Digitization-based power grid peak regulation demand response method and device, and medium

Abstract

The invention discloses a digitization-based power grid peak regulation demand response method and device, and a medium, and belongs to the technical field of power regulation. The method comprises the following steps: receiving an adjustment demand initiated by a power grid response platform, and determining a demand time period corresponding to the adjustment demand; inputting the demand time period into a pre-constructed business volume prediction model, and determining whether the station of the battery swap station has a corresponding supply space or not in combination with the adjustment demand; and if yes, determining a corresponding total response amount and a substation plan, and adjusting the operation state of the station of the battery swap station in response to the substation plan. By receiving the adjustment demand initiated by the power grid response platform, the corresponding demand time period can be quickly determined, the response speed and efficiency can be improved, the operation state of the battery swap station site can be optimized and adjusted, smooth peak regulation of the power grid can be ensured, and the stability and reliability of the power grid can be improved.

Description

[0001] This application claims priority from a Chinese patent application with the application number 202311870352.8 and the invention title "A Digital-based Grid Peak Shaving Demand Response Method, Device, and Medium", which was filed on December 29, 2023. This application incorporates the entire content of the above-mentioned Chinese patent application by reference. Technical Field

[0002] This application relates to the field of power regulation technologies, and particularly to a digital-based grid peak shaving demand response method, device, and medium. Background Art

[0003] Currently, with the rapid development of the national industry and the improvement of residents' living standards, the demand for electricity continues to grow. In this context, the power system faces challenges in terms of imbalanced electricity consumption over time and the difficulty of grid capacity regulation. To ensure the safe and stable operation of the power grid, demand-side peak shaving has become an important measure. Demand-side peak shaving refers to taking measures on the power demand side to reduce the grid load and improve grid stability by changing the electricity consumption pattern or increasing energy storage devices. At the same time, the popularization of electric vehicles has also increased the pressure on the power demand side. Electric vehicles are a type of transportation tool with energy storage functions and can be regarded as one of the entities for grid demand-side peak shaving. Through facilities such as battery swapping stations, electric vehicles can provide a certain amount of power support to the grid during peak electricity consumption periods and also absorb excess electricity for storage during off-peak periods. This two-way electricity consumption mode not only helps to balance power supply and demand but also improves the stability and reliability of the power grid. Therefore, as an energy storage facility and an entity with a large electricity demand, the battery swapping station has become an important role in the grid peak shaving system.

[0004] However, in the process of implementing demand-side peak shaving, it is necessary to balance the interests of multiple parties. On the one hand, it is necessary to meet the service needs of battery swapping users to ensure the normal operation of electric vehicles; on the other hand, it is necessary to consider the own benefits of battery swapping enterprises to ensure that they can obtain reasonable economic returns. In addition, it is also necessary to balance the grid demand-side peak shaving response and the satisfaction of battery swapping users' service needs to ensure the safe and stable operation of the power grid. Summary of the Invention

[0005] Embodiments of this application provide a digital-based grid peak shaving demand response method, device, and medium to solve the technical problem that existing grid peak shaving technologies are imbalanced in meeting the service needs of battery swapping users, the own benefits of battery swapping enterprises, and demand-side response, resulting in operation pressure and power usage contradictions.

[0006] On the one hand, embodiments of this application provide a digital-based grid peak shaving demand response method, including:

[0007] Receive the regulation requirements initiated by the power grid response platform and determine the demand period corresponding to the regulation requirements;

[0008] Input the demand period into the pre-constructed business volume prediction model and, in combination with the regulation requirements, determine whether the swapping station site has a corresponding supply space;

[0009] If so, determine the corresponding total response amount and sub-station plan, and adjust the operation status of the swapping station site in response to the sub-station plan.

[0010] A digital-based power grid peak shaving demand response method provided by an embodiment of the present application can quickly determine the corresponding demand period by receiving the regulation requirements initiated by the power grid response platform, improving the response speed and efficiency; the method of predicting whether the swapping station site has a supply space through the business volume prediction model can reduce resource waste and improve the accuracy of power grid peak shaving; when the swapping station site has a supply space, optimizing and adjusting the operation status of the swapping station site can ensure the smooth progress of power grid peak shaving and improve the stability and reliability of the power grid.

[0011] In an implementation manner of the present application, receiving the regulation requirements initiated by the power grid response platform and determining the demand period corresponding to the regulation requirements specifically includes:

[0012] Based on the pre-constructed data interface, receive the regulation requirements initiated by the power grid response platform and obtain the historical regulation requirements corresponding to the power grid response platform;

[0013] Based on the preset data mining algorithm, analyze the historical regulation requirements to determine the total energy demand corresponding to the power grid response platform at different times;

[0014] According to the magnitude relationship between the total energy demand and the preset energy threshold, determine the energy demand pattern corresponding to the power grid response platform at different times; the energy demand pattern includes a high energy demand pattern and a low energy demand pattern;

[0015] Analyze the regulation requirements to determine the demand period required by the power grid response platform, and determine the demand peak shaving period corresponding to the power grid response platform according to the energy demand pattern corresponding to the demand period.

