i-th type of user; i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j Let be the feature value related to the j-th user behavior or geographical location; p is the number of indicators related to the power distribution network operation parameters; I k This represents the average value of the k-th distribution network operation parameter within the statistical period.

[0095] In practical applications, the magnitude of the adjustment probability affects whether the composite control strategy needs to be optimized. When the probability of adjusting the composite control strategy is closer to 1, it indicates that the current user electricity consumption, distribution network operation status, and ecological environment conditions require adjustment of the composite control strategy parameters, meaning the current strategy is less compatible with the actual situation. Conversely, when the probability of adjusting the composite control strategy is closer to 0, it indicates that the current composite control strategy matches all aspects better, and no changes to the composite control strategy parameters are needed.

[0096] In one possible implementation, optimizing the composite control strategy includes at least one of the following methods: increasing the dynamic electricity price fluctuation range; reclassifying electricity consumption periods; and dynamically adjusting the power threshold of electrical equipment that has a high impact on users.

[0097] Among them, raising the dynamic electricity price fluctuation range refers to expanding the range of electricity price fluctuations set according to changes in electricity supply and demand at different times, which is used to regulate users' electricity consumption behavior through price levers.

[0098] For example, if users do not respond adequately to the current electricity pricing mechanism, the electricity price during the day (peak hours) is 1.5 times the base price and at night (off-peak hours) it is 0.5 times. After the fluctuation range is adjusted upward, the electricity price during the peak hours may become 2 times or even higher as an additional ecological surcharge, while the electricity price during the off-peak hours will drop to 0.3 times or lower.

[0099] Reclassifying electricity usage periods refers to reclassifying different electricity usage periods within a day or week based on factors such as the latest electricity usage patterns, grid load conditions, and renewable energy generation capacity.

[0100] For example, based on grid load forecasting and the characteristics of ecologically sensitive periods, the day was originally divided into four periods: morning peak, daytime flat period, evening peak, and nighttime off-peak. Now, considering the characteristics of solar power generation, a "midday super off-peak" period may be added, because solar radiation is strongest at this time, and photovoltaic power generation is at its maximum. Alternatively, the priority power supply period for agricultural irrigation can be adjusted to alleviate grid pressure and reduce interference with ecological activities.

[0101] Dynamically adjusting the power threshold of electrical equipment for high-impact users refers to changing the maximum power limit of electrical equipment that users who have a significant impact on the grid load are allowed to use in real time, based on the current load of the power grid and environmental conditions.

[0102] For example, based on actual energy consumption data of the equipment, during the high-temperature period in summer, when the power grid load is close to its limit, the system can automatically reduce the power limit of high-energy-consuming equipment such as air conditioners, or require these devices to operate within a specific time window to distribute the load pressure. For users around protected areas, the power limit of non-essential equipment can be further tightened to reduce its negative impact on the ecological environment.

[0103] In practical applications, the core rules of the strategy are reshaped in response to significant changes in the ecological environment or user needs: when critical changes occur in an ecological area, such as the establishment of a new protected area or the discovery of endangered species, control rules are immediately added or modified, such as a complete ban on industrial electricity use around the core area of ​​the protected area, or mandatory shutdown of nighttime lighting equipment in tourist areas. To address seasonal and sudden fluctuations in electricity demand, strategy priorities are adjusted; for example, during peak tourist seasons, the power supply guarantee level for commercial users in tourist-concentrated areas is upgraded, while non-essential industrial loads are restricted; under extreme weather conditions, priority is given to ensuring electricity supply for residential use, and the power allocation logic for industrial and residential users is restructured. Based on the evolution of user behavior, control strategies for different types of users are redefined; for example, for newly added users of new energy charging facilities, exclusive time-of-use pricing and load control rules are formulated to balance their electricity demand with grid stability.

[0104] The distribution network control method provided in this application provides an understanding of the latest situation of the entire ecological area after the issuance of control strategies by real-time monitoring of electricity consumption behavior information reflecting users' electricity consumption habits, distribution network operating parameters reflecting the power grid's operating status, and changes in ecological and environmental indicators. Based on this collected information, the power supply strategy is adjusted in a timely manner, improving the accuracy of distribution network control strategy formulation and thus enhancing the control accuracy of the distribution network, ensuring the stable operation of the power system.

