User side energy storage system and power pre-distribution method based on swan mongolian operating system

Abstract

The invention discloses a user side energy storage system based on a swan mongolian operating system and a power pre-distribution method, and the system comprises an energy storage device which is used for storing electric energy; the data processing unit is used for collecting and processing data of the energy storage equipment; the EMS system is installed on a user side and used for controlling the charging and discharging process of the energy storage equipment according to the collected data of the energy storage equipment and a preset strategy and monitoring the charging and discharging state and the electric energy quality of the energy storage equipment in real time; the EMS system comprises a prediction module, wherein the prediction module is used for predicting the future electric energy demand of each user according to the electric energy consumption data of the user side; and the swan operating system is used for processing system tasks, scheduling resources and controlling and managing the EMS system. According to the energy storage management system, the swan mongolian operation system is adopted as an operation and management platform of the EMS system, the energy storage management system has more efficient and more intelligent control and management capability, and the charging and discharging state and the electric energy demand of the energy storage equipment can be predicted, so that more efficient and more intelligent energy storage management and control are realized.

Description

Technical Field

[0001] The present invention relates to the fields of energy storage technology and smart grid technology, and in particular to a user-side energy storage system and power pre-distribution method based on the Hongmeng operating system. Background Art

[0002] With the widespread adoption of renewable energy and the continuous development of distributed power sources, behind-the-meter energy storage systems are gaining increasing attention. Energy storage systems can improve power quality and reliability, implement peak load shifting and demand response, and thus reduce both user electricity costs and grid operating costs.

[0003] However, most existing energy storage systems use traditional operating systems and control algorithms, which suffer from problems such as low control accuracy and response speed, insufficient system reliability and stability. Therefore, a new energy storage system is needed to achieve more efficient and intelligent control and management. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In order to solve the deficiencies in the prior art, the present invention provides a user-side energy storage system and power pre-distribution method based on the Hongmeng operating system, which overcomes the problems of low control accuracy and response speed, insufficient system reliability and stability, etc.

[0006] The present invention adopts the following technical solutions.

[0007] The first aspect of the present invention provides a user-side energy storage system based on the Hongmeng operating system, comprising: an energy storage device, a data processing unit, an EMS system, and the Hongmeng operating system. The energy storage device is used to store electrical energy; the data processing unit is used to collect and process data from the energy storage device; the EMS system is installed on the user side and is used to control the charging and discharging process of the energy storage device based on the collected energy storage device data and preset policies, and to monitor the charging and discharging status of the energy storage device and the power quality of the energy storage device in real time; the EMS system includes a prediction module, which is used to predict the future power demand of each user based on the user-side power consumption data; and the Hongmeng operating system is responsible for processing system tasks and scheduling resources, and controlling and managing the EMS system.

[0008] Optionally, the Hongmeng operating system also includes an artificial intelligence-based prediction algorithm, which is used to predict the charging and discharging status of energy storage devices, generate preset strategies, and transmit the preset strategies to the EMS system.

[0009] Optionally, the Hongmeng operating system also includes an optimization algorithm based on artificial intelligence, which is used to achieve optimal scheduling of energy storage equipment and the upper-level power grid. The optimal scheduling aims to minimize the operating cost and maintenance cost of the energy storage equipment.

[0010] Optionally, the system further includes an interface component for displaying data of the monitored energy storage device, and the interface component includes at least one of the following interfaces: system homepage, device monitoring, alarm, and system strategy.

[0011] The second aspect of the present invention provides a user-side power pre-distribution method, including: collecting the power consumption data of each user side, and generating a historical power consumption curve of each user side based on the power consumption data within a preset time; extracting characteristic values ​​for each historical power consumption curve, inputting the characteristic values ​​into the prediction analysis model in the prediction module, and calculating the power demand prediction value of each user side; pre-distributing electricity to each user side according to the power demand prediction value of each user side.

[0012] Optionally, the characteristic value includes one or more of a maximum power consumption value, a minimum power consumption value, a maximum power consumption change value, and a minimum power consumption change value.