[0016] The embodiments of this application can discover the rules and patterns hidden in the data through data analysis and mining, providing more accurate and reliable data support for subsequent power grid peak shaving; by determining the precise peak shaving method of the power grid response platform, resource waste can be reduced, and the efficiency and effect of power grid peak shaving can be improved; through real-time response, the demand-side response of power grid peak shaving can be understood in a timely manner, providing strong support for subsequent decision-making; by analyzing historical order data, the response method of power grid peak shaving can be continuously improved, the efficiency and effect of power grid peak shaving can be enhanced, and the stable operation of the power grid and the reasonable allocation of power resources can be ensured.

[0017] In one implementation of this application, the demand period is input into a pre-constructed business volume prediction model, and combined with the adjustment demand, it is determined whether the swapping station site has a corresponding supply space, specifically including:

[0018] The demand period corresponding to the adjustment demand is input into the trained business volume prediction model, and the response influencing factors corresponding to the demand period are obtained; the response influencing factors at least include weather and promotional activities;

[0019] Based on the response influencing factors and combined with the adjustment demand, the power supply capacity corresponding to the swapping station site is output; the power supply capacity includes the response power supply quantity and the power supply power;

[0020] According to the response power supply quantity and the power supply power, the charging power corresponding to the swapping station site is calculated, and according to the charging power and the adjustment demand, it is determined whether the swapping station site has a spare supply space.

[0021] The embodiments of this application can improve the prediction accuracy by predicting the supply capacity of the swapping station site during the demand period through the business volume prediction model, providing more accurate data support for subsequent power grid peak shaving; determining whether the swapping station site has a spare supply space according to the charging power and the adjustment demand of the swapping station site can provide more accurate supply space information for power grid peak shaving, helping to improve the efficiency and effect of power grid peak shaving; by determining the response power supply quantity and the power supply power in the supply capacity of the swapping station site, and calculating the charging power corresponding to the swapping station site according to the response power supply quantity and the power supply power, it can provide strong support for power grid peak shaving decision-making, thereby reducing the risk of decision-making errors and improving the reliability and accuracy of power grid peak shaving; based on the determined supply space of the swapping station site, more reasonable resource allocation can be carried out, and this resource optimization and allocation can reduce resource waste, improve the efficiency and effect of power grid peak shaving, and provide strong support for ensuring the safe and stable operation of the power grid.

[0022] In one implementation of this application, if so, the corresponding response total amount and the sub-station plan are determined, and in response to the sub-station plan, the operation status of the swapping station site is adjusted, specifically including:

[0023] When the swapping station site has a supply space corresponding to the regulation demand, determine the response scale of each swapping station site during the demand period respectively, and determine the total response corresponding to the regulation demand according to the response scale and power supply capacity of each swapping station site;

[0024] Based on a preset optimization algorithm, perform optimization processing on the total response to obtain a sub-station plan corresponding to the total response, and determine the target charging state of the charging bins corresponding to each swapping station site in the sub-station plan;

[0025] Perform operation control on the corresponding swapping station site according to the target charging state; the operation control includes at least one of stopping charging at the designated charging bin of the site, stopping charging at all charging bins of the site, shutting down the site, and performing reverse power supply from the site to the grid response platform.

[0026] The method for determining the response scale during the demand period in the embodiments of the present application can provide accurate data support for subsequent total response calculation and sub-station plan formulation; by optimizing the total response, resource waste can be reduced, and the efficiency and effect of power grid peak shaving can be improved; by optimizing the sub-station plan, the smooth progress of power grid peak shaving can be ensured, and the stability and reliability of the power grid can be improved; by performing operation control on the swapping station site, the smooth progress of power grid peak shaving can be ensured, resource waste can be reduced, and the stability and reliability of the power grid can be improved; by optimizing the management of the site, the smooth progress of power grid peak shaving can be ensured, resource waste can be reduced, and the stability and reliability of the power grid can be improved; not only can the power consumption of the power grid be reduced through three ways of stopping charging, but also the power supply capacity of the power grid can be increased by performing reverse power supply from the site to the grid response platform.

[0027] In an implementation manner of the present application, after performing operation control on the corresponding swapping station site according to the target charging state, the method further includes:

[0028] Send the operation information of each swapping station site after regulation to the user terminal corresponding to each platform user to adjust the swapping behavior of the platform user in combination with the sub-station plan; the operation information includes the current start-stop state of each swapping station site and the recovery time of the charging bin or the shutdown site that stops charging;

[0029] When the swapping station site is in the state of shutting down the site, determine the nearest available swapping station site and the corresponding available swapping position information within the preset range from the swapping station site according to the positioning information corresponding to the swapping station site, and send the available swapping station site and the corresponding available swapping position information to the client corresponding to each platform user.

