Energy storage configuration method and device for region, processor and electronic equipment

Abstract

The application discloses a regional energy storage configuration method and device, a processor and an electronic device. The method comprises the following steps: obtaining historical power consumption data of at least one power consumption device in a region and historical power generation data of at least one power generation device in the region; based on the historical power consumption data and the historical power generation data, predicting future power consumption data of the power consumption device and future power generation data of the power generation device; based on the future power consumption data, determining future energy storage data for compensating the future power generation data; and according to a capacity configuration strategy, respectively configuring energy storage capacities corresponding to the future energy storage data to a plurality of collaborative power generation devices. The application solves the technical problem of low efficiency of regional energy storage configuration.

Description

Technical Field

[0001] This application relates to the field of energy storage, and more specifically, to a method, apparatus, processor, and electronic device for configuring energy storage in a region. Background Technology

[0002] Currently, driven by the "dual carbon" goal, a multi-level and comprehensive green port has been formed. Among the related technologies, distributed photovoltaic technology is often used to convert solar energy into electricity using the port area rooftops and storage yard space, and offshore wind power technology is also often used to convert wind energy into electricity by leveraging the coastal advantages.

[0003] However, the above-mentioned methods of converting electrical energy are all affected by changes in the port environment, which can affect the efficiency of the energy conversion and lead to the technical problem of low efficiency in regional energy storage configuration.

[0004] There is currently no effective solution to the technical problem of low efficiency in energy storage configuration in the aforementioned areas. Summary of the Invention

[0005] This application provides a method, apparatus, processor, and electronic device for configuring energy storage in a region, to at least solve the technical problem of low efficiency in configuring energy storage in a region.

[0006] According to one aspect of the embodiments of this application, a method for configuring energy storage in a region is provided. The method includes: acquiring historical electricity consumption data of at least one electrical device in the region and historical power generation data of at least one power generation device in the region, wherein the historical electricity consumption data represents the electricity consumption status of the electrical device in a historical period, and the historical power generation data represents the power generation status of the power generation device in a historical period; predicting future electricity consumption data of the electrical device and future power generation data of the power generation device based on the historical electricity consumption data and the historical power generation data, wherein the future electricity consumption data represents the electricity consumption status of the electrical device in a future period, and the future power generation data represents the power generation status of the power generation device in a future period; determining future energy storage data for compensating for the future power generation data based on the future electricity consumption data, wherein the future energy storage data represents the energy storage capacity of multiple cooperating power generation devices that generate electricity in conjunction with the power generation devices; and configuring energy storage capacity corresponding to the future energy storage data to the multiple cooperating power generation devices according to a capacity configuration strategy, wherein the capacity configuration strategy represents the rules for configuring different energy storage capacities to the multiple cooperating power generation devices respectively.

[0007] Optionally, based on historical electricity consumption data and historical power generation data, future electricity consumption data and future power generation data of power-consuming equipment are predicted, including: inputting historical electricity consumption data into the feature extraction layer of the hybrid prediction model for feature extraction to obtain the electricity consumption features of historical electricity consumption data, and inputting historical power generation data into the feature extraction layer for feature extraction to obtain the power generation features of historical power generation data, wherein the hybrid prediction model is used to represent the correlation between historical electricity consumption data and historical power generation data and future electricity consumption data and future power generation data; inputting electricity consumption features into the relationship extraction layer of the hybrid prediction model for relationship extraction to obtain the electricity consumption dependency relationship between the previous electricity consumption feature and the next electricity consumption feature, and inputting power generation features into the relationship extraction layer for relationship extraction to obtain the power generation dependency relationship between the previous power generation feature and the next power generation feature; in the hybrid prediction model, future electricity consumption data and future power generation data are predicted based on electricity consumption features, power generation features, electricity consumption dependency relationship, and power generation dependency relationship.

[0008] Optionally, in the hybrid prediction model, future electricity consumption data and future power generation data are predicted based on electricity consumption characteristics, power generation characteristics, electricity consumption dependence, and power generation dependence. This includes inputting the electricity consumption characteristics, power generation characteristics, electricity consumption dependence, and power generation dependence into the prediction layer of the hybrid prediction model for prediction to obtain future electricity consumption data and future power generation data.

[0009] Optionally, based on future electricity consumption data, future energy storage data for compensating future power generation data is determined, including: determining the electricity consumption of the power generation equipment in the future time period from the future electricity consumption data, and determining the power generation of the power generation equipment in the future time period from the future power generation data; and determining the difference between the electricity consumption and the power generation as the energy storage capacity corresponding to the future energy storage data.

[0010] Optionally, the method further includes: searching the configuration library according to the energy storage capacity to obtain multiple candidate power generation devices that meet the configuration rules with the energy storage capacity, and determining the multiple candidate power generation devices as multiple collaborative power generation devices, wherein the configuration library includes: configuration rules between different energy storage capacities and different candidate power generation devices.

[0011] Optionally, the method further includes: determining reference indicators for multiple collaborative power generation devices to obtain multiple reference indicators, and determining weights for multiple reference indicators to obtain multiple weights; adjusting the reference indicators using the weights; and determining a preset capacity configuration strategy that matches the multiple adjusted reference indicators from a capacity configuration strategy library as a capacity configuration strategy, wherein the capacity configuration strategy library includes different preset capacity configuration strategies that match different reference indicators.

[0012] Optionally, the plurality of the collaborative power generation devices include: supercapacitors and lithium batteries, wherein, according to a capacity configuration strategy, the energy storage capacity corresponding to future energy storage data is configured for the plurality of collaborative power generation devices, including: determining the capacity configuration ratio of energy storage capacity in supercapacitors and lithium batteries according to the capacity configuration strategy; and configuring the energy storage capacity corresponding to future energy storage data for supercapacitors and lithium batteries respectively according to the capacity configuration ratio.

[0013] According to one aspect of the embodiments of this application, a regional energy storage configuration device is provided. The device may include: an acquisition unit, configured to acquire historical electricity consumption data of at least one electrical device in the region, and historical power generation data of at least one power generation device in the region, wherein the historical electricity consumption data represents the electricity consumption status of the electrical device in a historical period, and the historical power generation data represents the power generation status of the power generation device in a historical period; a prediction unit, configured to predict future electricity consumption data of the electrical device and future power generation data of the power generation device based on the historical electricity consumption data and the historical power generation data, wherein the future electricity consumption data represents the electricity consumption status of the electrical device in a future period, and the future power generation data represents the power generation status of the power generation device in a future period; a first determination unit, configured to determine future energy storage data for compensating for the future power generation data based on the future electricity consumption data, wherein the future energy storage data represents the energy storage capacity of multiple cooperating power generation devices that generate electricity in conjunction with the power generation devices; and a configuration unit, configured to configure the energy storage capacity corresponding to the future energy storage data to the multiple cooperating power generation devices according to a capacity configuration strategy, wherein the capacity configuration strategy represents the rules for configuring different energy storage capacities to the multiple cooperating power generation devices respectively.

