Energy Internet optimization method, system, device and readable storage medium

Abstract

The present invention discloses an energy internet optimization method, system, device, and readable storage medium. The method includes: obtaining historical power generation data of power generation devices in the energy internet; determining power supply information of the power generation devices in a future time period based on the historical power generation data; determining a distribution path for the power generated by the power generation devices in the future time period based on the power supply information, and sending the power supply distribution path to an energy router. The substantial effects of the present invention include: using historical power generation data to determine future power supply information and power supply distribution paths to optimize the energy internet and improve energy utilization efficiency and stability.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to an optimization method, system, device and readable storage medium for energy Internet. Background Art

[0002] The Energy Internet can interconnect a large number of distributed power generation devices, distributed energy storage devices, and distributed loads, forming a peer-to-peer energy exchange and sharing network with bidirectional energy flow. Devices in the Energy Internet can be interconnected through energy routers to achieve open, peer-to-peer energy exchange.

[0003] With the development of energy internet technology, the external world has put forward higher requirements on the energy utilization efficiency and stability of energy internet. Therefore, it is necessary to propose an energy internet optimization method to optimize the energy internet. Summary of the Invention

[0004] In response to the problem that the existing technology has unsatisfactory optimization effect on the energy Internet, the present invention provides an optimization method, system, device and readable storage medium for the energy Internet, which uses historical power generation data to determine future power supply information and power distribution path to optimize the energy Internet and improve energy utilization efficiency and stability.

[0005] The following are the technical solutions of the present invention.

[0006] An energy internet optimization method, comprising:

[0007] Obtaining historical power generation data of power generation devices in the energy internet;

[0008] determining power supply information of the power generation device in a future time period based on the historical power generation data;

[0009] Based on the power supply information, a distribution path of the power generated by the power generation device in the future time period is determined, and the distribution path of the power generated is sent to the energy router.

[0010] Preferably, determining a distribution path of the power generated by the power generation device in the future time period based on the power supply information, and sending the distribution path of the power generated to the energy router includes:

[0011] Determining, based on the power supply information, power supply changes corresponding to two adjacent sub-time periods in the future time period;

[0012] For each of the power supply changes, determining whether the power supply change is greater than a first power threshold;

[0013] When the power supply change is greater than the first power threshold, dividing the power supply generated by the sub-time period with the largest power supply in the two adjacent sub-time periods to obtain divided sub-power supplies;

[0014] Determine the distribution path of the sub-power supply and send the distribution path of the sub-power supply to the energy router, wherein a part of the divided sub-power supply is used to power the load in the energy Internet, and another part of the divided sub-power supply is used to be transmitted to the energy storage device in the energy Internet for energy storage.

[0015] As an option, it also includes:

[0016] Determine whether the power supply amount for the load in the sub-time period of the future time period is less than a second power threshold; when the power supply amount is less than the second power threshold, power the load based on the power generation device and the energy storage device in the sub-time period of the future time period.

[0017] Preferably, determining a distribution path of the power generated by the power generation device in the future time period based on the power supply information, and sending the distribution path of the power generated to the energy router includes:

[0018] Get weather forecast information;

[0019] adjusting the power supply information based on the weather forecast information to obtain adjusted power supply information;

[0020] Based on the adjusted power supply information, a distribution path of the power generated by the power generation device in the future time period is determined, and the distribution path of the power generated is sent to the energy router.

[0021] The present invention also provides an energy internet optimization system, comprising:

[0022] A first acquisition module is used to obtain historical power generation data of power generation devices in the energy internet;

[0023] A first determining module is configured to determine power supply information of the power generation device within a future time period based on the historical power generation data;

[0024] The second determining module is configured to determine a distribution path of the power generated by the power generation device in the future time period based on the power supply information, and send the distribution path of the power generated to the energy router.

[0025] Preferably, the second determining module is specifically configured to:

[0026] Determining, based on the power supply information, power supply changes corresponding to two adjacent sub-time periods in the future time period;

[0027] For each of the power supply changes, determining whether the power supply change is greater than a first power threshold;

[0028] When the power supply change is greater than the first power threshold, dividing the power supply generated by the sub-time period with the largest power supply in the two adjacent sub-time periods to obtain divided sub-power supplies;

[0029] Determine the distribution path of the sub-power supply and send the distribution path of the sub-power supply to the energy router, wherein a part of the divided sub-power supply is used to power the load in the energy Internet, and another part of the divided sub-power supply is used to be transmitted to the energy storage device in the energy Internet for energy storage.

[0030] Preferably, the system further comprises:

[0031] A determination module, configured to determine whether the power supply amount provided to the load in the sub-time period of the future time period is less than a second power threshold;

[0032] A power supply module is configured to supply power to the load based on the power generation device and the energy storage device in a sub-time period of the future time period when the power supply change is less than the second power threshold.

[0033] Preferably, the second determining module further includes:

[0034] Get weather forecast information;

[0035] adjusting the power supply information based on the weather forecast information to obtain adjusted power supply information;

[0036] Based on the adjusted power supply information, a distribution path of the power generated by the power generation device in the future time period is determined, and the distribution path of the power generated is sent to the energy router.

[0037] The present invention also provides an energy internet optimization device, comprising a processor, wherein the processor is configured to execute the above-mentioned energy internet optimization method.

[0038] The present invention also provides a readable storage medium storing computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the above-mentioned energy Internet optimization method.