[0030] In the embodiments of the present application, by updating in real time and providing the platform users with the detailed operation status of the battery swapping stations, users can plan their battery swapping trips in advance, avoid waiting time caused by station outages or unavailable charging bins, thereby improving the convenience of the battery swapping service and user satisfaction. At the same time, the operator can also adjust and optimize the resource allocation according to user behavior, reduce idle periods, and improve the overall operation efficiency. When the originally planned battery swapping station is unavailable for a user, the system can quickly provide an alternative solution and guide the user to the nearest available station, effectively reducing the user's troubles and inconveniences. This not only improves the user experience but also reflects the humanization and intelligence of the service, enhancing the user's trust and loyalty to the platform.

[0031] In one implementation manner of the present application, after adjusting the operation status of the battery swapping stations in response to the sub-station plan, the method further includes:

[0032] Sending the response results corresponding to each battery swapping station to the grid response platform respectively, and determining the energy usage corresponding to each response result through the grid response platform;

[0033] Analyzing each energy usage situation to determine the peak shaving effect of the corresponding battery swapping station during the demand period, and overall evaluating the peak shaving effect;

[0034] According to the corresponding overall evaluation result, optimizing the power supply strategies corresponding to each battery swapping station in the sub-station plan, and realizing the response to the adjustment requirements of the grid response platform according to the optimized power supply strategies.

[0035] The embodiments of the present application can provide accurate data support for formulating subsequent optimized power supply strategies through a precise peak shaving effect evaluation method; through an overall optimization method, it can ensure the smooth progress of grid peak shaving, improve the stability and reliability of the grid; through an intelligent decision-making method, it can reduce manual intervention, improve decision-making efficiency and accuracy; by formulating optimized power supply strategies corresponding to each battery swapping station, it can achieve efficient utilization of resources, reduce resource waste, and improve the efficiency and effect of grid peak shaving; through evaluation and optimization, it can continuously improve the demand-side response method of grid peak shaving, improve the efficiency and effect of grid peak shaving, and ensure the stable operation of the grid and the reasonable allocation of power resources.

[0036] In one implementation manner of the present application, determining whether a battery swapping station has a corresponding supply space specifically includes:

[0037] When the supply space of each battery swapping station is less than the to-be-responded electricity quantity corresponding to the adjustment demand, determining the electricity quantity difference between the to-be-responded electricity quantity and the supply space;

[0038] Determine the peak shaving demand level corresponding to the power difference according to the magnitude relationship between the power difference and the lack of supply space threshold, and determine the peak shaving strategy corresponding to the adjustment demand according to the peak shaving demand level; the peak shaving demand level includes the first-level peak shaving demand and the second-level peak shaving demand, and the peak shaving strategy includes site addition and optimization of the site layout strategy;

[0039] Determine that the peak shaving strategy corresponding to the first-level peak shaving demand is site addition, and determine the number of swapping stations to be added and the supply capacity corresponding to each swapping station according to the power difference;

[0040] Determine that the peak shaving strategy corresponding to the second-level peak shaving demand is optimization of the site layout strategy, and optimize the layout of the swapping stations and adjust the operation strategy of the swapping stations according to the power difference.

[0041] The embodiments of the present application can provide accurate data support for subsequent peak shaving demand level division and peak shaving strategy formulation by determining the power difference; can provide a basis for subsequent peak shaving strategy formulation by determining the peak shaving demand level, which helps to improve the efficiency and effect of power grid peak shaving; can provide guidance for subsequent power grid peak shaving by formulating the peak shaving strategy, which helps to improve the stability and reliability of the power grid; can increase the power supply capacity of the power grid through the site addition strategy to meet higher power demands; can improve the power supply efficiency and effect of the power grid through the optimization of the site layout strategy, which helps to improve the stability and reliability of the power grid; and can continuously improve the demand-side response method for power grid peak shaving by continuously monitoring and optimizing the power difference of the adjustment demand, the peak shaving demand level, and the peak shaving strategy.

[0042] In one implementation manner of the present application, before inputting the demand period into the pre-constructed traffic volume prediction model, the method further includes:

[0043] Construct a traffic volume prediction model corresponding to the swapping station based on the machine learning method;

[0044] Obtain a number of historical order information of the swapping station in the historical period, and respectively determine the demand period corresponding to each historical order information and the actual demand power corresponding to the demand period;

[0045] Input the historical order information marked with the demand period into the traffic volume prediction model to train the traffic volume prediction model until the predicted demand power output by the traffic volume prediction model matches the actual demand power corresponding to the historical order information, and complete the training of the traffic volume prediction model.

[0046] The embodiments of the present application can accurately predict future traffic volumes by constructing traffic volume prediction models; by obtaining historical order information, historical data can be fully utilized to provide more reference information for constructing traffic volume prediction models; using machine learning methods to construct traffic volume prediction models can flexibly handle various complex situations, and the prediction accuracy and stability of the models can be improved through training and optimization, enabling them to automatically update and improve, ensuring that the models always maintain the best state, and providing continuous and accurate data support for power grid peak shaving.