[0105] Figure 3 A flowchart illustrating the power distribution network control method provided in the embodiments of this application. Figure 3 ,like Figure 3 As shown, the method includes:

[0106] S301. Collect ecological data and user data within the ecological area using sensors and perform preprocessing.

[0107] Preprocessing involves performing real-time preprocessing of ecological and user data on data nodes, including data cleaning, noise reduction, and standardization, to ensure data quality and consistency.

[0108] S302. Analyze user data based on ecological data.

[0109] In practical applications, an ecosystem service value assessment model can be built to analyze user data. This model can filter data that affects the ecosystem from user data, analyze the value coefficient of user data, and calculate the comprehensive evaluation value of user data.

[0110] For example, to filter user data that impacts the ecosystem, ecological function categories can be categorized based on ecological region type. Relevant indicators can then be selected from both ecological and user data. This can be done using the Pearson correlation coefficient method or based on the cross-correlation between ecological and user data. Analyzing the value coefficients of user data can be done by quantifying economic value using the value equivalence method, determining the value coefficients for various services. The value coefficients are the direct output of the value equivalence method, representing the economic value of a unit of ecological service. Furthermore, the value coefficients can be adjusted based on the ecological region type, and the ecological economic losses from specific user behaviors can be calculated.

[0111] S303. Based on the analysis results, design composite control strategies for different ecological regions and user types.

[0112] The analysis results can refer to the data on the ecosystem that impacts the selected user data, serving as the basis for formulating a composite control strategy. Alternatively, the analysis results can be value coefficients calculated after analyzing user data, used to evaluate the probability of adjusting the composite control strategy. Finally, the analysis results can be a comprehensive evaluation value of the user data, further assessing the degree of user influence to determine whether to adjust the composite control strategy.

[0113] In another possible implementation, the synergistic impact of the strategy on ecological protection and power grid efficiency is quantified by comprehensively considering the value coefficient and the comprehensive evaluation value of user data.

[0114] S304. Real-time monitoring of user electricity consumption behavior, distribution network operation parameters, and changes in ecological environment indicators within the ecological area;

[0115] S305. Adjust the composite control strategy in a timely manner based on the monitoring and evaluation results.

[0116] Figure 4This is a schematic diagram of the structure of the power distribution network control device provided in the embodiments of this application, such as... Figure 4 As shown, the power distribution network control device 40 provided in this embodiment includes:

[0117] The data acquisition module 401 is used to acquire ecological data within the ecological area, as well as user data of various types of users within the ecological area. The ecological data includes at least one of meteorological data, environmental quality data, ecological physical characteristic data, and biodiversity data. The user data includes at least one of basic electricity consumption information data, electrical equipment data, electricity consumption behavior data, and geographical location data.

[0118] The type determination module 402 is used to determine the regional type of an ecological region based on ecological data. The ecological region includes at least one of the following: natural ecological protection zone, ecological agriculture zone, ecotourism zone, and urban ecological zone.

[0119] The strategy configuration module 403 is used to configure composite control strategies for users within the ecological area based on user data and area type.

[0120] The distribution network control module 404 is used to control the operation of power facilities in the distribution network supplying power to users and / or the power resources allocated to users according to a composite control strategy.

[0121] In one possible implementation, the strategy configuration module 403 is further configured to: determine a comprehensive evaluation value of user data based on user data; determine the user's influence level based on the comprehensive evaluation value of user data; determine a first control strategy for the user based on the influence level; determine a second control strategy for the user based on the region type; and determine a composite control strategy based on the first and second control strategies.

[0122] In one possible implementation, the strategy configuration module 403 is also used to configure the strategy via the formula: Calculate the overall evaluation value of user data;

[0123] Where U is the comprehensive evaluation value of user data; n is the number of types of user electrical equipment; P i T represents the rated power of the i-th type of electrical equipment; i E represents the total usage time of the i-th type of device within the statistical period; i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j β is the feature value related to the j-th user behavior or geographic location; j I represents the importance coefficient of the j-th feature; p represents the number of indicators related to the operating parameters of the distribution network; kγ is the average value of the k-th distribution network operation parameter index within the statistical period; k The associated weight of the k-th distribution network operation parameter index.