[0013] Optionally, the predictive analysis model is expressed as follows:

[0014]

[0015] m∈(1,n),p∈(1,n)

[0016] Among them, δ is the predicted value of electric energy demand, is the maximum power consumption value, β is the minimum power consumption value, v is the maximum power consumption change value, ε is the minimum power consumption change value, x m is the energy consumption value corresponding to the maximum energy consumption change value, x p The energy consumption value corresponding to the minimum energy consumption change value, x1, x2 and x n is the reference value selected from the historical power consumption curve, and n is the number of power consumption values ​​selected from the historical power consumption curve as reference values.

[0017] Optionally, the amount of electricity is pre-allocated based on the proportion of the power demand forecast value of each user side to the total demand forecast value.

[0018] Optionally, pre-allocating electricity to each user side based on the electricity demand forecast value of each user side includes: weighting the electricity demand forecast value of each user side according to user priority; calculating the weighted electricity demand forecast value of all user sides; and calculating the pre-allocated electricity to each user side based on the weighted electricity demand forecast value and the total power generation of the electric field.

[0019] A third aspect of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the above-mentioned user-side power pre-distribution method when loaded into the processor.

[0020] A fourth invention of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above-mentioned method for user-side power pre-distribution when executed by a processor.

[0021] Compared with the prior art, the beneficial effects of the present invention include at least:

[0022] This invention uses the Hongmeng operating system as the operating and management platform for configuring the EMS (Energy Management System), enabling more efficient and intelligent control and management, and improving system reliability and stability. The predictive analysis model in the prediction module predicts power demand, taking into account more comprehensive factors, thereby improving the accuracy of the predicted power demand; the basic formulas expressed in the model are more closely connected, improving the robustness of the prediction module. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0024] Figure 1 A schematic diagram of the framework structure of a user-side energy storage system based on the Hongmeng operating system provided in an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of the framework structure of another user-side energy storage system based on the Hongmeng operating system provided in an embodiment of the present invention;

[0026] Figure 3 A schematic diagram of the hardware architecture of a user-side energy storage system based on the Hongmeng operating system provided in an embodiment of the present invention;

[0027] Figure 4 A schematic diagram of the software architecture of a user-side energy storage system based on the Hongmeng operating system provided in an embodiment of the present invention;

[0028] Figure 5 A schematic flow chart of a method for implementing a user-side energy storage system based on the Hongmeng operating system provided in an embodiment of the present invention;

[0029] Figure 6 A schematic flow chart of a method for pre-distributing power on the user side provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0033] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0034] Example 1

[0035] Reference Figures 1 to 4 , which is a first embodiment of the present invention, provides a user-side energy storage system based on the Hongmeng operating system, including:

[0036] Energy storage devices are used to store electrical energy.

[0037] The data processing unit is used to collect and process data from the energy storage device.

[0038] The EMS system is installed on the user side and is used to control the charging and discharging process of the energy storage device based on the collected data of the energy storage device and the preset strategy, and to monitor the charging and discharging status and power quality of the energy storage device in real time; the EMS system includes a prediction module, which is used to predict the future power demand of each user based on the power consumption data on the user side.

[0039] HarmonyOS is responsible for processing system tasks and scheduling resources, controlling and managing the EMS system.

[0040] Specifically, the energy storage device includes a battery pack, a supercapacitor pack and a flywheel pack.

[0041] The data collected from energy storage devices include current, voltage, charge and discharge, etc.

[0042] Optionally, the data processing unit is based on the Hongmeng operating system and includes a data analysis and monitoring module; the data analysis and monitoring module has multiple functions:

[0043] 1. The energy storage system can monitor the current, voltage, temperature and other parameters of the energy storage system in real time, and provide comprehensive real-time data monitoring and alarm functions.

[0044] 2. The energy storage system can analyze and optimize the historical data of the energy storage system, provide data reports and suggestions, and help users understand energy consumption and optimize the performance of the energy storage system.