[0047] On the other hand, the embodiments of the present application also provide a digital-based power grid peak shaving demand response device, which includes:

[0048] A receiving module, configured to receive the adjustment demand initiated by the power grid response platform and determine the demand period corresponding to the adjustment demand;

[0049] A prediction module, configured to input the demand period into a pre-constructed traffic volume prediction model and determine whether there is a corresponding supply space at the swap station site in combination with the adjustment demand;

[0050] An adjustment module, configured to, if so, determine the corresponding total response amount and sub-station plan, and adjust the operating state of the swap station site in response to the sub-station plan.

[0051] On the other hand, the embodiments of the present application also provide an electronic device, which includes:

[0052] At least one processor;

[0053] And a memory communicatively connected to the at least one processor;

[0054] Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the digital-based power grid peak shaving demand response method as described in any one of the above.

[0055] On the other hand, the embodiments of the present application also provide a non-volatile computer storage medium storing computer-executable instructions, and when the computer executes the executable instructions, the digital-based power grid peak shaving demand response method as described in any one of the above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0057] Figure 1 is a schematic flowchart of a digital-based power grid peak shaving demand response method provided by an embodiment of the present application;

[0058] Figure 2 Schematic diagram of the internal structure of a digital-based power grid peak shaving demand response device provided by an embodiment of the present application;

[0059] Figure 3 Schematic diagram of the internal structure of an electronic device provided by an embodiment of the present application. Specific implementation manners

[0060] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0061] The embodiments of the present application provide a digital-based power grid peak shaving demand response method, device, and medium to solve the technical problems that there are imbalances among the existing power grid peak shaving technologies in meeting the service requirements of battery swapping users, the own benefits of battery swapping enterprises, and demand-side response, and there are operation pressures and power usage contradictions.

[0062] The following will describe in detail the technical solutions provided by each embodiment of the present application with reference to the drawings.

[0063] Figure 1 Flow chart of a digital-based power grid peak shaving demand response method provided by an embodiment of the present application.

[0064] The implementation of the analysis method involved in the embodiments of the present application can be a terminal device or a server, and the present application does not make special limitations thereon. For the convenience of understanding and description, the following embodiments will be described in detail taking the server as an example.

[0065] It should be noted that this server can be a single device or a system composed of multiple devices, that is, a distributed server, and the present application does not make specific limitations thereon.

[0066] As Figure 1 shown, a digital-based power grid peak shaving demand response method provided by an embodiment of the present application includes:

[0067] 101. Receive the adjustment demand initiated by the power grid response platform and determine the demand period corresponding to the adjustment demand.

[0068] In an embodiment of the present application, the server establishes a connection relationship between the battery swapping operation platform and the grid response platform through a pre-constructed data interface, including developing corresponding data interface programs on the two platforms respectively, and ensuring the stability and security of the data interface. When the grid response platform initiates an adjustment demand, it sends the adjustment demand to the battery swapping operation platform through the data interface. As a result, after receiving the adjustment demand, the battery swapping operation platform stores it in the corresponding database for subsequent analysis. After receiving the adjustment demand, the battery swapping operation platform obtains the corresponding historical adjustment demand from the grid response platform through the data interface. It should be noted that the historical adjustment demand in the embodiment of the present application includes detailed demand information, demand time period, total demand, etc.

[0069] When analyzing the historical adjustment demand to determine the total energy demand, select a data mining algorithm suitable for the historical adjustment demand, such as time series analysis, clustering analysis, etc. These algorithms can help us extract useful information from the historical adjustment demand, such as the trend and periodic changes of the total energy demand. Use the selected data mining algorithm to process and analyze the historical adjustment demand, mainly including steps such as cleaning, preprocessing, and feature extraction of the historical adjustment demand, so as to obtain more accurate analysis results. Then, based on the analysis results, determine the total energy demand corresponding to the grid response platform at different time periods, which can help understand the energy demand situation of the grid response platform and provide a basis for formulating subsequent response strategies.

[0070] When determining the energy demand pattern at different time periods, set corresponding energy thresholds according to the historical adjustment demand of the grid response platform and industry standards. When the total energy demand exceeds this threshold, it is considered that the demand side is in a high energy demand mode; otherwise, it is considered to be in a low energy demand mode. Compare the total energy demand obtained from the analysis of the historical adjustment demand with the preset energy threshold to determine the energy demand pattern corresponding to the grid response platform at different time periods.

[0071] When determining the demand peak shaving time period of the grid response platform, analyze the received adjustment demand to understand specific demand information, demand time period, total demand, etc., which helps to understand the response requirements and peak shaving objectives of the grid response platform. And according to the energy demand pattern and specific information obtained from the analysis of the historical adjustment demand, determine the required demand time period of the grid response platform, so as to understand the peak shaving objectives and peak shaving time range of the grid response platform. Then, based on the determined demand time period and the corresponding energy demand pattern, determine the corresponding peak shaving time period, so as to determine in which time periods peak shaving operations need to be carried out to meet the adjustment demand of the grid response platform and ensure the stable operation of the grid.

