Internet interaction system for energy industry

Abstract

The embodiment of the invention discloses an energy industry internet interaction system. The system can comprise a power grid twinborn body of a power grid master station cloud of a target power grid and an aggregation twinborn body of an aggregation cloud platform under the power grid master station cloud; wherein the aggregation twin body is used for sharing the platform operation state and the platform energy supply and demand condition of the aggregation cloud platform to the power grid twin body; and the power grid twins are used for determining the power grid energy supply and demand conditions of the target power grid, and performing energy scheduling on the target power grid according to the platform operation states and the platform energy supply and demand conditions shared by the plurality of aggregated twins and the power grid energy supply and demand conditions. According to the technical scheme provided by the embodiment of the invention, the problem that the digital twin cannot participate in the interactive operation of the power grid is solved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of energy industrial Internet, and in particular, to an energy industrial Internet interactive system. Background Art

[0003] Affected by random factors such as terrain, topography, climate conditions and the units themselves, the output of renewable energy is uncertain, which greatly reduces the reliability of renewable energy power generation systems and leads to a significant decline in the planned power generation and dispatching enthusiasm of the power grid dispatching center for renewable energy. At the same time, the energy industrial Internet with electricity as the core includes a variety of energy production, transmission, storage and consumption networks, and the topology changes dynamically, evolving into a giant dimensional system with complex structure, numerous equipment and complex technology, with typical nonlinear random characteristics and multi-scale dynamic characteristics, resulting in traditional mechanism model analysis and optimization control methods can no longer meet the requirements of energy industrial Internet planning, design, monitoring analysis and operation optimization.

[0004] In recent years, with the rapid development of computer technology and intelligent technology, digital twin technology has received widespread attention in various fields and has been applied to a certain extent. For the power sector, digital twin technology can also play a key role. The use of the energy industrial Internet digital twin system can improve the safety and stability of the power system, formulate and implement economic operation strategies, and implement emergency control and anti-accident control of the power system. The digital twin management mechanism and modeling technology, as one of the important foundations of the energy industrial Internet twin technology, are the key to solving these problems.

[0005] At present, different construction methods have been proposed for the digital twin framework of the energy industrial Internet, and corresponding digital twin modeling methods have been proposed for related power grid equipment. However, the constructed digital twins fail to participate in the interactive operation of the power grid (that is, the energy industrial Internet with electricity as the core), which will directly affect the digital management capabilities of the energy industrial Internet and needs to be urgently resolved. Summary of the invention

[0006] The embodiment of the present invention provides an energy industrial Internet interactive system, which solves the problem that the digital twin cannot participate in the interactive operation of the power grid.

[0007] According to one aspect of the present invention, an energy industrial Internet interactive system is provided, comprising: a power grid twin of a power grid master station cloud of a target power grid and an aggregated twin of an aggregated cloud platform under the power grid master station cloud; wherein,

[0008] The aggregate twin can be used to share the platform operation status and platform energy supply and demand of the aggregate cloud platform to the grid twin;

[0009] The power grid twin can be used to determine the grid energy supply and demand situation of the target power grid, and perform energy scheduling for the target power grid based on the platform operation status and platform energy supply and demand situation shared by multiple aggregated twins, as well as the grid energy supply and demand situation.

[0010] Optionally, the aggregated cloud platform corresponds to multiple energy devices, the aggregated twin, and is also used for:

[0011] For each energy device among the multiple energy devices, obtaining the device operation status and device energy supply and demand situation of the energy device;

[0012] The acquired operation statuses of multiple devices are aggregated to obtain the platform operation status, and the acquired energy supply and demand conditions of multiple devices are aggregated to obtain the platform energy supply and demand conditions.

[0013] On this basis, an optional, aggregate twin is also used to:

[0014] Energy scheduling is performed for multiple energy devices based on the acquired operating status of multiple devices, the energy supply and demand of multiple devices, and the energy supply and demand of the platform.

[0015] Alternatively, the platform operation status is represented by the operation status of multiple devices, and the platform energy supply and demand status is represented by the energy supply and demand status of multiple devices;

[0016] The aggregate twin is specifically used to encapsulate the operating status of multiple devices into an operating model, encapsulate the energy supply and demand conditions of multiple devices into a supply and demand model, and share the operating model and the supply and demand model to the power grid twin, so that the power grid twin can obtain the platform operating status through the received operating model and obtain the platform energy supply and demand conditions through the received supply and demand model.

[0017] Alternatively, the energy industrial Internet interactive system may further include: an edge computing device under the aggregated cloud platform; wherein the power station corresponding to the edge computing device has at least one target device, the target device is an energy device, and the edge computing device is used to:

[0018] For each target device, the device data of the target device is collected, and the device operating status and device energy supply and demand of the target device are determined based on the device data, and the determined device operating status and device energy supply and demand are shared with the aggregate twin.