[0014] According to another aspect of the embodiments of this application, a processor is also provided. The processor is used to run a program, wherein the program is executed by the processor to perform the methods described in the embodiments of this application.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided. This computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of the embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, the computer program product including a computer program, wherein the computer program implements the method in the embodiments of this application when executed by a processor.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the method in the embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods described in the embodiments of this application.

[0020] In this embodiment, historical electricity consumption data of at least one electrical device in the region and historical power generation data of at least one power generation device in the region are acquired. Based on the historical electricity consumption data and historical power generation data, future electricity consumption data of the electrical device and future power generation data of the power generation device are predicted. Based on the future electricity consumption data, future energy storage data for compensating for future power generation data is determined. According to a capacity configuration strategy, energy storage capacity corresponding to the future energy storage data is configured for multiple collaborative power generation devices. Since this embodiment combines the acquired historical electricity consumption data and historical power generation data, future power generation data can be predicted. Based on the predicted future electricity consumption data, future energy storage data for compensating for future power generation data can be determined. Furthermore, according to the rule of configuring different energy storage capacities for multiple collaborative power generation devices, energy storage capacity corresponding to the future energy storage data is configured for multiple collaborative power generation devices. This achieves the goal of avoiding the difficulty in guaranteeing regional energy storage due to environmental changes, thereby solving the technical problem of low efficiency in regional energy storage configuration and ultimately achieving the technical effect of improving the efficiency of regional energy storage configuration. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a schematic diagram illustrating an application scenario of a regional energy storage configuration method according to an embodiment of this application;

[0023] Figure 2 This is a flowchart of a regional energy storage configuration method according to an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of a port energy storage capacity determination system based on improved source load prediction, according to an embodiment of this application.

[0025] Figure 4 This is a flowchart of a port energy storage capacity determination method based on improved source load prediction, according to an embodiment of this application.

[0026] Figure 5This is a schematic diagram of a regional energy storage configuration device according to an embodiment of this application;

[0027] Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] According to an embodiment of this application, an embodiment of a regional energy storage configuration method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] As an optional implementation, the energy storage configuration method for the aforementioned areas can be applied, but is not limited to, to areas such as... Figure 1 The application scenarios shown. Figure 1 This is a schematic diagram illustrating an application scenario of a regional energy storage configuration method according to an embodiment of this application, such as... Figure 1As shown, in the application scenario, terminal device 10 can communicate with server 13 via network 11, but is not limited to this. Server 13 can perform operations on the database, such as write or read data operations. Terminal device 10 may include, but is not limited to, a human-computer interaction screen, a processor, and a memory. The human-computer interaction screen may be used to display virtual machines on the mobile terminal 10, but is not limited to this. Interaction device 12 may be used to respond to the aforementioned human-computer interaction operations, execute corresponding operations, or generate corresponding instructions and send the generated instructions to server 13.

[0032] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here. Specifically, the regional energy storage configuration method in this application may include: step S102, acquiring historical electricity consumption data of at least one electrical device in the region, and historical power generation data of at least one power generation device in the region; step S104, predicting future electricity consumption data of the electrical device and future power generation data of the power generation device based on the historical electricity consumption data and historical power generation data; step S106, determining future energy storage data for compensating for future power generation data based on the future electricity consumption data; and step S108, configuring the energy storage capacity corresponding to the future energy storage data to multiple coordinating power generation devices according to a capacity configuration strategy.

[0033] It should be noted that all relevant information and data involved in this application (including but not limited to historical electricity consumption data and historical power generation data) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of such data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0034] According to an embodiment of this application, a method for configuring energy storage in a region is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 2 This is a flowchart of a regional energy storage configuration method according to an embodiment of this application, such as... Figure 2 As shown, the method may include the following steps.

[0036] Step S201: Obtain historical electricity consumption data of at least one electrical device in the area, and historical power generation data of at least one power generation device in the area.

[0037] In the technical solution provided in step S201 of this application, the aforementioned historical electricity consumption data can be used to represent the electricity consumption status of electrical equipment during a historical period. Optionally, the aforementioned historical electricity consumption data can also be referred to as historical load data. For example, if the historical period is from October 11, 2025 to October 31, 2025, then the aforementioned historical electricity consumption data can be used to represent the electricity consumption status of electrical equipment during the period from October 11, 2025 to October 31, 2025. The values ​​here are only illustrative examples and are not specifically limited.

[0038] In this embodiment, the aforementioned historical power generation data can be used to represent the power generation status of the power generation equipment within a historical period. For example, if the power generation equipment is a wind turbine, the historical power generation data can be used to represent the power generation status of the wind turbine within a historical period; if the power generation equipment is a photovoltaic device, the historical power generation data can be used to represent the power generation status of the photovoltaic device within a historical period. This is merely an example and is not intended to be specific.

[0039] In this embodiment, historical electricity consumption data of at least one electrical device in the region and historical power generation data of at least one power generating device in the region are obtained. Optionally, this embodiment can determine the historical electricity consumption data of at least one electrical device in the region and the historical power generation data of at least one power generating device in the region from a power database. The power database may include historical electricity consumption data of at least one electrical device in different regions and historical power generation data of at least one power generating device in different regions. That is, by searching the power database according to region, the aforementioned historical electricity consumption data and historical power generation data can be obtained.

[0040] Optionally, historical electricity consumption data of electrical equipment can be obtained by accessing the electricity consumption logs of electrical equipment in the area, and historical power generation data of power generation equipment can be obtained by accessing the power generation logs of power generation equipment in the area.

[0041] Step S202: Based on historical electricity consumption data and historical power generation data, predict the future electricity consumption data of the electrical equipment and the future power generation data of the power generation equipment.

[0042] In the technical solution provided by step S202 of this application, the aforementioned future electricity consumption data can be used to represent the electricity consumption status of electrical equipment in a future time period. Optionally, the aforementioned future electricity consumption data can also be referred to as future load data.

[0043] In this embodiment, the aforementioned future power generation data can be used to represent the power generation status of the power generation equipment in a future period. Optionally, the aforementioned future power generation data can also be referred to as future source data. For example, if the aforementioned power generation equipment is a wind turbine, then the aforementioned future power generation data can be used to represent the power generation status of the wind turbine in a future period; if the aforementioned power generation equipment is a photovoltaic equipment, then the aforementioned future power generation data can be used to represent the power generation status of the photovoltaic equipment in a future period.

[0044] In this embodiment, after acquiring historical electricity consumption data of at least one electrical device and historical power generation data of at least one power generation device in the area, future electricity consumption data of the electrical device and future power generation data of the power generation device are predicted based on the historical electricity consumption data and historical power generation data. Optionally, based on the acquired historical electricity consumption data and historical power generation data, this embodiment can extract electricity consumption characteristics from the historical electricity consumption data and power generation characteristics from the historical power generation data. Combining the above-mentioned electricity consumption characteristics and power generation characteristics, future electricity consumption data of the electrical device and future power generation data of the power generation device can be predicted, thereby achieving the purpose of predicting the electricity consumption status of the electrical device and the power generation status of the power generation device in the future time period.

