Distributed intelligent management system and method based on residential electricity load aggregation

Abstract

The invention discloses a distributed intelligent management system and method based on residential electricity load aggregation. The system comprises a data acquisition and communication module; a central management system; a distributed energy management module; and a load management and scheduling module. The distributed intelligent management system has the advantage of centralized scheduling, and solves the problems that in the using process of an existing distributed intelligent management system, centralized scheduling of distributed renewable energy sources is inconvenient, the utilization rate of the renewable energy sources is not high due to the fact that effective centralized scheduling is not available, energy waste is caused in part of time periods, and power consumption is low. And due to unpredictability and intermittency of renewable energy sources, if no good scheduling strategy is provided for smoothing the fluctuations, the stability and reliability of the power grid may be threatened.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and particularly to a distributed intelligent management system and method based on residential electricity load aggregation. Background Art

[0002] The distributed intelligent management system and method based on residential electricity load aggregation is an advanced energy management technology, which aims to optimize the allocation and use of power resources through intelligent means. The core of this method is to aggregate and manage the power consumption of multiple households or small enterprises, so as to achieve more efficient power use as a whole, and can better adapt to the intermittent characteristics of renewable energy (such as solar energy, wind energy).

[0003] This method of load aggregation first collects the electricity consumption data of each household or user and treats them as a whole. In this way, power companies or third-party service providers can see a total load curve, so as to better predict demand and make corresponding adjustments.

[0004] It includes the following application scenarios: automatically adjusting the air conditioner temperature or delaying the start time of non-urgent electrical appliances during peak power demand periods, using electric vehicles (EVs) as energy storage units, charging when there is excess power and feeding power back to the grid when there is a shortage, and adjusting the working hours of household appliances according to weather forecasts and solar power generation conditions.

[0005] During the use of existing distributed intelligent management systems, it is not convenient to centrally dispatch distributed renewable energy. The lack of effective central dispatching will lead to low utilization rate of renewable energy, resulting in energy waste during some periods. Moreover, due to the unpredictability and intermittency of renewable energy, if there is no good dispatching strategy to smooth these fluctuations, it may pose a threat to the stability and reliability of the power grid. Summary of the Invention

[0006] The purpose of the present invention is to provide a distributed intelligent management system and method based on residential electricity load aggregation, which has the advantage of centralized dispatching, and solves the problems that during the use of existing distributed intelligent management systems, it is not convenient to centrally dispatch distributed renewable energy, the lack of effective central dispatching will lead to low utilization rate of renewable energy, resulting in energy waste during some periods, and due to the unpredictability and intermittency of renewable energy, if there is no good dispatching strategy to smooth these fluctuations, it may pose a threat to the stability and reliability of the power grid.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A distributed intelligent management system based on residential electricity load aggregation, comprising:

[0008] A data collection and communication module;

[0009] Central management system;

[0010] Distributed energy management module;

[0011] Load management and scheduling module.

[0012] Preferably, as a distributed intelligent management system based on residential electricity load aggregation of the present invention, the data acquisition and communication module includes a power consumption detection module, a communication module, and a data encryption module.

[0013] Preferably, as a distributed intelligent management system based on residential electricity load aggregation of the present invention, the power consumption detection module is an intelligent electricity meter, which is installed in residential users' homes to collect power consumption data. The communication module is a 5G module, and the 5G module is electrically connected to the intelligent electricity meter to transmit the detected data to the central management system. The data encryption module encrypts the data transmitted by the communication module to prevent the data from being tampered with.

[0014] Preferably, as a distributed intelligent management system based on residential electricity load aggregation of the present invention, the central management system includes a data receiving module, a data decryption module, a data storage module, a data processing module, and a data analysis module.

[0015] Preferably, as a distributed intelligent management system based on residential electricity load aggregation of the present invention, the data receiving module receives the data sent by the communication module and transmits it to the data decryption module. The data decryption module uses a predefined encryption algorithm and key to decrypt the received data packet. The decrypted data is transmitted to the data storage module in plain text. The data storage module stores the decrypted data in a database according to a preset data model. The database is a NoSQL database. The data processing module extracts the required data fields inside the data storage module, cleans the data, and converts the data format to make it suitable for further analysis. The data analysis module uses statistical methods and machine learning algorithms to analyze the processed data to obtain an analysis result.

[0016] Preferably, as a distributed intelligent management system based on residential electricity load aggregation of the present invention, the distributed energy management module includes a collection and aggregation module, a centralized management module, and a multi-channel output module.