[0019] On this basis, an optional edge computing device is also used to receive energy scheduling instructions returned by the aggregated cloud platform regarding the shared operating status of each device and the energy supply and demand situation of each device, and perform energy scheduling for at least some of the target devices based on the energy scheduling instructions.

[0020] On this basis, optionally, the above-mentioned energy industrial Internet interactive system also includes: energy twins and edge computing devices corresponding to each target device, which are also specifically used for:

[0021] The energy scheduling instructions are forwarded to each energy twin respectively, so that each energy twin performs energy scheduling among at least some of the target devices according to the received energy scheduling instructions.

[0022] On this basis, an optional energy twin is used to obtain instruction execution rules corresponding to the received energy scheduling instructions, and execute the energy scheduling instructions according to the instruction execution rules to perform energy scheduling between at least some of the target devices.

[0023] Alternatively, the energy twin is pre-built as follows:

[0024] Acquire device information of the corresponding target device, wherein the device information may include at least one of physical configuration, energy production capacity, power storage capacity, and charging power;

[0025] Based on the device information, an energy twin of the target device is constructed.

[0026] Alternatively, the edge computing device is also used to:

[0027] The collected device data corresponding to at least part of the target devices are processed, and energy scheduling is performed for at least part of the target devices according to the obtained processing results.

[0028] The energy industrial Internet interactive system described in the embodiment of the present invention may include the grid twin of the grid master station cloud of the target grid and the aggregated twin of the aggregated cloud platform under the grid master station cloud. On this basis, through the aggregated twin, the platform operation status and platform energy supply and demand of the aggregated cloud platform can be shared to the grid twin; further, through the grid twin, the grid energy supply and demand of the target grid can be determined, and according to the platform operation status and platform energy supply and demand shared by multiple aggregated twins, as well as the grid energy supply and demand, energy scheduling is performed for the target grid. The above technical scheme achieves the effect of the two digital twins, the aggregated twin and the grid twin, participating in the interactive operation of the target grid.

[0029] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0031] Figure 1 It is a schematic diagram of a multi-level digital twin architecture of the energy industrial Internet in various energy industrial Internet interactive systems provided according to an embodiment of the present invention;

[0032] Figure 2 It is a schematic diagram of a distributed multi-level twin interaction framework for cloud-pipe-edge-end collaboration in various energy industrial Internet interaction systems provided according to an embodiment of the present invention;

[0033] Figure 3 This is a structural diagram of an energy industrial Internet interactive system provided according to an embodiment of the present invention;

[0034] Figure 4 is a structural diagram of another energy industrial Internet interactive system provided according to an embodiment of the present invention;

[0035] Figure 5 This is a structural diagram of another energy industrial Internet interactive system provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The situations of "target", "original", etc. are similar and will not be repeated here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] Before introducing the embodiments of the present invention, the application scenarios of the embodiments of the present invention are first exemplified. By way of example, in order to solve the problem that the digital twin described above fails to participate in the interactive operation of the power grid, an energy industrial Internet platform integrated interactive architecture that supports wide-area collaborative interactive regulation is proposed here by way of example. It is oriented to the distributed multi-level collaborative interaction requirements of the energy industrial Internet and includes the following steps: (1) Proposing a multi-level digital twin architecture design method for the energy industrial Internet; (2) Proposing a distributed multi-level twin interaction method for cloud-pipe-edge collaboration, thereby improving the digital management capabilities of the energy industrial Internet and helping to enable distributed source-load-storage resources to participate in the interactive operation of the energy industrial Internet.

[0039] Specifically, for the multi-level digital twin architecture of the energy industrial Internet, in the physical system, the source, load and storage equipment can be hierarchically structured to the upper distributed resource aggregation cloud platform and power grid, as follows:

[0040] 1) Hierarchical architecture of source, load and storage equipment to the aggregated cloud platform: Source, load and storage equipment can upload its own equipment operation status and equipment energy supply and demand to the aggregated cloud platform in real time through communication networks and technical means. As an energy management and dispatching center, the aggregated cloud platform can centrally control and dispatch subordinate source, load and storage equipment to achieve optimal configuration and dispatch of energy. Among them, the "source" in the source, load and storage equipment can be understood as the energy supplier, for example, it can include renewable energy (such as solar energy, wind energy and hydropower, etc.) and traditional energy (such as coal, oil and natural gas, etc.); "load" can be understood as the end user of energy, such as residents, industrial and commercial, and electric vehicles (EV); "storage" can be understood as energy storage.

