Energy storage management regulation and control system and regulation and control method

Abstract

The invention discloses an energy storage management regulation and control system and a regulation and control method, which can optimize an energy scheduling mode and reduce power utilization cost by utilizing different prices of wave crests and wave troughs of a municipal power grid, and comprises an energy storage cabinet, a battery replacement station, a mobile energy storage vehicle, a photovoltaic system, a monitoring system and a management device, a power supply mode can be regulated and controlled by a management device according to wave crests and wave troughs of a power grid, energy storage conditions of an energy storage cabinet, power utilization requirements of a battery swap station, power utilization requirements of a mobile energy storage vehicle and power generation conditions of a photovoltaic system, so that efficient and stable operation of a power utilization system is ensured; the management device dynamically issues an energy scheduling strategy according to data detected by the monitoring system, including obtaining the state of the energy storage cabinet and determining the power consumption stage of the power grid, and based on the state of the battery and the power consumption stage of the power grid, the management device controls charging or discharging, balances the wave crest and the wave trough of the power grid, reduces the pressure of the power grid, and reduces the energy consumption of the power grid. The power generation income is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power energy storage and energy management, in particular to an energy storage management and control system and a control method. BACKGROUND

[0002] With the wide application of new energy vehicles, electric vehicles and various portable electronic devices, the demand for charging infrastructure has shown explosive growth. The traditional charging system mainly relies on municipal power grid for direct power supply. This single power supply mode has many disadvantages: on the one hand, it causes excessive load on the power grid during peak power consumption, and on the other hand, it leads to idle and waste of power generation resources during off-peak power consumption. According to statistics, the peak-valley load difference can reach 30% to 40% of the maximum load, forcing the generator set to start and stop frequently, which not only increases the consumption of energy such as coal and equipment wear and tear, but also causes serious energy waste and environmental pollution, ultimately leading to high social power supply cost.

[0003] In the current power system, especially in the power consumption environment of industrial parks, residential areas and the like, although peak-valley energy storage charging technology has been used to optimize energy allocation, and attempts have been made to integrate renewable energy such as photovoltaic power generation and energy storage devices. However, the existing technical solutions still have obvious deficiencies: there is a lack of intelligent collaborative working mechanism between various energy units, making it difficult to achieve dynamic optimal allocation of electric energy; the system architecture is rigid and cannot flexibly respond to changes in power consumption demand in different scenarios; there is a lack of a unified energy management platform, making it difficult to achieve effective complementation and global optimization of various energy forms. In particular, in emerging application scenarios such as mobile energy storage and battery replacement services, the existing system cannot achieve intelligent scheduling of electric energy between energy storage cabinets, mobile energy storage vehicles, battery replacement stations and other devices, nor can it respond dynamically according to the real-time state of the power grid.

[0004] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0005] The image fusion method and system proposed by the present application solve the above deficiencies in the prior art.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] An energy storage management and control system, characterized in that it comprises:

[0008] An energy storage cabinet for storing electric energy and outputting electric power externally;

[0009] A battery replacement station, a charging terminal, mainly used for small power battery replacement service;

[0010] A mobile energy storage vehicle, a charging terminal, mainly used for storing electric energy and providing charging and discharging service for other devices;

[0011] A photovoltaic system for converting light energy into electrical energy and outputting the electrical energy to other energy storage devices;

[0012] A monitoring system for collecting and monitoring information of all units in the municipal power grid and power system;

[0013] A management device in communication with the energy storage cabinet, battery swap station, mobile energy storage vehicle, photovoltaic system, and monitoring system, for formulating and issuing energy scheduling strategies for all units in the power system and for information interaction;

[0014] The management device dynamically issues energy scheduling strategies according to the data detected by the monitoring system, manages the energy storage cabinet, battery swap station, mobile energy storage vehicle, photovoltaic system, and monitors energy use.

[0015] The energy scheduling strategy includes obtaining the state of the energy storage cabinet and determining the power consumption stage of the power grid; wherein the state of the energy storage cabinet includes the state of charge value and the health state index value of the battery; the power consumption stage of the power grid includes peak power consumption and valley power consumption; based on the state of the battery and the power consumption stage of the power grid, the management device controls charging or discharging.

[0016] The energy scheduling strategy also includes constructing a predictive data-driven multi-energy collaborative scheduling and energy storage buffer system, which includes 1) a predictive energy management module, 2) a multi-energy collaborative scheduling module, and 3) a gradient energy storage buffer module.

[0017] An energy storage management and regulation method, characterized in that,

[0018] The management device is in communication with the energy storage cabinet, battery swap station, mobile energy storage vehicle, photovoltaic system, and monitoring system;

[0019] The management device dynamically issues energy scheduling strategies according to the data detected by the monitoring system; when the monitoring system monitors that the power grid is in a valley power consumption state, the management device controls the energy storage cabinet to obtain electrical energy from the power grid to charge the internal battery pack; when it is determined that the power grid is in a peak power consumption state, and the state of charge value of the battery of the energy storage cabinet is greater than a preset threshold, the energy storage cabinet is controlled to discharge to the battery swap station or mobile energy storage vehicle.

[0020] The energy scheduling strategy includes obtaining the state of the energy storage cabinet and determining the power consumption stage of the power grid, based on the battery state and the power consumption stage of the power grid, the management device controls charging or discharging, wherein the battery health state index value is realized by resistance measurement or cycle life prediction model, the power consumption stage of the power grid is realized by load curve analysis or electricity price period division model, which is used to identify the power grid peak shaving demand; the management device controls the energy storage cabinet to discharge to the battery swap station or the mobile energy storage vehicle within the range allowed by the battery health state; at the same time, the coupling analysis of battery performance parameters and power grid load state is carried out, and the safety boundary of charging and discharging operation is dynamically adjusted.

[0021] The energy scheduling strategy also includes establishing a predicted performance management module, a multi-energy collaborative scheduling module and a gradient energy storage buffer module:

[0022] The predicted performance management module is based on intelligent charging demand prediction, and a double-dimensional decision model is used to analyze the state parameters of energy storage equipment and the load parameters of the power grid; the double-dimensional decision model is a GAT-LSTM hybrid model, GAT is responsible for capturing the spatial topological relationship of the charging station network, and LSTM processes time series data; the multi-energy collaborative scheduling module is based on a distributed energy management system of edge computing architecture, which realizes multi-energy collaborative scheduling by combining edge computing and reinforcement learning; the gradient energy storage buffer module realizes peak-valley load transfer through a lithium battery and super capacitor hybrid energy storage system and V2G technology.

[0023] The energy scheduling strategy includes establishing an energy storage management and control system, including the following steps

[0024] Step one: improve the energy buffer capacity of charging infrastructure through diversified energy storage technology combination, build a multi-level adaptive energy storage collaborative scheduling system, which contains three layers of energy storage architecture:

[0025] Step two: improve the response speed of the system to demand changes through data-driven predicted performance management, build an intelligent optimization system based on AI-driven predicted performance management and multi-source collaborative scheduling;

[0026] Step three: reconstruct the organization mode of energy distribution through microgrid architecture, adopt enhanced double bus architecture and microgrid dynamic control system integrated with intelligent scheduling.

[0027] The organizational mode of reconstructing energy distribution through a micro-grid architecture adopts an enhanced double bus architecture and a micro-grid dynamic control system integrated with intelligent scheduling, and the specific steps are as follows: Step 1, a double bus structure is adopted, and through real-time monitoring of the bus voltage difference, automatic scheduling switching of the multi-energy system is realized; Step 2, a dynamic weight distribution algorithm: based on multi-source data fusion technology, combined with an evolutionary algorithm to optimize weight distribution; Step 3, a virtual synchronous generator control strategy: considering the transient frequency characteristics of different response stages of inertia control or primary frequency modulation, effectively absorbing fluctuating energy sources such as wind power and / or photovoltaic power, assisting the system to achieve frequency stability.

[0028] A digital twin energy storage management regulation system and regulation method model system are established based on an energy regulation strategy.