[0072] 102. Input the demand period into a pre-constructed business volume prediction model, and combine the adjusted demand to determine whether there is a corresponding supply space for the swapping station site.

[0073] In an embodiment of the present application, the demand period of the adjusted demand is used as an input and input into a trained business volume prediction model. At the same time, it is also necessary to obtain possible response influencing factors during this demand period, such as weather, promotional activities, time, holiday situations, etc., which will have an impact on the energy demand on the demand side.

[0074] Based on the analysis results of the response influencing factors and combined with the adjusted demand, the corresponding power supply capacity of the swapping station site is output. In this way, it is possible to understand the possible power supply capacity of the swapping station site during a specific period, and based on the response power supply quantity and power supply power in the predicted power supply capacity, determine the total demand power during this demand period, so as to understand the power supply situation required during a specific period.

[0075] According to the response power supply quantity and power supply power, the corresponding charging power of the swapping station site can be calculated. Furthermore, based on the charging power and the adjusted demand, it can be judged whether each swapping station site has a spare supply space. If the supply space is sufficient, it can meet the energy demand on the demand side; if the supply space is insufficient, corresponding peak shaving measures need to be taken to meet the demand.

[0076] It can be understood that by inputting the demand period of the adjusted demand into the trained business volume prediction model, predicting the response power supply quantity and power supply power in the power supply capacity based on the response influencing factors, and then combining the adjusted demand of the swapping station site, it can be judged whether each swapping station site has a spare supply space, thereby providing decision-making support for subsequent power grid peak shaving.

[0077] In an embodiment of the present application, by comparing the difference between the actual supply power quantity and the total demand power, the power quantity difference can be determined. The power quantity difference can reflect the demand situation of the supply space, and based on the magnitude relationship between the power quantity difference and the lack of supply space threshold, the corresponding peak shaving demand level can be determined. If the power quantity difference exceeds the threshold, it can be considered that there is a first-level peak shaving demand; if the power quantity difference does not exceed the threshold but is close to the threshold, it can be considered that there is a second-level peak shaving demand. Furthermore, according to the peak shaving demand level, the corresponding peak shaving strategy can be determined. For the first-level peak shaving demand, a strategy of increasing sites is adopted; for the second-level peak shaving demand, a strategy of optimizing the site layout strategy is adopted.

[0078] According to the first-level peak shaving demand and the power quantity difference, the number of swapping station sites that need to be added can be determined. At the same time, according to the supply capacity of each swapping station site, the supply capacity required for each new site can also be determined, so as to meet the first-level peak shaving demand and ensure the stable operation of the power grid.

[0079] According to the secondary peak shaving demand and the electricity quantity difference, the layout of the swapping stations can be optimized, including measures such as adjusting the location of the stations, adding new stations or merging existing stations. Moreover, according to the secondary peak shaving demand and the optimized result of the station layout, the operation strategy of the swapping stations can be adjusted, mainly including measures such as adjusting the charging plan, optimizing the resource allocation, and improving the operation efficiency. These measures can meet the secondary peak shaving demand and improve the stability and operation efficiency of the power grid.

[0080] It can be understood that by determining the peak shaving demand level according to the size relationship between the electricity quantity difference and the lack of supply space threshold, and formulating corresponding peak shaving strategies according to the peak shaving demand level, the demand-side response of the power grid peak shaving can be realized. At the same time, by determining the station addition strategy for the primary peak shaving demand and the optimized strategy for the station layout strategy for the secondary peak shaving demand, the stability and operation efficiency of the power grid can be further improved.

[0081] In an embodiment of the present application, before inputting the demand period into the pre-constructed business volume prediction model, a suitable machine learning method is selected, such as linear regression, support vector machine, neural network, etc. Then, based on the selected machine learning method, a model capable of predicting the business volume of the swapping operation platform is constructed, and several historical order information of the swapping operation platform within the historical time period is obtained. It should be noted that the order information in the embodiments of the present application includes data such as the date, time, and electricity demand of the order.

[0082] Using the selected machine learning method, the historical order information marked with the demand period is input into the business volume prediction model. This model can learn the rules and patterns in the historical data and is used to predict the future business volume. By training the business volume prediction model until the estimated order quantity output by the model matches the actual order quantity corresponding to the historical order information, the evaluation can be carried out by comparing the predicted value and the actual value. If the predicted value and the actual value differ greatly, it is necessary to adjust the model parameters or select other machine learning methods for re-training.

[0083] 103. If so, determine the corresponding total response amount and the sub-station plan, and in response to the sub-station plan, adjust the operating state of the swapping stations.

[0084] In an embodiment of the present application, when determining the total response amount corresponding to the regulation demand, according to the response power supply amount and the power supply power of each swapping station, determine the response scale of each swapping station during the demand period, so as to understand the power supply capacity and supply space of each swapping station during the demand period, and calculate the total response amount corresponding to the regulation demand according to the response scale and power supply capacity of each swapping station. This can help understand the total power supply required during a specific period and the contributions of each swapping station.