[0041] 2) The hierarchical architecture of the aggregated cloud platform to the upper aggregated cloud platform: In a large power system, there can be multiple aggregated cloud platforms, which can realize information interaction and sharing through communication networks and technical means. The upper aggregated cloud platform can collect the platform operation status and platform energy supply and demand of the subordinate aggregated cloud platforms, and conduct centralized control and scheduling to achieve higher-level energy management and optimization.

[0042] 3) The hierarchical architecture of the aggregation cloud platform to the power grid: As an energy management and dispatching center, the aggregation cloud platform can exchange information and dispatch energy with the power grid. The aggregation cloud platform can optimize the control and dispatch of the subordinate source, load and storage equipment according to the needs and limitations of the power grid to ensure the stable operation and energy balance of the power grid.

[0043] In general, source-load-storage equipment can realize the hierarchical architecture of the aggregation cloud platform, the upper aggregation cloud platform and the power grid through communication networks and technical means. This hierarchical architecture can achieve the optimal configuration and dispatch of energy, improve energy utilization efficiency, and promote the sustainable development of the power system.

[0044] On this basis, a multi-level digital twin architecture of source-load-storage device-level digital twin and grid-level digital twin is designed. Figure 1 . The digital twins of the source, load and storage equipment level and the digital twins of the grid level are integrated and interacted at multiple levels to achieve comprehensive digital mapping and interaction of energy management and optimization. This architecture can pass data such as the equipment operation status and equipment energy supply and demand of the source, load and storage equipment upward step by step, and can also pass energy management and optimized energy scheduling instructions downward step by step to achieve comprehensive automation and intelligence of energy configuration and scheduling. In particular, the focus is on the design and implementation of the distributed source, load and storage equipment level digital twin construction method, the source, load and storage equipment level digital twin dynamic aggregation method (i.e., the source, load and storage equipment level digital twin interacts with the aggregate cloud platform digital twin), the aggregate cloud platform digital twin construction method, and the multi-level aggregate cloud platform digital twin hierarchical control method.

[0045] Further, for a distributed multi-level twin interaction framework for cloud-pipe-edge-end collaboration, see Figure 2 , built ① a cloud-based distributed multi-level digital twin collaborative interaction mode, ② an edge-based distributed photovoltaic system, energy storage system, charging pile and other digital twin collaborative interaction mode based on IoT edge computing technology, and ③ an interactive instruction execution mode of physical devices such as distributed photovoltaic system, energy storage system and charging pile coupled with digital twin. These three modes mainly involve the following six contents:

[0046] Cloud management: Building distributed multi-level digital twin interactions in the cloud can collect and analyze data from all digital twins and physical devices, and issue energy scheduling instructions to edge computing devices and physical devices.

[0047] Edge computing: Edge computing devices are located at the edge of the network and can quickly process and respond to data from physical devices and digital twins. Edge computing devices can automatically adjust the device operation status of physical devices through predefined rules and algorithms, and report data from digital twins and physical devices upward.

[0048] Physical equipment configuration: The configuration of physical equipment needs to match the digital twin. For example, the power generation of the distributed photovoltaic system needs to match the prediction of the digital twin, and the charging power of the charging pile needs to match the charging demand of the digital twin.

[0049] Digital twin modeling: For each distributed photovoltaic system, energy storage system and charging pile, a corresponding digital twin can be established. The digital twin may include key equipment information such as the physical configuration of the physical equipment, equipment operating status, energy production capacity, power storage capacity and charging power.

[0050] Instruction execution rules: Based on the predictions and actual needs of the digital twin, a set of instruction execution rules can be formulated. These rules may include the power generation adjustment rules of the distributed photovoltaic system, the power charging and discharging rules of the energy storage system, and the charging power adjustment rules of the charging piles.

[0051] Instruction execution optimization: During the instruction execution process, optimization strategies can be considered to maximize energy efficiency. For example, the power generation of distributed photovoltaic systems and the charging power of charging piles can be adjusted according to power demand and price fluctuations to minimize energy costs.

[0052] Next, the above three modes will be elaborated in detail. Figure 3 The energy industry Internet interactive system presented mainly describes the cloud-based distributed multi-level digital twin collaborative interaction mode. Figure 4 Another energy industrial Internet interactive system displayed mainly describes the collaborative interaction mode of digital twins such as edge-end distributed photovoltaic systems, energy storage systems and charging piles based on IoT edge computing technology. Figure 5 What is displayed is another energy industrial Internet interactive system, which mainly explains the interactive instruction execution mode of coupling physical equipment such as distributed photovoltaic systems, energy storage systems and charging piles with digital twins.