[0039] The substantial effects of the present invention include: using historical power generation data to determine future power supply information and power distribution paths to optimize the energy Internet and improve energy utilization efficiency and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of an application scenario of the energy internet optimization method according to some embodiments of this specification;

[0041] Figure 2 is an exemplary flow chart for determining a distribution path for power generated by a power generation device within a future time period according to some embodiments of this specification;

[0042] Figure 3 is an exemplary flow chart of determining a distribution path for sub-power supply according to some embodiments of this specification;

[0043] Figure 4 is an exemplary flow chart of supplying power to a load based on a power generation device and an energy storage device according to some embodiments of this specification;

[0044] Figure 5 This is another exemplary flow chart for determining a distribution path for the power generated by a power generation device in a future time period according to some embodiments of this specification. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0047] It should be understood that in the present invention, "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes 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 these processes, methods, products or apparatuses.

[0048] The technical solution of the present invention is described in detail below with reference to specific embodiments. The embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0049] Example:

[0050] Figure 1It is a schematic diagram of an application scenario of the energy internet optimization method shown in some embodiments of this specification.

[0051] The optimization method of energy internet can be used to optimize energy internet. Figure 1 As shown, the application scenario of the energy internet optimization method may include energy internets 110 - 1 to 110 - n , an energy router 120 and a processor 130 .

[0052] The Energy Internet can refer to a network whose internal structures enable bidirectional, on-demand energy transmission and dynamic, balanced usage. The Energy Internet can include power generation devices 111, loads 112, and energy storage devices 113. Power generation devices 111 can include wind power generation devices 111-1 and solar power generation devices 111-2. Power generation devices 111, loads 112, and energy storage devices 113 can be connected via energy routers 120 to enable energy exchange. For example, power generation devices 111 and loads 112 can be connected via energy routers 120 to transmit the electricity generated by power generation devices 111 to loads 112. Another example is power generation devices 111 and energy storage devices 113 connected via energy routers 120 to transmit the electricity generated by power generation devices 111 to energy storage devices 113. Another example is power storage devices 113 and loads 112 connected via energy routers to transmit the electricity stored in energy storage devices 113 to loads 112.

[0053] Energy router 120 can connect to power generation device 111, load 112, and energy storage device 113 to control the flow of energy within the energy internet. In some embodiments, the energy router can control the flow of energy within one or more energy internets. For example, energy router 120 can control the flow of energy within energy internets 110-1 through 110-n.

[0054] The processor 130 can process data and / or information from the energy router. The processor can communicate with the energy router to provide various functions of the service. For example, the processor can obtain data from the energy router (such as historical power generation data of the power generation device), predict the power supply information of the power generation device in the future time period, and then send the predicted data to the energy router. The processor can also be used to process data and / or information from external data sources (for example, cloud data centers) outside the application scenario of the optimization method of the energy Internet. For example, the processor can be used to process weather forecast information to adjust the power supply information of the power generation device in the future time period. In some embodiments, the processor can be a single server or a server group. The server group can be centralized or distributed (for example, the processor can be a distributed system). In some embodiments, the processor can be regional or remote. In some embodiments, the processor can be implemented on a cloud platform or provided in a virtual manner. By way of example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc. or any combination thereof.

[0055] It should be noted that the above description of the application scenarios of the energy internet optimization method is only for the convenience of description and does not limit this specification to the scope of the embodiments cited.

[0056] The energy internet optimization method can be applied to an energy internet optimization system. In some embodiments, the energy internet optimization system can include a first acquisition module, a first determination module, and a second determination module.

[0057] The first acquisition module can be used to obtain the historical power generation data of the power generation device in the energy internet. For more information about power generation devices and historical power generation data, see Figure 2 The related descriptions will not be repeated here.

[0058] The first determination module can be used to determine the power supply information of the power generation device in the future time period based on the historical power generation data. For more information about the future time period and power supply information, see Figure 2 The related descriptions will not be repeated here.

[0059] The second determination module can be used to determine the distribution path of the power generated by the power generation device in the future time period based on the power supply information, and send the distribution path of the power supply to the energy router. For more information about power supply, distribution path, and energy router, see Figure 2 The related descriptions will not be repeated here.

[0060] In some embodiments, the second determination module can also be used to determine the power supply change corresponding to two adjacent sub-time periods in a future time period based on the power supply information; for each power supply change, determine whether the power supply change is greater than a first power threshold; when the power supply change is greater than the first power threshold, divide the power supply generated by the sub-time period with the largest power supply in the two adjacent sub-time periods to obtain the divided sub-power supplies; determine the distribution path of the sub-power supplies, and send the distribution path of the sub-power supplies to the energy router, wherein a part of the divided sub-power supplies are used to power the loads in the energy Internet, and another part of the divided sub-power supplies are used to be transmitted to the energy storage device in the energy Internet for energy storage.

[0061] In some embodiments, the second determination module can also be used to obtain weather forecast information; based on the weather forecast information, adjust the power supply information to obtain adjusted power supply information; based on the adjusted power supply information, determine the distribution path of the power supply generated by the power generation device in the future time period, and send the distribution path of the power supply to the energy router.

[0062] In some embodiments, the energy internet optimization system may further include a judgment module and a power supply module.

[0063] The judgment module can be used to judge whether the power supply amount of the load in the sub-time period in the future time period is less than the second power threshold. Figure 3 For more information about the second power threshold, see Figure 4 The related descriptions will not be repeated here.

[0064] The power supply module can be used to supply power to the load based on the power generation device and the energy storage device in a sub-time period in the future time period when the power supply amount is less than the second power threshold. Figure 3 The related descriptions will not be repeated here.

[0065] It should be understood that the optimization system and modules of the energy internet can be implemented in various ways. For example, in some embodiments, the processing device and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system (for example, a microprocessor or dedicated design hardware). Those skilled in the art will understand that the above-mentioned processing device and its modules can be implemented by computer-executable instructions. The system and its modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays and programmable logic devices, but also by software implemented by, for example, various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0066] It should be noted that the above description of the optimization system and modules of the energy internet is for convenience of description only and does not limit this specification to the scope of the embodiments cited. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various modules, or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, the various modules of the optimization system of the energy internet can be different modules in a system, or a module can realize the functions of two or more modules mentioned above. For example, the modules can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0067] Figure 2 This is an exemplary flow chart for determining a distribution path for the power generated by a power generation device in a future time period according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps:

[0068] Step 210: Acquire historical power generation data of power generation devices in the energy internet. Step 210 may be performed by a first acquisition module.