[0029] Compared with the prior art, the beneficial effects of the present application are:

[0030] The present application proposes a kind of energy storage management regulation method, management unit is integrated with scheduling power grid, energy storage cabinet and mobile energy storage vehicle, photovoltaic and multiple energy sources, so that it realizes the best distribution of electric power in time and space through efficient cooperation, can significantly improve the energy efficiency and economy during power consumption, balance the power grid peak and valley, reduce the pressure of power grid at the same time, improve power generation income.

[0031] Through modular design, energy storage cabinets, mobile energy storage vehicles and other power consumption terminals can be flexibly accessed or removed as needed, with high flexibility and scalability, which can be extended to other diversified application scenarios. Mobile energy storage vehicles can also constitute redundant emergency power supply for battery swap stations, which can ensure stable key charging demand during power grid failure or peak power consumption period, thereby improving the reliability of system power supply.

[0032] The present application realizes the time-space transfer of power grid load, stores off-peak electricity and reasonably distributes it during peak periods; through the cooperation of mobile energy storage vehicles and photovoltaic systems, temporary power supply nodes are formed in areas without power grid coverage; the multi-unit collaborative control of the management device makes the charging and discharging behavior of the energy storage equipment real-time match the state of the power grid, reducing the frequency of generator set frequency modulation. The linkage mechanism of battery swap stations and energy storage cabinets effectively reduces the peak value of power grid load during peak periods, while improving the utilization rate of energy storage equipment. Through prediction algorithm, the load demand and electricity price of power grid are predicted, and the charging and discharging strategy is formulated in advance. According to different electricity price periods, the charging and discharging power of the energy storage system is adjusted. Peak-valley arbitrage is realized, and the cost of electricity is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A schematic diagram of the energy storage management regulation system according to the present application;

[0034] Figure 2 A schematic diagram of the peak-valley management strategy according to the present application;

[0035] Figure 3 The core flow diagram of the energy storage management regulation system of the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application. It should be noted that: similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0037] In the prior art, with the popularization of new energy vehicles and electric devices, the charging infrastructure is facing the problems of power supply pressure and peak-valley load imbalance. The traditional charging system relies on single power supply mode of municipal power grid, and power shortage easily occurs in peak period, and energy idling occurs in valley period. For example, in a summer peak period, the centralized charging of an industrial park causes the sudden increase of power grid load, and the standby generator set needs to be frequently started, causing energy waste and equipment wear. Although the prior art introduces auxiliary energy such as photovoltaic, it lacks a multi-unit collaborative mechanism and cannot realize dynamic scheduling and load balancing.

[0038] In order to solve the above problems, the researchers observed that the peak-valley difference of the power grid can be more than 30% of the total load, and the frequent peak regulation of the generator set will increase the energy consumption. Through analysis, it is found that the energy storage device and the distributed charging network can alleviate the centralized power supply pressure, but the existing system lacks a unified scheduling platform. Therefore, the idea of integrating energy storage units, mobile charging terminals and clean energy is generated, and the monitoring data is used to dynamically adjust the charging and discharging strategy to form a multi-source collaborative solution.

[0039] Therefore, as Figure 1 For the energy storage management regulation system described in the present application, the present application proposes a system architecture including an energy storage cabinet, a battery swap station, a mobile energy storage vehicle, a photovoltaic system, a monitoring system and a management device. The energy storage cabinet stores and outputs electric energy; the battery swap station provides small power battery replacement service; the mobile energy storage vehicle stores electric energy and charges and discharges for devices; the photovoltaic system converts light energy to supply the mobile energy storage vehicle; the monitoring system identifies the peak-valley state of the power grid; and the management device formulates and issues energy scheduling strategies.

[0040] Wherein, the energy storage cabinet refers to a fixed energy storage device configured with energy storage battery modules, which can be implemented by lithium iron phosphate battery pack, for storing off-peak electricity and releasing in peak period. The battery swap station refers to a service terminal equipped with a battery replacement mechanical arm, which can realize fast battery disassembly and assembly through an automatic track system, for reducing centralized charging load. The mobile energy storage vehicle refers to a charging device mounted with a mobile battery pack, which can be implemented by a vehicle-mounted energy storage system, for providing emergency power supply in weak power grid areas. The photovoltaic system refers to a power generation unit composed of solar panels and inverters, which can be implemented by monocrystalline silicon components, for supplementing clean energy supply. The monitoring system refers to a device for collecting real-time power grid load data, which can be implemented by smart meters and data analysis modules, for identifying peak and valley periods. The management device refers to a control platform integrated with a communication module, which can be implemented by an edge computing server, for coordinating the operation strategies of each unit.

[0041] Specifically, the energy storage cabinet is charged by the municipal power grid during the off-peak period of the power grid and discharged to the battery swap station and the mobile energy storage vehicle during the peak period. The battery swap station provides battery replacement services for small devices such as electric vehicles, reducing the impact of centralized charging on the power grid. The monitoring system continuously collects power grid load data and transmits peak and valley states to the management device. The management device formulates charging and discharging strategies according to the state of charge of the battery and the stage of the power grid and issues instructions, such as preferentially charging the energy storage cabinet during the off-peak period and controlling the energy storage cabinet to discharge to the battery swap station during the peak period. The mobile energy storage vehicle is also charged during the off-peak period or receives power generated by the photovoltaic system or wind energy system during idle time, and provides power to large devices in power-intensive areas. The photovoltaic system directly charges the mobile energy storage vehicle when the light is sufficient, forming a clean energy closed loop.

[0042] The application further provides that the energy storage cabinet and the mobile energy storage vehicle are both internally provided with energy storage battery modules, and the energy storage cabinet is used for power delivery to the battery swap station and the mobile energy storage vehicle.

[0043] Wherein, the energy storage battery module refers to a device for storing electrical energy, which can be implemented by lithium ion battery pack or flow battery pack, and the state of charge and health are monitored by the battery management system. The module provides basic energy storage capability for the energy storage unit, enabling the energy storage cabinet and the mobile energy storage vehicle to have independent charging and discharging functions. Wherein, the power delivery refers to the process of transferring electrical energy from the energy storage cabinet to other units, which can be implemented by connecting a bidirectional inverter to a DC bus, supporting bidirectional energy flow. This feature makes the energy storage cabinet a key energy dispatching hub, realizing cross-unit energy allocation.

[0044] Specifically, the energy storage cabinet stores electric energy through the built-in energy storage battery module, and establishes a power connection with the battery swap station and the mobile energy storage vehicle. When the battery swap station needs to supplement the electric quantity, the energy storage cabinet transmits electric energy to it through the power interface; when the mobile energy storage vehicle needs to be charged, the energy storage cabinet completes energy transmission through the fast charging port. Through the energy storage management and control system, the energy data of each device is monitored in real time, and the energy gap is balanced by linkage energy storage; at the same time, after the mobile energy storage vehicle receives electric energy through the energy storage battery module, it can be transferred to other scenes to supply power to the equipment. The mobile energy storage vehicle can also provide emergency power supply for places without power supply for temporary use; by detecting the distance between the mobile energy storage vehicle and the energy storage cabinet, the charging moving distance of the mobile energy storage vehicle and the energy storage cabinet is dynamically calculated and distributed, and the mobile energy storage vehicle is automatically moved and matched with the charging interface for charging; a collaborative network of fixed energy storage facilities and mobile energy storage carriers is constructed, the energy storage cabinet is used as a core node to dynamically distribute the electric energy path, which not only meets the continuous power supply demand of the battery swap station, but also supports the flexible application of the mobile energy storage vehicle in different scenes. The traditional energy storage system relies on a single energy storage unit for power supply, and there is a lack of energy interaction mechanism between units, resulting in fixed power supply path and limited expandability. The present scheme forms an expandable hierarchical power supply network through the bidirectional power transmission architecture of the energy storage cabinet and the mobile energy storage vehicle, which not only realizes the energy complementation of fixed scenes and mobile scenes, but also reduces the risk of overload of a single unit through distributed energy storage nodes.

[0045] The present application solves the problem of low energy distribution efficiency caused by insufficient coordination between energy storage units, and realizes flexible scheduling of energy across units through the active electric quantity transmission of the energy storage cabinet to the battery swap station and the mobile energy storage vehicle. At the same time, the mobile energy storage vehicle as a mobile energy storage node expands the system power supply range, so that it can adapt to temporary power demand or remote scene power supply, and enhances the expandability of the system topology.