[0053] Figure 3This is a structural block diagram of an energy industrial Internet interactive system provided by an embodiment of the present invention. This embodiment is applicable to the case where a digital twin participates in the interactive operation of an energy industrial Internet with electricity as the core, that is, it is applicable to the case where a digital twin participates in the interactive operation of a power grid.

[0054] See also Figure 3 The energy industrial Internet interactive system described in the embodiment of the present invention may include: a power grid twin 10 of the power grid master station cloud of the target power grid and an aggregated twin 11 of the aggregated cloud platform under the power grid master station cloud; wherein,

[0055] The aggregate twin 11 is used to share the platform operation status and platform energy supply and demand of the aggregate cloud platform to the power grid twin 10;

[0056] The power grid twin 10 is used to determine the power grid energy supply and demand situation of the target power grid, and perform energy scheduling for the target power grid according to the platform operation status and platform energy supply and demand situation shared by multiple aggregated twins 11, as well as the power grid energy supply and demand situation.

[0057] The cloud-based distributed multi-level digital twin collaborative interaction mode may include two-level clouds: the power grid master cloud and the aggregate cloud platform. The target power grid corresponding to the power grid master cloud can be understood as the power grid that the digital twin needs to participate in; the power grid twin 10 can be understood as the digital twin of the power grid master cloud; and the aggregate twin 11 can be understood as the digital twin of the aggregate cloud platform.

[0058] In the embodiment of the present invention, multiple aggregated twins 11 within the power grid master station cloud participate in collaborative interaction, and the aggregated cloud platform may include interactions between multiple distributed entities such as photovoltaic storage and charging microgrids. This interaction can achieve optimal scheduling and management of energy, improve energy utilization efficiency and management level, promote the consumption of renewable energy, and provide strong support for building an intelligent, efficient and reliable energy industrial Internet.

[0059] Specifically, the grid master cloud can be understood as the basic unit of energy supply and consumption, including multiple aggregated cloud platforms and distributed energy sites (i.e., distributed energy devices). The grid twin 10 can simulate and predict the grid energy supply and demand of the entire target grid, as well as the grid operation status and performance of the target grid. This level of interaction mainly involves participants such as grid operators, energy suppliers, and electricity users.

[0060] Within the power grid master cloud, multiple aggregated twins 11 participate in collaborative interaction to achieve energy dispatch and management of the entire target power grid. Aggregated twins 11 can interact in the following ways:

[0061] For data sharing, each aggregated twin 11 can share its own platform operating status and platform energy supply and demand conditions in order to achieve energy dispatch and management of the entire target power grid.

[0062] For energy scheduling, each aggregated twin 11 can be centrally managed and controlled through an energy management system, and the target power grid energy can be optimally scheduled and managed based on the target power grid's energy supply and demand conditions and the platform operating status and platform energy supply and demand conditions of each aggregated twin 11.

[0063] On this basis, optionally, for emergency control, in the event of a target power grid failure or insufficient energy supply, each aggregate twin 11 can implement safe and stable operation of the target power grid through emergency control measures.

[0064] Optionally, the power grid master cloud and the aggregation cloud platform can interact based on any of the three interaction modes: physical model, quotation data, and package model.

[0065] ① Interaction mode based on physical models: This interaction mode is based on physical models, that is, interaction is achieved by simulating and predicting the behavior of physical systems. In the power grid master cloud and aggregation cloud platform, the physical model can include the dynamic model of the power system and the operation model of energy equipment. By using physical models, high-precision simulation and prediction of energy systems can be achieved, thereby performing energy scheduling and management.

[0066] ② Interaction mode based on quotation data: This interaction mode is based on quotation data, that is, interaction is carried out through quotation data provided by energy suppliers. In the power grid master cloud and aggregation cloud platform, energy suppliers can provide different energy prices and supply information, and power users can choose to purchase appropriate energy based on this information. By using quotation data, energy optimization scheduling and management can be achieved, and the economic benefits of power users can also be improved.

[0067] ③ Interaction mode based on encapsulation model: This interaction mode is implemented based on the encapsulation model, that is, the interaction is carried out by encapsulating the equipment operation status and equipment energy supply and demand into the model. In the power grid master cloud and aggregation cloud platform, the encapsulation model can include the operation model of the energy equipment and the energy production and consumption model (i.e., supply and demand model). By using the encapsulation model, remote monitoring and management of energy equipment can be achieved, and optimal scheduling and management of energy can also be achieved.