[0045] 3. The energy storage system supports remote monitoring and control functions. Users can remotely monitor the operating status of the energy storage system through mobile phones or computers, and perform real-time control and adjustments.

[0046] Optionally, the data processing unit further includes a machine learning module for analyzing energy usage and generating energy-saving recommendations.

[0047] HarmonyOS is a future-oriented, full-scenario distributed operating system that supports a variety of terminal devices such as mobile phones, tablets, smart wearables, smart screens, etc. It is suitable for the usage scenarios of various IoT devices, and can drive artificial intelligence algorithms and manage a large number of IoT devices.

[0048] In this embodiment, by performing data processing based on the HarmonyOS operating system, the data processing speed can be improved. Moreover, since the HarmonyOS operating system adopts a microkernel architecture, it can be installed on various hardware devices to realize the interconnection of all things, thereby enabling more efficient and intelligent control and management of energy storage system-related equipment.

[0049] Optionally, the EMS system also includes a virtual power plant management system, which is used to combine energy storage equipment with other dispatchable resources (such as solar photovoltaic panels, wind turbines, etc.) to form a virtual power plant and achieve coordinated dispatch with the upper-level power grid.

[0050] Optionally, the EMS system uses the Nova IoT platform developed by Qingzhou Energy, including core technologies such as distributed time-series database and encrypted network tunnel, to solve the problems of full data synchronization and data compression in seconds, and financial-level security issues of cloud-edge communication, to achieve two-way data flow between energy storage stations and cloud platforms, and realize the cloud-edge integration effect at the system level, ensuring that station data is losslessly reported to the cloud platform in real time, and the instructions of the cloud platform can be safely and real-timely transmitted to the station, ultimately aiming to reduce network communication costs, server storage costs and power station safety operation and maintenance risks.

[0051] Optionally, the Hongmeng operating system also includes an artificial intelligence-based prediction algorithm, which is used to predict the charging and discharging status of energy storage devices, generate preset strategies, and transmit the preset strategies to the EMS system.

[0052] Specifically, first, real-time data of the energy storage device is collected, including the current battery power, charging status, load conditions, etc. Then, a machine learning algorithm is used to predict the discharge status of the energy storage device based on historical data and real-time data. Finally, based on the prediction results, the charging and discharging plan of the energy storage device is adjusted to meet the needs of the power system.

[0053] Optionally, the machine learning algorithm includes a stochastic model, a support vector machine, or a neural network.

[0054] Optionally, the Hongmeng operating system also includes an optimization algorithm based on artificial intelligence, which is used to achieve optimal scheduling of energy storage equipment and the upper-level power grid. The optimal scheduling aims to minimize the operating cost and maintenance cost of the energy storage equipment.

[0055] Optionally, the artificial intelligence-based optimization algorithm includes a reinforcement learning algorithm.

[0056] Specifically, first, based on the real-time situation and forecast results of the power system, a deep deterministic policy gradient is used to determine the scheduling plan of the energy storage equipment. Then, while meeting the needs of the power system, the operating cost and maintenance cost of the energy storage equipment are minimized. Finally, considering the volatility and uncertainty of renewable energy, the scheduling plan of the energy storage equipment is optimized to ensure the stability and reliability of the system.

[0057] Optionally, the HarmonyOS operating system includes an IoT edge computing architecture, using AWS Greengrass services, hardware chip Edge TPU and software stack Cloud IoT Edge.