[0046] Compared with the prior art, the traditional scheme relies on single power grid power supply and lacks dynamic scheduling capability, and cannot coordinate multiple types of energy storage units. The present scheme integrates fixed energy storage cabinets and mobile energy storage vehicles to form a hierarchical energy storage structure; uses a photovoltaic system to supplement clean energy supply and reduce dependence on municipal power grids; the management device dynamically adjusts the strategy based on real-time monitoring data to realize multi-energy complementation. The battery swap station in the prior art only has the function of battery replacement, and the present scheme links it with the energy storage cabinet to directly supply power through the energy storage cabinet during peak periods, avoiding the load superposition caused by the battery swap station taking power from the power grid.

[0047] By the technical scheme, the application realizes the time-space transfer of the power grid load, stores the off-peak power and reasonably distributes the off-peak power in the peak period; through cooperation of the mobile energy storage vehicle and the photovoltaic system, a temporary power supply node is formed in the area without power grid coverage; the multi-unit collaborative control of the management device makes the charging and discharging behavior of the energy storage equipment real-time match the state of the power grid, and reduces the frequency of generator set frequency modulation. The linkage mechanism of the battery swap station and the energy storage cabinet effectively reduces the peak value of the power grid load in the peak period, and improves the utilization rate of the energy storage equipment. The load demand and the electricity price of the power grid are predicted through the prediction algorithm, and the charging and discharging strategy is formulated in advance. According to different electricity price periods, the charging and discharging power of the energy storage system is adjusted. The peak-valley arbitrage is realized, and the power cost is reduced.

[0048] The following embodiment 1 takes the summer electricity peak of an industrial park as an example to show the practical application of the present scheme.

[0049] The whole energy storage management and control system is planned and designed:

[0050] The industrial park electricity data (historical load curve, peak-valley period, electricity price structure), geographical layout (power distribution room location, available roof area, traffic route) and existing facilities (charging pile, generator) are stored and retrieved at the same time.

[0051] Modeling is adopted by using simulation software (such as MATLAB / Simulink), and the system capacity and layout are optimized. For example, the capacity of the energy storage cabinet is calculated according to the peak load gap, and the capacity of the photovoltaic system is designed according to the sunshine hours.

[0052] Energy storage cabinet: choose standardized products, such as Tesla Powerpack, parameters including capacity 500kWh, cycle life ≥6000 times. Installed near the power distribution room, the infrastructure includes concrete platform and rainproof shed. The electrical connection is interconnected with the power grid and the battery swap station through the cable, and the national standard (such as GB / T 34131) is followed.

[0053] Mobile energy storage vehicle: install battery pack and charging interface. The battery pack uses Ningde Times lithium iron phosphate, and is equipped with Schneider Electric inverter. Set up a special parking space and charging pile, and the charging pile supports V2G (vehicle to grid) function.

[0054] Photovoltaic system: install solar panels on the roof or open space, and also arrange photovoltaic system (photovoltaic panel) on the roof of the mobile energy storage vehicle to further improve the energy self-sufficiency; the angle is adjusted to the best inclination angle of the local area. The photovoltaic system on the roof of the mobile energy storage vehicle has a solar tracking system to adjust the angle in real time, and the inverter is connected with the power grid and the energy storage cabinet. Use Longi monocrystalline silicon components, and the inverter selects Huawei intelligent photovoltaic solution.

[0055] Monitoring System: Install smart meters and sensors at key points such as grid access points, energy storage cabinets, and battery swap stations. Use shielded cables for wiring to reduce interference. Choose Siemens brand for smart meters and Honeywell series for sensors.

[0056] Management Device Software: Deploy on edge servers and use a development database like MySQL to store historical data. Use a web interface and mobile app for user interface, supporting real-time monitoring and manual intervention. Use Dell PowerEdge for edge servers and open-source platforms like Apache Kafka for software development.

[0057] Calculate Return on Investment (ROI) considering equipment cost, installation cost, operation and maintenance cost, and energy saving benefits. Expect 3-5 years of investment recovery period.

[0058] Battery Swap Station: Deploy in parking lots or entrances, install automated mechanical arms and battery racks. Communicate with management devices through Ethernet network. Set up LAN and WAN, use 4G / 5G wireless backup. Use MQTT for device communication and HTTPS for secure data transmission.

[0059] Integration Testing: Verify component communication: management device issues instructions, test energy storage cabinet charging and discharging, battery replacement in battery swap station, and mobile energy storage vehicle dispatching.

[0060] Simulate grid peak and valley scenarios: Use load simulators to generate load fluctuations to check system response time and policy implementation.

[0061] Optimization Adjustment: Calibrate monitoring system data accuracy, adjust MPC algorithm parameters (such as prediction time domain, control time domain).

[0062] Safety Testing: Including overload protection, insulation detection, and network security (firewall, encrypted transmission).

[0063] Daily Operations: Management device automatically runs, operators monitor abnormalities through the interface. Generate reports regularly, including energy consumption analysis, device status, and cost savings.

[0064] Maintenance Plan: Check BMS and converters monthly, perform capacity tests every six months. Check battery health after daily charging, perform mechanical component maintenance weekly. Clean solar panels quarterly, check inverters annually.

[0065] Expansion and Upgrade: Add more energy storage units (including lithium battery + supercapacitor hybrid energy storage, hydrogen energy storage, etc.) or integrate other renewable energy sources (such as wind power) as needed.

[0066] The charging and discharging needs of new energy vehicles and electric vehicles are equivalent to the charging of electric devices, and the theory is the same.

[0067] Assume the industrial park area is 100,000 square meters, and the main load comes from manufacturing workshops and electric vehicle charging. The summer daily peak period (13:00-16:00) load reaches 1000kW, and the trough period (00:00-06:00) load is 500kW. The original system relies on the municipal power grid and a backup generator, which frequently starts during peak hours, with a monthly fuel cost of about 50,000 yuan.

[0068] System configuration: Energy storage cabinets: 2, each with a capacity of 500kWh and a power of 250kW, installed near the workshop. Battery swap station: 1, supporting 50 electric bicycle battery replacements, with a battery capacity of 2kWh each. Mobile energy storage vehicles: 2, each with a capacity of 100kWh and a power of 50kW, deployed for patrol in the park. Photovoltaic system: installed on the roof, with a peak power of 50kW and a daily average power generation of 200kWh. Monitoring system: photovoltaic power generation system can be installed on the power station or above the mobile energy storage vehicle; smart meters are installed at the power grid entrance, and sensors cover key lines. Management device: edge server located in the control center, running the MPC algorithm.

[0069] Workflow:

[0070] Trough period (00:00-06:00): The monitoring system detects that the grid load drops to 500kW, and the electricity price is 0.3 yuan / kWh. The management device issues instructions, and the energy storage cabinet charges at 100kW, and the mobile energy storage vehicle charges at the same time. After charging is completed, the energy storage cabinet SOC reaches 95%, and the mobile energy storage vehicle SOC reaches 100%.

[0071] Normal period (06:00-10:00): The photovoltaic system starts generating power, and the power gradually rises to 50kW. The management device controls the photovoltaic power to directly charge the mobile energy storage vehicle, and the remaining power is stored in the energy storage cabinet. The battery swap station takes power from the energy storage cabinet to pre-charge the battery inventory, preparing for daytime service.

[0072] Peak period (13:00-16:00): The monitoring system detects that the load increases to 1000kW, and the electricity price is 1.0 yuan / kWh. The management device performs scheduling: the energy storage cabinet discharges at 200kW to the battery swap station to meet the battery replacement demand (load 150kW). The mobile energy storage vehicle moves to the designated location to provide power for large equipment (output 50kW). The photovoltaic system generates 30kW of power, which is directly supplied to the mobile energy storage vehicle.

[0073] Results: The actual load of the power grid decreases from 1000kW to 800kW, avoiding the start of the backup generator. Emergency response: at 15:00, the monitoring system detects that the power grid is locally overloaded, and the management device immediately dispatches the mobile energy storage vehicle to provide an additional 20kW of support for 30 minutes.

