New energy equipment interconnection AI energy intelligent management monitoring system and method

Abstract

The invention relates to the technical field of intelligent management and monitoring systems, and discloses a new energy equipment interconnection AI energy intelligent management and monitoring system. The light storage equipment is connected with a photovoltaic panel, an energy storage battery, a charging pile, an intelligent electric meter, a diesel engine and an intelligent charging socket; the charging pile is connected with an electric vehicle; the intelligent ammeter is connected with a power grid; the intelligent socket is connected with a water heater and common equipment; the light storage device, the energy storage battery, the charging pile, the intelligent electric meter, the diesel engine and the intelligent charging socket are connected with a cloud server through a router. The invention provides a new energy equipment interconnection AI energy intelligent management monitoring system. The system has the advantages that the hardware sales volume is doubled; according to the invention, insurance renewal service is provided for the user who has passed the equipment warranty period, usage of the user is ensured, insurance renewal service fund is increased, user experience is improved, power is continuously saved for the user through the system, an optimal power utilization strategy service is started for the user in a payment subscription mode, and software earnings except hardware are increased.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management and monitoring systems, and in particular to an AI energy intelligent management and monitoring system for interconnected new energy equipment. Background Art

[0002] Current photovoltaic new energy equipment has many deficiencies in energy management, which are mainly reflected in the following aspects: Single monitoring function: Existing photovoltaic new energy equipment mainly monitors data, and most of them are independent single equipment data monitoring. Power generation and energy flow can only be absorbed by the equipment itself, and it is impossible to actively control energy-consuming equipment to complete the absorption of excess photovoltaic energy, resulting in serious waste of photovoltaic energy.

[0003] System control topology defects: Some existing energy management systems connect all devices to the cloud, which controls the energy flow of photovoltaic new energy devices. However, this approach has problems such as untimely data updates and control delays, and is highly dependent on the network, so system stability cannot be guaranteed.

[0004] Poor user experience: The existing monitoring system performs poorly in terms of user experience. Multiple devices can only be configured to access the Internet individually, the system operation mode needs to be set manually, the data is not real-time, and it cannot provide the optimal energy consumption plan.

[0005] Single revenue model: Equipment manufacturers mainly generate revenue through the sale of hardware machines. They lack software system services to maximize the use of equipment, and thus are unable to bring higher additional energy benefits to users.

[0006] In order to solve the above problems, this application proposes an AI energy intelligent management and monitoring system for interconnected new energy equipment. Summary of the invention

[0007] 1. Purpose of the invention In order to solve the technical problems existing in the background technology, the present invention proposes an AI energy intelligent management and monitoring system for interconnected new energy equipment. The present invention has the functions of doubling hardware sales; providing renewal services for users whose equipment has expired, ensuring user use while increasing renewal service fees, and improving user experience; continuously saving electricity for users through the system, and providing users with optimal electricity consumption strategy services through paid subscription, thereby increasing software revenue other than hardware.

[0008] (II) Technical solution To solve the above problems, the present invention provides a new energy equipment interconnected AI energy intelligent management and monitoring system, including: a photovoltaic storage device; The photovoltaic storage device is connected with photovoltaic panels, energy storage batteries, charging piles, smart meters, diesel engines, and smart charging sockets; The charging pile is connected to an electric vehicle; Smart meters are connected to the grid; The smart socket is connected to the water heater and common appliances; The photovoltaic storage equipment, energy storage batteries, charging piles, smart meters, diesel engines, and smart charging sockets are connected to the cloud server and local devices through a router to form a local area network.

[0009] Preferably, the cloud server is connected to a mobile phone APP.

[0010] A method for intelligent management and monitoring of energy by interconnecting new energy equipment with AI, including a cloud-edge collaborative system, in which the cloud is responsible for complex data processing, and uses AI technology to train and generate energy management operation strategies on the equipment side, and finally sends the strategies to the edge device side at one time; Control strategy generation is an important link. The AI ​​big model will generate control strategy scenarios based on grid electricity price information, meteorological information, electricity consumption habits, equipment rated power, battery SOC capacity, and equipment performance status indicators.

[0011] Preferably, the peak state: When photovoltaic power is insufficient, energy storage batteries are used to power the loads first, and the system does not feed power to the grid, and the system remains in a zero-feed state.

[0012] Preferably, trough state: When photovoltaic power is insufficient, the grid is used to charge the energy storage battery and supply power to the load, and the system remains in a zero-feed state.