[0058] AWS Greengrass is software that allows users to securely run local compute, messaging, data caching, synchronization, and ML inference functions on connected devices. By using AWS Lambda, Greengrass ensures that users' IoT devices can quickly respond to local events, interact with local resources using Lambda functions running on the Greengrass Core, maintain intermittent connectivity, and maintain updates through over-the-air updates, minimizing the cost of transmitting IoT data to the cloud. With Greengrass, users can create and test device software in the cloud using familiar languages ​​and programming models before deploying it to their devices. Users can program AWS Greengrass to filter device data and transmit only necessary information back to the cloud. AWS Greengrass uses the security and access management features of AWS IoT Core to authenticate and encrypt device data at all connection points. This ensures that data is never exchanged between devices when communicating with each other and the cloud without verified identity. ; The Edge Tensor Processing Unit (TPU) is an ASIC (Application Specific Integrated Circuit) chip designed specifically for running TensorFlow Lite ML models at the edge, providing a more efficient AI solution in edge computing. It is optimized for TensorFlow Lite and can efficiently run machine learning models on edge devices, thereby reducing dependence on cloud services and improving the real-time and security of data processing; The software stack Cloud IoT Edge is an open platform that integrates network, computing, storage, and application core capabilities at the edge close to the source of objects or data. It is deployed on edge hardware to achieve collaborative management of edge hardware and the cloud, and can provide computing and intelligent services nearby to meet basic needs in real-time business, application intelligence, security and privacy protection, etc. It has independent access and computing capabilities, supports multiple operating systems such as Huawei Cloud EulerOS, CentOS, Ubuntu, etc., and can adapt to different hardware specifications, including containerized and non-containerized (process-based) deployment methods.

[0059] In this embodiment, by using AWS Lambda, Greengrass ensures that users' IoT devices can quickly respond to local events, use Lambda functions running on Greengrass Core to interact with local resources, maintain intermittent connectivity, and maintain updates through over-the-air updates, minimizing the cost of transmitting IoT data to the cloud. Using the Edge TPU, a more efficient AI solution is provided for edge computing. It is optimized for TensorFlow Lite and can efficiently run machine learning models on edge devices, reducing reliance on cloud services and improving the real-time and security of data processing.

[0060] Specifically, the Hongmeng operating system includes hardware architecture and software architecture.

[0061] The hardware architecture includes: (1) Using domestically produced chips, with a CPU core count of no less than 4 cores and a main frequency of no less than 1GHz, domestically produced ARM chips, supporting at least 4G memory, 16G storage, and wide temperature characteristics. (2) Communication: Supporting RS232 / 485, Ethernet port and 4G wireless, etc. (3) Encryption: Using a combination of software and hardware encryption, supporting secure smart TF cards or soft encryption, etc. (4) Panel: Supporting power, operation, fault, communication indicator lights, etc. In the software architecture, based on domestically produced ARM chips and equipped with Hongmeng operating system, customized power IoT equipment is designed and developed. It connects to the IoT management platform through MQTT and HTTP protocols, integrates modular APP containers, security protection functions and other services, and obtains perception data through RS485, Ethernet, and wireless communication. The edge computing system is deployed in the form of containers, supports dynamic optimization, has strong compatibility, and supports the one-time development, transplantation and reconstruction of IoT APPs.

[0062] In this embodiment, the distributed architecture of the Hongmeng operating system enables the system to work collaboratively across devices and platforms. This distributed architecture enables the system to obtain and analyze energy data from various devices in real time, thereby making more accurate energy management decisions. The EMS system achieves a cloud-edge integration effect at the system level, enabling two-way data flow between the energy storage station and the cloud platform, effectively reducing network communication costs, server storage costs, and power station security operation and maintenance risks. The user-side energy storage system based on the Hongmeng operating system has a powerful security protection mechanism. The Hongmeng operating system adopts a microkernel architecture and hardware-level security protection technology, which can effectively prevent various security attacks and vulnerabilities. In the energy storage system, this security protection mechanism can ensure the data security and privacy protection of the user-side energy storage device, and prevent malicious attacks and data leakage.

[0063] Combine Figure 1 and Figure 2As shown, optionally, the system further includes an interface component for displaying the data of the monitored energy storage equipment, and the interface component includes at least one of the following interfaces: system homepage, equipment monitoring, alarm and system strategy.