[0017] Preferably, in a distributed intelligent management system based on residential electricity load aggregation of the present invention, the acquisition and aggregation module uses an inverter to connect to a distributed power source, converts reverse current electricity into direct current electricity, and uses voltage and current regulating devices to regulate the voltage and current of the direct current electricity. The centralized management module includes an energy management system and a power storage device. The energy management system uniformly manages and schedules the electricity output by the acquisition and aggregation module. The power storage facility is electrically connected to the energy management system and uses an energy storage device to temporarily store electricity for use during peak demand or when intermittent energy cannot be supplied. The multi-channel output module includes a distribution box with multiple output ports and intelligent circuit breakers. The distribution box with multiple output ports delivers electrical energy to different household user loads as needed. The intelligent circuit breaker automatically cuts off the power supply when detecting overload or faults to protect the system safety.

[0018] Preferably, in a distributed intelligent management system based on residential electricity load aggregation of the present invention, the load management and scheduling module includes a load forecasting module, a load classification module, a management strategy formulation module, and a real-time scheduling module.

[0019] Preferably, in a distributed intelligent management system based on residential electricity load aggregation of the present invention, the load forecasting module receives the analysis results of the central management system and identifies the electricity consumption patterns and trends of users based on the analysis result data. The load classification module classifies users into different types of loads according to the electricity consumption patterns and trends of users. The management strategy formulation module formulates different management strategies for different classified load users. The real-time scheduling module controls the multi-channel output module according to the formulated management strategies to perform load distribution.

[0020] A distributed intelligent management method based on residential electricity load aggregation includes the following steps:

[0021] S1. Installation of smart meters. Install smart meters in each residential user's home to monitor and record electricity consumption data in real time.

[0022] S2. Detection of electricity consumption. The smart meters regularly detect the electricity consumption of users and record the relevant data.

[0023] S3. Data transmission. The smart meters send the collected data to the central management system through a 5G module. The data encryption module encrypts the received data to ensure the security and integrity of the data during transmission.

[0024] S4. Data processing and analysis. The data receiving module in the central management system receives the data packets from the 5G module. The data decryption module decrypts the data packets using a predefined encryption algorithm and key.

[0025] S5. Data storage. The decrypted data is stored in a NoSQL database, organized and stored according to a preset data model. The data processing module extracts the required fields from the database, cleans the data, removes invalid or incorrect data, and converts the cleaned data into a format suitable for further analysis.

[0026] S6. Data analysis. The data analysis module uses statistical methods and machine learning algorithms to analyze the processed data, identify the user's electricity consumption patterns and trends, and predict future load demands.

[0027] S7. Distributed energy management. An inverter is used to convert the direct current generated by the distributed power source into alternating current, and the converted alternating current is regulated through voltage and current regulating devices to ensure that it meets the grid standards.

[0028] S8. Power distribution. The energy management system uniformly manages and schedules the collected and aggregated power. Energy storage devices are used to temporarily store excess power for emergencies. A distribution box with multiple output ports distributes power to different household users according to demand. The intelligent circuit breaker automatically cuts off the power when overload or fault is detected to ensure the safe operation of the system.

[0029] S9. Load management and scheduling. The load forecasting module receives the analysis results provided by the central management system and predicts future load demands based on this data. According to the user's electricity consumption patterns and trends, the load classification module classifies users into different categories.

[0030] S10. Management strategy formulation. The management strategy formulation module formulates corresponding load management strategies according to the characteristics of different categories of users. The real-time scheduling module dynamically adjusts power distribution by controlling the multi-channel output module according to the formulated management strategies to cope with different load demands.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] 1. The present invention uses an intelligent electricity meter to monitor the electricity consumption of residents in real time and transmits the data to the central management system efficiently and quickly through the 5G network. The data encryption module ensures the data security during the transmission process. The central management system is responsible for receiving, decrypting, storing, processing, and analyzing the information transmitted from the data acquisition and communication module. By using a NoSQL database to store a large amount of unstructured data and using data processing and analysis technologies to mine useful information, it provides a basis for subsequent decision-making.

[0033] 2. The present invention converts different types of power sources into available alternating current through an inverter, and ensures power quality through voltage and current regulation devices. At the same time, the energy management system is responsible for overall planning of the use of these energies, and the power storage device can provide supplementation during peak demand periods or when the power generation of renewable energy is insufficient. The load management and scheduling module is responsible for predicting future loads based on the actual electricity consumption of users, and formulating reasonable electricity usage strategies. Through real-time scheduling, the system can dynamically adjust power supply to ensure that while meeting user needs, the operating efficiency of the power grid is optimized.

