Power grid intelligent management method and related device

Abstract

The invention provides a power grid intelligent management method and a related device. The power grid intelligent management method comprises the following steps: acquiring power grid operation data; fitting to obtain a relation curve of the load and the time; generating a peak period evaluation coefficient and an initial electric energy scheduling strategy corresponding to the electric energy data of the load side; when the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold, determining a peak period and peak period electricity consumption; determining compensation interval time according to the peak time period, and determining compensation parameters according to the electricity consumption in the peak time period; and compensating the initial electric energy scheduling strategy according to the compensation interval time and the compensation parameter to obtain a target electric energy scheduling strategy, and managing the power grid. According to the intelligent management method for the power grid, the influence of the peak period on the electric energy dispatching strategy is fully considered, automatic and intelligent management is achieved for peak regulation and energy storage of the power grid, the operation efficiency and reliability of the power grid are improved, and the method better adapts to the access of new energy and the change of a consumption mode.

Description

Technical Field

[0001] The present disclosure relates to the technical field of smart grids, and in particular to a method for smart grid management and related devices. Background Art

[0002] The multi-dimensional and in-depth management of digital power grid intelligence aims to improve the operational efficiency, safety and service quality of the power grid through technological innovation and intelligent upgrades, while promoting the transformation and sustainable development of the energy structure. Building a digital energy ecosystem and promoting the coordinated regulation of energy storage and energy supply and energy consumption systems requires multi-faceted efforts and innovations, strengthening the construction of power grid peak-shaving energy storage and intelligent dispatching capabilities, promoting large-scale and high-quality development of energy storage on the power supply side, the grid side, and the load side, building a flexible and intelligent power grid dispatching system, and forming a power system regulation capability that is compatible with development.

[0003] At present, advanced information technologies such as cloud computing, big data, the Internet of Things, and artificial intelligence are usually used to build intelligent dispatching systems to achieve real-time monitoring and optimized dispatching of the power grid operation status, and to use technologies such as load forecasting and energy management to predict the supply and demand of the power grid in advance, thereby generating dispatching decisions. However, the existing dispatching strategies are not smart enough to control peak load regulation, and cannot achieve automation and intelligence of power grid peak load regulation and energy storage. Summary of the invention

[0004] The purpose of the embodiments of the present disclosure is to provide a power grid intelligent management method and related devices to solve the technical problem that the existing technology is difficult to achieve automation and intelligence of power grid peak load storage.

[0005] In order to solve the above technical problems, the first aspect of the present invention provides a method for intelligent management of a power grid, including: obtaining power grid operation data received by a power grid intelligent data terminal, the power grid operation data including power supply side power data, power grid side power data, load side power data and load type; fitting based on the load side power data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve; based on the load-time relationship curve, generating a peak period evaluation coefficient and an initial power scheduling strategy corresponding to the load side power data; when the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold, determining the peak time period and peak period power consumption in the load side power data; determining a compensation interval time according to the peak time period, and determining a compensation parameter according to the peak period power consumption; compensating the initial power scheduling strategy according to the compensation interval time and the compensation parameter to obtain a target power scheduling strategy, and managing the power grid based on the target power scheduling strategy.

[0006] Furthermore, based on the load-time relationship curve, a peak period evaluation coefficient corresponding to the load-side electric energy data is generated, including: taking the power consumption time data and the load-side electric energy data as coordinate axes, determining the coordinate system and the change trend of the load-time relationship curve in the coordinate system; determining the peak period evaluation coefficient based on the change trend; wherein the change trend includes: the change rate of the peak time period in the load-time relationship curve; or, the duration of the peak time period in the load-time relationship curve; or, the change rate of the peak time period and the duration of the peak time period in the load-time relationship curve.

[0007] Furthermore, based on the load-time relationship curve, an initial power dispatching strategy corresponding to the load-side power data is generated, including: extracting a feature identifier in the load-time relationship curve in response to a user-triggered strategy generation operation; determining an initial power dispatching strategy corresponding to the feature identifier from a strategy database, wherein the strategy database is generated based on historical relationship curve data and historical feature identifiers, and is pre-configured in a power grid intelligent data terminal.