[0045] Optionally, under the above-mentioned power generation characteristics, the above-mentioned power consumption characteristics are input into the power consumption prediction model for prediction, and future power consumption data of the electrical equipment can be obtained. The above-mentioned power consumption prediction model can be used to represent the correlation between the power consumption characteristics and the future power consumption data, and the above-mentioned correlation is affected by the above-mentioned power generation characteristics.

[0046] Optionally, under the aforementioned electricity consumption characteristics, the aforementioned power generation characteristics are input into the power generation prediction model for prediction, thereby obtaining future power generation data of the power generation equipment. The aforementioned power generation prediction model can be used to represent the correlation between the power generation characteristics and the future power generation data, and the aforementioned correlation is affected by the aforementioned electricity consumption characteristics.

[0047] Step S203: Based on future electricity consumption data, determine the future energy storage data to compensate for future power generation data.

[0048] In the technical solution provided in step S203 of this application, the aforementioned future energy storage data can be used to represent the energy storage capacity of multiple co-generating power generation devices that work in conjunction with the generating equipment. These multiple co-generating power generation devices can include: a first type of generating power generation device and a second type of generating power generation device. For example, the first type of generating power generation device can be a supercapacitor, a lithium battery, or a graphene battery, etc., and the second type of generating power generation device can be a supercapacitor, a lithium battery, or a graphene battery, etc.

[0049] In this embodiment, after predicting the future electricity consumption data of the power-consuming equipment and the future power generation data of the power-generating equipment based on historical electricity consumption data and historical power generation data, future energy storage data for compensating for the future power generation data is determined based on the future electricity consumption data. Optionally, in this embodiment, based on the predicted future electricity consumption data, the aforementioned future electricity consumption data is input into an energy storage prediction model to perform energy storage prediction, thereby obtaining future energy storage data. The aforementioned energy storage prediction model can be used to represent the correlation between the future electricity consumption data and the future energy storage data.

[0050] Optionally, by using the aforementioned future electricity consumption data to perform compensation calculations on the aforementioned future power generation data, future energy storage data for compensating the future power generation data can be obtained, thereby achieving the goal of determining the energy storage capacity of multiple cooperating power generation devices that generate power in conjunction with the power generation equipment.

[0051] Step S204: According to the capacity configuration strategy, configure the energy storage capacity corresponding to the future energy storage data to multiple collaborative power generation devices respectively.

[0052] In the technical solution provided by step S204 of this application, the capacity configuration strategy can be used to represent the rules for configuring different energy storage capacities to multiple collaborative power generation devices respectively.

[0053] In this embodiment, after determining the future energy storage data to compensate for future power generation data based on future electricity consumption data, the energy storage capacity corresponding to the future energy storage data is configured for multiple collaborative power generation devices according to the capacity configuration strategy.

[0054] Optionally, based on the determination of future energy storage data, according to the capacity configuration strategy, the energy storage capacity corresponding to the future energy storage data is configured to the first type of power generation equipment and the second type of power generation equipment respectively, thereby achieving the purpose of allocating the appropriate energy storage capacity to multiple collaborative power generation equipment.

[0055] Optionally, multiple reference indicators are determined for each of the collaborative power generation devices, resulting in multiple reference indicators. A preset configuration ratio corresponding to one reference indicator is determined as a capacity configuration ratio of the energy storage capacity among the collaborative power generation devices corresponding to that reference indicator. According to the aforementioned capacity configuration ratio, the energy storage capacity satisfied by the aforementioned capacity configuration ratio is configured for the collaborative power generation devices corresponding to that reference indicator.

[0056] In steps S201 to S204 of this application, historical electricity consumption data of at least one electrical device in the region and historical power generation data of at least one power generation device in the region are obtained. Based on the historical electricity consumption data and historical power generation data, future electricity consumption data of the electrical device and future power generation data of the power generation device are predicted. Based on the future electricity consumption data, future energy storage data for compensating for future power generation data is determined. According to the capacity configuration strategy, the energy storage capacity corresponding to the future energy storage data is configured for multiple collaborative power generation devices. Since the embodiments of this application combine the obtained historical electricity consumption data and historical power generation data, future power generation data can be predicted. Based on the predicted future electricity consumption data, future energy storage data for compensating for future power generation data can be determined. According to the rule of configuring different energy storage capacities for multiple collaborative power generation devices, the energy storage capacity corresponding to the future energy storage data is configured for multiple collaborative power generation devices. This achieves the goal of avoiding the difficulty in guaranteeing regional energy storage due to environmental changes, thereby solving the technical problem of low efficiency in regional energy storage configuration and achieving the technical effect of improving the efficiency of regional energy storage configuration.

[0057] The following section further describes the steps of predicting future electricity consumption data of electrical equipment and future power generation data of power generation equipment based on historical electricity consumption data and historical power generation data in this embodiment.

[0058] As an optional embodiment, step S202, based on historical electricity consumption data and historical power generation data, predicts future electricity consumption data of electrical equipment and future power generation data of power generation equipment, including: inputting historical electricity consumption data into the feature extraction layer of the hybrid prediction model for feature extraction to obtain the electricity consumption features of historical electricity consumption data, and inputting historical power generation data into the feature extraction layer for feature extraction to obtain the power generation features of historical power generation data; inputting the electricity consumption features into the relationship extraction layer of the hybrid prediction model for relationship extraction to obtain the electricity consumption dependency relationship between the previous and subsequent electricity consumption features, and inputting the power generation features into the relationship extraction layer for relationship extraction to obtain the power generation dependency relationship between the previous and subsequent power generation features; in the hybrid prediction model, based on the electricity consumption features, power generation features, electricity consumption dependency relationship, and power generation dependency relationship, predicts future electricity consumption data and future power generation data.

[0059] In this embodiment, the hybrid prediction model described above can be used to represent the correlation between historical electricity consumption data and historical power generation data, and future electricity consumption data and future power generation data. For example, the hybrid prediction model described above can be a hybrid neural network.

[0060] In this embodiment, the feature extraction layer can be a convolutional layer in a hybrid neural network.

[0061] In this embodiment, the aforementioned electricity consumption characteristics and the aforementioned power generation characteristics are local time-series characteristics.

[0062] In this embodiment, after acquiring historical electricity consumption data of at least one electrical device in the region and historical power generation data of at least one power generation device in the region, the historical electricity consumption data is input into the feature extraction layer of the hybrid prediction model for feature extraction to obtain the electricity consumption features of the historical electricity consumption data, and the historical power generation data is input into the feature extraction layer for feature extraction to obtain the power generation features of the historical power generation data.

[0063] Optionally, the aforementioned historical load data can be input into the convolutional layer of a hybrid neural network for feature extraction to obtain the load characteristics of the historical load data, and the historical power generation data can be input into the convolutional layer of a hybrid neural network for feature extraction to obtain the power generation characteristics of the historical power generation data. This achieves the purpose of extracting electricity consumption characteristics and power generation characteristics.