[0034] 3. Through centralized scheduling, the present invention can better match power generation capacity and actual electricity demand, reduce energy waste. Through effective management of distributed energy, it can relieve the power grid pressure brought by the volatility of renewable energy and maintain the stable operation of the power grid. Reasonable scheduling can reduce the dependence on traditional energy, and at the same time reduce the demand for maintaining standby power generation capacity, thereby reducing the overall operating cost. Through load forecasting and classification, the system can provide personalized services according to user habits, avoid situations such as sudden power outages, improve service quality, and the reasonable utilization of renewable energy helps to reduce carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the system of the present invention;

[0036] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] Embodiment 1

[0038] Please refer to Figure 1 , a distributed intelligent management system based on residential electricity load aggregation, including:

[0039] Data acquisition and communication module;

[0040] Central management system;

[0041] Distributed energy management module;

[0042] Load management and scheduling module.

[0043] Furthermore, the data acquisition and communication module includes a power consumption detection module, a communication module, and a data encryption module.

[0044] Furthermore, the power consumption detection module is an intelligent electric meter, and the intelligent electric meter is installed in residential users' homes to collect power consumption data. The communication module is a 5G module, and the 5G module is electrically connected to the intelligent electric meter to transmit the detected data to the central management system. The data encryption module encrypts the data transmitted by the communication module to prevent the data from being tampered with.

[0045] Furthermore, the central management system includes a data receiving module, a data decryption module, a data storage module, a data processing module, and a data analysis module.

[0046] Furthermore, the data receiving module receives the data sent by the communication module and transfers it to the data decryption module. The data decryption module uses a predefined encryption algorithm and key to decrypt the received data packet. The decrypted data is transferred to the data storage module in plain text. The data storage module stores the decrypted data in the database according to a preset data model. The database is a NoSQL database. The data processing module extracts the required data fields inside the data storage module, cleans the data, and converts the data format to make it suitable for further analysis. The data analysis module uses statistical methods and machine learning algorithms to analyze the processed data and obtains the analysis result.

[0047] Furthermore, the distributed energy management module includes a collection and aggregation module, a centralized management module, and a multi-channel output module.

[0048] Furthermore, the collection and aggregation module uses an inverter to connect to the distributed power source, converts the reverse current electricity into direct current electricity, and uses voltage and current regulating devices to regulate the voltage and current of the direct current electricity. The centralized management module includes an energy management system and a power storage device. The energy management system uniformly manages and schedules the electricity output by the collection and aggregation module. The power storage facility is electrically connected to the energy management system and uses the energy storage device to temporarily store the electricity for use during peak demand or when intermittent energy cannot be supplied. The multi-channel output module includes a distribution box with multiple output ports and an intelligent circuit breaker. The distribution box with multiple output ports delivers the electric energy to different household user loads as needed. The intelligent circuit breaker automatically cuts off the power supply when detecting overload or fault to protect the system safety.

[0049] Furthermore, the load management and scheduling module includes a load forecasting module, a load classification module, a management strategy formulation module, and a real-time scheduling module.

[0050] Furthermore, the load forecasting module receives the analysis result of the central management system and identifies the user's electricity consumption pattern and trend based on the analysis result data. The load classification module classifies the users into different types of loads according to the user's electricity consumption pattern and trend. The management strategy formulation module formulates different management strategies for different classified load users. The real-time scheduling module controls the multi-channel output module according to the formulated management strategy to perform load distribution.

[0051] The data acquisition and communication module is the foundation of the entire system. It monitors the electricity consumption of residents in real time through smart meters and transmits the data to the central management system efficiently and quickly via the 5G network. The data encryption module ensures the data security during the transmission process.

[0052] The central management system is responsible for receiving, decrypting, storing, processing, and analyzing the information transmitted from the data acquisition and communication module. It stores a large amount of unstructured data using a NoSQL database and utilizes data processing and analysis technologies to mine useful information, providing a basis for subsequent decision-making.

[0053] The distributed energy management module focuses on managing and dispatching the electricity from distributed energy sources (such as solar panels and wind turbines). It converts different types of power sources into usable alternating current through inverters and ensures the power quality through voltage and current regulation devices. At the same time, the energy management system is responsible for overall planning of the use of these energies, and the power storage devices can provide supplements during peak demand periods or when the renewable energy generation is insufficient.

[0054] The load management and scheduling module is responsible for predicting the future load based on the actual electricity consumption of users and formulating reasonable electricity usage strategies accordingly. Through real-time scheduling, the system can dynamically adjust the power supply to ensure optimizing the operation efficiency of the power grid while meeting the user's needs.