[0069] A power generation device may refer to a device that converts various forms of energy (e.g., solar energy, wind energy, etc.) into electrical energy. In some embodiments, the power generation device may include a wind power generation device and a solar power generation device. In some embodiments, the power generation device may also include a device that converts other energy sources into electrical energy. For example, the power generation device may also include a hydroelectric power generation device that converts the potential energy of water into electrical energy.

[0070] In some embodiments, the energy internet may include one or more power generation devices. In some embodiments, the multiple power generation devices in the energy internet may be of the same or different types. For example, the energy internet may include two different power generation devices: a wind power generation device and a solar power generation device.

[0071] Historical power generation data may refer to the amount of power generated by a power generation device in an energy internet in the past time period. In some embodiments, the historical power generation data may be directly obtained from an energy router in the energy internet.

[0072] Step 220: Determine the power supply information of the power generation device in the future time period based on the historical power generation data. Step 220 may be performed by the first determination module.

[0073] The future time period may refer to a time period after the current time. In some embodiments, the length of the future time period may be directly preset. For example, the future time period may be preset to be the next three days.

[0074] In some embodiments, the future time period can be divided into multiple sub-time periods. The lengths of the sub-time periods in the future time period can also be pre-set. For example, the future time period can be preset to the next three days, and each 12 hours in the next three days is a sub-time period.

[0075] The power supply information may refer to the power supply information of the power generation devices in the energy internet corresponding to the amount of power provided in each time period.

[0076] In some embodiments, the power supply information of the power generation device in the future time period can be determined based on a machine learning model.

[0077] In some embodiments, historical power generation data of the total power supply of multiple power generation devices within a preset past time period can be used as input to the total power supply prediction model, and the output is power supply information of the power generation devices within a future time period. The preset past time period can be pre-set.

[0078] For example, at 00:00 on October 4, 2030, the historical power generation data {800, 900, 1000, 950, 900, 900} of the total power generation of multiple power generation devices in the past three days, i.e., from October 1, 2030 to October 3, 2030, can be input into the total power supply prediction model to determine the power supply information of the power generation devices in the next three days, i.e., from October 4, 2030 to October 6, 2030, as {600, 800, 900, 1050, 900, 950}. Among them, the historical power generation data {800, 900, 1000, 950, 900, 900} indicates that the total power generation of multiple power generation devices in the energy internet from 00:00 to 12:00 on October 1, 2030, from 12:00 to 24:00 on October 1, 2030, from 00:00 to 12:00 on October 2, 2030, from 12:00 to 24:00 on October 2, 2030, from 00:00 to 12:00 on October 3, 2030, and from 12:00 to 24:00 on October 3, 2030 is 800kWh, 900kWh, 1000kWh, 950kWh, 900kWh, 900kWh, and 900kWh respectively. kWh; the power supply information {600, 800, 900, 1050, 900, 950} indicates that the predicted total power generation of multiple power generation devices from 00:00 to 12:00 on October 4, 2030, from 12:00 to 24:00 on October 4, 2030, from 00:00 to 12:00 on October 5, 2030, from 12:00 to 24:00 on October 5, 2030, from 00:00 to 12:00 on October 6, 2030, and from 12:00 to 24:00 on October 6, 2030 is 600kWh, 800kWh, 900kWh, 1050kWh, 900kWh, and 950kWh respectively.

[0079] It should be understood that the length of historical power generation data has no direct relationship to the length of the future time period to be predicted. For example, it is also possible to predict the power supply information of a power generation device for the next three days based on the historical power generation data of the total power generation of multiple power generation devices over the past year. In addition, similar examples in this specification will also be used to define every 12-hour period as a sub-time period. Therefore, when the future time period or the time period of historical power generation data is three days, there are six sub-time periods; when the future time period or the time period of historical power generation data is two days, there are four time periods. This is explained here, and the division of sub-time periods will not be repeated in similar examples in this specification.

[0080] In some embodiments, the total power supply prediction model may include but is not limited to a support vector machine model, a logistic regression model, a naive Bayes classification model, a Gaussian distribution Bayes classification model, a decision tree model, a random forest model, a KNN classification model, and a neural network model.

[0081] In some embodiments, the total power supply prediction model can be obtained by training based on a large amount of historical power generation data.

[0082] In some embodiments, historical total power generation data for multiple power generation devices corresponding to a sample historical time period can be used as training samples. The identifiers for the training samples can be the historical total power generation data for each power generation device corresponding to the sample time period. The sample time period is the time period following the sample historical time period. A large number of identified training samples are input into an initial total power supply prediction model. The parameters of the initial total power supply prediction model are updated through training. When the trained model meets preset conditions, training concludes, and a trained total power supply prediction model is obtained.

[0083] In some embodiments, the historical power generation data of each power generation device can be input into its corresponding energy supply prediction model to determine the power supply information corresponding to each power generation device in the future time period. For example, the historical power generation data of a wind power generation device can be input into its corresponding wind power supply prediction model to determine the power supply information corresponding to the wind power generation device in the Energy Internet in the future time period. For another example, the historical power generation data of a solar power generation device can be input into its corresponding solar power supply prediction model to determine the power supply information corresponding to the solar power generation device in the Energy Internet in the future time period.

[0084] In some embodiments, a wind power supply prediction model can be used to obtain power supply information corresponding to multiple wind turbines in a future time period based on input data representing the relationships between multiple wind turbines. The input data of the wind power supply prediction model is the historical power generation data of multiple wind turbines, as well as the relationships between each wind turbine (e.g., the relationships between wind turbines include distance relationships, azimuth relationships, etc.). The output data of the wind power supply prediction model is the power supply information corresponding to each wind turbine in the future time period.