[0064] In this embodiment, the aforementioned relationship extraction layer can be a two-layer bidirectional long short-term memory network layer in a hybrid neural network.

[0065] In this embodiment, after inputting historical electricity consumption data into the feature extraction layer of the hybrid prediction model to extract features and obtaining historical power generation data into the feature extraction layer to extract features and obtain historical power generation data into the power generation layer, the electricity consumption features are input into the relationship extraction layer of the hybrid prediction model to extract relationships and obtain the electricity consumption dependency relationship between the previous and subsequent electricity consumption features. Similarly, the power generation features are input into the relationship extraction layer to extract relationships and obtain the power generation dependency relationship between the previous and subsequent power generation features.

[0066] Optionally, by inputting load features into a two-layer bidirectional long short-term memory network layer in a hybrid neural network for relation extraction, the load dependency relationship between previous and subsequent load features can be obtained. Similarly, by inputting power generation features into a two-layer bidirectional long short-term memory network layer in a hybrid neural network for relation extraction, the power generation dependency relationship between previous and subsequent power generation features can be obtained.

[0067] In this embodiment, after inputting electricity consumption features into the relationship extraction layer of the hybrid prediction model to extract relationships and obtain the electricity consumption dependency relationship between the previous and subsequent electricity consumption features, and inputting power generation features into the relationship extraction layer to extract relationships and obtain the power generation dependency relationship between the previous and subsequent power generation features, the hybrid prediction model predicts future electricity consumption data and future power generation data based on the electricity consumption features, power generation features, electricity consumption dependency relationship, and power generation dependency relationship.

[0068] Optionally, given electricity consumption characteristics and electricity consumption dependence, a hybrid neural network can be used to predict power generation characteristics by referring to power generation dependence, thereby obtaining future power generation data. Similarly, given power generation characteristics and power generation dependence, a hybrid neural network can be used to predict electricity consumption characteristics by referring to electricity consumption dependence, thereby obtaining future electricity consumption data. This achieves the goal of predicting the electricity consumption status of electrical equipment and the power generation status of power generation equipment in future periods, thus improving the accuracy of future electricity consumption and future power generation data.

[0069] The following section further describes the steps in the above-described hybrid prediction model of this embodiment to predict future electricity consumption data and future power generation data based on electricity consumption characteristics, power generation characteristics, electricity consumption dependence, and power generation dependence.

[0070] As an optional implementation method, in the hybrid prediction model, future electricity consumption data and future power generation data are predicted based on electricity consumption characteristics, power generation characteristics, electricity consumption dependence, and power generation dependence. This includes: inputting the electricity consumption characteristics, power generation characteristics, electricity consumption dependence, and power generation dependence into the prediction layer of the hybrid prediction model for prediction to obtain future electricity consumption data and future power generation data.

[0071] In this embodiment, the prediction layer can be a fully connected layer in a hybrid neural network.

[0072] In this embodiment, after inputting electricity consumption features into the relationship extraction layer of the hybrid prediction model to extract relationships and obtain the electricity consumption dependency relationship between the previous and subsequent electricity consumption features, and inputting power generation features into the relationship extraction layer to extract relationships and obtain the power generation dependency relationship between the previous and subsequent power generation features, the electricity consumption features, power generation features, electricity consumption dependency relationship and power generation dependency relationship are input into the prediction layer of the hybrid prediction model for prediction to obtain future electricity consumption data and future power generation data.

[0073] Optionally, given load characteristics and electricity consumption dependence, a fully connected layer in a hybrid neural network can be used to predict power generation characteristics by referencing power generation dependence, thereby obtaining future source-end data. Similarly, given power generation characteristics and power generation dependence, a fully connected layer in a hybrid neural network can be used to predict load characteristics by referencing load dependence, thereby obtaining future load data. This achieves the goal of predicting the electricity consumption status of electrical equipment and the power generation status of power generation equipment in future periods, thus improving the accuracy of future electricity consumption and power generation data.

[0074] For example, by using the convolutional layers in a hybrid neural network to extract features from the aforementioned historical load data and historical power generation data, local time-series features can be obtained. The relation extraction layer can utilize the two-layer bidirectional long short-term memory network in the hybrid neural network to extract dependencies on the local time-series features, thereby obtaining the dependencies between forward and backward time-series features. The fully connected layer can combine the local time-series features and dependencies to predict and output future source data and future load data.

[0075] The following section further describes the steps of determining future energy storage data to compensate for future power generation data based on future electricity consumption data in this embodiment.

[0076] As an optional embodiment, step S203, based on future electricity consumption data, determines future energy storage data to compensate for future power generation data, including: determining the electricity consumption of the power generation equipment in the future time period from the future electricity consumption data, and determining the power generation of the power generation equipment in the future time period from the future power generation data; and determining the difference between the electricity consumption and the power generation as the energy storage capacity corresponding to the future energy storage data.

[0077] In this embodiment, after predicting the future electricity consumption data of the power-consuming equipment and the future power generation data of the power-generating equipment based on historical electricity consumption data and historical power generation data, the electricity consumption of the power-generating equipment in the future time period is determined from the future electricity consumption data, and the power generation of the power-generating equipment in the future time period is determined from the future power generation data. Then, the difference between the electricity consumption and the power generation is determined as the energy storage capacity corresponding to the future energy storage data.

[0078] Optionally, by detecting future electricity consumption data, the electricity consumption of the power generation equipment over the future period can be obtained; similarly, by detecting future power generation data, the power generation of the power generation equipment over the future period can be obtained. Then, the difference between the electricity consumption and the power generation is calculated to obtain the electricity difference between the two. This electricity difference is then used to determine the energy storage capacity corresponding to the future energy storage data. This achieves the goal of determining the energy storage capacity of multiple cooperating power generation devices, thereby improving the accuracy of energy storage capacity.

[0079] The energy storage configuration method for this region in this embodiment will be further described below.

[0080] As an optional embodiment, the method further includes: searching a configuration library according to the energy storage capacity to obtain multiple candidate power generation devices that meet the configuration rules with the energy storage capacity, and determining the multiple candidate power generation devices as multiple collaborative power generation devices.

[0081] In this embodiment, the configuration library may include configuration rules between different energy storage capacities and different candidate power generation devices.

[0082] In this embodiment, the configuration library is searched according to the energy storage capacity. That is, the energy storage capacity corresponding to future energy storage data is searched in the configuration library. If the different energy storage capacities mentioned above include the energy storage capacity corresponding to the future energy storage data, the search process is completed, and multiple candidate power generation devices that meet the configuration rules with the energy storage capacity are obtained. The multiple candidate power generation devices found are then identified as multiple collaborative power generation devices, thereby achieving the goal of identifying multiple collaborative power generation devices and thus realizing the technical effect of improving the availability of collaborative power generation devices.

[0083] The energy storage configuration method for this region in this embodiment will be further described below.

[0084] As an optional embodiment, the method further includes: determining reference indicators for multiple collaborative power generation devices to obtain multiple reference indicators, and determining weights for the multiple reference indicators to obtain multiple weights; adjusting the reference indicators using the weights; and determining a preset capacity configuration strategy that matches the multiple adjusted reference indicators from a capacity configuration strategy library as a capacity configuration strategy.