[0064] Specifically, as shown in Table 1, the system homepage includes the current time, number of days of safe operation, total charge capacity, total discharge capacity, daily charge capacity, daily discharge capacity display of the energy storage system, charge and discharge status, SOC (State of Charge, remaining battery capacity), SOH (State of Health, battery health status), charge and discharge power curve, main equipment status: PCS (Power Conversion System, energy storage converter), BMS (Battery Management System, battery management system), air conditioning; equipment monitoring is divided into PCS monitoring, BMS monitoring, status display, alarm information and power curve setting (peak shaving and valley filling). PCS monitoring includes controlling PCS power on and off, real-time power setting, grid connection and disconnection, alarm elimination operations, displaying important alarm status, displaying important PCS analog information, displaying PCS power curve, displaying operation data, and BMS monitoring includes displaying BMS SOC curve, battery cluster overall information, battery cluster alarm status, battery cluster analog information, battery PACK information, battery cell voltage, temperature information, status display including power cabinet temperature and humidity data and alarm information, battery cabinet temperature, humidity, smoke, hydrogen, carbon monoxide, VOC data and alarm information, access control status, flooding status, emergency stop status, fan status, lightning protection status, circuit breaker status, alarm information including real-time alarm presentation and historical alarm information; the platform can issue control parameters according to the set peak and valley time periods to control the PCS to charge and discharge during the specified time period to ensure economy. The PCS start and stop can start and stop one by one at time intervals to ensure smooth changes in power; the alarm is to receive instructions from the load control master station or flexible consumption equipment to adjust the energy storage output in real time; the system strategy has two aspects. On the one hand, it is anti-backflow protection. During the energy storage discharge process, the gateway meter power is monitored and the energy storage output is dynamically adjusted according to the power load power to prevent backflow; on the other hand, it is over-demand protection. During the energy storage charging process, the gateway meter power is monitored and, based on the degree of power approaching the transformer capacity, the total load of energy storage charging and load power consumption does not exceed the transformer demand to prevent over-demand.

[0065] Table 1

[0066]

[0067]

[0068]

[0069] Optionally, the system further includes a communication interface, connected to and adapted to an external system, so that the external system can communicate with the system through the communication interface.

[0070] Combine Figure 5 As shown, the use process includes the following steps:

[0071] S1. Install and start the HarmonyOS operating system on a computing device.

[0072] S2. Configure the data processing unit to collect energy device and sensor data.

[0073] S3. Configure the EMS system to control the charging and discharging process of the energy storage device based on real-time data and predetermined strategies.

[0074] S4. Communicate with external devices and services through the communication interface.

[0075] S5. Use the interface component to view energy usage, set energy usage preferences, and receive user input instructions.

[0076] S6. Update and improve energy scheduling algorithms and battery health management strategies as needed.

[0077] In this embodiment, users can flexibly configure the energy storage system according to their needs, including flexibly configuring the energy devices and sensor devices required for collection, and flexibly selecting the required external services or external device communications. This can further enhance the user experience, and the energy storage system is highly scalable and can be adapted to more user scenarios.

[0078] Example 2

[0079] Combine Figure 6 As shown, embodiment 2 of the present invention provides a user-side power pre-allocation method, which is applied to the user-side energy storage system based on the above-mentioned Hongmeng operating system, and the method includes:

[0080] S100: The data processing unit collects the power consumption data of each user side, and generates a historical power consumption curve of each user side according to the power consumption data within a preset time.

[0081] Specifically, the preset time is 30 days, 40 days, or 50 days. It is understood that those skilled in the art can set a specific value of the preset time according to actual conditions, and the embodiment of the present invention does not limit this.

[0082] S200: The data processing unit extracts characteristic values ​​from each historical power consumption curve, inputs the characteristic values ​​into the prediction analysis model in the prediction module, and calculates and obtains the power demand prediction value of each user side.

[0083] In S200 , the characteristic value includes one or more of a maximum power consumption value, a minimum power consumption value, a maximum power consumption change value, and a minimum power consumption change value.