[0013] Preferably, negative electricity price state: Reduce photovoltaic power generation, give priority to using the power grid for charging energy storage batteries and powering loads, turn on smart loads (charging columns, smart sockets, water heaters, heat pumps, household appliances), and keep the system in a zero-feed state.

[0014] Preferably, intelligent linkage: Photovoltaic power generation is maximized, energy storage batteries are fully charged first, and intelligent loads are switched on and off according to the photovoltaic surplus.

[0015] Preferably, guard mode: In special weather conditions, priority is given to ensuring that the energy storage battery is fully charged, reducing battery power consumption, and keeping the system in a zero-power-feed state; When the device receives the cloud control strategy, it will perform more precise energy control based on the production strategy: The next mode is the peak mode. Regardless of whether the household load is using electricity or not, the battery is guaranteed to be fully charged or above the power consumption habitually used by the user during the peak period (if the battery power is insufficient, it is necessary to use the grid AC to charge the battery to the specified value before the peak period arrives); In peak mode, when there is a charging pile for smart appointment charging, when the household electricity uses the AC power of the grid, the charging column is reversely controlled to release the electric vehicle power for household loads, reducing the AC power consumption on the grid side; The next mode is the valley mode, when there is household load power consumption, the battery power is released to the maximum extent; In valley mode, when there is a charging pile for smart scheduled charging, the rated power of the charging pile and the car power are calculated to select the time period with the lowest electricity price to ensure that the charging pile is charged until it is fully charged. The next mode is the negative electricity price mode, when household load electricity consumption is maximized by releasing the power of energy storage batteries.

[0016] The above technical solution of the present invention has the following beneficial technical effects: 1. In the early stage, sales revenue can be achieved by selling edge modules or products of monitoring communication hardware to equipment manufacturers. Compared with the hardware requirements of traditional non-networking equipment, the energy networking system can double the hardware sales; 2. Through continuous AI learning, the system can realize intelligent energy strategy control, continuously improve energy efficiency for users, minimize grid electricity consumption, and save electricity expenses. With the in-depth application of users, different levels of energy-saving control strategies can be introduced. By default, users are provided with standard version control strategies. Advanced and more energy-saving control strategies require users to pay for subscriptions (monthly / yearly) or flexibly pay for daily subscriptions. The revenue from software payment can be shared with equipment manufacturers. Our company has new long-term benefits and creates new revenue paths for equipment manufacturers. The two sides complement each other and develop in the long run.

[0017] 3. As the number of users continues to increase, we can customize advertising services for equipment manufacturers (release new products for promotion); add a recommended list of energy interconnection equipment to the app, collect service commissions from manufacturers in need, and help manufacturers increase additional hardware sales as much as possible; provide renewal services for users whose equipment has expired, ensuring user use while increasing renewal premium income. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a structural schematic diagram of the AI ​​energy intelligent management and monitoring system for interconnected new energy equipment proposed by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0020] like Figure 1 As shown, the present invention proposes a new energy equipment interconnected AI energy intelligent management and monitoring system, including: a photovoltaic storage device; The photovoltaic storage device is connected with photovoltaic panels, energy storage batteries, charging piles, smart meters, diesel engines, and smart charging sockets; The charging pile is connected to an electric vehicle; Smart meters are connected to the grid; The smart socket is connected to the water heater and common appliances; The photovoltaic storage equipment, energy storage batteries, charging piles, smart meters, diesel engines, and smart charging sockets are connected to the cloud server and local devices through a router to form a local area network.

[0021] The cloud server is connected to the mobile APP.

[0022] A method for intelligent management and monitoring of energy by interconnecting new energy equipment with AI, including a cloud-edge collaborative system, in which the cloud is responsible for complex data processing, and uses AI technology to train and generate energy management operation strategies on the equipment side, and finally sends the strategies to the edge device side at one time; Control strategy generation is an important link. The AI ​​big model will generate control strategy scenarios based on grid electricity price information, meteorological information, electricity consumption habits, equipment rated power, battery SOC capacity, and equipment performance status indicators.

[0023] Peak state: When photovoltaic power is insufficient, energy storage batteries are used to power the loads first, and the system does not feed power to the grid, and the system remains in a zero-feed state.

[0024] Trough state: When photovoltaic power is insufficient, the grid is used to charge the energy storage battery and supply power to the load, and the system remains in a zero-feed state.

[0025] Negative electricity price status: Reduce photovoltaic power generation, give priority to using the power grid for charging energy storage batteries and powering loads, turn on smart loads (charging columns, smart sockets, water heaters, heat pumps, household appliances), and keep the system in a zero-feed state.