[0085] In this embodiment, the aforementioned reference indicators may include: economic benefits, output power, and electricity cost savings, etc.

[0086] In this embodiment, the capacity configuration strategy library may include different preset capacity configuration strategies that match different reference metrics.

[0087] In this embodiment, after determining reference indicators for multiple collaborative power generation devices, obtaining multiple reference indicators, and determining weights for multiple reference indicators, obtaining multiple weights, the reference indicators are adjusted using the weights.

[0088] Optionally, the economic benefits, output power, and electricity cost savings of multiple coordinated power generation devices are determined separately, resulting in multiple economic benefits, output power, and electricity cost savings. Weights for each of the multiple economic benefits, output power, and electricity cost savings are also determined. The economic benefits are adjusted using the weights of the economic benefits to obtain adjusted economic benefits; the output power is adjusted using the weights of the output power to obtain adjusted output power; and the electricity cost savings are adjusted using the weights of the electricity cost savings to obtain adjusted electricity cost savings.

[0089] In this embodiment, after adjusting the reference indicators using weights, a preset capacity configuration strategy that matches multiple adjusted reference indicators is determined as the capacity configuration strategy from the capacity configuration strategy library.

[0090] Optionally, multiple adjusted reference indicators are searched in the capacity configuration strategy library. If different reference indicators include multiple adjusted reference indicators, the reference indicator search process is completed, and a preset capacity configuration strategy matching the multiple adjusted reference indicators is obtained. The preset capacity configuration strategy obtained is then determined as the capacity configuration strategy. This achieves the goal of determining the rules for configuring different energy storage capacities to multiple collaborative power generation devices, thereby realizing the technical effect of improving the reliability of the capacity configuration strategy.

[0091] The following section further describes the steps of configuring the energy storage capacity corresponding to future energy storage data for multiple collaborative power generation devices according to the capacity configuration strategy described above in this embodiment.

[0092] As an optional embodiment, the multiple collaborative power generation devices include: supercapacitors and lithium batteries. In step S204, according to a capacity configuration strategy, the energy storage capacity corresponding to future energy storage data is configured for each of the multiple collaborative power generation devices. This includes: determining the capacity configuration ratio of energy storage capacity in the supercapacitors and lithium batteries according to the capacity configuration strategy; and configuring the energy storage capacity corresponding to future energy storage data for the supercapacitors and lithium batteries according to the capacity configuration ratio.

[0093] In this embodiment, the multiple collaborative power generation devices may include: supercapacitors and lithium batteries.

[0094] In this embodiment, after determining the future energy storage data to compensate for future power generation data based on future electricity consumption data, the capacity configuration ratio of energy storage capacity in supercapacitors and lithium batteries is determined according to the capacity configuration strategy.

[0095] Optionally, based on the determined capacity configuration strategy, the energy storage capacity can be configured and divided according to the above capacity configuration strategy to obtain the capacity configuration ratio of energy storage capacity in supercapacitors and lithium batteries. That is, the first capacity configuration ratio of energy storage capacity in supercapacitors and the second capacity configuration ratio of energy storage capacity in lithium batteries can be obtained.

[0096] In this embodiment, after determining the capacity configuration ratio of energy storage capacity in supercapacitors and lithium batteries according to the capacity configuration strategy, the energy storage capacity corresponding to future energy storage data is configured for supercapacitors and lithium batteries respectively according to the capacity configuration ratio.

[0097] Optionally, according to the first capacity configuration ratio, the energy storage capacity satisfied by the first capacity configuration ratio is configured to the supercapacitor, and according to the second capacity configuration ratio, the energy storage capacity satisfied by the second capacity configuration ratio is configured to the lithium battery. This achieves the goal of configuring the energy storage capacity corresponding to future energy storage data to the supercapacitor and the lithium battery respectively according to the capacity configuration ratio, thereby realizing the technical effect of improving the efficiency of regional energy storage configuration.

[0098] In this embodiment, by combining the acquired historical electricity consumption data and historical power generation data, future power generation data can be predicted. Based on the predicted future electricity consumption data, future energy storage data to compensate for future power generation data can be determined. In accordance with the rule of configuring different energy storage capacities for multiple collaborative power generation devices, the energy storage capacity corresponding to the future energy storage data is configured for each of the multiple collaborative power generation devices. This achieves the goal of avoiding the difficulty in guaranteeing regional energy storage due to environmental changes, thereby solving the technical problem of low efficiency in regional energy storage configuration and thus realizing the technical effect of improving the efficiency of regional energy storage configuration.

[0099] The technical solutions of the embodiments of this application will be illustrated below with reference to preferred embodiments.

[0100] Currently, driven by the "dual carbon" goal, a multi-level and comprehensive green port has been formed. Among the related technologies, distributed photovoltaic technology is often used to convert solar energy into electricity using the port area rooftops and storage yard space, and offshore wind power technology is also often used to convert wind energy into electricity by leveraging the coastal advantages.

[0101] However, the above-mentioned methods of converting electrical energy are all affected by changes in the port environment, which can affect the efficiency of the energy conversion and lead to the technical problem of low efficiency in regional energy storage configuration.

[0102] To address the aforementioned technical problems, this application proposes a regional energy storage configuration method. By combining historical electricity consumption data and historical power generation data, future power generation data can be predicted. Based on the predicted future electricity consumption data, future energy storage data to compensate for future power generation data can be determined. Furthermore, according to the rule of configuring different energy storage capacities for multiple collaborative power generation devices, the energy storage capacity corresponding to the future energy storage data is configured for each of the multiple collaborative power generation devices. This achieves the goal of avoiding the difficulty in guaranteeing regional energy storage due to environmental changes, thereby solving the technical problem of low efficiency in regional energy storage configuration and ultimately achieving the technical effect of improving the efficiency of regional energy storage configuration.

[0103] In this embodiment, a port energy storage capacity-determining system based on improved source-load prediction can output an energy storage configuration scheme that meets the port's energy storage needs. For example, Figure 3 This is a schematic diagram of a port energy storage capacity determination system based on improved source load prediction, according to an embodiment of this application. Figure 3 As shown, the system 300 may include: a data acquisition module 301, a source-load prediction module 302, an energy storage optimization module 303, and an output configuration module 304. The source-load prediction module 302 may include: a feature extraction layer 3021, a relationship extraction layer 3022, and a fully connected layer 3023. The energy storage optimization module 303 may include: a data acquisition layer 3031, a solution layer 3032, an update layer 3033, and an optimization layer 3034.

[0104] In this embodiment, the data acquisition module 301 can transmit the historical load data of the electrical equipment and the historical power generation data of the power generation equipment to the source-load prediction module 302.