[0008] Furthermore, the initial power scheduling strategy is compensated according to the compensation interval and the compensation parameters to obtain a target power scheduling strategy, including: discretization processing is performed on the load-time relationship curve, and the compensation interval is set according to the processing result; according to the compensation interval and a pre-configured compensation parameter database, compensation parameters are queried to obtain the compensation parameters, the compensation parameters include a total power supply compensation value, a power distribution compensation value, and a power usage time compensation value; the initial power scheduling strategy is compensated one by one according to the compensation parameters to obtain the target power scheduling strategy, and the target power scheduling strategy includes the total power supply, power distribution data, and the ratio of power usage time in the target power usage area.

[0009] Further, the fitting based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain the load-time relationship curve includes: using a nonlinear model to fit the load-side electric energy data, the load type and the corresponding power consumption time data to obtain the load-time relationship curve:

[0010] y i =β 0 +β 1 sin(β 2 x)+...+β n-1 sin(β n x)+ε;

[0011] Among them, y i represents the load side power data, i represents the load type, βn represents the given term coefficient, x represents the electricity consumption time data, and ε represents the error term.

[0012] Furthermore, it also includes: when a fault event is detected, the area where the fault event is located is cut off and isolated, and the backup load capacity is obtained; according to the load-side electric energy data, the load type and the peak period evaluation coefficient, the power transfer priority of the area to be powered is calculated; based on the order of the power transfer priority, the interconnecting feeders corresponding to the backup load capacity are selected in turn to restore the power supply to the area to be powered.

[0013] Further, according to the load-side electric energy data, the load type and the peak period evaluation coefficient, the power transfer priority of the area to be powered is calculated, including:

[0014]

[0015] Among them, P represents the priority of power transfer, μ represents the power outage impact coefficient, q represents the peak period evaluation coefficient, i represents the load type, k represents the load type, i It represents the priority coefficient corresponding to the load type, and W represents the load capacity in the load side power data.

[0016] The second aspect of the present invention provides a power grid intelligent management system, which is communicatively connected to a power grid intelligent data terminal and includes: a data acquisition module, which is used to acquire power grid operation data received by the power grid intelligent data terminal, wherein the power grid operation data includes power supply side power data, power grid side power data, load side power data and load type; a curve fitting module, which is used to fit the load side power data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve; a strategy generation module, which is used to generate a peak period evaluation coefficient and an initial power scheduling strategy corresponding to the load side power data based on the load-time relationship curve; a peak determination module, which is used to determine the peak period and peak period power consumption in the load side power data when the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold; a compensation determination module, which is used to determine the compensation interval time according to the peak period and determine the compensation parameter according to the peak period power consumption; a power scheduling module, which is used to compensate the initial power scheduling strategy according to the compensation interval time and the compensation parameter to obtain a target power scheduling strategy, and manage the power grid based on the target power scheduling strategy.

[0017] A third aspect of the present invention provides a storage medium storing a computer program, which implements the steps of the power grid intelligent management method when executed by a processor.

[0018] A fourth aspect of the present invention provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the power grid intelligent management method when executing the computer program in the memory.

[0019] The intelligent power grid management method of the present invention determines the compensation parameters by calculating the peak period evaluation coefficient in the load-time relationship curve, and then uses the compensation parameters to compensate the initial power dispatching strategy to obtain the target power dispatching strategy, thereby managing the power grid based on the target power dispatching strategy; the present invention fully considers the impact of the peak period on the power dispatching strategy, realizes automated and intelligent management of power grid peak load storage, improves the operating efficiency and reliability of the power grid, and better adapts to the access of new energy and changes in consumption patterns.

[0020] The storage medium and electronic device in the present invention have all the beneficial effects of the above-mentioned intelligent power grid management method, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0022] Figure 1 A schematic diagram of the steps of the power grid intelligent management method provided by the present invention;

[0023] Figure 2 This is a schematic diagram of the structure of the power grid intelligent management system provided by the present invention. DETAILED DESCRIPTION

[0024] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0025] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0026] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0027] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0028] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0029] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0030] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0031] The first aspect of the present invention provides a method for intelligent management of a power grid, Figure 1 As shown, including:

[0032] S100, acquiring power grid operation data received by the power grid intelligent data terminal, wherein the power grid operation data includes power supply side power data, power grid side power data, load side power data and load type;

[0033] S200, performing fitting based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve;

[0034] S300, based on the load-time relationship curve, generating a peak period evaluation coefficient and an initial power dispatch strategy corresponding to the load-side power data;

[0035] S400, when the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold, determining the peak period and peak period power consumption in the load side power data;

[0036] S500, determining a compensation interval according to the peak time period, and determining a compensation parameter according to the power consumption during the peak time period;

[0037] S600: Compensate the initial power dispatching strategy according to the compensation interval time and the compensation parameters to obtain a target power dispatching strategy, and manage the power grid based on the target power dispatching strategy.