[0124] In one possible implementation, the strategy configuration module 403 is further configured to restrict the user's high-energy-consuming equipment from accessing the distribution network when the user is at a high-impact level; reduce the price of the electricity resources allocated to the user when the user is at a medium-impact level; and determine whether the user's electricity consumption behavior matches the preset personalized electricity consumption plan when the user is at a low-impact level, and reduce the price of the electricity resources allocated to the user when the user's electricity consumption behavior matches the preset personalized electricity consumption plan.

[0125] In one possible implementation, the distribution network control module 404 is also used to monitor in real time the electricity consumption behavior of users within the ecological area, the operating parameters of the distribution network, and changes in ecological and environmental indicators. The electricity consumption behavior includes electricity consumption time distribution, power consumption characteristics, frequency of use of electrical equipment, and participation in demand response. The operating parameters of the distribution network include voltage, current, power factor, frequency, and load factor. The ecological and environmental indicators include meteorological indicators, environmental quality indicators, ecophysical characteristic indicators, and biodiversity indicators. Based on the changes in electricity consumption behavior, distribution network operating parameters, and ecological and environmental indicators, real-time monitoring results are obtained. Based on the real-time monitoring results, the composite control strategy is optimized and adjusted.

[0126] In one possible implementation, the distribution network control module 404 is further configured to acquire, based on real-time detection results, the number of types of user electrical equipment, the user's comprehensive evaluation value of user data, the average energy efficiency of the electrical equipment, the standard energy efficiency of the electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operating parameters, and the average value of distribution network operating parameter indicators within a statistical period; determine the adjustment probability of the composite control strategy based on the number of types of user electrical equipment, the user's comprehensive evaluation value of user data, the average energy efficiency of the electrical equipment, the standard energy efficiency of the electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operating parameters, and the average value of distribution network operating parameter indicators within a statistical period; and determine whether to optimize and adjust the composite control strategy based on the adjustment probability.

[0127] In one possible implementation, the distribution network control module 404 is also used to: Calculate the adjustment probability of the composite control strategy; where P is the adjustment probability of the composite control strategy; n is the number of types of user electrical equipment; U i E represents the comprehensive evaluation value of user data for the i-th type of user;i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j Let be the feature value related to the j-th user behavior or geographical location; p is the number of indicators related to the power distribution network operation parameters; I k This represents the average value of the k-th distribution network operation parameter within the statistical period.

[0128] In one possible implementation, the distribution network control module 404 is also used to increase the dynamic electricity price fluctuation range; redivide the electricity consumption period; and dynamically adjust the power threshold of electrical equipment for users with high impact.

[0129] The power distribution network control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0130] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0131] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0132] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0133] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0134] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0135] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0136] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0137] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0138] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0139] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0140] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0143] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0145] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A power distribution network control method, characterized in that, include: Acquire ecological data within the ecological area, as well as user data of various types of users within the ecological area. The ecological data includes at least one of meteorological data, environmental quality data, ecological physical characteristic data, and biodiversity data. The user data includes at least one of basic electricity consumption information data, electrical equipment data, electricity consumption behavior data, and geographical location data. Based on the ecological data, the regional type of the ecological area is determined; Based on the user data and the region type, configure a composite control strategy for the user within the ecological region; According to the composite control strategy, the operation of power facilities in the distribution network supplying power to the user and / or the power resources allocated to the user are controlled.

2. The method according to claim 1, characterized in that, The step of configuring a composite control strategy for users within the ecological region based on the user data and the region type includes: Based on the user data, determine the comprehensive evaluation value of the user data; Based on the comprehensive evaluation value of the user data, the user's influence level is determined; Based on the level of impact, determine the user's first control strategy; Based on the region type, determine the user's second control policy; The composite control strategy is determined based on the first control strategy and the second control strategy.