[0105] In this embodiment, the source-load prediction module 302 can receive the historical load data and the historical power generation data, and predict future source-end data and future load data based on the historical load data and the historical power generation data. Specifically, the feature extraction layer 3021 can be used to extract features from the historical load data and the historical power generation data using convolutional layers in a hybrid neural network to obtain local time-series features. The relationship extraction layer 3022 can be used to extract dependencies from the local time-series features using a two-layer bidirectional long short-term memory network in a hybrid neural network, obtaining the dependency relationship between forward and backward time-series features. The fully connected layer 3023 can be used to combine the local time-series features and dependencies to predict and output future source-end data and future load data.

[0106] In this embodiment, the energy storage optimization module 303 can be used to receive future source data and future load data, and based on the future source data and future load data, determine an energy storage configuration scheme that meets the port's energy storage needs. Specifically, the data acquisition layer 3031 can be used to acquire future source data and future load data from the source-load prediction module 302; the solution layer 3032 can be used to combine the future source data and future load data to seek the optimal solution, that is, to seek a configuration method for the energy storage capacity; the update layer 3033 can be used to update the configuration method for the energy storage capacity, wherein the configuration method is to configure supercapacitors and lithium batteries in the port; and the optimization layer 3034 can be used to determine an energy storage configuration scheme that meets the port's energy storage needs based on the weights of the following indicators for supercapacitors and lithium batteries: economic benefits, output power, and electricity cost savings. The scheme can include: the energy storage capacity to be allocated to supercapacitors and lithium batteries, and the cost required to allocate the energy storage capacity to supercapacitors and lithium batteries.

[0107] In this embodiment, the output configuration module 304 can be used to output an energy storage configuration scheme for the port's energy storage needs, so as to prompt the port to configure supercapacitors and lithium batteries according to the above scheme.

[0108] For example, by implementing a port energy storage capacity determination method based on improved source-load forecasting, an energy storage configuration scheme that meets the port's energy storage needs can be output. For instance, Figure 4 This is a flowchart of a port energy storage capacity determination method based on improved source load prediction, according to an embodiment of this application. Figure 4 As shown, the method may include the following steps.

[0109] Step S401: Obtain historical load data of electrical equipment and historical power generation data of power generation equipment.

[0110] After acquiring historical load data and historical power generation data, step S402 is executed, in which the convolutional layer in the hybrid neural network is used to extract features from the historical load data and historical power generation data to obtain local time-series features.

[0111] In the technical solution provided by step S402 of this application, the hybrid neural network is a neural network that combines convolutional layers and a two-layer bidirectional long short-term memory network.

[0112] After extracting features from the historical load data and historical power generation data through the convolutional layers in the hybrid neural network to obtain local time-series features, step S403 is executed. The dependency relationship between the forward time-series features and the backward time-series features is extracted through the two-layer bidirectional long short-term memory network in the hybrid neural network.

[0113] After extracting the dependencies of local temporal features through a two-layer bidirectional long short-term memory network in a hybrid neural network, and obtaining the dependencies between forward and backward temporal features, step S404 is executed to combine local temporal features and dependencies to predict and output future source data and future load data.

[0114] After combining local time-series characteristics and dependencies to predict and output future source data and future load data, step S405 is executed to combine the future source data and future load data to seek the configuration method of energy storage capacity.

[0115] After combining future source data and future load data to determine the configuration method for energy storage capacity, step S406 is executed to update the configuration method for energy storage capacity.

[0116] After updating the configuration method of energy storage capacity, step S407 is executed to determine the energy storage configuration scheme that meets the energy storage needs of the port based on the following weights of supercapacitors and lithium batteries: economic benefits, output power and electricity cost savings.

[0117] In the technical solution provided by step S407 of this application, the energy storage configuration scheme may include: the energy storage capacity to be allocated to the supercapacitor and the lithium battery, and the cost required to complete the allocation of the energy storage capacity to the supercapacitor and the lithium battery.

[0118] After determining the energy storage configuration scheme that meets the port's energy storage needs, step S408 is executed to output the energy storage configuration scheme that meets the port's energy storage needs.

[0119] In this embodiment, by combining the acquired historical electricity consumption data and historical power generation data, future power generation data can be predicted. Based on the predicted future electricity consumption data, future energy storage data to compensate for future power generation data can be determined. In accordance with the rule of configuring different energy storage capacities for multiple collaborative power generation devices, the energy storage capacity corresponding to the future energy storage data is configured for each of the multiple collaborative power generation devices. This achieves the goal of avoiding the difficulty in guaranteeing regional energy storage due to environmental changes, thereby solving the technical problem of low efficiency in regional energy storage configuration and thus realizing the technical effect of improving the efficiency of regional energy storage configuration.

[0120] According to an embodiment of this application, a regional energy storage configuration device is also provided. It should be noted that this regional energy storage configuration device can be used to execute a regional energy storage configuration method as described in the embodiments.

[0121] Figure 5 This is a schematic diagram of a regional energy storage configuration device according to an embodiment of this application. Figure 5As shown, the energy storage configuration device 500 in this area may include: an acquisition unit 501, a prediction unit 502, a first determination unit 503, and a configuration unit 504.

[0122] The acquisition unit 501 is used to acquire historical electricity consumption data of at least one electrical device in the area and historical power generation data of at least one power generation device in the area. The historical electricity consumption data is used to represent the electricity consumption status of the electrical device in a historical period, and the historical power generation data is used to represent the power generation status of the power generation device in a historical period.

[0123] The prediction unit 502 is used to predict the future electricity consumption data of the electrical equipment and the future power generation data of the power generation equipment based on historical electricity consumption data and historical power generation data. The future electricity consumption data is used to represent the electricity consumption status of the electrical equipment in the future period, and the future power generation data is used to represent the power generation status of the power generation equipment in the future period.

[0124] The first determining unit 503 is used to determine future energy storage data for compensating future power generation data based on future electricity consumption data, wherein the future energy storage data is used to represent the energy storage capacity of multiple cooperating power generation devices that generate power in conjunction with the power generation equipment.

[0125] Configuration unit 504 is used to configure the energy storage capacity corresponding to future energy storage data to multiple collaborative power generation devices according to the capacity configuration strategy, wherein the capacity configuration strategy is used to represent the rules for configuring different energy storage capacities to multiple collaborative power generation devices respectively.

[0126] Optionally, the prediction unit 502 may include: a first extraction module, used to input historical electricity consumption data into the feature extraction layer of the hybrid prediction model for feature extraction to obtain the electricity consumption features of the historical electricity consumption data, and input historical power generation data into the feature extraction layer for feature extraction to obtain the power generation features of the historical power generation data, wherein the hybrid prediction model is used to represent the correlation between historical electricity consumption data and historical power generation data, and future electricity consumption data and future power generation data; a second extraction module, used to input the electricity consumption features into the relationship extraction layer of the hybrid prediction model for relationship extraction to obtain the electricity consumption dependency relationship between the previous electricity consumption feature and the next electricity consumption feature, and input the power generation features into the relationship extraction layer for relationship extraction to obtain the power generation dependency relationship between the previous power generation feature and the next power generation feature; and a prediction module, used to predict future electricity consumption data and future power generation data in the hybrid prediction model based on the electricity consumption features, power generation features, electricity consumption dependency relationship, and power generation dependency relationship.