[0085] The input data can be data features of wind turbines, as well as data features of relationships between wind turbines, represented by a graph in the sense of graph theory. A graph is a data structure consisting of nodes and edges / paths, and can include multiple nodes and multiple edges / paths connecting multiple nodes. Nodes correspond to historical power generation data for wind turbines, and edges correspond to relationships between wind turbines.

[0086] The attributes of a node may include historical power generation data of a wind power generation device, wherein the historical power generation data of a wind power generation device may include the historical time period in which the wind power generation device supplies power and the power supply corresponding to the time period. For example, a certain energy Internet contains two wind power generation devices, and the historical power generation data of one wind power generation device over the past three days is {100, 120, 150, 140, 130, 150}, and the historical power generation data of the other wind power generation device over the past three days is {200, 210, 180, 160, 190, 170}. The historical power generation data of the above two wind power generation devices can be used as two nodes.

[0087] Edge attributes can include the direction and distance relationship between wind turbines. For example, an energy internet contains two wind turbines, with wind turbine A as the center point and wind turbine B located at 10 meters north of the node.

[0088] The wind power supply prediction model can be a graph neural network. Using the aforementioned nodes, edges, and their attributes as inputs, the graph neural network can generate output data corresponding to the nodes to be predicted. Each node to be predicted corresponds to a wind turbine. A graph neural network is a type of neural network that operates directly on a graph. Based on an information propagation mechanism, each node in the graph can exchange attribute information via edges, continuously updating its node information until a stopping condition is met. Based on its updated node information, each node to be predicted corresponding to a wind turbine outputs power supply information for the future time period corresponding to that wind turbine.

[0089] For example, the historical power generation data of wind power generation device A and wind power generation device B over the past three days can be {100, 120, 150, 140, 130, 130} and {200, 210, 180, 160, 160, 170} respectively as the input of the nodes of the wind power supply prediction model, and the relationship between wind power generation devices A and B, that is, wind power generation device B is located 10M north of wind power generation device A, can be used as the input of the edge between the nodes of wind power generation devices A and B in the wind power supply prediction model, and it is determined that the power supply information of wind power generation device A and wind power generation device B in the next three days are {120, 120, 140, 150, 150, 140} and {180, 200, 170, 150, 160, 170} respectively.

[0090] The parameters of the wind power supply prediction model can be obtained through training. The training samples include historical power generation data of sample wind turbines within a first sample historical time period, as well as the relationships between the sample wind turbines. The labels used during training are historical power generation data of sample wind turbines within a second sample historical time period, where the second sample historical time period is the time period after the first sample historical time period. The historical power generation data in the training samples can be directly obtained from historical data of the Energy Internet, and the relationships between the sample wind turbines can be obtained by measuring the locations of the sample wind turbines.

[0091] In real life, it's difficult to determine the changing patterns of different winds, but certain winds have a causal relationship in terms of location. For example, a certain wind blows from south to north, from point A to point B. When a wind turbine uses wind to generate electricity, it affects the size and direction of the wind, thereby affecting the power generation of wind turbines in other locations. Therefore, some embodiments of this specification can use graphs to represent data between multiple wind turbines. While reflecting the characteristics of the historical power generation data of the wind turbines themselves, they can also better reflect the relationship between and mutual influence between the various wind turbines, thereby improving the accuracy of the power supply information of each wind turbine in the future time period predicted by the wind power supply prediction model.

[0092] In some embodiments, the power supply information of the corresponding energy generation device in the future time period can also be determined based on other energy power supply prediction models. For example, the historical power generation data of the solar power generation device can be input into the solar power supply prediction model to determine the corresponding power supply information of the solar power generation device in the future time period.

[0093] Exemplarily, the historical electricity consumption data {150, 170, 160, 180, 150, 170} of the solar power generation device in the past three days is input into the solar power supply prediction model, and it is determined that the power supply information of the solar power generation device in the next three days is {160, 170, 160, 190, 170, 200}.

[0094] In some embodiments, the solar power supply prediction model can be trained and obtained based on a large amount of historical power generation data of solar power generation devices.

[0095] In some embodiments, historical power generation data from a solar power generation device during a sample historical time period can be used as training samples. The identification of the training samples can be the historical power generation data from the solar power generation device during the sample time period. The sample time period is the time period following the sample historical time period. A large number of identified training samples are input into an initial solar power supply prediction model. The parameters of the initial solar power supply prediction model are updated through training. When the trained model meets preset conditions, the training ends, and the trained solar power supply prediction model is obtained. In some embodiments, the solar power generation device can be implemented based on a deep neural network.

[0096] In some embodiments, based on the power supply information of each power generation device in the future time period, the power supply information of the power generation device in the energy internet in the future time period is determined.

[0097] In some embodiments, the power supply of each power generation device in a sub-time period of a future time period can be summed to determine the power supply information of the power generation devices in the energy internet in the future time period. For example, a certain energy internet contains wind power generation device A, wind power generation device B, and a solar power generation device. It has been determined that the power supply information of wind power generation device A, wind power generation device B, and solar power generation device in the next three days are {120, 120, 140, 150, 150, 140}, {180, 200, 170, 150, 160, 170}, and {160, 170, 160, 190, 170, 200}, respectively. Based on this, the power supply information of the power generation devices in the energy internet in the next three days can be determined to be {460, 490, 470, 490, 480, 510}.

[0098] Step 230: Based on the power supply information, determine the distribution path of the power generated by the power generation device in the future time period, and send the distribution path of the power generated to the energy router. Step 230 can be performed by the second determination module.