[0038] In the present invention, the peak period evaluation coefficient in the load-time relationship curve is calculated to determine the compensation parameter, and then the initial power dispatching strategy is compensated by the compensation parameter to obtain the target power dispatching strategy, so as to manage the power grid based on the target power dispatching strategy. The present invention fully considers the impact of the peak period on the power dispatching strategy, and can realize automation and intelligence for power grid peak load storage, improve the operation efficiency and reliability of the power grid, and better adapt to the access of new energy and changes in consumption patterns.

[0039] In step S100, the grid operation data received by the grid intelligent data terminal is obtained, wherein the grid operation data includes power supply side power data, grid side power data, load side power data and load type.

[0040] It should be noted that the digital power grid intelligent management method in the present invention is implemented by a digital power grid intelligent management system, and the digital power grid intelligent management system is connected to the power grid intelligent data terminal in communication. Through the intelligent meter and the metering system, the power grid can read the meter in real time or quasi-real time, obtain detailed load characteristics and power outage records, and monitor information such as power quality. These data are not only used for monitoring and analysis, but also support a variety of applications such as power market transactions, power demand side response, energy efficiency monitoring and management, which are of great significance for building a new power system, realizing efficient utilization of energy and optimizing power resource allocation. The power grid intelligent data terminal can monitor and optimize the operation status of the power grid in real time through the Internet of Things system, and the digital power grid intelligent management system receives the power grid operation data through the power grid intelligent data terminal, and the power grid operation data includes power data on the power supply side, power data on the power grid side, power data on the load side, and load type.

[0041] In step S200, fitting is performed based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve.

[0042] Specifically, in an optional embodiment, a nonlinear model is used to fit the load-side electric energy data, the load type, and the corresponding power consumption time data to obtain a load-time relationship curve:

[0043] y i =β 0 +β 1 sin(β 2 x)+...+β n-1 sin(β nx)+ε;

[0044] Among them, y i represents the load side power data, i represents the load type, β n represents the given term coefficient, x represents the electricity consumption time data, and ε represents the error term.

[0045] Since the load-side electric energy data and power consumption time data have different responses to different load types, and there are differences in usage scenarios between load types, the corresponding load-time relationship curve is fitted according to the obtained load-side electric energy data, the load type and the corresponding power consumption time data. In this embodiment, a nonlinear model is used to fit the data, and the form of the load-time relationship curve obtained by the final fitting is shown in the above formula. Of course, in other implementations, curve fitting can also be performed by machine learning methods, such as neural networks, support vector machines, etc., and the present invention is not limited to this.

[0046] In step S300, based on the load-time relationship curve, a peak period evaluation coefficient and an initial power dispatch strategy corresponding to the load-side power data are generated.

[0047] In an optional embodiment, it specifically includes:

[0048] Taking the power consumption time data and the load-side power data as coordinate axes, determining a coordinate system and a change trend of the load-time relationship curve in the coordinate system;

[0049] Determining the peak period evaluation coefficient based on the change trend;

[0050] The change trends include:

[0051] The rate of change of the peak time period in the load-time curve; or, the duration of the peak time period in the load-time curve; or, the rate of change of the peak time period and the duration of the peak time period in the load-time curve.

[0052] In this embodiment, the change trend of the load-time relationship curve in the coordinate system is used to reflect the peak period evaluation coefficient. Exemplarily, the rate of change of the peak time period in the load-time relationship curve can be represented by the tangent slope of the curve in the peak section. The higher the slope, the more sudden the change in the peak period, and the more it is necessary to consider the layout in the scheduling strategy to extract the reserved peak capacity. Exemplarily, the peak period evaluation coefficient can also be obtained by the duration of the peak time period in the load-time relationship curve. The longer the duration, the more peak capacity is reserved in the corresponding scheduling strategy. In addition, the rate of change of the peak time period and the duration of the peak time period can be calculated according to the preset weights to obtain the peak period evaluation coefficient.