[0068] The energy industrial Internet interactive system described in the embodiment of the present invention may include the grid twin of the grid master station cloud of the target grid and the aggregated twin of the aggregated cloud platform under the grid master station cloud. On this basis, through the aggregated twin, the platform operation status and platform energy supply and demand of the aggregated cloud platform can be shared to the grid twin; further, through the grid twin, the grid energy supply and demand of the target grid can be determined, and according to the platform operation status and platform energy supply and demand shared by multiple aggregated twins, as well as the grid energy supply and demand, energy scheduling is performed for the target grid. The above technical scheme achieves the effect that the two digital twins, the aggregated twin and the grid twin, participate in the interactive operation of the target grid.

[0069] An optional technical solution is that the aggregated cloud platform corresponds to multiple energy devices, aggregated twins, and is also used for:

[0070] For each energy device among the multiple energy devices, obtaining the device operation status and device energy supply and demand situation of the energy device;

[0071] The acquired operation statuses of multiple devices are aggregated to obtain the platform operation status, and the acquired energy supply and demand conditions of multiple devices are aggregated to obtain the platform energy supply and demand conditions.

[0072] Among them, energy equipment can be understood as energy-related equipment corresponding to the aggregated cloud platform, such as the source load storage equipment described above, specifically, it can be a distributed photovoltaic system, energy storage system, charging piles, microgrids and other multiple distributed entities. On this basis, combined with the application scenarios that this technical solution may involve, optionally, the energy equipment can also be called a physical device.

[0073] Specifically, the aggregate twin can simulate and predict the equipment operation status and equipment energy supply and demand of each energy device within it, which can be reflected in the production and consumption of energy. This level of interaction mainly involves participants such as aggregate operators, energy suppliers and electricity users.

[0074] Within the scope of the aggregated cloud platform, multiple distributed entities, including photovoltaic storage and charging microgrids, interact to achieve energy scheduling and management within the aggregated cloud platform. These entities can interact in the following ways:

[0075] For data sharing, each subject (i.e., energy equipment) can share its own equipment operating status and equipment energy supply and demand situation, so that the aggregated cloud platform can aggregate the acquired operating status of multiple devices to obtain the platform operating status, and aggregate the acquired energy supply and demand situations of multiple devices to obtain the platform energy supply and demand situation, so as to realize energy scheduling and management within the aggregated cloud platform.

[0076] The above technical solution, through data aggregation, realizes the accurate acquisition of the platform operation status and platform energy supply and demand situation.

[0077] On this basis, optionally, for energy scheduling, each entity can achieve centralized management and control through the aggregated cloud platform, and realize energy scheduling of multiple energy devices within the aggregated cloud platform according to the platform energy supply and demand situation of the aggregated cloud platform and the equipment operating status and equipment energy supply and demand situation of each energy device.

[0078] Optionally, for emergency control, in the event of a failure within the aggregated cloud platform or insufficient energy supply, each entity can implement emergency control measures to achieve safe and stable operation of the aggregated cloud platform.

[0079] Alternatively, the platform operation status is represented by the operation status of multiple devices, and the platform energy supply and demand situation is represented by the energy supply and demand situation of multiple devices. The aggregate twin is specifically used to encapsulate the operation status of multiple devices into an operation model, encapsulate the energy supply and demand situation of multiple devices into a supply and demand model, and share the operation model and the supply and demand model with the power grid twin, so that the power grid twin obtains the platform operation status through the received operation model and obtains the platform energy supply and demand situation through the received supply and demand model. This optional solution, by using the encapsulation model, can realize remote monitoring and management of energy equipment, and can also realize optimal scheduling and management of energy.

[0080] Figure 4 It is a structural block diagram of another energy industrial Internet interactive system provided by an embodiment of the present invention. This embodiment is optimized based on the above-mentioned technical solutions. In this embodiment, optionally, the above-mentioned energy industrial Internet interactive system also includes: an edge computing device under the aggregated cloud platform; wherein the power station corresponding to the edge computing device has at least one target device, and the target device is an energy device, and the edge computing device is used to: for each target device, collect the device data of the target device, and determine the device operating status and device energy supply and demand of the target device based on the device data, and share the determined device operating status and device energy supply and demand to the aggregated twin. Among them, the explanations of the terms that are the same or corresponding to the above-mentioned embodiments are not repeated here.