[0085] When determining the sub - station plan, select an optimization algorithm suitable for the requirements, such as linear programming, integer programming, etc. Based on these algorithms, the optimal sub - station plan can be found from multiple battery - swapping station sites to meet the total required electricity and rationally utilize resources. Then, use the selected optimization algorithm to optimize the total response amount, formulate a sub - station plan that meets the requirements, and further determine the power supply amount and power distribution of each battery - swapping station site during a specific period.

[0086] When operating and regulating the battery - swapping station sites, according to the sub - station plan and the actual situation of each battery - swapping station site, determine the target charging state of the corresponding charging bins of each battery - swapping station site, so as to understand the charging requirements and target situations of each charging bin during a specific period. And operate and regulate the corresponding battery - swapping station sites according to the target charging state, which mainly includes at least one of stopping charging at a specified charging bin of the site, stopping charging at all charging bins of the site, shutting down the site, and reverse power supply from the site to the grid response platform. Through these measures, the operation strategies of each battery - swapping station site can be adjusted according to the actual situation to meet the energy requirements on the demand side and ensure the stable operation of the power grid.

[0087] It can be understood that by determining the response scale of each battery - swapping station site during the demand period, calculating the total response amount, formulating the sub - station plan, and determining the target charging state of the corresponding charging bins of each battery - swapping station site, the operation and regulation of the corresponding battery - swapping station sites can be achieved.

[0088] In an embodiment of the present application, the operation status of each battery - swapping station site is collected and analyzed in real - time, including but not limited to whether it is currently enabled, the charging bins in progress of charging, the number of completed battery - swapping times, and any sudden shutdown information. Whenever the status of the battery - swapping station changes (such as changing from the operating state to the shutdown state, or a charging bin changing from in - use to idle), a message containing the latest operation information will be automatically generated. These information are not only limited to the current start - stop state of the battery - swapping station and the details of the charging bins with stopped charging, but also include the estimated time for the shutdown site to resume use, so that users can plan in advance. Based on the message push service, these operation information are immediately sent to the mobile application clients of each registered user, ensuring that users can always keep abreast of the real - time dynamics of nearby battery - swapping stations.

[0089] Combined with big - data analysis, generate a daily or weekly sub - station operation plan according to historical battery - swapping demands, traffic flow forecasts, and the operation and maintenance plans of battery - swapping stations. This plan aims to balance the loads of each site and avoid overcrowding at some sites while other sites are idle. After the user terminal receives the operation information, the system will, according to the user's current location, vehicle battery level, and the sub - station plan, intelligently recommend the most suitable battery - swapping station and time, guiding users to avoid peak hours or shutdown sites and optimize the battery - swapping route.

[0090] When a swapping station is temporarily out of service, an emergency response mechanism is immediately activated. First, using the built-in Geographic Information System (GIS), based on the geographical location of the out-of-service station, search for all other swapping stations within a preset radius (such as 5 kilometers, 10 kilometers), and evaluate the availability of these candidate stations (such as the remaining swapping positions, current waiting time, etc.) to determine the swapping station that is closest to the out-of-service station and can be used immediately. Subsequently, key information such as the location information of the alternative swapping station, the number of remaining swapping positions, and the estimated waiting time is quickly conveyed to all platform users who may be affected through push notifications, helping to guide users to adjust their trips in a timely manner and go to the available swapping station to complete the swapping.

[0091] In an embodiment of the present application, after adjusting the operating status of the swapping station sites in response to the substation plan, the response results corresponding to each swapping station site are respectively sent to the power grid response platform, and through the power grid response platform, the energy usage conditions corresponding to each response result are obtained. It should be noted that the energy usage conditions in the embodiments of the present application include data such as the electricity usage conditions and charging status of each swapping station site during the demand period. By analyzing the obtained energy usage conditions of each, it is possible to determine the peak shaving effect of the corresponding swapping station site during the demand period, which includes indicators such as the peak shaving volume, peak shaving time, and peak shaving efficiency of each swapping station site.

[0092] By comprehensively evaluating the peak shaving effects of each swapping station site, the overall peak shaving effect of the power grid peak shaving can be obtained. It should be noted that the peak shaving effects in the embodiments of the present application include indicators such as the total peak shaving volume and total peak shaving efficiency. According to the overall evaluation results, optimize the power supply strategies corresponding to each swapping station site in the substation plan, including measures such as adjusting the power supply amount of each swapping station site and optimizing the charging plan. These strategies can further improve the stability of the power grid, reduce energy consumption, and reduce emissions. According to the optimized power supply strategies, respond to the adjustment requirements of the power grid response platform, mainly including measures such as adjusting the operating strategies of each swapping station site and optimizing resource allocation. These measures can ensure that there is sufficient power supply in the power grid during peak hours, and at the same time reduce power waste during off-peak hours.