[0053] It is worth noting that the evaluation coefficient threshold can be set by the user. Among them, when the peak period evaluation coefficient is greater than the preset evaluation coefficient threshold, it indicates that the initial power dispatching strategy at this time regulates the peak-shaving energy storage of the power grid beyond the original estimate, and it is necessary to determine the peak time period and peak period power consumption in the load-side power data and compensate for it. When the peak period evaluation coefficient is not greater than the preset evaluation coefficient threshold, it indicates that the initial power dispatching strategy at this time regulates the peak-shaving energy storage of the power grid within the original estimate, and the power grid can be managed based on the generated initial power dispatching strategy.

[0054] In an optional embodiment, based on the load-time relationship curve, generating an initial power dispatching strategy corresponding to the load-side power data includes:

[0055] In response to a user-triggered policy generation operation, extracting a feature identifier from the load-time relationship curve;

[0056] An initial electric energy dispatching strategy corresponding to the characteristic identifier is determined from a strategy database, wherein the strategy database is generated according to historical relationship curve data and historical characteristic identifiers and is pre-configured in a power grid intelligent data terminal.

[0057] Among them, the characteristic identifier is selected from the relationship curve between load and time, such as the peak time period, the peak duration period, the user type corresponding to the load, etc. Each characteristic identifier corresponds to an initial power scheduling strategy.

[0058] Exemplarily, the present invention can support online collection of data to update the strategy database, and by uploading the power grid operation data collected by the Internet of Things system, collect the power grid operation data of big data and upload it to the power grid intelligent data terminal. Through big data, refined modeling can be performed for different load-time relationship curves and different load types to reduce the phenomenon of inaccurate feature extraction due to individual differences between loads. Deep learning models deployed in the cloud, such as RNN (Recurrent Neural Network), Transformer and other mainstream regression prediction models, are used to train and fit the data, build the relationship between feature identification and initial power dispatch strategy, and accurately match based on big data models. Finally, the generated initial power dispatch strategy is configured in the power grid intelligent data terminal through the network, which is convenient for the digital power grid intelligent management system to obtain.

[0059] In step S400, when the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold, the peak time period and peak period power consumption in the load-side electric energy data are determined.

[0060] It is worth noting that in modern urban power grids, electricity demand fluctuates greatly throughout the day. In order to better manage the power grid load, it is necessary to accurately evaluate and predict the peak hours so as to rationally allocate resources and ensure the stable operation of the power grid. Based on historical data and power grid operation experience, an evaluation coefficient threshold is set, such as 0.8, which is used to determine whether it has entered the peak period. The system monitors the load-side power data in real time, including power consumption, power consumption time, etc.

[0061] The peak time period evaluation coefficient is calculated based on the real-time data. For example, when the peak time period evaluation coefficient is obtained based on the rate of change of the peak time period in the load-time relationship curve, when the slope is greater than the slope threshold, the current time period is determined to be the peak time period, and the corresponding peak time period power consumption is obtained; or when the duration of the peak time period in the load-time relationship curve is greater than the time threshold, the current time period is determined to be the peak time period, and the corresponding peak time period power consumption is obtained.

[0062] Alternatively, the peak period evaluation coefficient is calculated according to the change rate of the peak period in the load-time relationship curve and the duration of the peak period according to the preset weight. For example, the peak period evaluation coefficient can be calculated more comprehensively to reflect the load characteristics of the power grid during the peak period. The calculation formula of the peak period evaluation coefficient is as follows:

[0063] PTAF=w 1 ×Rate+w 2 ×Duration

[0064] Among them, PTAF is the peak period assessment coefficient, w1 is the weight of the change rate in the peak time period, which is a pre-set value used to indicate the relative importance of the change rate in the evaluation. 2 It is the weight of the duration of the peak time period. It is also a pre-set value used to indicate the relative importance of duration in the evaluation. Rate is the rate of change of the peak time period, which can be defined as the difference between the maximum power consumption during the peak period and the power consumption in the same period before the peak, divided by the time interval. Duration is the duration of the peak time period, usually measured in hours.

[0065] In practical applications, the weight w 1 and w 2 The value of will be determined according to the grid operator’s goals and strategies. For example, if the operator is more concerned with reducing the sharp changes during peak hours, w 1 will give a higher weight to the rate of change; if you are more concerned about the duration of the peak period, w 2 Will give higher weight to duration.

[0066] Among them, peak hours and power consumption are determined:

[0067] Peak time period: The system records and marks the current time period as a peak time period, such as 7:00-9:00 a.m. and 6:00-8:00 p.m.