[0127] Optionally, the prediction module may include a prediction submodule, which is used to input electricity consumption characteristics, power generation characteristics, electricity consumption dependence and power generation dependence into the prediction layer of the hybrid prediction model for prediction, so as to obtain future electricity consumption data and future power generation data.

[0128] Optionally, the first determining unit 503 may include: a first determining module, used to determine the electricity consumption of the power generation equipment in the future time period from the future electricity consumption data, and to determine the power generation of the power generation equipment in the future time period from the future power generation data; and a second determining module, used to determine the difference between the electricity consumption and the power generation as the energy storage capacity corresponding to the future energy storage data.

[0129] Optionally, the energy storage configuration device 500 in the region may further include: a second determining unit, configured to search the configuration library according to the energy storage capacity to obtain multiple candidate power generation devices that meet the configuration rules with the energy storage capacity, and to determine the multiple candidate power generation devices as multiple collaborative power generation devices, wherein the configuration library includes: configuration rules between different energy storage capacities and different candidate power generation devices.

[0130] Optionally, the energy storage configuration device 500 in this area may further include: a third determining unit, configured to determine reference indicators for multiple collaborative power generation devices respectively, to obtain multiple reference indicators, and to determine weights for multiple reference indicators respectively, to obtain multiple weights; an adjusting unit, configured to adjust the reference indicators using the weights; and a fourth determining unit, configured to determine a preset capacity configuration strategy that matches the multiple adjusted reference indicators from a capacity configuration strategy library as a capacity configuration strategy, wherein the capacity configuration strategy library includes: different preset capacity configuration strategies that match different reference indicators.

[0131] Optionally, the plurality of the collaborative power generation devices include: supercapacitors and lithium batteries, wherein the configuration unit 504 may include: a third determining module, used to determine the capacity configuration ratio of energy storage capacity in supercapacitors and lithium batteries according to a capacity configuration strategy; and a configuration module, used to configure the energy storage capacity corresponding to future energy storage data to supercapacitors and lithium batteries respectively according to the capacity configuration ratio.

[0132] In this embodiment, a regional energy storage configuration device is provided. The device may include: an acquisition unit, configured to acquire historical electricity consumption data of at least one electrical device in the region, and historical power generation data of at least one power generation device in the region, wherein the historical electricity consumption data represents the electricity consumption status of the electrical device within a historical time period, and the historical power generation data represents the power generation status of the power generation device within a historical time period; and a prediction unit, configured to predict future electricity consumption data of the electrical device and future power generation data of the power generation device based on the historical electricity consumption data and historical power generation data, wherein the future electricity consumption data represents the electricity consumption status of the electrical device within a future time period, and the future power generation data represents the power generation status of the power generation device within a future time period. The system comprises: a power generation status; a first determining unit, configured to determine future energy storage data to compensate for future power generation data based on future electricity consumption data, wherein the future energy storage data represents the energy storage capacity of multiple cooperating power generation devices that generate power in conjunction with the power generation equipment; and a configuration unit, configured to allocate the energy storage capacity corresponding to the future energy storage data to the multiple cooperating power generation devices according to a capacity configuration strategy, wherein the capacity configuration strategy represents the rules for configuring different energy storage capacities to the multiple cooperating power generation devices respectively. This achieves the goal of avoiding the difficulty in guaranteeing regional energy storage due to environmental changes, thereby solving the technical problem of low efficiency in regional energy storage configuration and thus realizing the technical effect of improving the efficiency of regional energy storage configuration.

[0133] According to an embodiment of this application, a processor is also provided for running a program, wherein the program is executed by the processor to perform the methods described in the embodiment.

[0134] According to an embodiment of this application, an electronic device is also provided. Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application, such as... Figure 6 As shown, the electronic device 600 may include a memory 610 and a processor 620, wherein the memory 610 is used to store an executable program; and the processor 620 is used to run the program stored in the memory 610, wherein the program executes the methods in various embodiments of this application when it runs.

[0135] In this application, "multiple" refers to two or more.

[0136] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0137] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0138] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0139] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided. This computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method described in the embodiments.

[0140] Computer-readable storage media, also known as computer storage media, may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. These propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable storage media can transmit, propagate, or transfer programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0141] The program code contained in a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, or any suitable combination thereof.

[0142] According to an embodiment of this application, a computer program product is also provided, which includes a computer program, wherein the computer program, when executed by a processor, implements the method in the embodiment.

[0143] According to an embodiment of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the method described in the embodiment.

[0144] According to an embodiment of this application, a computer program is also provided, which, when executed by a processor, implements the method described in the embodiment.

[0145] Optionally, when the above-mentioned computer program is executed by the processor, the program code implements the following steps: acquiring historical electricity consumption data of at least one electrical device in the region, and historical power generation data of at least one power generation device in the region, wherein the historical electricity consumption data is used to represent the electricity consumption status of the electrical device in a historical period, and the historical power generation data is used to represent the power generation status of the power generation device in a historical period; based on the historical electricity consumption data and the historical power generation data, predicting the future electricity consumption data of the electrical device and the future power generation data of the power generation device, wherein the future electricity consumption data is used to represent the electricity consumption status of the electrical device in a future period, and the future power generation data is used to represent the power generation status of the power generation device in a future period; based on the future electricity consumption data, determining the future energy storage data used to compensate for the future power generation data, wherein the future energy storage data is used to represent the energy storage capacity of multiple cooperating power generation devices that generate electricity in tandem with the power generation device; and configuring the energy storage capacity corresponding to the future energy storage data to the multiple cooperating power generation devices according to the capacity configuration strategy, wherein the capacity configuration strategy is used to represent the rules for configuring different energy storage capacities to the multiple cooperating power generation devices respectively.

[0146] Optionally, when the above-mentioned computer program is executed by the processor, the program code implements the following steps: inputting historical electricity consumption data into the feature extraction layer included in the hybrid prediction model for feature extraction to obtain the electricity consumption features of the historical electricity consumption data, and inputting historical power generation data into the feature extraction layer for feature extraction to obtain the power generation features of the historical power generation data, wherein the hybrid prediction model is used to represent the correlation between historical electricity consumption data and historical power generation data, and future electricity consumption data and future power generation data; inputting the electricity consumption features into the relationship extraction layer included in the hybrid prediction model for relationship extraction to obtain the electricity consumption dependency relationship between the previous electricity consumption feature and the next electricity consumption feature, and inputting the power generation features into the relationship extraction layer for relationship extraction to obtain the power generation dependency relationship between the previous power generation feature and the next power generation feature; in the hybrid prediction model, based on the electricity consumption features, power generation features, electricity consumption dependency relationship, and power generation dependency relationship, predicting future electricity consumption data and future power generation data.

[0147] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: inputting electricity consumption characteristics, power generation characteristics, electricity consumption dependence, and power generation dependence into the prediction layer included in the hybrid prediction model for prediction, so as to obtain future electricity consumption data and future power generation data.