[0093] It can be understood that by analyzing the energy usage conditions, evaluating the peak shaving effects, formulating optimized power supply strategies, and implementing demand-side response, it is possible to respond to the adjustment requirements of the power grid response platform and improve the stability and operating efficiency of the power grid.

[0094] The above is the method embodiment proposed in the present application. Based on the same inventive concept, the embodiments of the present application also provide a digital-based power grid peak shaving demand response device, the structure of which is as Figure 2 shown.

[0095] Figure 2The figure is a schematic internal structure diagram of a digital-based power grid peak shaving demand response device provided by an embodiment of the present application. As Figure 2 shown, the device includes:

[0096] A receiving module 201, configured to receive the adjustment demand initiated by the power grid response platform and determine the demand period corresponding to the adjustment demand;

[0097] A prediction module 202, configured to input the demand period into a pre-constructed traffic prediction model and, in combination with the adjustment demand, determine whether the swapping station site has a corresponding supply space;

[0098] An adjustment module 203, configured to, if so, determine the corresponding total response amount and sub-station plan, and adjust the operating state of the swapping station site in response to the sub-station plan.

[0099] Figure 3 The figure is a schematic internal structure diagram of an electronic device provided by an embodiment of the present application. As Figure 3 shown, the device includes:

[0100] At least one processor;

[0101] And a memory communicatively connected to the at least one processor;

[0102] Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the digital-based power grid peak shaving demand response method described in any of the above embodiments.

[0103] In an embodiment of the present application, the above-mentioned processor is capable of executing: receiving the adjustment demand initiated by the power grid response platform and determining the demand period corresponding to the adjustment demand;

[0104] Inputting the demand period into a pre-constructed traffic prediction model and, in combination with the adjustment demand, determining whether the swapping station site has a corresponding supply space;

[0105] If so, determining the corresponding total response amount and sub-station plan, and adjusting the operating state of the swapping station site in response to the sub-station plan.

[0106] An embodiment of the present application further provides a non-volatile computer storage medium storing computer-executable instructions, and when the computer executes the executable instructions, it can execute the digital-based power grid peak shaving demand response method described in any of the above embodiments.

[0107] In an embodiment of the present application, the above-mentioned processor is capable of executing: receiving the adjustment demand initiated by the power grid response platform and determining the demand period corresponding to the adjustment demand;

[0108] Input the demand period into a pre-constructed business volume prediction model, and in combination with the adjusted demand, determine whether the swapping station site has corresponding supply space;

[0109] If so, determine the corresponding total response amount and the sub-station plan, and in response to the sub-station plan, adjust the operation status of the swapping station site.

[0110] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the equipment and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0111] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] The equipment and medium provided in the embodiments of the present application correspond one-to-one with the method. Therefore, the equipment and medium also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the equipment and medium will not be elaborated here.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0114] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementation in the processFigure 1 means for the functions specified in one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0115] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means, and the instruction means implements the processes Figure 1 means for the functions specified in one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the processes Figure 1 means for the functions specified in one or more processes and / or blocks Figure 1 steps for the functions specified in one or more blocks

[0117] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory

[0118] The memory may include non-permanent memory in computer-readable media, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media

[0119] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves

[0120] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the said element.

[0121] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A digital-based power grid peak shaving demand response method, characterized in that The method includes: Receiving an adjustment demand initiated by a power grid response platform and determining a demand period corresponding to the adjustment demand; Inputting the demand period into a pre-constructed business volume prediction model and, in combination with the adjustment demand, determining whether there is a corresponding supply space at the swap station site; If so, determining a corresponding total response amount and a sub-station plan, and in response to the sub-station plan, adjusting the operating state of the swap station site.

2. The digital-based power grid peak shaving demand response method according to claim 1, wherein The receiving an adjustment demand initiated by a power grid response platform and determining a demand period corresponding to the adjustment demand specifically includes: Based on a pre-constructed data interface, receiving an adjustment demand initiated by a power grid response platform and obtaining the historical adjustment demands corresponding to the power grid response platform; Analyzing the historical adjustment demands based on a preset data mining algorithm to determine the total energy demand corresponding to the power grid response platform at different time periods; Determining the energy demand pattern corresponding to the power grid response platform at different time periods according to the size relationship between the total energy demand and a preset energy threshold; the energy demand pattern includes a high energy demand pattern and a low energy demand pattern; Analyzing the adjustment demand to determine the demand period required by the power grid response platform, and determining the demand peak regulation period corresponding to the power grid response platform according to the energy demand pattern corresponding to the demand period.

3. A digital-based power grid peak shaving demand response method according to claim 1, characterized in that, The inputting the demand period into a pre-constructed business volume prediction model and, in combination with the adjustment demand, determining whether there is a corresponding supply space at the swap station site specifically includes: Inputting the demand period corresponding to the adjustment demand into a trained business volume prediction model and obtaining the response influencing factors corresponding to the demand period; the response influencing factors at least include weather and promotional activities; Based on the response influencing factors and in combination with the adjustment demand, outputting the power supply capacity corresponding to the swap station site; the power supply capacity includes the response power supply amount and the power supply power; Calculating the charging power corresponding to the swap station site according to the response power supply amount and the power supply power, and determining whether there is a spare supply space at the swap station site according to the charging power and the adjustment demand.