[0099] A distribution path can refer to the flow of power generated by a power generation device. Based on the distribution path, the purpose of the power generated by the power generation device can be clarified. For example, if the distribution path of the power generated by the power generation device in a future time period is a load path, this means that the power generated by the power generation device is used to power the load.

[0100] In some embodiments, the distribution path of the power generated by the power generation device in the future time period can be one or more. For example, the distribution path of the power generated by the power generation device in the future time period can be a load path and an energy storage device path. For more information about loads and energy storage devices, see Figure 3 The related descriptions will not be repeated here.

[0101] In some embodiments, modeling or various data analysis algorithms, such as regression analysis and discriminant analysis, can be used to process the power supply information to determine the distribution path of the power generated by the power generation device in the future time period.

[0102] In some embodiments, the power supply information may be processed based on the first power threshold to determine a distribution path for the power generated by the power generation device in a future time period. For more information on processing the power supply information based on the first power threshold to determine a distribution path for the power generated by the power generation device in a future time period, see Figure 3 The related descriptions will not be repeated here.

[0103] An energy router may refer to a device that allocates electrical energy within the Energy Internet. In some embodiments, a power distribution path may be sent to the energy router. The energy router distributes the electrical energy generated by the power generation device based on the received power distribution path.

[0104] In some embodiments, an energy router can control the flow of electrical energy within an energy interconnection. For example, an energy router can control the flow of power provided by a power supply device in a particular energy interconnection to loads in that energy interconnection. In some embodiments, an energy router can control the flow of electrical energy between multiple energy interconnections. For example, an energy router can control the flow of power provided by a power supply device in one energy interconnection to loads in another energy interconnection.

[0105] Figure 3 FIG. 1 is an exemplary flow chart of determining a distribution path for sub-power supply according to some embodiments of this specification. Figure 3 As shown, the process 230 includes the following steps: In some embodiments, the process 230 may be executed by the second determination module.

[0106] Step 231 : determining power supply changes corresponding to two adjacent sub-time periods in a future time period based on the power supply information.

[0107] A sub-time period is a time period formed by dividing a future time period. For more information about sub-time periods, see Figure 2 and its related descriptions.

[0108] The power supply change may refer to the absolute change in the power supply of a power generation device in the energy internet corresponding to two adjacent sub-time periods in a future time period.

[0109] In some embodiments, the power supply change can be determined based on the power supply of two adjacent sub-time periods of a future time period. In some embodiments, when there are multiple future time periods, the power supply change between the last sub-time period of the previous future time period and the first sub-time period of the next future time period can be determined to determine the subsequent power supply split.

[0110] In some embodiments, the power supply variation can be determined by formula (1):

[0111] l n→n+1 =|P n+1 -P n | (1)

[0112] Among them, l n→n+1 P is the power supply change of the power generation device from the nth time period to the n+1th time period in the future time period, n+1 P is the power supply of the power generation device in the energy internet in the n+1th time period in the future, n P is the power supply of the power generation device in the energy Internet in the nth time period in the future time period. n+1 With P n See the method for determining Figure 2 The related descriptions will not be repeated here.

[0113] For example, the power supply of the power generation device in the energy Internet in the first sub-time period in the future time period is 150kWh, and the power supply in the second sub-time period is 100kWh. Then the power supply change of the power generation device in the energy Internet from the first sub-time period to the second sub-time period is 50kWh.

[0114] Step 232 : for each power supply change, determine whether the power supply change is greater than a first power threshold.

[0115] The first power threshold may refer to a threshold used to determine whether the power supply of a power generation device in the energy internet varies too much in different time periods.

[0116] In some embodiments, the first power threshold can be preset. For example, the first power threshold can be preset to 50 kWh.

[0117] When the power supply change corresponding to two adjacent sub-time periods in the future time period is less than or equal to the first power threshold, then in the previous sub-time period of the two adjacent sub-time periods in the future time period, all power generated by the power generation device in the energy Internet is provided to the load for use.

[0118] A load can refer to a device that uses electrical energy. In some embodiments, the load can be a device that uses electrical energy for production, such as a machine tool that uses electrical energy to produce automotive parts. In some embodiments, the load can also be other devices, such as a smart home.

[0119] Step 233 : when the power supply variation is greater than the first power threshold, the power supply generated by the sub-time period with the largest power supply in the sub-time period where the power supply variation is located is divided to obtain divided sub-power supplies.

[0120] Sub-power supply can refer to the power supply obtained by dividing the power supply generated by the power generation device in the energy Internet.

[0121] In some embodiments, when the power supply change is greater than a first power threshold, the time period with the smallest power supply in the sub-time period where the power supply change is located may not be processed, and the power supply generated by the time period with the largest power supply in the sub-time period where the power supply change is located may be split to obtain two sub-power supplies after the split. For example, if the power supply of the first sub-time period of the future time period is 180kWh, the power supply of the second sub-time period is 100kWh, and the first power threshold is 50kWh, then it can be determined that the second sub-time period does not need to be split, and the power supply of the first sub-time period needs to be split.

[0122] For more details on how to determine the amount of sub-power supply, please refer to step 234 of this manual and will not be repeated here.

[0123] Step 234, determine the distribution path of the sub-power supply, and send the distribution path of the sub-power supply to the energy router, wherein a part of the divided sub-power supply is used to power the load in the energy Internet, and another part of the divided sub-power supply is used to be transmitted to the energy storage device in the energy Internet for energy storage.

[0124] An energy storage device refers to a device that can store electrical energy and supply the stored electrical energy to a load.

[0125] In some embodiments, the sub-power supply for energy storage in the energy storage device in the energy internet can be determined by formula (2):

[0126] p1=l n→n+1 -l0 (2)

[0127] Among them, p1 is the sub-power supply for energy storage in the energy Internet, l n→n+1 is the power supply change of the power generation device from the nth sub-time period to the (n+1)th sub-time period in the future time period, and l0 is the first power threshold.