[0055] Through centralized scheduling, the power generation capacity and the actual electricity demand can be better matched, reducing energy waste. Through the effective management of distributed energy, the grid pressure brought by the volatility of renewable energy can be alleviated, maintaining the stable operation of the grid. Reasonable scheduling can reduce the dependence on traditional energy sources and also reduce the demand for maintaining standby power generation capacity, thus reducing the overall operating cost. Through load forecasting and classification, the system can provide personalized services according to user habits, avoid situations such as sudden power outages, improve the service quality, and the reasonable utilization of renewable energy helps to reduce carbon emissions.

[0056] Embodiment 2

[0057] Please refer to Figure 2 , a distributed intelligent management method based on the aggregation of residential electricity loads, including the following steps:

[0058] S1. Installation of smart meters: Install smart meters in each residential user's home for real-time monitoring and recording of electricity consumption data;

[0059] S2. Detection of electricity consumption: The smart meters regularly detect the electricity consumption of users and record the relevant data;

[0060] S3. Data Transmission: The smart meter sends the collected data to the central management system through the 5G module. The data encryption module encrypts the received data to ensure the security and integrity of the data during transmission.

[0061] S4. Data Processing and Analysis: The data receiving module in the central management system receives the data packets from the 5G module. The data decryption module decrypts the data packets using the predefined encryption algorithm and key.

[0062] S5. Data Storage: The decrypted data is stored in the NoSQL database and organized for storage according to the preset data model. The data processing module extracts the required fields from the database, cleans the data to remove invalid or incorrect data, and converts the cleaned data into a format suitable for further analysis.

[0063] S6. Data Analysis: The data analysis module uses statistical methods and machine learning algorithms to analyze the processed data, identify the user's electricity consumption patterns and trends, and predict future load demands.

[0064] S7. Distributed Energy Management: The inverter is used to convert the direct current generated by the distributed power source into alternating current, and the converted alternating current is regulated by the voltage and current regulation device to ensure that it meets the grid standards.

[0065] S8. Power Distribution: The energy management system uniformly manages and schedules the collected and aggregated power. The energy storage device is used to temporarily store the excess power for future needs. The distribution box with multiple output ports distributes the power to different household users according to the demand. The intelligent circuit breaker automatically cuts off the power when detecting overload or fault to ensure the safe operation of the system.

[0066] S9. Load Management and Scheduling: The load forecasting module receives the analysis results provided by the central management system and predicts future load demands based on this data. According to the user's electricity consumption patterns and trends, the load classification module classifies users into different categories.

[0067] S10. Management Strategy Formulation: The management strategy formulation module formulates corresponding load management strategies according to the characteristics of different categories of users. The real-time scheduling module dynamically adjusts the power distribution by controlling the multi-channel output module according to the formulated management strategies to cope with different load demands.

[0068] By monitoring the user's electricity consumption in real time through the smart meter and combining data analysis to predict future load demands, the power supply can be adjusted more precisely to avoid power waste.

[0069] By centrally managing and dispatching distributed energy, renewable energy can be utilized more effectively. At the same time, energy storage devices can store energy during periods of excess power generation and release it during peak demand, thereby optimizing the allocation of power resources.

[0070] Intelligent circuit breakers can automatically cut off the power supply when detecting overload or faults, protecting the system from damage and thus ensuring the safe and stable operation of the power grid.

[0071] By accurately predicting and dispatching electricity, unnecessary power reserves can be reduced, the costs of grid operators can be lowered, and ultimately consumers can benefit.

[0072] Based on the user's electricity consumption patterns and trends, the system can provide personalized services to ensure that users obtain sufficient power supply when needed, improving service quality.

[0073] This method encourages the use of renewable energy and reduces dependence on fossil fuels through optimized dispatching, helping to reduce greenhouse gas emissions and promote green development.

[0074] The application of encryption technology and intelligent circuit breakers improves the security of the system, preventing data leakage and power system failures.

[0075] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A distributed intelligent management system based on residential electricity load aggregation, characterized in that: include: Data acquisition and communication module; Central management system; Distributed energy management module; Load management and scheduling module.

2. A distributed intelligent management system based on residential electricity load aggregation according to claim 1, characterized in that: The data acquisition and communication module includes a power consumption detection module, a communication module and a data encryption module.

3. A distributed intelligent management system based on residential electricity load aggregation according to claim 2, characterized in that: The power consumption detection module is a smart meter, which is installed in the homes of residential users to collect power consumption data. The communication module is a 5G module, which is electrically connected to the smart meter to transmit the detected data to the central management system. The data encryption module encrypts the data transmitted by the communication module to prevent the data from being tampered with.