[0074] Benefit analysis, reduce the grid power purchase cost by 15% (about 20,000 yuan) per month, reduce the frequency of generator use by 70%, save fuel cost 35,000 yuan. Photovoltaic power generation saves electricity bill 5,000 yuan per month. The peak valley difference of power grid is reduced from 30% to 15%, and the utilization rate of energy storage equipment is increased to 80%. Reduce carbon emissions by 10 tons per month, increase clean energy ratio by 25%.

[0075] In addition to the above industrial park scene, the energy storage management and control system can also be arranged in residential communities, except that the power equipment is replaced by new energy vehicles or electric bicycles, etc., it can also provide emergency and standby power for residential power supply, and even provide more energy-saving solutions for community life and public power supply; store low valley electricity and reasonably distribute it during peak periods; through the collaborative control of multiple units of the management device, the charging and discharging behavior of the energy storage equipment is matched with the real-time state of the power grid, and the battery swap station and the energy storage cabinet can provide cheap charging and battery replacement services for delivery riders; according to different electricity price periods, adjust the charging and discharging power of the energy storage system. Peak valley arbitrage is realized, and the cost of electricity is reduced.

[0076] The application further proposes that the management device is in communication connection with the energy storage cabinet, the battery swap station, the mobile energy storage vehicle, the photovoltaic system and the monitoring system, the monitoring system monitors whether the municipal power grid is in a peak or valley state, and the management device dynamically issues energy dispatching strategies and manages the running state and energy use of each unit.

[0077] Among them, the management device refers to a control unit with data processing and instruction generation capability, which can be realized by combining an embedded system with a communication module, and is used for integrating unit operation data and generating dispatching instructions. Communication connection refers to a data interaction channel based on wired or wireless transmission protocol, which can be realized by industrial Ethernet or 5G communication module, and is used for realizing real-time information interaction between the management device and each unit. The monitoring system refers to a power grid state recognition device, which can be realized by combining a smart meter with a load analysis algorithm, and is used for real-time acquisition of power grid voltage and current parameters and judgment of peak and valley states. The energy dispatching strategy refers to a set of charging and discharging instructions generated based on the power grid state and unit operation data, which can be realized by combining a dynamic programming algorithm with a real-time optimization model, and is used for coordinating the charging and discharging time sequence and power distribution of the energy storage equipment.

[0078] See the wave peak and valley management strategy schematic diagram shown in the figure. Figure 2 The management device obtains power consumption information and judges whether it is a wave peak or valley. When it is judged to be a wave peak, it is preferred to use the energy storage cabinet to charge other power equipment, including delivering power to the battery swap station or the mobile energy storage vehicle. When the monitoring system identifies whether the power grid is in a valley power consumption stage, terminate part or all of the energy storage equipment from the power grid, until it is judged to be a wave peak period, and preferentially use the energy storage cabinet to charge.

[0079] Specifically, the management device obtains the battery state of charge of the energy storage cabinet, the battery replacement demand of the battery swap station, the remaining power of the mobile energy storage vehicle, the power generation of the photovoltaic system, and the grid peak valley state data of the monitoring system in real time through the communication connection. When the monitoring system identifies that the grid is in the valley power consumption stage, the management device generates a charging instruction to control the energy storage cabinet to absorb power from the grid and store it. When the grid is in the peak power consumption stage and the battery state of charge of the energy storage cabinet is higher than the preset threshold, the management device generates a discharging instruction to dispatch the energy storage cabinet to deliver power to the battery swap station or the mobile energy storage vehicle. At the same time, the management device continuously monitors the power generation of the photovoltaic system, preferentially allocates photovoltaic power to the mobile energy storage vehicle charging port, and switches to the energy storage cabinet storage after the mobile energy storage vehicle is fully charged. By periodically collecting the operation data of each unit and updating the scheduling strategy, dynamic optimization of energy distribution is realized.

[0080] Compared with the prior art, the existing charging system usually uses an independent controller to manage a single energy unit, and there is a lack of data intercommunication and coordination mechanism between units. For example, the energy storage device cannot dynamically adjust the discharging power according to the demand of the battery swap station, and the switching between photovoltaic power generation and grid power supply relies on manual operation. The present scheme establishes a unified communication network and scheduling logic through the management device, so that the energy storage cabinet, the battery swap station and other units can automatically adjust the operation mode according to the real-time grid state and system load, eliminating the delay of manual intervention, and supporting adaptive adjustment of the strategy when a new unit is connected.

[0081] Through the above technical scheme, the present application solves the problem of low coordination efficiency of multiple energy units, realizes precise matching of the charging and discharging behavior of the energy storage device and the grid load state, and avoids the conflict between photovoltaic power generation and grid power supply. Through centralized processing of energy data and generation of strategies by the unified management platform, the response speed of the system to sudden power demand is improved, and the system expandability is enhanced through the modular communication architecture, so that the new unit can be quickly connected to the existing scheduling network.

[0082] The present application further proposes that the energy storage cabinet is connected to the battery swap station and the mobile energy storage vehicle, and charges the battery swap station and the mobile energy storage vehicle according to the instruction of the management device.

[0083] The energy storage cabinet refers to a power storage device with built-in energy storage battery modules, which can be implemented by combining lithium ion battery packs and bidirectional converters, and is used to receive or release power and maintain system energy balance. The device is physically connected to the battery swap station and the mobile energy storage vehicle through power lines to form a power transmission network, providing a basic framework for dynamic scheduling. The management device instruction refers to a scheduling signal generated based on the grid load state, which can be implemented by combining edge computing devices and communication modules. By receiving the grid state data of the monitoring system in real time, the charging strategy is generated based on the battery state of charge value of the energy storage cabinet to drive the energy storage cabinet to perform power delivery operations.

[0084] Specifically, the energy storage cabinet and the battery swap station and the mobile energy storage vehicle establish a two-way power transmission channel through a standardized interface. When the monitoring system detects that the power grid is in a low valley power consumption stage, the management device sends a charging instruction to the energy storage cabinet to absorb power from the power grid and store it in the battery pack. When the power grid enters a peak power consumption stage and the state of charge of the battery of the energy storage cabinet exceeds a set threshold, the management device generates a discharge instruction to control the energy storage cabinet to supplement the small power battery inventory of the battery swap station through the pre-set fast charging port or provide emergency power support for the mobile energy storage vehicle. Through the cooperation of the physical connection architecture and the management instruction, the mechanism realizes the dynamic allocation of energy storage resources in the time and space dimensions, and synchronously improves the battery replacement service capability of the battery swap station and the power supply flexibility of the mobile energy storage vehicle.

[0085] The fast charging port refers to a special interface that supports high-power power transmission, which can be implemented by adopting direct current fast charging technology conforming to CHAdeMO or CCS standards. It breaks through the power limit of traditional charging interfaces by improving the current carrying capacity and optimizing the voltage matching mechanism. The power delivery refers to an energy transmission process based on dynamic load detection, which can be realized by the cooperative work of a bidirectional inverter and an intelligent power distribution module. It adjusts the output power according to the real-time demand of the mobile energy storage vehicle to maximize the efficiency of power transmission.

[0086] Specifically, during the low valley stage of the power grid, the management device controls the energy storage cabinet to inject power into the mobile energy storage vehicle through the fast charging port. The port shortens the charging time of the mobile energy storage vehicle to 30%-50% of the original time by improving the charging current density and optimizing the heat dissipation structure, so that the charging power reaches 2-3 times of that of the traditional interface. When the mobile energy storage vehicle needs to supply power to other equipment, the fast charging port switches to the discharge mode and matches the input specifications of the target equipment through a pre-set power regulation algorithm to realize lossless transmission of electric energy.

[0087] The conventional charging interface is limited by the alternating current conversion efficiency and the cable current carrying capacity, and cannot meet the fast charging and discharging requirements of the mobile energy storage vehicle in the emergency dispatching scenario. The direct current fast charging technology directly improves the power transmission rate, and the intelligent temperature control system maintains the stability of the port under high voltage and large current working conditions, avoiding the charging interruption problem caused by frequent triggering of the overheat protection in the traditional scheme. The mobile energy storage vehicle can complete 80% power replenishment within 15-30 minutes, significantly reducing the energy dispatching lag caused by charging delay. Through the adaptive power regulation mechanism, the energy storage cabinet and the mobile energy storage vehicle can maintain the best power transmission efficiency under different working conditions, providing timely and reliable energy support for sudden power demand.