[0128] In some embodiments, the sub-power supply amount for powering the loads in the energy internet can be determined by formula (3):

[0129] p2=max(P n , P n+1 )-p1 (3)

[0130] Among them, p2 is the sub-power supply used to power the load in the energy Internet, P n P is the power supply of the power generation device in the energy internet in the nth sub-time period in the future time period. n+1 is the power supply of the power generation device in the energy internet in the n+1th sub-time period in the future time period, and p1 is the sub-power supply used to be transmitted to the energy storage device in the energy internet for energy storage.

[0131] In some embodiments, if the sub-time period that needs to be divided is the later time period of two adjacent sub-time periods, when the power supply change of the next adjacent sub-time period is judged, the judgment is made based on the sub-power supply used to power the load after the sub-time period of the later time period of the two adjacent sub-time periods is divided and the power supply of the next sub-time period. For example, the preset future time period can be the next two days, and every 12 hours in the next two days can be regarded as a sub-time period. The power supply information of the energy internet in the next two days is {100, 180, 120, 130}, and the first power threshold is 40kWh. Since the power supply change of the first sub-time period with a power supply of 100kWh and the second sub-time period with a power supply of 180kWh is greater than the first power threshold, the second sub-time period needs to be divided, of which 40kWh of the power supply is used to transmit to the energy storage device in the energy internet for energy storage, and 140kWh of the power supply is used to power the load. At this time, the power supply amount used to power the load in the second sub-time period is 140kWh, and the power supply change with the 120kWh power supply amount in the third sub-time period is less than the first power threshold, so there is no need to divide the power supply amount of the second sub-time period again, nor is there any need to divide the power supply amount of the third sub-time period.

[0132] In some embodiments, the power supply of the sub-time periods in which the power supply variation between two adjacent sub-time periods is too large may be divided in other ways.

[0133] In some embodiments, a sub-power allocation path can be determined based on the usage of different sub-power allocations, and the sub-power allocation path can be sent to the energy router. The sub-power allocation used to power loads in the energy internet can be transmitted to the load path, and the sub-power allocation used to store energy in energy storage devices in the energy internet can be transmitted to the energy storage device path.

[0134] When the power supply between adjacent sub-time periods varies significantly, it can cause excessive voltage fluctuations within the Energy Internet, impacting not only the normal operation of the Energy Internet but also potentially damaging the loads within it. By segmenting the power supply between adjacent sub-time periods with significant power supply variations, the problem of excessive power supply variations between adjacent sub-time periods, which can lead to excessive voltage fluctuations within the Energy Internet, is avoided. This improves energy utilization efficiency, stabilizes the voltage supplied to the load, reduces potential losses to the load within the Energy Internet due to excessive voltage fluctuations, and increases the service life of the load. Some embodiments described herein can determine not only the power supply variations between adjacent sub-time periods within a future time period, but also the power supply variations between adjacent sub-time periods across multiple future time periods, thus avoiding the negative impact on the Energy Internet caused by excessive power supply variations between multiple future time periods.

[0135] Figure 4 This is an exemplary flow chart of supplying power to a load based on a power generation device and an energy storage device according to some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps:

[0136] Step 410: Determine whether the power supply amount for the load in the sub-time period of the future time period is greater than a second power threshold. Step 410 may be performed by a determination module.

[0137] The second power threshold may refer to the minimum power required to maintain normal operation of loads in the energy internet.

[0138] In some embodiments, the second power thresholds of multiple sub-time periods in the future time period may be the same or different.

[0139] In some embodiments, the second power threshold may be determined according to the load.

[0140] In some embodiments, the second power threshold can be determined based on the minimum power required to operate the load in a sub-period within a future time period in the Energy Internet. The minimum power required by the load can be pre-set. For example, if the load in the Energy Internet consumes at least 20 kWh of power per hour, and each sub-period in the future time period is 8 hours long, the second power threshold can be determined to be 160 kWh.

[0141] In some embodiments, the power consumption of the load in each sub-time period in the future time period can be predicted based on the load power consumption model to determine the second power threshold. For example, if the power consumption of the load in a sub-time period in the future time period is determined to be 150kWh based on the load power consumption model, the second power threshold corresponding to the sub-time period can be determined to be 150kWh. For more information on predicting the power consumption of the load in each sub-time period in the future time period, see Figure 5 The relevant instructions will not be repeated here.

[0142] By predicting the power consumption of the load in the sub-time period of the future time period, the second power threshold is determined. This operation can make the obtained data closer to reality, thereby ensuring the normal operation of the load in the energy internet.

[0143] In some embodiments, the second power threshold may also be determined by other means, for example, it may be directly pre-set and determined.

[0144] In some embodiments, when the power supply of the power generation device in the energy internet in a sub-time period within the future time period is greater than or equal to a second power threshold, the power generation device in the energy internet is used to supply power to the load.

[0145] In some embodiments, it may also be determined whether the power supply amount provided to the load in a sub-time period in the future time period is greater than a third power threshold.

[0146] The third power threshold may refer to the maximum power required to maintain normal operation of loads in the energy internet.

[0147] In some embodiments, the third power threshold can be determined based on the load. In some embodiments, the third power threshold can be determined based on the maximum power required to operate the load in a sub-time period within a future time period in the Energy Internet. The maximum power required by the load can be pre-set. For example, if the load in the Energy Internet consumes a maximum of 30kWh of power per hour, and each sub-time period in the future time period is 8 hours long, then the third power threshold can be determined to be 240kWh.

[0148] In some embodiments, the third power threshold may also be determined by other means, for example, it may be directly preset and determined.