[0084] In S200, the prediction analysis model is expressed as follows:

[0085]

[0086] m∈(1,n),p∈(1,n)

[0087] Among them, δ is the predicted value of electric energy demand, is the maximum power consumption value, β is the minimum power consumption value, v is the maximum power consumption change value, ε is the minimum power consumption change value, x m is the energy consumption value corresponding to the maximum energy consumption change value, x p is the electric energy consumption value corresponding to the minimum electric energy consumption change value, x1 and x2 are reference values ​​selected from the historical electric energy consumption curve, and n is the number of electric energy consumption values ​​selected as reference values ​​in the historical electric energy consumption curve.

[0088] Specifically, the historical electricity consumption curve uses time as the horizontal coordinate and the electricity consumption value as the vertical coordinate. The maximum derivative value of the historical electricity consumption curve can be taken as the maximum electricity digestion change value, and the vertical coordinate corresponding to the maximum derivative value of the curve is the electricity consumption value corresponding to the maximum electricity consumption change value; the minimum derivative value of the historical electricity consumption curve can be taken as the minimum electricity digestion change value, and the vertical coordinate corresponding to the minimum derivative value of the curve is the electricity consumption value corresponding to the minimum electricity consumption change value.

[0089] In the prediction analysis model expression, the first term It expresses the influence of the maximum energy consumption value of the historical energy consumption curve; the second item It expresses the influence of the minimum power consumption value of the historical power consumption curve; the selection of 1 in the expression can save computing power, increase computing speed, and thus improve the efficiency of the system; the third item It expresses the influence of the maximum energy consumption change value of the historical energy consumption curve; the fourth item It expresses the influence of the minimum energy consumption change value of the historical energy consumption curve; the fifth item It expresses the influence of the difference between the reference values ​​corresponding to different reference points. The influence refers to the variable contribution of each characteristic value in a curve.

[0090] In this embodiment, the user-side power demand at a certain moment in the future is calculated by the variable contribution of each characteristic value of the curve. That is, the future user-side energy demand is predicted by studying the historical power consumption on the user side, and then the power allocation strategy is further formulated according to the predicted energy demand. This can improve the accuracy of energy management decisions of the energy storage system and achieve more intelligent and efficient control and management.

[0091] S300, the EMS system pre-allocates electricity to each user side according to the predicted value of the electricity demand of each user side.

[0092] Optionally, the amount of electricity is pre-allocated based on the proportion of the power demand forecast value of each user side to the total demand forecast value.

[0093] Specifically, the power is pre-allocated to each user side according to the following formula:

[0094]

[0095] Among them, H i is the pre-allocated power at the i-th user side; H total is the total power generation of the electric field; δ i is the predicted value of the electric energy demand at the i-th user side.

[0096] Optionally, S300 includes:

[0097] The power demand forecast value of each user side is weighted according to the user priority;

[0098] Calculate the weighted power demand forecast value of all users;

[0099] The pre-allocated power volume for each user is calculated based on the weighted power demand forecast value and the total power generation of the electric field.

[0100] Specifically, the power is pre-allocated to each user side according to the following formula:

[0101]

[0102] Among them, H i is the pre-allocated power at the i-th user side, w i is the weight of the i-th user side, H total is the total power generation of the electric field; δ i is the predicted value of the power demand of the i-th user. The higher the user priority, the greater the corresponding weight.

[0103] Optionally, during the pre-allocation process, it is checked whether the pre-allocated power on the user side exceeds the total power generation of the electric field. If so, the pre-allocated power on the user side whose priority is lower than the preset priority threshold is reduced proportionally.

[0104] It is understandable that those skilled in the art may set the preset priority threshold and the user-side priority according to actual needs, and the embodiments of the present invention do not limit this.

[0105] It should be understood that the size of the serial numbers of the steps in the above embodiments 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.

[0106] Embodiment 3 of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the user-side power pre-distribution method described in embodiment 2.

[0107] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the user-side power pre-distribution method according to embodiment 2 is implemented.

[0108] Compared with the prior art, the beneficial effects of the present invention include at least:

[0109] This invention uses the Hongmeng operating system as the operating and management platform for configuring the EMS (Energy Management System), enabling more efficient and intelligent control and management, and improving system reliability and stability. The predictive analysis model in the prediction module predicts power demand, taking into account more comprehensive factors, thereby improving the accuracy of the predicted power demand; the basic formulas expressed in the model are more closely connected, improving the robustness of the prediction module.