[0088] Compared with the prior art, the traditional charging system lacks a command-driven directional charging and discharging mechanism between the energy storage unit and the power terminal, and each unit can only independently respond to the change in the power grid state. The present scheme builds an electric energy transmission network under the control of instructions, so that the energy storage cabinet can implement accurate electric energy transmission to a specific power terminal according to the real-time power grid load state, forming a dynamic matching between the energy storage resources and the power demand, and effectively overcoming the rigid problem of energy distribution existing in the traditional system.

[0089] Through the above technical scheme, the present application realizes the optimal configuration of energy storage resources in the peak and valley period of the power grid, so that the small power battery replacement service capability of the battery swap station is stably guaranteed in the peak period, and at the same time ensures that the mobile energy storage vehicle can quickly obtain power supply in the sudden power demand scenario. The charging and discharging operation of the energy storage cabinet forms a linkage mechanism with the power grid load state, which not only reduces the direct dependence on the municipal power grid in the peak period, but also improves the overall utilization rate of the energy storage system.

[0090] The present application further proposes an energy dispatching strategy, which includes obtaining the state of the energy storage cabinet and determining the power consumption stage of the power grid, wherein the state of the energy storage cabinet includes the battery state of charge value and the battery health state index value, and the power consumption stage of the power grid includes peak power consumption and valley power consumption. Based on the battery state and the power consumption stage of the power grid, the management device controls charging or discharging.

[0091] The battery state of charge value refers to the percentage of the current remaining power of the energy storage device, which can be realized by using the voltage-capacity correspondence or the coulomb counting method, and is used to reflect the real-time energy level of the energy storage device. The battery health state index value refers to the degree of attenuation of the battery performance relative to the initial state, which can be realized by using the internal resistance measurement or the cycle life prediction model, and is used to evaluate the battery charging and discharging capacity and safety boundary. The power consumption stage of the power grid refers to the period when the municipal power grid load is at a peak or valley, which can be realized by using the load curve analysis or the electricity price period division model, and is used to identify the power grid peak shaving demand.

[0092] Specifically, the battery state of charge value and the health state index value of the energy storage cabinet are obtained in real time, and the power consumption stage of the power grid is identified by a monitoring system. In the valley power consumption stage of the power grid, the management device judges the charging safety threshold according to the battery health state index value, controls the energy storage cabinet to charge from the power grid to store low-price electric energy; in the peak power consumption stage of the power grid, the management device controls the energy storage cabinet to discharge to the battery swap station or the mobile energy storage vehicle within the range allowed by the battery health state, in combination with the battery state of charge value and the preset threshold. By coupling the battery performance parameters with the power grid load state, the safety boundary of the charging and discharging operation is dynamically adjusted, avoiding the overloading of the equipment or the waste of energy storage capacity caused by the single dependence on the power grid state.

[0093] It needs to be added here that the battery health state monitoring in the present solution has the function of automatic repair of the system, which can be realized by adjusting the combination of current and voltage, including the following common methods: high voltage and large current repair method, by increasing the charging voltage to 1.3-1.5 times of the nominal voltage (such as 48V charger for 36V battery), and increasing the charging current to 1.5-2.0 times (such as 3-4A current for 20AH battery), quickly activating the chemical reaction. Avoid overcharging by monitoring the system to control the change of battery parameters in real time. Full charge and full discharge repair method, when the battery meets the repair scheme, the user can be reminded to repair the battery by full charge and full discharge repair method at this time, and when the user agrees, the battery is fully charged with the charger to ensure that the voltage reaches the rated value. Discharge to voltage below 1.0V can activate deep active material and restore capacity. It is recommended to operate once every 3-6 months. Pulse repair method, using special equipment to output 60-300V pulse voltage to eliminate sulfuration through transient high voltage. Low voltage (such as 60V) is suitable for extending the life, and high voltage (such as 300V) should be used with caution to avoid damage to the plate. Battery repair needs to be combined with the battery health state data detected by the monitoring system to remind the user to replace the battery or perform manual physical repair, and the repair method of adjusting the current and voltage provided by the system should avoid potential risks.

[0094] Through the above technical solutions, the present application can dynamically adjust the charging and discharging strategy according to the grid load state and the health state of the energy storage device, and solve the problem of uncoordinated energy scheduling caused by the difference between peak and valley loads in the traditional system. The introduction of the battery health state index value quantizes the safety boundary of the charging and discharging operation, avoiding the risk of overcharging or overdischarging the battery; the coordinated analysis of the grid power consumption stage and the battery state of charge realizes the accurate matching of the energy storage capacity and the peak shaving demand, reducing energy waste.

[0095] The present application further proposes a specific way for the management device to control charging or discharging, specifically: when it is determined that the grid is in a low valley power consumption state, the energy storage cabinet is controlled to obtain power from the grid to charge the internal battery pack; when it is determined that the grid is in a high peak power consumption state, the energy storage cabinet is controlled to discharge to the battery swap station or mobile energy storage vehicle if the battery state of charge value of the energy storage cabinet is greater than the preset threshold value.

[0096] The low-valley electricity refers to the low-load operation stage of the municipal power grid, which can be achieved by real-time monitoring of the power grid power or based on historical electricity consumption data to divide the period. The role is to use the low-price period for low-cost charging. The peak electricity refers to the high-load operation stage of the municipal power grid, which can be judged by monitoring the real-time power of the power grid or combining the preset load threshold. The role is to identify the key period that needs to be peak shaving. The battery state of charge value refers to the percentage of the current remaining capacity of the energy storage cabinet battery to the rated capacity, which can be estimated by voltage integration method or Kalman filtering algorithm. The role is to quantify the available energy of the energy storage device. The preset threshold refers to the minimum energy ratio to trigger the discharging operation, for example, set to 80%, which can be dynamically adjusted according to the battery type and cycle life requirements. The role is to prevent excessive discharge from causing battery performance degradation.

[0097] Specifically, in the low valley stage of the power grid, the load of the municipal power grid is low and the price is usually in the low valley interval. At this time, the management device obtains the power grid state information through the communication module, sends the charging instruction to the energy storage cabinet, and the energy storage cabinet absorbs the power from the power grid and stores it in the battery pack. In the peak stage of the power grid, the management device monitors the battery state of charge value of the energy storage cabinet in real time. When the value exceeds the preset threshold, it indicates that the energy storage cabinet has sufficient discharging capacity, and then sends the discharging instruction to the energy storage cabinet to supply power to the battery swap station or the mobile energy storage vehicle. Through the above time period control logic, the energy storage cabinet completes the power storage in the low valley period of the power grid load and releases the power in the peak period to relieve the pressure of the power grid, while avoiding the shortening of the service life of the battery due to frequent deep discharge.

[0098] The traditional charging system usually adopts a fixed time charging and discharging strategy or relies on a single power grid state for control, lacking dynamic evaluation of the battery state. For example, it may force discharge in the peak period, causing the battery to run out of power and continue to discharge, exacerbating battery wear and tear, often ignoring the economic factors of time-of-use electricity prices. The present scheme introduces a battery state of charge threshold as a discharge condition, combining the power grid load state with the state of the energy storage device, ensuring the effectiveness of the discharging process and protecting the battery health through threshold limitation. The present scheme can adopt preferential charging in the low valley period to directly reduce the electricity cost.

[0099] Through the above technical scheme, the present application realizes dynamic energy transfer in the peak and valley periods of the power grid load, reduces the direct dependence on the municipal power grid during peak electricity consumption, and reduces energy waste caused by frequent start and stop of the generator set. At the same time, by presetting the threshold to control the discharging time, the capacity degradation problem caused by excessive discharge of the battery is avoided, and the service life of the energy storage device is prolonged. In addition, the cooperative operation of low valley charging and peak discharging can effectively utilize the price difference to reduce the overall electricity cost.