[0068] Peak hour electricity usage: The system calculates and records the total electricity usage during peak hours, for example, the electricity usage from 7:00-9:00 am is 1000 kWh.

[0069] In step S500, the compensation interval is determined according to the peak time period, and the compensation parameter is determined according to the power consumption during the peak time period.

[0070] Exemplarily, to determine the compensation interval, the peak period is first identified. Peak periods of the day, such as morning peak and evening peak, are determined through data analysis. When setting the compensation interval, compensation parameters, such as the compensation interval, are set according to the duration of the peak period and the load of the power grid. For example, if the peak period lasts for 2 hours, the compensation interval may be set to compensate every 15 minutes or every hour.

[0071] In step S600, the initial power dispatching strategy is compensated according to the compensation interval time and the compensation parameters to obtain a target power dispatching strategy, and the power grid is managed based on the target power dispatching strategy.

[0072] In an optional embodiment, compensating the initial power scheduling strategy according to the compensation interval time and the compensation parameter to obtain a target power scheduling strategy includes:

[0073] Performing discretization processing on the load-time relationship curve, and setting a compensation interval time according to the processing result;

[0074] According to the compensation interval and a pre-configured compensation parameter database, query to obtain compensation parameters, wherein the compensation parameters include a total power supply compensation value, a power distribution compensation value, and a power usage time compensation value;

[0075] The initial power scheduling strategy is compensated one by one according to the compensation parameters to obtain the target power scheduling strategy, which includes the total power supply in the target power consumption area, power distribution data, and the ratio of power consumption time.

[0076] In this embodiment, the compensation interval can be set, and discretization processing is performed on the basis of the load-time relationship curve, and the compensation interval is set. The compensation parameter database is configured in the management system in the form of a compensation table. Further, this embodiment can configure different compensation intervals according to actual usage requirements. It includes but is not limited to isochronous compensation: compensation is performed at fixed time intervals, such as compensation once for every accumulated 1 hour of electricity consumption; compensation is performed once for each interval of a peak electricity consumption, such as compensation is performed once every two peak times according to the compensation parameter list. Regardless of the compensation method adopted, each compensation will be implemented within the peak time. Users can select the built-in compensation parameter table according to their own needs, or they can set the compensation parameter table themselves. Among them, the compensation parameters include the total power supply compensation value, the power distribution compensation value, and the power consumption time compensation value; the target power scheduling strategy includes the total power supply, power distribution data, and the ratio of power consumption time in the target power consumption area.

[0077] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0078] In a preferred embodiment, the power grid intelligent management method further includes:

[0079] When a fault event is detected, the area where the fault event is located is cut off and isolated, and the spare load capacity is obtained;

[0080] Calculate the power transfer priority of the area to be powered based on the load-side power data, the load type and the peak period evaluation coefficient;

[0081] Based on the order of the transfer priorities, interconnecting feeders are selected in sequence from the interconnecting feeders corresponding to the standby load capacity to restore power supply to the area to be supplied.

[0082]

[0083] Among them, P represents the priority of power transfer, μ represents the power outage impact coefficient, q represents the peak period evaluation coefficient, i represents the load type, k represents the load type, i It represents the priority coefficient corresponding to the load type, and W represents the load capacity in the load side power data.

[0084] In this embodiment, a backup power supply is usually used when power is restored. The backup power supply is connected to the area to be powered through a contact switch and a contact feeder. Power supply can only be restored by closing the contact switch between the backup power supply and the area to be powered. Since the load capacity of the backup power supply is limited, it is necessary to sort the priority of the transfer of power to the area to be powered, and restore power supply in sequence according to the sorting until the load capacity of the backup power supply is exhausted. Through this scheduling method, the fault adaptive capability is improved, the grid load is balanced, and the stability of power supply is increased.

[0085] In the application scenario of the digital power grid intelligent management method, when the power grid detects a fault event, the system will automatically execute a series of emergency response measures to ensure the stable operation of the power grid and the reliability of power supply.

[0086] For example, when a power grid fault occurs, the smart grid management system will immediately detect the abnormality and automatically cut off and isolate the faulty area to prevent the fault from spreading to other areas. This process can be achieved through feeder automation technology, which can quickly complete the faulty area identification, isolation, and restore power supply to non-faulty areas.

[0087] While isolating the fault area, the system will obtain backup load capacity, such as starting backup generators, adjusting the grid operation mode, or calling energy storage equipment, to enhance active power regulation capabilities and ensure safe and stable operation of the grid.