[0026] Smart load refers to an electrical load device with intelligent, programmable and high-precision characteristics, such as charging columns, smart sockets, water heaters, heat pumps, and household appliances. It plays an important role in power system testing, power equipment research and development, battery testing, and electronic product research and development.

[0027] Smart loads use advanced power electronics technology and control algorithms to achieve precise control and simulation of electric energy. They can simulate various actual load conditions and test and debug power supply equipment, batteries, and motors. The working principle of smart loads mainly includes power conversion, control algorithms, communication interfaces, and human-computer interaction interfaces, through which precise adjustment and monitoring of load power, voltage, and current parameters can be achieved.

[0028] Intelligent linkage: Photovoltaic power generation is maximized, energy storage batteries are fully charged first, and intelligent loads are switched on and off according to the photovoltaic surplus.

[0029] Guardian Mode: In special weather conditions, priority is given to ensuring that the energy storage battery is fully charged, reducing battery power consumption, and keeping the system in a zero-power-feed state; When the device receives the cloud control strategy, it will perform more precise energy control based on the production strategy: The next mode is the peak mode. Regardless of whether the household load is using electricity or not, the battery is guaranteed to be fully charged or above the power consumption habitually used by the user during the peak period (if the battery power is insufficient, it is necessary to use the grid AC to charge the battery to the specified value before the peak period arrives); In peak mode, when there is a charging pile for smart appointment charging, when the household electricity uses the AC power of the grid, the charging column is reversely controlled to release the electric vehicle power for household loads, reducing the AC power consumption on the grid side; The next mode is the valley mode, when there is household load power consumption, the battery power is released to the maximum extent; In valley mode, when there is a charging pile for smart scheduled charging, the rated power of the charging pile and the car power are calculated to select the time period with the lowest electricity price to ensure that the charging pile is charged until it is fully charged. The next mode is the negative electricity price mode, when household load electricity consumption is maximized by releasing the power of energy storage batteries.

[0030] In negative electricity price mode: Application 1-Electricity: The battery is charged to the maximum and the smart load is turned on; Application 2 - Feeding: The battery is discharged to the maximum extent, and the intelligent load is turned off (when the next period is the peak, the battery needs to make AI predictions based on the energy consumption habit value × (above 100%) & weather information to see whether it can continue to discharge. If it is greater than the energy consumption habit value × (above 100%), the battery continues to discharge; if it is less than the energy consumption habit value × (above 100%), the battery needs to stop discharging and be ready to charge at any time (based on the real-time power consumption, the battery starts charging when the peak comes while it is still using power); Intelligent linkage mode (photovoltaic linkage), Application 1: When the battery is fully charged / charged at maximum power, the PV surplus power 1 starts the smart load, and the PV surplus power 2 is fed into the grid; Application 2: When the household load is turned on, the photovoltaic surplus power is insufficient, the smart load is turned off, and the battery discharge compensation is performed (before the battery is discharged, a logic is added to determine whether the peak / valley / negative electricity price in 1 and 2 above is the mode to be executed. (1) If it is a peak, the battery needs to make an AI prediction based on the energy consumption habit value × (above 100%) & weather information to determine whether it can continue to discharge. If it is greater than the energy consumption habit value × (above 100%), the battery continues to discharge; if it is less than the energy consumption habit value × (above 100%), the battery needs to stop discharging, the household load needs to be powered by the grid AC, and the battery should be ready to charge at any time (based on the real-time power consumption, the battery starts charging when the peak is approaching while the battery is still using power); (2) If it is a valley / negative electricity price, the battery will be discharged to the maximum extent. At the same time, the battery needs to use AI to predict whether the battery can be fully charged during the valley / negative electricity price period after being discharged to a certain percentage.

[0031] In guard mode (in bad weather), the battery is kept fully charged when the grid AC and photovoltaic power are powered all day long, and the battery power is not released; conversely, when the grid AC is cut off, the household load actively starts to use electricity and the battery is discharged. A battery power reminder is given every time the power consumption SOC exceeds 10%.