[0100] Further combined with the following case 2:

[0101] Firstly, the midday period of the day uses photovoltaic energy storage and evening peak power supply scheduling

[0102] Prediction accuracy improvement: The prediction of the management device should not be based only on historical data, but should integrate ultra-short-term photovoltaic power generation prediction (using real-time sky imaging, satellite cloud map data, etc.) and load prediction (based on park production plans, weather temperature, holiday patterns). This can ensure that the charging plan for mobile energy storage vehicles is dynamically adjusted when photovoltaic power generation actually fluctuates. Multi-objective optimization charging strategy: When the mobile energy storage vehicle takes power from the energy storage cabinet during the "flat period, i.e. non-peak and low period", the strategy needs to be optimized: complete charging at the optimal power before the end of the flat period electricity price, which is the lowest cost. Avoid high current fast charging, use a gentle charging curve that meets the state of health (SOH) of the battery, which has the longest service life. Use the built-in algorithm of the management device to calculate the "electricity price time window" and "battery charging acceptance curve", and give the optimal solution of cost and life balance.

[0103] Secondly, during the evening peak power supply period, switching from "energy storage combined power supply" to "grid supplementary power purchase" should be smooth and undisturbed. This requires the management device to have virtual synchronous machine technology, enabling the energy storage cabinet and mobile energy storage vehicle to simulate the inertia and damping characteristics of traditional generators, enabling seamless switching of collaborative power supply, supporting grid frequency stability, and avoiding voltage flicker caused by sudden switching.

[0104] Thirdly, charging during the night low power consumption period and daytime peak power supply scheduling

[0105] Charging safety and balance: Nighttime high-power concentrated charging is a high-risk period for battery thermal runaway. The BMS of the energy storage cabinet and mobile energy storage vehicle must implement an active balancing strategy to ensure consistency between cells and work with the thermal management system to adjust the charging power based on the ambient temperature. Here, the management device is connected to the specific area's power consumption data through the networking system. At this time, the mobile energy storage vehicle needs dynamic path planning: The dispatch of mobile energy storage vehicles during the day is not only "sent to the office area or production workshop", but also a dynamic optimization problem (here, mobile energy storage vehicles in residential areas can partially replace new energy vehicles or electric vehicles, and of course the corresponding management quantity also needs to be adjusted accordingly). Input real-time load demand, park road congestion, mobile energy storage vehicle's current power (SOC) and location of each area. The output is to plan the optimal service point, service duration and travel route for each mobile energy storage vehicle to minimize its own energy consumption (mobile power consumption) and maximize power supply efficiency. Combination with grid frequency regulation service: During daytime power supply, if the energy storage device still has redundant capacity, the management device can respond to the grid's frequency regulation auxiliary service signal and adjust the output power within seconds to create additional income for the park. This requires the system to have extremely high communication and response speed.

[0106] The existing only relies on the grid load state or energy storage device state single dimension for charging and discharging control, such as only according to the electricity price period to execute charging and discharging, or only according to the remaining battery power to judge the operation opportunity. Such method cannot synchronously solve the influence of device performance attenuation on charging and discharging safety, and it is also difficult to realize the dynamic matching of grid peak shaving demand and energy storage device state. The scheme of the present application will analyze the energy storage device state parameters and the grid load parameters at the same time, establish a two-dimensional decision model, so that the charging and discharging strategy can meet the grid peak shaving demand and adapt to the change of battery performance.

[0107] Specifically, a predictive data-driven multi-energy collaborative scheduling and energy storage buffer system can be constructed by increasing the energy prediction large model self-learning and automatically distributing the scheduling strategy, which can specifically include three core modules: 1) a predictive energy management module: a graph attention network (GAT) and a long short-term memory network (LSTM) hybrid model is used for charging demand prediction, by analyzing multi-dimensional data such as historical charging mode, weather data, traffic flow, etc., the charging demand prediction accuracy can reach more than 85%, and the energy allocation can be completed in advance for 30 minutes; 2) a multi-energy collaborative scheduling module: a distributed energy management system based on edge computing architecture integrates municipal power grid, photovoltaic power generation, energy storage system and other energies, and realizes optimal energy allocation through reinforcement learning algorithm, which reduces the peak load by 25%; 3) a gradient energy storage buffer module: combined with fixed energy storage unit (lithium battery + super capacitor hybrid energy storage, response time <10ms) and mobile energy storage resource (V2G technology, calling idle electric vehicle battery capacity), a three-level energy storage buffer pool is constructed to realize peak-valley load transfer efficiency >90%. The system uses blockchain technology to ensure transparent energy transaction, and through demand side response mechanism and dynamic price incentive strategy to guide users to staggered charging, which reduces the system operation cost by more than 20%.

[0108] The present scheme aims to construct a high-precision energy storage management or electric vehicle charging demand prediction system, and realizes multi-energy collaborative scheduling by combining edge computing and reinforcement learning, and realizes peak-valley load transfer through lithium battery and super capacitor hybrid energy storage system and V2G technology. The goal is to improve the charging demand prediction accuracy to more than 95%, the peak-valley load transfer efficiency to 85%, and the system response time to be controlled within 100ms, to meet the high requirements of smart grid on distributed energy management.

[0109] In order to solve the full utilization of multiple energy sources, such as wind energy, solar energy, and even rainwater energy generation, the full absorption and storage of electric energy are effectively utilized through the energy storage management control system. Based on the energy scheduling strategy, the inventor further proposes an intelligent charging demand prediction and multi-energy collaborative scheduling system design scheme based on a GAT-LSTM hybrid model, further realizes and perfects the power grid, energy storage, and energy storage management control; At the same time, the inventor further proposes to construct a digital twin energy storage management control system and control method model system mapped with the physical system, and the specific digital twin system is realized based on the following system steps, and the method process is shown in the following Figure 3 : Energy storage management control system core process schematic diagram:

[0110] Step one: First, improve the energy buffer capacity of the charging infrastructure through diversified energy storage technology combination, build a multi-level adaptive energy storage collaborative scheduling system, and the system design includes three layers of energy storage architecture:

[0111] 1. Fast response layer: For example, use super capacitor array (power density >10kW / kg) and or flywheel energy storage (response time <20ms), with a total capacity of 30% of the peak load, to specifically deal with sudden demand; Stand in front of the battery, "filter" the high-frequency and violent power fluctuations in the power grid; Deal with solar and wind power systems and other fluctuations;

[0112] 2. Intermediate buffer layer: Use lithium-titanium battery (10C charge-discharge rate) and flow battery hybrid energy storage system, capacity is 50% of the daily fluctuation load, response time <5 seconds;

[0113] 3. Long-period energy storage layer: Combine hydrogen energy storage system (energy density >500Wh / kg) and gradient utilization power battery pack, capacity is 80% of the basic load.

[0114] Step two: Improve the response speed of the system to demand changes through data-driven prediction performance management, and build an intelligent optimization system based on AI-driven prediction performance management and multi-source collaborative scheduling. This system combines a double-layer LSTM neural network model with model predictive control (MPC), integrates distributed energy storage units and neural network prediction accuracy to establish a multi-level intelligent optimization system. Through the LSTM model, the time-dependent relationship of the battery is captured, achieving more than 90% of the charging demand prediction accuracy, and outputting individualized balancing strategies.

[0115] Step three: Through the microgrid architecture, the organization of energy distribution is restructured to improve system flexibility, and an enhanced double-bus architecture and intelligent scheduling integrated microgrid dynamic control system is used, the specific implementation steps are as follows:

[0116] 1). Enhanced dual bus architecture: The system adopts a dual bus structure design, through real-time monitoring of the bus voltage difference (threshold 15V), realizing the automatic scheduling switching of multi-energy system, improving the stability and reliability of the system.

[0117] 2). Dynamic weight distribution algorithm: Based on multi-source data fusion technology, combined with evolutionary algorithm to optimize weight distribution, realize accurate estimation of the state of integrated energy system, improve energy management efficiency.

[0118] 3). Virtual synchronous generator control strategy: Considering the transient frequency characteristics of different response stages such as inertia control and primary frequency modulation, effectively absorbing fluctuating energy sources such as wind power and photovoltaic, assisting the system to achieve frequency stability.

[0119] The core of the system adopts a hybrid model architecture of graph attention network (GAT) and long short-term memory network (LSTM). GAT is responsible for capturing the spatial topological relationship of the charging station network, while LSTM processes time series data.