[0088] Next, the system will analyze the load-side power data, including information such as power consumption, power consumption mode, and type of power equipment, as well as consider different load types (such as industrial, commercial, residential, etc.). These data are crucial for calculating the transfer priority of the area to be powered. At the same time, the system will calculate the peak period assessment coefficient, analyze historical data, apply prediction models, and monitor the current power grid status in real time. The peak period assessment coefficient helps determine the load pressure and power supply demand of the power grid.

[0089] Then, the transfer priority calculation will be carried out. Based on the load-side power data, load type and peak period evaluation coefficient, the system will calculate the transfer priority of the area to be powered. This priority will take into account which areas or users are more dependent on power supply, and which areas can temporarily reduce power consumption or adjust power consumption time.

[0090] Finally, the interconnection feeder is selected and the power supply is restored. Based on the calculated transfer priority, the system will select the interconnection feeder from the interconnection feeders corresponding to the backup load capacity in turn to restore the power supply to the area to be powered, optimize the allocation of power resources and improve power supply efficiency.

[0091] Furthermore, continuous monitoring and optimization can be performed. After power supply is restored, the system will continue to monitor the operation status of the power grid to ensure the stability and reliability of power supply. At the same time, the system will continuously optimize the operation strategy of the power grid based on real-time data and prediction models to cope with possible future failures or load changes.

[0092] Through the intelligent power grid management method of this embodiment, it is possible to achieve a rapid response to faults, minimize power outage time, and improve the operating efficiency and power supply reliability of the power grid. The implementation of these methods can not only guarantee the power demand of power users, but also promote the sustainable development of the power grid and the efficient use of energy.

[0093] In summary, in the intelligent power grid management method of the present invention, the peak period evaluation coefficient in the load-time relationship curve is calculated to determine the compensation parameter, and then the initial power dispatching strategy is compensated by using the compensation parameter to obtain the target power dispatching strategy, so as to manage the power grid based on the target power dispatching strategy; the present invention fully considers the impact of peak periods on the power dispatching strategy, realizes automated and intelligent management of power grid peak load storage, improves the operating efficiency and reliability of the power grid, and better adapts to the access of new energy and changes in consumption patterns.

[0094] The second aspect of the present invention provides a power grid intelligent management system, the power grid intelligent management system is connected to the power grid intelligent data terminal in communication, combined with Figure 2 As shown, including:

[0095] A data acquisition module is used to acquire the power grid operation data received by the power grid intelligent data terminal, wherein the power grid operation data includes power supply side power data, power grid side power data, load side power data and load type;

[0096] A curve fitting module, used for fitting based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve;

[0097] A strategy generation module, for generating a peak period evaluation coefficient and an initial power dispatch strategy corresponding to the load-side power data based on the load-time relationship curve;

[0098] A peak determination module, for determining the peak time period and peak time power consumption in the load-side electric energy data when the peak time period evaluation coefficient is greater than a preset evaluation coefficient threshold;

[0099] A compensation determination module, used to determine a compensation interval according to the peak time period, and to determine a compensation parameter according to the power consumption during the peak time period;

[0100] The electric energy scheduling module is used to compensate the initial electric energy scheduling strategy according to the compensation interval time and the compensation parameters to obtain a target electric energy scheduling strategy, and manage the power grid based on the target electric energy scheduling strategy.

[0101] In an optional embodiment, the strategy generation module includes:

[0102] A trend determination module, used to determine a coordinate system and a change trend of the load-time relationship curve in the coordinate system by taking the power consumption time data and the load-side power data as coordinate axes;

[0103] A coefficient determination module, used for determining the peak period evaluation coefficient based on the change trend;

[0104] The change trends include:

[0105] The rate of change of the peak time period in the load-time curve; or, the duration of the peak time period in the load-time curve; or, the rate of change of the peak time period and the duration of the peak time period in the load-time curve.

[0106] In an optional implementation, the strategy generation module includes:

[0107] A feature extraction module, configured to extract feature identifiers from the load-time relationship curve in response to a user-triggered policy generation operation;

[0108] The dispatching strategy module is used to determine the initial power dispatching strategy corresponding to the characteristic identifier from a strategy database, wherein the strategy database is generated according to historical relationship curve data and historical characteristic identifiers and is pre-configured in the power grid intelligent data terminal.