3. The method according to claim 2, characterized in that, The step of determining the comprehensive evaluation value of user data based on the user data includes: Through the formula: Calculate the comprehensive evaluation value of the user data; Wherein, U is the comprehensive evaluation value of the user data; n is the number of types of user electrical equipment; P i T represents the rated power of the i-th type of electrical equipment; i E represents the total usage time of the i-th type of device within the statistical period; i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j β is the feature value related to the j-th user behavior or geographic location; j I represents the importance coefficient of the j-th feature; p represents the number of indicators related to the operating parameters of the distribution network; k γ is the average value of the k-th distribution network operation parameter index within the statistical period; k The associated weight of the k-th distribution network operation parameter index.

4. The method according to claim 2, characterized in that, The process of determining the user's first control strategy based on the impact level includes: If the user is classified as a high-impact user, the user's high-energy-consuming equipment shall be restricted from accessing the power distribution network. If the user is of medium impact level, reduce the price of the electricity resources allocated to the user; If the user is at a low impact level, determine whether the user's electricity consumption behavior matches the preset personalized electricity consumption plan, and if the electricity consumption behavior matches the preset personalized electricity consumption plan, reduce the price of the electricity resources allocated to the user.

5. The method according to claim 1, characterized in that, The method further includes: Real-time monitoring of user electricity consumption behavior, distribution network operation parameters, and changes in ecological environment indicators within the ecological area. The electricity consumption behavior includes electricity consumption time distribution, power consumption characteristics, frequency of use of electrical equipment, and participation in demand response. The distribution network operation parameters include voltage, current, power factor, frequency, and load factor. The ecological environment indicators include meteorological indicators, environmental quality indicators, ecological physical characteristic indicators, and biodiversity indicators. Real-time monitoring results are obtained based on the changes in electricity consumption behavior, power distribution network operating parameters, and ecological environment indicators. Based on the real-time monitoring results, the composite control strategy is optimized and adjusted.

6. The method according to claim 5, characterized in that, The method further includes: Based on the real-time detection results, the following data are obtained: the number of types of user electrical equipment, the comprehensive evaluation value of user data, the average energy efficiency of electrical equipment, the standard energy efficiency of electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operation parameters, and the average value of distribution network operation parameter indicators within the statistical period. The adjustment probability of the composite control strategy is determined based on the types and number of user electrical equipment, the comprehensive evaluation value of user data, the average energy efficiency of electrical equipment, the standard energy efficiency of electrical equipment, the number of features related to user behavior and geographical location, the feature values ​​related to user behavior or geographical location, the number of indicators related to distribution network operation parameters, and the average value of distribution network operation parameter indicators within the statistical period. Based on the adjustment probability, determine whether to optimize the composite control strategy.

7. The method according to claim 6, characterized in that, Determining the adjustment probability of the composite control strategy includes: Through the formula: Calculate the adjustment probability of the composite control strategy; Wherein, P is the adjustment probability of the composite control strategy; n is the number of types of user electrical equipment; U i E represents the comprehensive evaluation value of user data for the i-th type of user; i E represents the average energy efficiency of the i-th type of user equipment. 0i The standard energy efficiency of this type of device; m is the number of features related to user behavior and geographic location; S j Let be the feature value related to the j-th user behavior or geographical location; p is the number of indicators related to the power distribution network operation parameters; I k This represents the average value of the k-th distribution network operation parameter within the statistical period.

8. The method according to claim 5, characterized in that, The optimization and adjustment of the composite control strategy includes at least one of the following methods: The range of dynamic electricity price fluctuations will be increased. Re-divide electricity usage periods; Dynamically adjust the power threshold of electrical equipment that has a high impact on users.

9. A power distribution network control device, characterized in that, include: The data acquisition module is used to acquire ecological data within the ecological area, as well as user data of various types of users within the ecological area. The ecological data includes at least one of meteorological data, environmental quality data, ecological physical characteristic data, and biodiversity data. The user data includes at least one of basic electricity consumption information data, electrical equipment data, electricity consumption behavior data, and geographical location data. The type determination module is used to determine the region type of the ecological region based on the ecological data, wherein the ecological region includes at least one of the following: natural ecological protection zone, ecological agriculture zone, ecotourism zone, and urban ecological zone. The strategy configuration module is used to configure a composite control strategy for the user within the ecological area based on the user data and the area type. The distribution network control module is used to control the operation of power facilities in the distribution network supplying power to the user and / or the power resources allocated to the user, according to the composite control strategy.

10. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

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