[0081] For details, see Figure 4 The energy industrial Internet interactive system described in this embodiment may include: a power grid twin 20 of the power grid master station cloud of the target power grid, an aggregated twin 21 of the aggregated cloud platform under the power grid master station cloud, and an edge computing device 22 under the aggregated cloud platform, wherein the aggregated cloud platform corresponds to multiple energy devices, and the power station corresponding to the edge computing device 22 has at least one target device, which is an energy device; wherein,

[0082] The edge computing device 22 is used to collect device data of each target device, determine the device operation status and device energy supply and demand of the target device according to the device data, and share the determined device operation status and device energy supply and demand with the aggregate twin 21;

[0083] Aggregate twin 21, used to aggregate the acquired operation status of multiple devices to obtain the platform operation status of the aggregate cloud platform, and aggregate the acquired energy supply and demand conditions of multiple devices to obtain the platform energy supply and demand conditions of the aggregate cloud platform, and share the platform operation status and the platform energy supply and demand conditions to the power grid twin 20;

[0084] The power grid twin 20 is used to determine the power grid energy supply and demand situation of the target power grid, and perform energy scheduling for the target power grid based on the platform operation status and platform energy supply and demand situation shared by multiple aggregated twins 21, as well as the power grid energy supply and demand situation.

[0085] Among them, the number of edge computing devices 22 under the aggregated cloud platform can be one or more. On this basis, for each edge computing device 22, the edge computing device 22 corresponds to a power station, which can be a power station including at least one of a photovoltaic power station, an energy storage power station, and a charging and swapping power station, etc. The power station has one or more energy devices, and the edge computing device 22 can be understood as a device responsible for edge computing for the one or more energy devices. In the embodiment of the present invention, in order to facilitate the distinction between energy devices under different power stations, the energy device under the power station under discussion is referred to as a target device.

[0086] Through the edge computing device 22, for each target device, the device data of the target device can be collected, and the device operating status and device energy supply and demand of the target device can be determined based on the device data, and then the determined device operating status and device energy supply and demand can be shared to the aggregate twin 21.

[0087] The technical solution of the embodiment of the present invention realizes the effective sharing of the equipment operation status and equipment energy supply and demand to the aggregate twin through the application of edge computing devices.

[0088] An optional technical solution, edge computing devices, are also used to:

[0089] Receive energy scheduling instructions returned by the aggregation cloud platform for the shared operating status of each device and the energy supply and demand of each device, and perform energy scheduling for at least part of the target devices according to the energy scheduling instructions.

[0090] The above technical solution receives the energy scheduling instructions returned by the aggregate cloud platform through the edge computing device, and then realizes the energy scheduling between at least some of the target devices with resource scheduling requirements among all the target devices according to the energy scheduling instructions.

[0091] Another alternative technology solution, edge computing devices, is also used to:

[0092] The collected device data corresponding to at least part of the target devices are processed, and energy scheduling is performed for at least part of the target devices according to the obtained processing results.

[0093] The above technical solution processes at least part of the device data that has processing requirements among all the collected device data through edge computing devices, and on this basis realizes energy scheduling between target devices.

[0094] On this basis, for the various technical solutions given above related to the collaborative interaction mode of digital twins such as edge-end distributed photovoltaic systems, energy storage systems and charging piles based on IoT edge computing technology, an optional example is used for exemplary explanation below.

[0095] Exemplarily, the edge side is based on the interactive operation control of the digital twin, that is, different entities such as the photovoltaic storage and charging microgrid establish their digital twins, provide their physical models, quotation data or packaging models accordingly according to the above-mentioned interaction mode, and receive energy scheduling instructions returned by the aggregated twin.

[0096] On this basis, for data collection, edge computing technology can use sensors and data acquisition equipment to collect real-time equipment data of physical equipment such as distributed photovoltaic systems, energy storage systems and charging piles. These equipment data may include at least one of power generation, battery status and charging requirements.

[0097] For data processing, the collected device data is processed and analyzed in real time through edge computing devices. Edge computing devices can adjust the output of distributed photovoltaic systems in real time to achieve maximum power generation, and optimize power distribution according to the equipment operating status of energy storage systems and charging piles.

[0098] For collaborative interaction, edge computing devices can coordinate the work of distributed photovoltaic systems, energy storage systems, and charging piles by collecting and processing real-time device data of each physical device. For example, during peak electricity demand, edge computing devices can instruct distributed photovoltaic systems to increase power generation while reducing the charging power of charging piles to maximize electricity demand.

[0099] For interactive optimization, edge computing devices can automatically adjust the device operating status of each physical device based on real-time device data collection and analysis, as well as preset optimization algorithms, to maximize energy efficiency.

[0100] For cloud connectivity, edge computing devices can connect and share data with other edge computing devices through cloud networks. This can achieve energy optimization on a larger scale, while also providing energy data and service support for other physical devices and systems.

[0101] Through the above model, intelligent management and optimization of various physical equipment can be achieved, energy efficiency can be improved, energy costs can be reduced, and it can also help promote and apply renewable energy.