[0032] According to the above control strategies, the system can quickly refresh data. All networked device display data can be refreshed in as fast as 5 seconds, and system operation information can be synchronized in real time, providing a better user experience. The regular data refresh rate is increased by 60 times by refreshing every 5 minutes. The system performs real-time status self-detection. When the system's networked devices are abnormal, the corresponding associated devices will enter a self-protection state to prevent the disorderly execution of system policies. After the device data is collected in the cloud, the cloud server performs data classification based on AI analysis, presents quantitative reports, and outputs detailed equipment energy consumption analysis reports. At the same time, through big data screening, it recommends the replacement of old and new equipment and recommends low-energy brands. The system is an ecosystem open to all equipment manufacturers. The local edge networking protocol is a unified standard. The monitoring communication hardware edge module or product of AECC has a rich hardware connection interface (UART / USB / RS485 / RS232 / CAN / WALN), with standard SDK development and API interface. As long as it is built-in or external to photovoltaic equipment, charging columns, electric meters, sockets, heat pumps, water heaters, and household appliances, it will take a week to transform the traditional monitoring system into an interconnected AI energy smart management monitoring system. The interconnection networking protocol is standard, so the equipment of different brands of manufacturers with built-in hardware can cross the manufacturer's barriers to interconnection, which can enable manufacturers with incomplete equipment product categories to build their own energy application ecosystem, and have the ability to compete with the ecosystem of large manufacturers to enhance product competitiveness and value. The equipment is a developed ecosystem, which can provide users with more brand choices when building an energy control system ecosystem, and can also bring more promotion and sales to equipment manufacturers.

[0033] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A new energy equipment interconnected AI energy intelligent management and monitoring system, characterized in that: include: Optical storage equipment; The photovoltaic storage device is connected with photovoltaic panels, energy storage batteries, charging piles, smart meters, diesel engines, and smart charging sockets; The charging pile is connected to an electric vehicle; Smart meters are connected to the grid; The smart socket is connected to the water heater and common appliances; The photovoltaic storage equipment, energy storage batteries, charging piles, smart meters, diesel engines, and smart charging sockets are connected to the cloud server and local devices through a router to form a local area network.

2. According to claim 1, a new energy equipment interconnected AI energy intelligent management and monitoring system is characterized in that: The cloud server is connected to the mobile APP.

3. A method for intelligent management and monitoring of new energy equipment interconnected by AI energy, characterized in that: Including the cloud-edge collaborative system, the cloud is responsible for complex data processing, using AI technology to train and generate energy management operation strategies on the device side, and finally sending the strategies to the edge device side at one time; Control strategy generation is an important link. The AI ​​big model will generate control strategy scenarios based on grid electricity price information, meteorological information, electricity consumption habits, equipment rated power, battery SOC capacity, and equipment performance status indicators.

4. According to claim 3, a new energy equipment interconnected AI energy intelligent management and monitoring method is characterized in that: Peak state: When photovoltaic power is insufficient, energy storage batteries are used to power the loads first, and the system does not feed power to the grid, and the system remains in a zero-feed state.

5. According to claim 4, a new energy equipment interconnected AI energy intelligent management and monitoring method is characterized in that: Trough state: When photovoltaic power is insufficient, the grid is used to charge the energy storage battery and supply power to the load, and the system remains in a zero-feed state.

6. A new energy equipment interconnected AI energy intelligent management and monitoring method according to claim 5, characterized in that: Negative electricity price status: Reduce photovoltaic power generation, give priority to using the power grid for charging energy storage batteries and powering loads, turn on smart loads, and keep the system in a zero-feed state.

7. A new energy equipment interconnected AI energy intelligent management and monitoring method according to claim 6, characterized in that: Intelligent linkage: Photovoltaic power generation is maximized, energy storage batteries are fully charged first, and intelligent loads are switched on and off according to the photovoltaic surplus.

8. A method for intelligent management and monitoring of new energy equipment interconnected by AI energy according to claim 7, characterized in that: Guardian Mode: In special weather conditions, priority is given to ensuring that the energy storage battery is fully charged, reducing battery power consumption, and keeping the system in a zero-power-feed state; When the device receives the cloud control strategy, it will perform more precise energy control based on the production strategy: The next mode is the peak mode, which ensures that the battery is fully charged or above the user's power consumption habits during the peak period, regardless of whether the household load is using electricity or not. In peak mode, when there is a charging pile for smart appointment charging, when the household electricity uses the AC power of the grid, the charging column is reversely controlled to release the electric vehicle power for household loads, reducing the AC power consumption on the grid side; The next mode is the valley mode, when there is household load power consumption, the battery power is released to the maximum extent; In valley mode, when there is a charging pile for smart scheduled charging, the rated power of the charging pile and the car power are calculated to select the time period with the lowest electricity price to ensure that the charging pile is charged until it is fully charged. The next mode is the negative electricity price mode, when household load electricity consumption is maximized by releasing the power of energy storage batteries.

Images