[0120] The inventors propose a solution for the digital twin energy storage management and control system and control method model system, which is based on the purpose of efficient management and intelligent prediction and scheduling strategy. Since the charging infrastructure needs to respond quickly to power demand when demand is large, while avoiding problems such as untimely energy distribution and sudden increase in grid load, there is a bidirectional mapping relationship between the digital twin system and the real physical system. A large number of sensor networks need to be added in the real physical system to collect real hardware data in real time and drive the virtual model to update synchronously. At the same time, the prediction and analysis of the virtual model can optimize the operation of the real hardware.

[0121] In the above scheme, step one is based on the intelligent charging demand prediction and multi-energy collaborative scheduling system design scheme of the above GAT-LSTM hybrid model. The core of the system is a distributed edge computing controller, which adopts a three-level control algorithm of prediction-feedback-adaptation, predicts future demand for 4 hours or more based on historical data and real-time load, and starts resource scheduling 15 minutes in advance. The digital twin system is established through the V2G bidirectional energy interface, integrating idle electric vehicle batteries in the park as dynamic energy storage resources, which can mobilize energy storage resources to meet 200% rated load short-term demand within 30 seconds, avoiding the start of standby generator sets. The system adopts modular design and can be flexibly expanded according to actual needs.

[0122] Or adopt fusion V2G and edge computing, realize intelligent energy storage response optimization; Due to the low user participation rate, the utilization rate of V2G system is not high, which affects the load fluctuation regulation effect of power grid. Therefore, this scheme combines the V2G bidirectional energy flow technology and the real-time processing capability of edge computing to improve the load regulation efficiency of power grid and the user participation rate. When the load of power grid is high but the electricity price is low, the user's power consumption mode and market electricity price are analyzed in real time through edge computing, so as to encourage users to participate in V2G system. In terms of energy storage management, more intelligent dynamic electricity price evaluation and load management strategy are applied, and adaptive graph attention mechanism is adopted to accurately detect and warn the abnormality of distributed energy. Through the combination of these technologies, efficient demand response, flexible load balancing and more accurate price signal guidance are realized. It is expected that the response capability of the system can be controlled within 30 seconds in the edge layer, improving the reaction capability of 200% to sudden load, and enhancing the overall efficiency and economic benefit of the power grid system.

[0123] This scheme puts forward a more intelligent and comprehensive energy storage control method with strong adaptability, improves the economic benefit and operation efficiency of the system, reduces the time delay of the system through edge computing, improves the reliability of the system, collects distributed energy equipment data in real time and uses adaptive graph attention to establish abnormal analysis rules, improves the stability, reliability and efficiency of the distributed energy system, provides V2G charging and discharging system and its control method, improves the system utilization rate to enhance the regulation effect of power grid load fluctuation.

[0124] In step two, due to the insufficient robustness of existing energy management systems in dealing with uncertain disturbances, and the high dependence on accurate prediction information limits the adaptability and efficiency of the system. Therefore, this scheme combines a double-layer LSTM neural network model with model predictive control (MPC), integrates the prediction accuracy of distributed energy storage units and neural networks to establish a multi-level intelligent optimization system. Through the LSTM model to capture the time-dependent relationship of the battery, more than 90% of the charging demand prediction accuracy is realized, and individualized balancing strategy is output. At the same time, integrate distributed energy storage technology to improve the reliability and efficiency of energy utilization, and effectively play a role in peak and valley regulation of load curve. In order to overcome the model uncertainty and computational complexity of MPC, a more robust control strategy is developed to keep the system efficient and stable in changing environments. The results show that this system can reduce the starting frequency of standby generators by 80%, reduce peak load by 25%, and improve renewable energy utilization by 40%, and control the scheduling delay to within 1 minute. This integrated approach significantly improves the system's ability to deal with power grid uncertainties, achieving a balance from fine time spans to long-term energy management.

[0125] The improvement effects include providing a resource interaction system for distributed energy storage in transformer area, effectively improving the reliability of transformer area power consumption and power supply service capability, capturing the time dependence of battery performance parameters through LSTM model, realizing high-precision remaining service life prediction, and outputting personalized balancing strategies for battery health status to prolong battery service life. By developing a hybrid control strategy with better robustness and adaptability, combining the advantages of ILC and MPC to improve the effectiveness of industrial control.

[0126] In step three, due to the deficiencies of traditional micro-grid systems in energy scheduling accuracy and response sensitivity, the system stability and efficiency are affected. Therefore, an enhanced double-bus architecture is constructed, combined with intelligent scheduling strategies and dynamic control algorithms to improve the energy scheduling accuracy and response speed of the micro-grid system. The system uses a dynamic weight distribution algorithm for multi-source data fusion and model optimization to ensure accurate response and flexible switching under different energy states, and stabilizes the frequency response through a virtual synchronous generator control strategy, especially when new energy is connected, effectively absorbing fluctuating energy sources such as wind and photovoltaic. The technical solution includes real-time monitoring of bus voltage difference, automatically scheduling each energy system when the difference exceeds 15V, reducing the peak-valley difference by 70%, with a response time of less than 200ms, achieving more than 95% self-sufficiency of charging demand within the park. The system is designed modularly, making it easy to expand to more complex energy networks, supporting evolutionary algorithm optimization to ensure the reliability and adaptability of long-term performance.

[0127] The technical solution shows good performance indicators in theory, such as a 70% reduction in peak-valley difference, a response time of less than 200ms, and more than 95% self-sufficiency of charging demand within the park. However, these data are mainly based on theoretical derivation or system modeling, and there is still a lack of verification data in actual operating environments. The virtual synchronous generator control strategy has a certain research foundation in the stability of new energy grid connection, but the overall system reliability still needs to be verified through actual deployment.

[0128] The technical solution is at TRL 4-5 level (technology verification stage), and the core technologies such as dynamic weight distribution algorithm and virtual synchronous generator control strategy have related technologies, but the overall system integration has not yet reached full maturity. The performance indicators proposed in the solution (such as a 70% reduction in peak-valley difference and a response time of less than 200ms) exceed the general level of existing micro-grid energy management systems, but have not yet formed an industry standard. The technology needs further prototype verification and small-scale pilot applications to verify its performance in actual industrial park environments.

[0129] The problems encountered in the implementation of this scheme are: 1. System complexity challenge: The coordinated control of multi-energy systems involves the integration of multiple energy forms such as electricity and heat, increasing the complexity of system design and operation and maintenance. 2. Real-time algorithm requirements: Dynamic weight allocation algorithm needs to respond within 200ms, which puts higher requirements on computing resources and communication networks. 3. Energy prediction accuracy limitations: Industrial park charging demand is affected by multiple factors, and the prediction model may face the problem of insufficient accuracy. 4. Hardware investment cost: The double bus architecture and supporting equipment require a large initial investment, which may affect the promotion of the scheme. 5. Long technical verification period: Long-term operation verification is needed to confirm the stability and reliability of the system under various extreme conditions.

[0130] The above scheme improves the adaptability and accuracy of the model by dynamically adjusting the weight of each data source and optimizing the weight distribution with evolutionary algorithm. It improves the scheduling accuracy of multi-energy integration in microgrid systems. The control strategy of the invention can consider the inertia control of virtual synchronous generators, primary frequency modulation and other different response characteristics to assist the system to achieve frequency stability and better absorb wind power, photovoltaic and other new energy.

[0131] In specific embodiments, deep learning technology has made significant progress in the field of power system prediction. LSTM model, with its ability to capture temporal dependencies, has shown superior performance in charging load prediction. However, a single LSTM model is difficult to handle the spatial topology of the charging station network. The GAT-LSTM hybrid model combines graph attention network (GAT) and long short-term memory network (LSTM) to capture spatial dependency and temporal correlation, providing a new approach to charging demand prediction.

[0132] The core of this technical solution is:

[0133] GAT-LSTM hybrid architecture: The graph attention network and LSTM network are fused to realize joint modeling of the space-time characteristics of the charging station network. The GAT layer captures the spatial correlation between charging stations through a dynamic adjacency matrix, and the LSTM layer processes time series features, and the two are optimized through a fusion unit to interact with space-time features.

[0134] Edge computing and reinforcement learning: Use edge computing to reduce system latency and use deep reinforcement learning (DRL) to optimize resource allocation to achieve real-time response and intelligent scheduling of charging demand.