4. A digital-based grid peak shaving demand response method according to claim 1, characterized in that The if so, determining a corresponding total response amount and a sub-station plan, and in response to the sub-station plan, adjusting the operating state of the swap station site specifically includes: In the case that the swap station site has a supply space corresponding to the adjustment demand, respectively determining the response scale corresponding to each swap station site during the demand period, and determining the total response amount corresponding to the adjustment demand according to the response scale and the power supply capacity of each swap station site; Based on a preset optimization algorithm, performing an optimization process on the total response amount to obtain a sub-station plan corresponding to the total response amount, and determining the target charging state of the charging bins corresponding to each swap station site in the sub-station plan; Performing operation regulation on the corresponding swap station site according to the target charging state; the operation regulation includes at least one of stopping charging at a designated charging bin of the site, stopping charging at all charging bins of the site, shutting down the site, and the site performing reverse power supply to the power grid response platform.

5. A digital-based power grid peak shaving demand response method according to claim 4, characterized in that After the corresponding swapping station site is operationally regulated according to the target charging state, the method further includes: Sending the operation information of each regulated swapping station site to the client corresponding to each platform user, so as to adjust the swapping behavior of the platform user in combination with the sub-station plan; the operation information includes the current start-stop state of each swapping station site and the restoration time of the stopped charging bin or the out-of-service site. When the swapping station site is out of service, according to the positioning information corresponding to the swapping station site, determining the nearest available swapping station site within the preset range and the corresponding available swapping position information thereto, and sending the available swapping station site and the corresponding available swapping position information to the client corresponding to each platform user.

6. A digital-based grid peak shaving demand response method according to claim 1, characterized in that After the operation state of the swapping station site is adjusted in response to the sub-station plan, the method further includes: Sending the response results corresponding to each swapping station site to the grid response platform respectively, and determining the energy usage corresponding to each response result through the grid response platform. Analyzing each energy usage to determine the peak shaving effect of the corresponding swapping station site during the demand period, and overall evaluating the peak shaving effect. According to the corresponding overall evaluation result, optimizing the power supply strategy corresponding to each swapping station site in the sub-station plan, and realizing the response to the adjustment demand of the grid response platform according to the optimized power supply strategy.

7. A digital-based power grid peak shaving demand response method according to claim 1, characterized in that, The determination of whether the swapping station site has a corresponding supply space specifically includes: When the supply space of each swapping station site is less than the electricity quantity to be responded corresponding to the adjustment demand, determining the electricity quantity difference between the electricity quantity to be responded and the supply space. According to the magnitude relationship between the electricity quantity difference and the lack of supply space threshold, determining the peak shaving demand level corresponding to the electricity quantity difference, and determining the peak shaving strategy corresponding to the adjustment demand according to the peak shaving demand level; the peak shaving demand levels include primary peak shaving demand and secondary peak shaving demand, and the peak shaving strategies include site addition and site layout strategy optimization. Determining that the peak shaving strategy corresponding to the primary peak shaving demand is site addition, and determining the number of swapping station sites to be added and the supply capacity corresponding to each swapping station site according to the electricity quantity difference. Determining that the peak shaving strategy corresponding to the secondary peak shaving demand is site layout strategy optimization, and optimizing the layout of the swapping station sites and adjusting the operation strategy of the swapping station sites according to the electricity quantity difference.

8. A digital-based power grid peak shaving demand response method according to claim 1, characterized in that, Before inputting the demand period into the pre-constructed business volume prediction model, the method further includes: Constructing a business volume prediction model corresponding to the swapping station site based on a machine learning method. Obtaining a plurality of historical order information of the swapping station site within the historical time period, and respectively determining the demand period corresponding to each historical order information and the actual demand electricity quantity during the demand period. Input the historical order information marked with the demand period into the business volume prediction model to train the business volume prediction model until the estimated demand power output by the business volume prediction model matches the actual demand power corresponding to the historical order information, and complete the training of the business volume prediction model.

9. A digital-based power grid peak shaving demand response device, characterized in that, The device includes: A receiving module, configured to receive the adjustment demand initiated by the power grid response platform and determine the demand period corresponding to the adjustment demand; A prediction module, configured to input the demand period into a pre-constructed business volume prediction model and determine whether there is a corresponding supply space at the swap station site in combination with the adjustment demand; An adjustment module, configured to, if so, determine the corresponding total response amount and the sub-station plan, and adjust the operating state of the swap station site in response to the sub-station plan.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the digital-based power grid peak shaving demand response method according to any one of claims 1-8.

11. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, When the computer executes the executable instructions, it implements the digital-based power grid peak shaving demand response method according to any one of claims 1-8.

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