[0109] It should be noted that the power grid intelligent management system provided in an embodiment of the present invention is used to execute all the process steps of a digital power grid intelligent management method in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0110] In the intelligent management system for power grids of the present invention, the peak period evaluation coefficient in the load-time relationship curve is calculated to determine the compensation parameters, and then the initial power dispatching strategy is compensated by using the compensation parameters to obtain the target power dispatching strategy, so as to manage the power grid based on the target power dispatching strategy; the present invention fully considers the impact of peak periods on the power dispatching strategy, realizes automated and intelligent management of power grid peak load storage, improves the operating efficiency and reliability of the power grid, and better adapts to the access of new energy and changes in consumption patterns.

[0111] A third aspect of the present invention provides a storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of implementing the power grid intelligent management method include:

[0112] S100, acquiring power grid operation data received by the power grid intelligent data terminal, wherein the power grid operation data includes power supply side power data, power grid side power data, load side power data and load type;

[0113] S200, performing fitting based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve;

[0114] S300, based on the load-time relationship curve, generating a peak period evaluation coefficient and an initial power dispatch strategy corresponding to the load-side power data;

[0115] S400, when the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold, determining the peak period and peak period power consumption in the load side power data;

[0116] S500, determining a compensation interval according to the peak time period, and determining a compensation parameter according to the power consumption during the peak time period;

[0117] S600: Compensate the initial power dispatching strategy according to the compensation interval time and the compensation parameters to obtain a target power dispatching strategy, and manage the power grid based on the target power dispatching strategy.

[0118] In the storage medium of the present invention, the computer program stored therein, when executed by the processor, determines the compensation parameters by calculating the peak period evaluation coefficient in the load-time relationship curve, and then uses the compensation parameters to compensate the initial power scheduling strategy to obtain the target power scheduling strategy, so as to manage the power grid based on the target power scheduling strategy; the present invention fully considers the impact of peak periods on the power scheduling strategy, realizes automated and intelligent management of power grid peak-shaving energy storage, improves the operating efficiency and reliability of the power grid, and better adapts to the access of new energy and changes in consumption patterns.

[0119] A fourth aspect of the present invention provides an electronic device, the electronic device comprising at least a memory and a processor, the memory storing a computer program, the processor implementing the steps of the power grid intelligent management method when executing the computer program on the memory, including:

[0120] S100, acquiring power grid operation data received by the power grid intelligent data terminal, wherein the power grid operation data includes power supply side power data, power grid side power data, load side power data and load type;

[0121] S200, performing fitting based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve;

[0122] S300, based on the load-time relationship curve, generating a peak period evaluation coefficient and an initial power dispatch strategy corresponding to the load-side power data;

[0123] S400, when the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold, determining the peak period and peak period power consumption in the load side power data;

[0124] S500, determining a compensation interval according to the peak time period, and determining a compensation parameter according to the power consumption during the peak time period;

[0125] S600: Compensate the initial power dispatching strategy according to the compensation interval time and the compensation parameters to obtain a target power dispatching strategy, and manage the power grid based on the target power dispatching strategy.

[0126] In the electronic device of the present invention, the computer program stored in its memory, when executed by the processor, determines the compensation parameters by calculating the peak period evaluation coefficient in the load-time relationship curve, and then uses the compensation parameters to compensate the initial power scheduling strategy to obtain the target power scheduling strategy, so as to manage the power grid based on the target power scheduling strategy; the present invention fully considers the impact of peak periods on the power scheduling strategy, realizes automated and intelligent management of power grid peak load storage, improves the operating efficiency and reliability of the power grid, and better adapts to the access of new energy and changes in consumption patterns.

[0127] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0128] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0131] If the integrated module is implemented in the form of 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 present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned motor torque control method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0132] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for intelligent management of a power grid, characterized in that: include: Acquire power grid operation data received by the power grid intelligent data terminal, wherein the power grid operation data includes power supply side power data, power grid side power data, load side power data and load type; Fitting is performed based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve; Based on the load-time relationship curve, generating a peak period evaluation coefficient and an initial power dispatch strategy corresponding to the load-side power data; When the peak period evaluation coefficient is greater than a preset evaluation coefficient threshold, determining the peak period and peak period power consumption in the load side power data; Determine the compensation interval time according to the peak time period, and determine the compensation parameter according to the power consumption during the peak time period; The initial electric energy dispatching strategy is compensated according to the compensation interval time and the compensation parameter to obtain a target electric energy dispatching strategy, and the power grid is managed based on the target electric energy dispatching strategy.