[0148] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: determining the electricity consumption of the power generation equipment in the future time period from the future electricity consumption data, and determining the power generation of the power generation equipment in the future time period from the future power generation data; determining the difference between the electricity consumption and the power generation as the energy storage capacity corresponding to the future energy storage data.

[0149] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: searching the configuration library according to the energy storage capacity to obtain multiple candidate power generation devices that meet the configuration rules with the energy storage capacity, and determining the multiple candidate power generation devices as multiple collaborative power generation devices, wherein the configuration library includes: configuration rules between different energy storage capacities and different candidate power generation devices.

[0150] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: determining reference indicators for multiple collaborative power generation devices to obtain multiple reference indicators, and determining weights for multiple reference indicators to obtain multiple weights; adjusting the reference indicators using the weights; and determining a preset capacity configuration strategy that matches the multiple adjusted reference indicators from the capacity configuration strategy library as the capacity configuration strategy, wherein the capacity configuration strategy library includes different preset capacity configuration strategies that match different reference indicators.

[0151] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: determining the capacity configuration ratio of energy storage capacity in supercapacitors and lithium batteries according to the capacity configuration strategy; configuring the energy storage capacity corresponding to future energy storage data to supercapacitors and lithium batteries respectively according to the capacity configuration ratio.

[0152] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0153] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0154] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0155] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0156] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0157] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0158] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for configuring energy storage in a region, characterized in that, include: Historical electricity consumption data of at least one electrical device in the region and historical power generation data of at least one power generation device in the region are obtained, wherein the historical electricity consumption data is used to represent the electricity consumption status of the electrical device in a historical period, and the historical power generation data is used to represent the power generation status of the power generation device in the historical period. Based on the historical electricity consumption data and the historical power generation data, the future electricity consumption data of the electrical equipment and the future power generation data of the power generation equipment are predicted, wherein the future electricity consumption data is used to represent the electricity consumption status of the electrical equipment in a future period, and the future power generation data is used to represent the power generation status of the power generation equipment in the future period. Based on the future electricity consumption data, future energy storage data is determined to compensate for the future power generation data, wherein the future energy storage data is used to represent the energy storage capacity of multiple cooperating power generation devices that generate power in conjunction with the power generation device. According to the capacity configuration strategy, the energy storage capacity corresponding to the future energy storage data is configured for each of the multiple collaborative power generation devices, wherein the capacity configuration strategy is used to represent the rules for configuring different energy storage capacities for each of the multiple collaborative power generation devices.

2. The method of claim 1, wherein, Based on the historical electricity consumption data and the historical power generation data, the future electricity consumption data of the electrical equipment and the future power generation data of the power generation equipment are predicted, including: The historical electricity consumption data is input into the feature extraction layer of the hybrid prediction model for feature extraction to obtain the electricity consumption characteristics of the historical electricity consumption data, and the historical power generation data is input into the feature extraction layer for feature extraction to obtain the power generation characteristics of the historical power generation data. The hybrid prediction model is used to represent the correlation between the historical electricity consumption data and the historical power generation data, and the future electricity consumption data and the future power generation data. The electricity consumption features are input into the relationship extraction layer of the hybrid prediction model to extract the relationship, thereby obtaining the electricity consumption dependency relationship between the previous electricity consumption feature and the next electricity consumption feature. The power generation features are input into the relationship extraction layer to extract the relationship, thereby obtaining the power generation dependency relationship between the previous power generation feature and the next power generation feature. In the hybrid prediction model, the future electricity consumption data and the future power generation data are predicted based on the electricity consumption characteristics, the power generation characteristics, the electricity consumption dependency, and the power generation dependency.

3. The method of claim 2, wherein, In the hybrid prediction model, based on the electricity consumption characteristics, the power generation characteristics, the electricity consumption dependency, and the power generation dependency, the future electricity consumption data and the future power generation data are predicted, including: The electricity consumption characteristics, the power generation characteristics, the electricity consumption dependency, and the power generation dependency are input into the prediction layer of the hybrid prediction model for prediction, so as to obtain the future electricity consumption data and the future power generation data.

4. The method of claim 1, wherein, Based on the future electricity consumption data, future energy storage data is determined to compensate for the future power generation data, including: From the future electricity consumption data, determine the electricity consumption of the power generation equipment in the future time period, and from the future power generation data, determine the power generation of the power generation equipment in the future time period; The difference between the electricity consumption and the electricity generation is determined as the energy storage capacity corresponding to the future energy storage data.

5. The method of claim 4, wherein, The method further includes: According to the energy storage capacity, the configuration library is searched to obtain multiple candidate power generation devices that meet the configuration rules with the energy storage capacity, and the multiple candidate power generation devices are determined as multiple collaborative power generation devices, wherein the configuration library includes: configuration rules between different energy storage capacities and different candidate power generation devices.

6. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Reference indicators for multiple collaborative power generation devices are determined to obtain multiple reference indicators, and weights for multiple reference indicators are determined to obtain multiple weights. The reference index is adjusted using the weights. From the capacity configuration strategy library, preset capacity configuration strategies that match multiple adjusted reference indicators are determined as the capacity configuration strategies, wherein the capacity configuration strategy library includes different preset capacity configuration strategies that match different reference indicators.

7. The method of claim 6, wherein, The plurality of the collaborative power generation devices include: supercapacitors and lithium batteries, wherein, according to a capacity configuration strategy, the energy storage capacity corresponding to the future energy storage data is configured for each of the plurality of collaborative power generation devices, including: According to the capacity configuration strategy, the capacity configuration ratio of the energy storage capacity in the supercapacitor and the lithium battery is determined; According to the capacity configuration ratio, the energy storage capacity corresponding to the future energy storage data is configured for the supercapacitor and the lithium battery, respectively.

8. An energy storage configuration for a region, comprising: include: The acquisition unit is used to acquire historical electricity consumption data of at least one electrical device in the area, and historical power generation data of at least one power generation device in the area, wherein the historical electricity consumption data is used to represent the electricity consumption status of the electrical device in a historical period, and the historical power generation data is used to represent the power generation status of the power generation device in the historical period. The prediction unit is used to predict the future electricity consumption data of the electrical equipment and the future power generation data of the power generation equipment based on the historical electricity consumption data and the historical power generation data, wherein the future electricity consumption data is used to represent the electricity consumption status of the electrical equipment in a future time period, and the future power generation data is used to represent the power generation status of the power generation equipment in the future time period. The first determining unit is configured to determine future energy storage data for compensating the future power generation data based on the future electricity consumption data, wherein the future energy storage data is used to represent the energy storage capacity of multiple cooperating power generation devices that generate power in conjunction with the power generation device. A configuration unit is configured to configure the energy storage capacity corresponding to the future energy storage data to multiple collaborative power generation devices according to a capacity configuration strategy, wherein the capacity configuration strategy is used to represent the rules for configuring different energy storage capacities to multiple collaborative power generation devices respectively.

9. A processor, comprising: The processor is used to run a program, wherein the program is executed by the processor to perform the method according to any one of claims 1 to 7.

10. An electronic device, comprising: include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

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