4. A distributed intelligent management system based on residential electricity load aggregation according to claim 3, characterized in that: The central management system includes a data receiving module, a data decryption module, a data storage module, a data processing module and a data analysis module.

5. A distributed intelligent management system based on residential electricity load aggregation according to claim 4, characterized in that: The data receiving module receives the data sent by the communication module and transmits it to the data decryption module. The data decryption module uses a predefined encryption algorithm and key to decrypt the received data packet. The decrypted data is transmitted to the data storage module in plain text. The data storage module stores the decrypted data in a database according to a preset data model. The database is a NoSQL database. The data processing module extracts the required data fields inside the data storage module, cleans the data, and converts the data format to make it suitable for further analysis. The data analysis module uses statistical methods and machine learning algorithms to analyze the processed data to obtain analysis results.

6. A distributed intelligent management system based on residential electricity load aggregation according to claim 5, characterized in that: The distributed energy management module includes a collection and aggregation module, a centralized management module and a multi-channel output module.

7. A distributed intelligent management system based on residential electricity load aggregation according to claim 6, characterized in that: The collection and collection module uses an inverter to connect to the distributed power source, converts the reverse current into direct current, and uses voltage and current regulating equipment to regulate the voltage and current of the direct current. The centralized management module includes an energy management system and a power storage device. The energy management system uniformly manages and dispatches the power output by the collection and collection module. The power storage facility and the energy management system are electrically connected, and the energy storage device is used to temporarily store the power for use during peak demand or when intermittent energy cannot be supplied. The multi-channel output module includes a distribution box with multiple output ports and an intelligent circuit breaker. The distribution box with multiple output ports transmits electrical energy to different household user loads as needed. The intelligent circuit breaker automatically cuts off the power supply when an overload or fault is detected to protect the system safety.

8. A distributed intelligent management system based on residential electricity load aggregation according to claim 7, characterized in that: The load management and scheduling module includes a load forecasting module, a load classification module, a management strategy formulation module and a real-time scheduling module.

9. A distributed intelligent management system based on residential electricity load aggregation according to claim 8, characterized in that: The load forecasting module receives the analysis results of the central management system and identifies the user's power usage pattern and trend based on the analysis result data. The load classification module classifies users into different types of loads based on their power usage patterns and trends. The management strategy formulation module formulates different management strategies based on different categories of load users. The real-time scheduling module controls the multi-channel output module based on the formulated management strategy to perform load distribution.

10. A distributed intelligent management method based on residential electricity load aggregation, applicable to the distributed intelligent management system based on residential electricity load aggregation as described in any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Smart meter installation: smart meters are installed in every household to monitor and record electricity consumption data in real time; S2. Power consumption detection: the smart meter regularly detects the user's power consumption and records the relevant data; S3, data transmission: the smart meter sends the collected data to the central management system through the 5G module, and the data encryption module encrypts the received data to ensure the security and integrity of the data during transmission; S4, data processing and analysis, the data receiving module in the central management system receives the data packet from the 5G module, and the data decryption module decrypts the data packet using a predefined encryption algorithm and key; S5. Data storage. The decrypted data is stored in a NoSQL database and organized according to a preset data model. The data processing module extracts the required fields from the database and cleans the data to remove invalid or erroneous data, and converts the cleaned data into a format suitable for further analysis. S6. Data analysis: The data analysis module uses statistical methods and machine learning algorithms to analyze the processed data, identify users' electricity usage patterns and trends, and predict future load demand; S7, Distributed energy management, using inverters to convert the DC power generated by distributed power sources into AC power, and regulating the converted AC power through voltage and current regulation equipment to ensure that it meets the grid standards; S8, power distribution, the energy management system is used to uniformly manage and dispatch the collected and aggregated power, and the energy storage device is used to temporarily store the excess power for emergency use. The distribution box with multiple output ports distributes power to different household users according to demand. The intelligent circuit breaker automatically cuts off the power supply when an overload or fault is detected to ensure the safe operation of the system; S9, load management and scheduling, the load forecasting module receives the analysis results provided by the central management system and predicts the future load demand based on these data. The load classification module divides users into different categories according to their power consumption patterns and trends; S10, management strategy formulation. The management strategy formulation module formulates corresponding load management strategies according to the characteristics of different categories of users. The real-time scheduling module dynamically adjusts power distribution by controlling the multi-channel output module according to the formulated management strategy to cope with different load demands.

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