[0102] Figure 5 It is a structural block diagram of another energy industrial Internet interactive system provided by an embodiment of the present invention. This embodiment is optimized based on the above-mentioned technical solutions. In this embodiment, optionally, the above-mentioned energy industrial Internet interactive system may also include: an energy twin corresponding to each target device, an edge computing device, and is also specifically used to: forward the energy scheduling instruction to each energy twin respectively, so that each energy twin performs energy scheduling between at least some of the target devices according to the received energy scheduling instruction. Among them, the explanations of the terms that are the same or corresponding to the above-mentioned embodiments are not repeated here.

[0103] For details, see Figure 5 The energy industrial Internet interactive system described in this embodiment includes: a power grid twin 30 of the power grid master station cloud of the target power grid, an aggregated twin 31 of the aggregated cloud platform under the power grid master station cloud, and an edge computing device 32 under the aggregated cloud platform. The aggregated cloud platform corresponds to multiple energy devices, and the power station corresponding to the edge computing device 22 has at least one target device, which is an energy device. The system also includes: an energy twin 33 corresponding to each target device; wherein,

[0104] The edge computing device 32 is used to collect device data of each target device, determine the device operation status and device energy supply and demand of the target device according to the device data, and share the determined device operation status and device energy supply and demand with the aggregate twin 31;

[0105] The edge computing device 32 is also used to receive the energy scheduling instructions returned by the aggregation cloud platform for the shared operation status of each device and the energy supply and demand of each device, and forward the energy scheduling instructions to each energy twin 33 respectively, so that each energy twin 33 performs energy scheduling between at least some of the target devices according to the received energy scheduling instructions;

[0106] Aggregate twin 31, used to aggregate the acquired operation status of multiple devices to obtain the platform operation status of the aggregate cloud platform, and aggregate the acquired energy supply and demand conditions of multiple devices to obtain the platform energy supply and demand conditions of the aggregate cloud platform, and share the platform operation status and the platform energy supply and demand conditions to the power grid twin 30;

[0107] The power grid twin 30 is used to determine the power grid energy supply and demand situation of the target power grid, and perform energy scheduling for the target power grid based on the platform operation status and platform energy supply and demand situation shared by multiple aggregated twins 31, as well as the power grid energy supply and demand situation.

[0108] Among them, the energy twin 33 can be understood as the digital twin of the target device (i.e., the energy device). In the implementation of the present invention, optionally, the energy twin 33 can be pre-constructed in the following manner: obtaining the device information of the corresponding target device, wherein the device information includes at least one of the physical configuration, energy production capacity, power storage capacity, and charging power; constructing the energy twin 33 of the target device according to the device information, thereby realizing the effective construction of the energy twin 33.

[0109] The energy scheduling instructions are forwarded to each energy twin 33 through the edge computing device 32, so that each energy twin 33 can perform energy scheduling between at least some target devices with resource scheduling needs according to the energy scheduling instructions.

[0110] On this basis, optionally, for the energy twin 33, an instruction execution rule corresponding to the energy scheduling instruction can be obtained, so that the energy scheduling instruction can be executed according to the instruction execution rule, so as to perform energy scheduling between at least some of the target devices.

[0111] The technical solution of the embodiment of the present invention realizes energy scheduling between at least some target devices with resource scheduling requirements by executing energy scheduling instructions through the energy twin.

[0112] On this basis, for the various technical solutions given above related to the interactive instruction execution mode of coupling physical equipment such as distributed photovoltaic systems, energy storage systems and charging piles with digital twins, an optional example is used for exemplary explanation below.

[0113] For example, through the interaction between cloud management, edge computing devices and physical devices, a closed loop of cloud-pipe-edge-end can be achieved. The "cloud" in "cloud-pipe-edge-end" can be understood as cloud computing, which is the core of data processing and computing, responsible for storage and management; "pipe" can be understood as network and communication, covering management, control and data transmission; "edge" can be understood as edge computing, which is close to physical devices and provides fast data processing; "end" can be understood as physical devices located at the terminal.

[0114] Cloud management can collect and analyze data from all digital twins and physical devices, and issue energy dispatch instructions to edge computing devices and physical devices. Edge computing devices can automatically adjust the device operation status of physical devices and report the data of digital twins and physical devices upward. At the same time, physical devices and digital twins can send data to edge computing devices to achieve two-way data interaction.

[0115] On this basis, for the configuration of physical equipment, first of all, physical equipment such as distributed photovoltaic systems, energy storage systems and charging piles can be configured. These physical devices need to meet the required power requirements and be able to interact with the digital twin.

[0116] For digital twin modeling, a corresponding digital twin can be established for each distributed photovoltaic system, energy storage system and charging pile. These digital twins can include key equipment information such as the physical configuration of the physical equipment, equipment operating status, energy production capacity, power storage capacity and charging power.