[0149] In some embodiments, when the power supply of a sub-time period in a future time period to supply power to a load is greater than a third power threshold, the portion of the power supply that is greater than the third power threshold is divided, and the portion of the power supply is transmitted to the energy storage device in the energy internet for energy storage. For example, the power supply of a sub-time period in a future time period is 300kWh, and the third power threshold is 240kWh. Then, the power supply of the sub-time period is divided, and the portion of the power supply that is greater than the third power threshold, that is, 60kWh of the power supply, is transmitted to the energy storage device in the energy internet for energy storage, and the power supply after the sub-time period is divided, that is, 240kWh of the power supply is used to supply power to the load. In some embodiments, when the power supply of a sub-time period in a future time period to supply power to a load is greater than the third power threshold, there is no need to divide the power supply based on the third power threshold.

[0150] Step 420: When the power supply is less than the second power threshold, power the load based on the power generation device and the energy storage device in a sub-time period of the future time period. Step 420 may be performed by the power supply module.

[0151] In some embodiments, when the power supply of a sub-time period in a future time period is less than a second power threshold, in the sub-time period in the future time period, in addition to the original power supply based entirely on the power generation device, an energy storage device is added to supply power to the load.

[0152] In some embodiments, the amount of power supplied by the energy storage device to the load may be determined based on the second power threshold. The amount of power supplied by the energy storage device to the load may be determined by formula (4):

[0153] l en =l2-P n (4)

[0154] Among them, l en is the power supply of the energy storage device to the load in the nth sub-time period of the future time period, l2 is the second power threshold, P n The power supply of the power generation device in the energy internet in the nth sub-time period in the future time period.

[0155] For example, the power supply of a sub-time period in the future time period is 100 kWh, and the second power threshold is 150 kWh. It can be determined that the power supply required by the energy storage device to supply power to the load in the sub-time period is 50 kWh.

[0156] In some embodiments, the power supply amount of the energy storage device to the load may be determined by other means, for example, by directly presetting the power supply amount of the energy storage device to the load.

[0157] In some cases, due to the excessive change in power supply between two adjacent sub-time periods in a future time period, it is necessary to divide the power supply of the two sub-time periods of the future time period, resulting in the divided power supply being less than the second power supply, which may cause the load to malfunction. In some cases, due to weather changes, the amount of power provided by the power generation device is low, which may also cause the load to malfunction. When the power supply provided by the power generation device is too small to maintain the load and cannot work, the normal operation of the energy internet is ensured by supplementing the power supply to the load by the energy storage device on the basis of the power supply to the load by the power generation device. In addition, by dividing the portion of the power supply of the power generation device in the sub-time period that is greater than the third power threshold, the excess power generated by the power generation device is stored while ensuring the normal operation of the load in the energy internet, thereby improving energy utilization.

[0158] Figure 5 This is another exemplary flow chart for determining a distribution path for the power generated by a power generation device in a future time period according to some embodiments of this specification. Figure 5 As shown, the process 500 includes the following steps: In some embodiments, the process 230 may be performed by the second determination module.

[0159] Step 510: Obtain weather forecast information.

[0160] Weather forecast information may refer to forecast information about the weather in a future time period. Weather forecast information may include cloud maps, temperature, wind speed, sunshine intensity, sunshine time, etc. In some embodiments, weather forecast information may be obtained in a variety of ways, for example, directly through the Internet.

[0161] Step 520: Adjust the power supply information based on the weather forecast information to obtain adjusted power supply information.

[0162] In some embodiments, the power supply information can be adjusted based on the weather forecast information and preset rules to obtain the adjusted power supply information. The preset rules can be preset based on experience. For example, the power generation device in a certain energy Internet is a solar power generation device, and the predicted weather forecast information is that the power supply information for the next three days is {600, 700, 650, 720, 680, 730}, but according to the obtained cloud map for the next three days, the predicted cloud cover in the second time period of 700kWh is greater than the cloud cover threshold. Therefore, according to the preset rules, the power supply in the second time period in the power supply information is multiplied by the adjustment coefficient of 0.6, thereby obtaining the adjusted power supply information for the future time period as {600, 420, 650, 720, 680, 730}, wherein the cloud cover threshold and the adjustment coefficient can be predetermined. For the prediction of power supply information in future time periods, see Figure 2 The related descriptions will not be repeated here.

[0163] By adjusting the power supply information in the predicted future time period based on weather forecast information, the adjusted power supply information can be made closer to the actual situation, thereby improving the accuracy of the prediction and ensuring the normal operation of the energy Internet.

[0164] In some embodiments, weather forecast information can also be used as input to the energy power supply prediction model. Based on weather forecast data and historical power generation data, the power supply information of the power generation device in the future time period is determined. For example, weather forecast data and historical power generation data of the solar power generation device can be input into the solar power supply prediction model to determine the power supply information of the solar power device in the future time period. Accordingly, when training the solar power supply prediction model, in addition to the training data Figure 2 In addition to the training samples of the solar power supply prediction model described above, historical weather data can also be included, wherein the historical weather data can be directly obtained through the Internet.

[0165] In some embodiments, when the power consumption of a load in the Energy Internet is affected by weather, the power consumption of the load in each sub-time period of a future time period can be determined based on weather forecast information. Weather forecast information for each sub-time period of a future time period can be input into a load power consumption prediction model, and the output is the power consumption of the load in each sub-time period of the future time period. For example, if the load is an air conditioner, the power consumption of the air conditioner in each sub-time period of the future time period can be determined based on weather forecast information for the future time period.

[0166] In some embodiments, the load power consumption prediction model may include but is not limited to a support vector machine model, a logistic regression model, a naive Bayes classification model, a Gaussian distribution Bayes classification model, a decision tree model, a random forest model, a KNN classification model, and a neural network model.

[0167] In some embodiments, the load power consumption prediction model can be obtained by training based on a large amount of historical data.