2. The intelligent power grid management method according to claim 1, characterized in that: Based on the load-time relationship curve, a peak period evaluation coefficient corresponding to the load-side electric energy data is generated, including: Taking the power consumption time data and the load-side power data as coordinate axes, determining a coordinate system and a change trend of the load-time relationship curve in the coordinate system; Determining the peak period evaluation coefficient based on the change trend; The change trends include: The rate of change of the peak time period in the load-time curve; or, the duration of the peak time period in the load-time curve; or, the rate of change of the peak time period and the duration of the peak time period in the load-time curve.

3. The intelligent management method for power grid according to claim 1, characterized in that: Based on the load-time relationship curve, an initial electric energy dispatching strategy corresponding to the load-side electric energy data is generated, including: In response to a user-triggered policy generation operation, extracting a feature identifier from the load-time relationship curve; An initial electric energy dispatching strategy corresponding to the characteristic identifier is determined from a strategy database, wherein the strategy database is generated according to historical relationship curve data and historical characteristic identifiers and is pre-configured in a power grid intelligent data terminal.

4. The intelligent management method for power grid according to claim 1, characterized in that: The initial electric energy scheduling strategy is compensated according to the compensation interval time and the compensation parameter to obtain a target electric energy scheduling strategy, including: Performing discretization processing on the load-time relationship curve, and setting a compensation interval time according to the processing result; According to the compensation interval and a pre-configured compensation parameter database, query to obtain compensation parameters, wherein the compensation parameters include a total power supply compensation value, a power distribution compensation value, and a power usage time compensation value; The initial power scheduling strategy is compensated one by one according to the compensation parameters to obtain the target power scheduling strategy, which includes the total power supply in the target power consumption area, power distribution data, and the ratio of power consumption time.

5. The intelligent management method for power grid according to claim 1, characterized in that: The fitting based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve includes: The load-side electric energy data, the load type and the corresponding power consumption time data are fitted using a nonlinear model to obtain a load-time relationship curve: y i =β0+β1sin(β2x)+...+β n-1 sin(β n x)+e; Among them, y i represents the load side power data, i represents the load type, β n represents the given term coefficient, x represents the electricity consumption time data, and ε represents the error term.

6. The intelligent management method for power grid according to claim 1, characterized in that: Also includes: When a fault event is detected, the area where the fault event is located is cut off and isolated, and the spare load capacity is obtained; Calculate the power transfer priority of the area to be powered based on the load-side power data, the load type and the peak period evaluation coefficient; Based on the order of the transfer priorities, interconnecting feeders are selected in sequence from the interconnecting feeders corresponding to the standby load capacity to restore power supply to the area to be supplied.

7. The intelligent management method for power grid according to claim 6, characterized in that: The power transfer priority of the area to be powered is calculated according to the load-side power data, the load type and the peak period evaluation coefficient, including: Among them, P represents the priority of power transfer, μ represents the power outage impact coefficient, q represents the peak period evaluation coefficient, i represents the load type, k represents the load type, i It represents the priority coefficient corresponding to the load type, and W represents the load capacity in the load side power data.

8. A power grid intelligent management system, characterized in that: The power grid intelligent management system is communicatively connected with the power grid intelligent data terminal, including: A data acquisition module is used to acquire the power grid operation data received by the power grid intelligent data terminal, wherein the power grid operation data includes power supply side power data, power grid side power data, load side power data and load type; A curve fitting module, used for fitting based on the load-side electric energy data, the load type and the corresponding power consumption time data to obtain a load-time relationship curve; A strategy generation module, used to generate a peak period evaluation coefficient and an initial power dispatch strategy corresponding to the load-side power data based on the load-time relationship curve; A peak determination module, used to determine the peak time period and peak time power consumption in the load-side electric energy data when the peak time period evaluation coefficient is greater than a preset evaluation coefficient threshold; A compensation determination module, used to determine a compensation interval according to the peak time period, and to determine a compensation parameter according to the power consumption during the peak time period; The electric energy scheduling module is used to compensate the initial electric energy scheduling strategy according to the compensation interval time and the compensation parameters to obtain a target electric energy scheduling strategy, and manage the power grid based on the target electric energy scheduling strategy.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the power grid intelligent management method according to any one of claims 1 to 7 are implemented.

10. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the power grid intelligent management method according to any one of claims 1 to 7 when executing the computer program on the memory.

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