[0117] For instruction execution rules, a set of instruction execution rules can be formulated based on the prediction and actual needs of the digital twin. These rules can include the power generation adjustment rules of the distributed photovoltaic system, the power charging and discharging rules of the energy storage system, and the charging power adjustment rules of the charging piles.

[0118] For instruction execution optimization, during the instruction execution process, optimization strategies can be considered to maximize energy efficiency. For example, the power generation of the distributed photovoltaic system and the charging power of the charging pile can be adjusted according to power demand and price fluctuations to minimize energy costs.

[0119] For the interaction between digital twins and physical devices, the digital twins are connected and interacted with the corresponding physical devices through the Internet of Things technology and communication protocols. When the digital twin receives the energy dispatch instruction, it can be forwarded to the corresponding physical device through the network to realize the automatic control and adjustment of the physical device.

[0120] For real-time monitoring and feedback, the operation status of physical equipment can be monitored and fed back in real time during the interaction process. If a physical device fails or experiences an abnormality, the digital twin needs to respond quickly and issue an alarm or control command to ensure the normal operation of the physical device and the stability of energy supply.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An energy industry Internet interactive system, characterized in that: include: The grid twin of the grid master station cloud of the target grid and the aggregated twin of the aggregated cloud platform under the grid master station cloud; wherein, The aggregate twin is used to share the platform operation status and platform energy supply and demand of the aggregate cloud platform with the power grid twin; The power grid twin is used to determine the power grid energy supply and demand situation of the target power grid, and perform energy scheduling for the target power grid based on the platform operation status and the platform energy supply and demand situation shared by multiple aggregated twins, as well as the power grid energy supply and demand situation.

2. The system according to claim 1, characterized in that The aggregate cloud platform corresponds to a plurality of energy devices, and the aggregate twin is also used for: For each of the plurality of energy devices, obtaining a device operating status and a device energy supply and demand situation of the energy device; The acquired operation states of the multiple devices are aggregated to obtain the operation state of the platform, and the acquired energy supply and demand conditions of the multiple devices are aggregated to obtain the energy supply and demand condition of the platform.

3. The system according to claim 2, characterized in that The polymer twin is also used for: Energy scheduling is performed for the multiple energy devices according to the acquired operating states of the multiple devices and the energy supply and demand conditions of the multiple devices, as well as the energy supply and demand conditions of the platform.

4. The system according to claim 2, characterized in that The platform operation state is represented by the operation states of the plurality of devices, and the platform energy supply and demand is represented by the energy supply and demand of the plurality of devices. The aggregate twin is specifically used for: The operating states of the multiple devices are encapsulated into an operating model, the energy supply and demand conditions of the multiple devices are encapsulated into a supply and demand model, and the operating model and the supply and demand model are shared with the power grid twin, so that the power grid twin obtains the operating state of the platform through the received operating model and obtains the energy supply and demand condition of the platform through the received supply and demand model.

5. The system according to claim 2, characterized in that Also includes: The edge computing device under the aggregated cloud platform; wherein the power station corresponding to the edge computing device has at least one target device, the target device is the energy device, and the edge computing device is used to: For each of the target devices, the device data of the target device is collected, and the device operating status and the device energy supply and demand of the target device are determined based on the device data, and the determined device operating status and the device energy supply and demand are shared with the aggregate twin.

6. The system according to claim 5, characterized in that The edge computing device is also used for: Receiving energy dispatching instructions returned by the aggregation cloud platform for the shared operation status of each of the devices and the energy supply and demand of each of the devices; According to the energy scheduling instruction, energy scheduling is performed for at least part of the target devices.

7. The system according to claim 6, characterized in that Also includes: The energy twin corresponding to each of the target devices, the edge computing device, is further specifically used for: The energy scheduling instruction is forwarded to each of the energy twins respectively, so that each of the energy twins performs energy scheduling among at least part of the target devices according to the received energy scheduling instruction.

8. The system according to claim 7, characterized in that The energy twin is used for: Acquire an instruction execution rule corresponding to the received energy dispatch instruction; The energy scheduling instruction is executed according to the instruction execution rule to perform energy scheduling among at least part of the target devices.

9. The system according to claim 7, characterized in that The energy twin is pre-constructed in the following manner: Acquire device information of the corresponding target device, wherein the device information includes at least one of physical configuration, energy production capacity, power storage capacity, and charging power; Based on the device information, the energy twin of the target device is constructed.

10. The system according to claim 5, characterized in that The edge computing device is also used for: The collected device data corresponding to at least part of the target devices are processed, and energy scheduling is performed for at least part of the target devices according to the obtained processing results.

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