[0110] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0111] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0112] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0113] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A user-side energy storage system based on Hongmeng operating system, characterized in that: include: Energy storage devices, used to store electrical energy; A data processing unit, used for collecting and processing data of the energy storage device; The EMS system is installed on the user side and is used to control the charging and discharging process of the energy storage device according to the collected data of the energy storage device and the preset strategy, and to monitor the charging and discharging status and power quality of the energy storage device in real time; the EMS system includes a prediction module, which is used to predict the future power demand of each user based on the power consumption data on the user side; Hongmeng operating system is responsible for processing system tasks and scheduling resources, controlling and managing the EMS system.

2. The user-side energy storage system based on Hongmeng operating system according to claim 1 is characterized in that: The Hongmeng operating system also includes an artificial intelligence-based prediction algorithm, which is used to predict the charging and discharging status of energy storage devices, generate preset strategies, and transmit the preset strategies to the EMS system.

3. The user-side energy storage system based on Hongmeng operating system according to claim 2 is characterized in that: The Hongmeng operating system also includes an optimization algorithm based on artificial intelligence, which is used to achieve optimal scheduling of energy storage equipment and the upper-level power grid. The optimal scheduling aims to minimize the operating cost and maintenance cost of the energy storage equipment.

4. The user-side energy storage system based on Hongmeng operating system according to any one of claims 1 to 3, characterized in that: The system also includes an interface component for displaying the monitored data of the energy storage device, and the interface component includes at least one of the following interfaces: system homepage, equipment monitoring, alarm and system strategy.

5. The user-side energy storage system based on Hongmeng operating system according to any one of claims 1 to 3, characterized in that: The system also includes a communication interface, which is connected to and adapted to an external system, so that the external system can communicate with the system through the communication interface.

6. A user-side power pre-allocation method, applied to the user-side energy storage system based on the Hongmeng operating system as described in any one of claims 1 to 5, characterized in that: include: Collect the power consumption data of each user side, and generate the historical power consumption curve of each user side according to the power consumption data within a preset time; Extract characteristic values ​​from each historical power consumption curve, input the characteristic values ​​into the prediction analysis model in the prediction module, and calculate the power demand prediction value of each user side; Pre-allocate electricity to each user based on the predicted power demand of each user.

7. The user-side power pre-distribution method according to claim 6, characterized in that: The characteristic value includes one or more of a maximum power consumption value, a minimum power consumption value, a maximum power consumption change value, and a minimum power consumption change value.

8. The user-side energy storage power pre-allocation method according to claim 7 is characterized in that: The predictive analysis model is expressed as follows: m∈(1,n),p∈(1,n) Among them, δ is the predicted value of electric energy demand, is the maximum power consumption value, β is the minimum power consumption value, v is the maximum power consumption change value, ε is the minimum power consumption change value, x m is the energy consumption value corresponding to the maximum energy consumption change value, x p is the energy consumption value corresponding to the minimum energy consumption change value, x1, x2 and x n is the reference value selected from the historical power consumption curve, and n is the number of power consumption values ​​selected from the historical power consumption curve as the reference value.

9. The user-side power pre-distribution method according to claim 8, characterized in that: The amount of electricity is pre-allocated based on the proportion of each user's predicted electricity demand to the total predicted demand.

10. The user-side power pre-distribution method according to claim 8, characterized in that: Pre-allocating power to each user side based on the predicted power demand value of each user side includes: The predicted value of electric energy demand at each user side is weighted according to the user priority; Calculate the weighted power demand forecast value of all users; The pre-allocated electricity amount for each user is calculated based on the weighted electricity demand forecast value and the total power generation of the electric field.

11. An electronic device, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the user-side power pre-distribution method according to any one of claims 6 to 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the user-side power pre-distribution method described in any one of claims 6 to 10 are implemented.

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