[0168] In some embodiments, weather information corresponding to each historical time period can be used as training samples. The identification of the training samples can be the load power consumption corresponding to each historical time period. The weather information corresponding to each historical time period can be obtained from the Internet, and the load power consumption corresponding to each historical time period can be obtained from historical data of the Energy Internet. The identified training samples are input into the initial load power consumption prediction model, and the parameters of the initial load power consumption prediction model are updated through training. When the trained model meets preset conditions, the training ends, and the trained load power consumption prediction model is obtained.

[0169] Step 530 : Based on the adjusted power supply information, determine a distribution path for the power generated by the power generation device in a future time period, and send the distribution path for the power generated to the energy router.

[0170] The process of step 530 is basically the same as that of step 230, see Figure 2 The related descriptions will not be repeated here.

[0171] It should be noted that the above descriptions of the various processes are for illustration and purpose only and do not limit the scope of application of this specification. Those skilled in the art may make various modifications and alterations to the above processes under the guidance of this specification. However, such modifications and alterations are still within the scope of this specification.

[0172] This specification also provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer implements the aforementioned method for optimizing the energy internet.

[0173] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0174] In the embodiments provided in this application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, structure or unit, which can be electrical, mechanical or other forms.

[0175] Units described as separate components may or may not be physically separate, and components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0176] In addition, the functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or software functional units.

[0177] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0178] The above content is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An energy internet optimization method, characterized in that: include: Obtaining historical power generation data of power generation devices in the energy internet; determining power supply information of the power generation device in a future time period based on the historical power generation data; determining, based on the power supply information, a distribution path for the power generated by the power generation device in the future time period, and sending the distribution path for the power generated to the energy router; The determining, based on the power supply information, a distribution path of the power generated by the power generation device in the future time period, and sending the distribution path of the power generated to the energy router includes: Determining, based on the power supply information, power supply changes corresponding to two adjacent sub-time periods in the future time period; For each of the power supply changes, determining whether the power supply change is greater than a first power threshold; When the power supply change is greater than the first power threshold, dividing the power supply generated by the sub-time period with the largest power supply in the two adjacent sub-time periods to obtain divided sub-power supplies; Determine a distribution path for the sub-power amounts, and send the distribution path for the sub-power amounts to the energy router, wherein a portion of the divided sub-power amounts is used to power loads in the energy internet, and another portion of the divided sub-power amounts is used to be transmitted to an energy storage device in the energy internet for energy storage; If the sub-time period that needs to be divided is the later time period of the two adjacent sub-time periods, when judging the power supply change of the next adjacent sub-time period, the judgment is made based on the sub-power supply amount used to power the load after the sub-time period with the later time period of the two adjacent sub-time periods is divided and the power supply amount of the next sub-time period.

2. The energy internet optimization method according to claim 1, characterized in that: Also includes: Determining whether the power supply amount provided to the load in the sub-time period of the future time period is less than a second power threshold; When the power supply amount is less than the second power threshold, power is supplied to the load based on the power generation device and the energy storage device in a sub-time period of the future time period.

3. The energy internet optimization method according to claim 1, characterized in that: The determining, based on the power supply information, a distribution path of the power generated by the power generation device in the future time period, and sending the distribution path of the power generated to the energy router includes: Get weather forecast information; adjusting the power supply information based on the weather forecast information to obtain adjusted power supply information; Based on the adjusted power supply information, a distribution path of the power generated by the power generation device in the future time period is determined, and the distribution path of the power generated is sent to the energy router.

4. An energy internet optimization system, characterized in that: include: A first acquisition module is used to obtain historical power generation data of power generation devices in the energy internet; A first determining module is configured to determine power supply information of the power generation device within a future time period based on the historical power generation data; A second determining module is configured to determine, based on the power supply information, a distribution path of the power generated by the power generation device in the future time period, and send the distribution path of the power generated to the energy router; The second determining module is specifically configured to: Determining, based on the power supply information, power supply changes corresponding to two adjacent sub-time periods in the future time period; For each of the power supply changes, determining whether the power supply change is greater than a first power threshold; When the power supply change is greater than the first power threshold, dividing the power supply generated by the sub-time period with the largest power supply in the two adjacent sub-time periods to obtain divided sub-power supplies; Determine a distribution path for the sub-powered amounts, and send the distribution path for the sub-powered amounts to the energy router, wherein a portion of the divided sub-powered amounts is used to power loads in the energy internet, and another portion of the divided sub-powered amounts is used to transmit electricity to an energy storage device in the energy internet for energy storage; If the sub-time period that needs to be divided is the later time period of the two adjacent sub-time periods, when judging the power supply change of the next adjacent sub-time period, the judgment is made based on the sub-power supply amount used to power the load after the sub-time period with the later time period of the two adjacent sub-time periods is divided and the power supply amount of the next sub-time period.

5. The energy internet optimization system according to claim 4, characterized in that: The system further comprises: A determination module, configured to determine whether the power supply amount provided to the load in the sub-time period of the future time period is less than a second power threshold; A power supply module is configured to supply power to the load based on the power generation device and the energy storage device in a sub-time period of the future time period when the power supply change is less than the second power threshold.

6. The energy internet optimization system according to claim 4, characterized in that: The second determining module further includes: Get weather forecast information; adjusting the power supply information based on the weather forecast information to obtain adjusted power supply information; Based on the adjusted power supply information, a distribution path of the power generated by the power generation device in the future time period is determined, and the distribution path of the power generated is sent to the energy router.

7. An energy internet optimization device, comprising a processor, characterized in that: The processor is used to execute the energy internet optimization method according to any one of claims 1 to 3.

8. A readable storage medium storing computer instructions, characterized in that: After the computer reads the computer instructions in the storage medium, the computer executes the energy internet optimization method according to any one of claims 1 to 3.

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