[0135] Hybrid energy storage system: Combining the advantages of lithium batteries and supercapacitors, lithium batteries provide high energy density, and supercapacitors provide high power density, through collaborative control to extend the system life and improve response speed.

[0136] V2G technology integration: realize the two-way energy flow between electric vehicles and the power grid, use the idle time of vehicles to provide peak shaving services for the power grid, and optimize the load curve of the power grid.

[0137] Further, the photovoltaic system or the wind energy system can also provide electric energy to the fixed energy storage unit or the mobile energy storage vehicle unit, so that the electric energy reaches 100%.

[0138] The photovoltaic system or the wind energy system functions to provide clean energy input for the mobile energy storage vehicle or the energy storage cabinet, the battery swap station, etc., independently of the power grid, and functions to receive photovoltaic energy or wind energy and store it to the full capacity state. Providing electric energy means establishing an energy transmission path, which can be realized by using a direct current fast charging interface and a power control module, and can be realized by using a battery management system to monitor the state of charge in real time and trigger a charging cutoff instruction, which functions to ensure that the energy storage unit has the maximum available capacity.

[0139] Specifically, for the photovoltaic system, when the light conditions are met, the light energy is converted into direct current electric energy and directly transmitted to the energy storage battery. When the battery management system detects that the state of charge of the energy storage unit is lower than the set threshold, the photovoltaic system starts the charging program, controls the charging current and voltage through the power regulation module, and automatically terminates the charging when the state of charge of the energy storage battery reaches 100%. When the photovoltaic system is in an effective power generation state, its output electric energy is directly connected to the charging port of the mobile energy storage vehicle through the direct current bus. In this process, the photovoltaic system acts as an independent power supply and can complete the full capacity supply of the energy storage unit without relying on the municipal power grid, and through energy closed-loop management, it avoids the reverse feedback of electric energy into the power grid. Under sufficient light conditions, the output power of the photovoltaic system and the charging demand of the mobile energy storage vehicle form a dynamic matching relationship until the energy storage unit reaches the full charge state. This process is realized through a priority control strategy, which ensures that the photovoltaic power is used for the complete charging of the mobile energy storage vehicle first, rather than being directly fed back to the power grid or supplied to other loads.

[0140] The present application establishes a dedicated power supply path between the photovoltaic system and the mobile energy storage vehicle, directly completes the full capacity charging of the energy storage unit when the light resource is sufficient, reduces the load pressure of the power grid, and realizes efficient consumption of clean energy. If wind energy or other clean energy is added, the same scheme can be used to dynamically combine it with the mobile energy storage unit under certain conditions to form an independent energy supply system that can operate independently of the power grid.

[0141] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An energy storage management and control system, characterized in that, include: An energy storage cabinet is used to store electrical energy and can output electricity to other systems. Battery swapping stations and charging terminals are mainly used for replacing small power batteries. Mobile energy storage vehicles and charging terminals are mainly used to store electrical energy and provide charging and discharging services for other devices. Photovoltaic systems are used to convert light energy into electrical energy and output that electrical energy to other energy storage devices. Monitoring system: Collects and monitors information from all units in the municipal power grid and electricity consumption system; Management device: It is communicatively connected to the energy storage cabinet, battery swapping station, mobile energy storage vehicle, photovoltaic system, and monitoring system to formulate and issue energy dispatch strategies and exchange information with all units in the power system; The management device dynamically issues energy dispatch strategies based on the data detected by the monitoring system, manages the energy storage cabinet, battery swapping station, mobile energy storage vehicle, photovoltaic system, and monitors energy usage.

2. The energy storage management and control system according to claim 1, characterized in that, The energy dispatch strategy includes: acquiring the status of the energy storage cabinet and determining the power consumption stage of the power grid; wherein, the status of the energy storage cabinet includes: battery state of charge value and battery health status index value; the power consumption stage of the power grid includes: peak power consumption and off-peak power consumption; based on the battery status and the power consumption stage of the power grid, the management device controls charging or discharging.

3. The energy storage management and control system according to claim 2, characterized in that, The energy dispatch strategy also includes constructing a predictive data-driven multi-energy collaborative dispatch and energy storage buffer system, which includes 1) a predictive energy management module; 2) a multi-energy collaborative dispatch module; and 3) a tiered energy storage buffer module.

4. A control method based on an energy storage management and control system, characterized in that, The management device is communicatively connected to the energy storage cabinet, battery swapping station, mobile energy storage vehicle, photovoltaic system, and monitoring system. The management device dynamically issues energy dispatch strategies based on data detected by the monitoring system. When the monitoring system detects that the power grid is in a low-peak electricity consumption state, the management device controls the energy storage cabinet to obtain power from the power grid to charge the internal battery pack. When it is determined that the power grid is in a peak electricity consumption state, and the battery state of charge value of the energy storage cabinet is greater than a preset threshold, the management device controls the energy storage cabinet to discharge to the battery swapping station or mobile energy storage vehicle.

5. The control method based on an energy storage management and control system according to claim 4, characterized in that, The energy dispatch strategy includes acquiring the status of the energy storage cabinet and determining the power consumption stage of the power grid. Based on the battery status and the power consumption stage of the power grid, the management device controls charging or discharging. The battery health status index is realized by internal resistance measurement or cycle life prediction model, and the power consumption stage of the power grid is realized by load curve analysis or electricity price time period division model to identify the peak-shaving demand of the power grid. Within the allowable range of battery health status, the management device controls the energy storage cabinet to discharge to the battery swapping station or mobile energy storage vehicle. At the same time, the battery performance parameters are coupled with the power grid load status for analysis, and the safety boundary of charging and discharging operations is dynamically adjusted.

6. The control method based on an energy storage management and control system according to claim 5, characterized in that, The energy dispatch strategy also includes establishing a predictive performance management module, a multi-energy coordinated dispatch module, and a tiered energy storage buffer module.

7. The control method based on an energy storage management and control system according to claim 6, characterized in that, The predictive performance management module is based on intelligent charging demand prediction and simultaneously analyzes the energy storage device status parameters and grid load parameters using a two-dimensional decision model. The dual-dimensional decision model is a GAT-LSTM hybrid model, where GAT is responsible for capturing the spatial topology of the charging station network, while LSTM processes the time-series data. The multi-energy collaborative scheduling module is based on a distributed energy management system with an edge computing architecture, and achieves multi-energy collaborative scheduling by combining edge computing and reinforcement learning. The tiered energy storage buffer module achieves peak-valley load transfer through a hybrid energy storage system of lithium batteries and supercapacitors and V2G technology.

8. The control method based on an energy storage management and control system according to claim 7, characterized in that, The energy dispatch strategy includes establishing an energy storage management and control system, comprising the following steps: Step 1: Enhance the energy buffering capacity of charging infrastructure through a diversified combination of energy storage technologies, and construct a multi-level adaptive energy storage collaborative scheduling system. This system includes a three-layer energy storage architecture: Step 2: Improve the system's response speed to demand changes through data-driven predictive energy management, and build an intelligent optimization system based on AI-driven predictive energy management and multi-source collaborative scheduling; Step 3: Reconstruct the organization of energy distribution through microgrid architecture, and adopt an enhanced dual-bus architecture and a microgrid dynamic control system with intelligent dispatch integration.

9. The control method based on an energy storage management and control system according to claim 8, characterized in that, The proposed method for reconstructing energy distribution through a microgrid architecture employs an enhanced dual-bus architecture and an integrated intelligent dispatch microgrid dynamic control system. The specific steps are as follows: Step 1. Adopting a dual-bus structure design, real-time monitoring of bus voltage differences enables automatic dispatching and switching of multiple energy systems; Step 2. Dynamic weight allocation algorithm: Based on multi-source data fusion technology, combined with an evolutionary algorithm, weight allocation is optimized; Step 3. Virtual synchronous generator control strategy: Considering the transient frequency characteristics of different response stages of inertial control or primary frequency regulation, effectively absorbing the fluctuating energy of wind power and / or photovoltaic power, assisting the system to achieve frequency stability.

10. The control method based on an energy storage management and control system according to claim 9, characterized in that, A digital twin-based energy storage management and control system and control method model system are established based on energy dispatch strategies.

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