New energy storage optimization method based on artificial intelligence

Abstract

The invention relates to the technical field of new energy, and discloses a new energy storage optimization method based on artificial intelligence, and the method comprises the following steps: 1, collecting real-time data from a new energy power generation system; 2, establishing a prediction model by using a deep learning algorithm and a machine learning technology, and predicting the generating capacity and the power demand; 3, dynamically adjusting a charging and discharging strategy of the energy storage system according to the prediction model and the real-time data so as to efficiently store and release energy; and step 4, integrating the energy storage optimization method with an energy efficiency management system, and providing energy efficiency analysis, demand response management and carbon emission monitoring functions. Through a high-precision prediction model established by adopting deep learning and machine learning technologies, the power generation amount and the power consumption demand in a period of time in the future can be accurately predicted, and according to the prediction results, the system can dynamically adjust the charging and discharging strategy of the energy storage equipment, so that efficient storage and release of energy are realized, and energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a new energy storage optimization method based on artificial intelligence. Background Art

[0002] With the continuous growth of global energy demand and the increasing severity of environmental problems, new energy power generation systems such as wind power, solar power and other renewable energy sources have received more and more attention. However, new energy power generation is intermittent and volatile, which poses a challenge to the stable operation of the power system. In order to alleviate this problem, energy storage technology is widely used in new energy power generation systems to balance the difference between power generation and power consumption by storing excess power for use during peak demand periods.

[0003] Traditional energy storage systems mostly rely on preset charging and discharging strategies, which are usually based on historical data and fixed rules and are difficult to cope with real-time changes in power generation and consumption. In addition, existing energy storage systems lack intelligent energy efficiency management methods and cannot dynamically adjust energy storage strategies to optimize energy efficiency. With the development of the energy market, peer-to-peer energy transactions have gradually increased, but traditional trading methods have problems with data security and lack of transparency, which affects the fairness of transactions and user trust.

[0004] At the same time, the maintenance of energy storage equipment is usually based on regular inspections or passive repairs after failures, lacking real-time monitoring of the equipment's operating status and predictive maintenance methods. This not only increases the maintenance cost of the equipment, but may also cause the equipment to suddenly shut down due to failure to detect the fault in time, affecting the overall operating efficiency of the system. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a new energy storage optimization method based on artificial intelligence, which solves the problem that most traditional energy storage systems rely on preset charging and discharging strategies, which are usually based on historical data and fixed rules and are difficult to cope with real-time changes in power generation and consumption.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a new energy storage optimization method based on artificial intelligence, comprising the following steps:

[0007] Step 1: Collect real-time data from renewable energy power generation systems;

[0008] Step 2: Use deep learning algorithms and machine learning techniques to build a prediction model to predict power generation and electricity demand;

[0009] Step 3: Dynamically adjust the charging and discharging strategy of the energy storage system according to the prediction model and real-time data to efficiently store and release energy;

[0010] Step 4: Integrate the energy storage optimization method with an energy efficiency management system to provide energy efficiency analysis, demand response management, and carbon emission monitoring functions;

[0011] Step 5: Use blockchain technology to conduct peer-to-peer energy transactions between energy storage systems and power grids to ensure data security and transparency;

[0012] Step 6: Perform predictive maintenance on energy storage equipment to detect and handle potential faults in advance by real-time monitoring of equipment status and operating parameters.

[0013] Preferably, the energy storage system includes a battery energy storage device and a supercapacitor.

[0014] Preferably, the collection of multi-dimensional real-time data includes obtaining power generation, power demand and weather condition data through sensors and Internet of Things devices.

[0015] Preferably, the prediction model includes: time series analysis, regression analysis, support vector machine and neural network.

[0016] Preferably, the energy efficiency management system displays energy efficiency analysis results and management suggestions through a user interface.

[0017] Preferably, the blockchain technology is used to record and verify energy transactions between the energy storage system and the power grid, ensure the immutability and transparency of transaction data, and support smart contract functions to automatically execute energy transactions.

[0018] Preferably, the predictive maintenance includes using machine learning algorithms to analyze equipment operating data, predict the remaining service life and potential failures of the equipment, and generate maintenance recommendations and early warning notifications.

[0019] Preferably, the real-time monitoring includes monitoring the voltage, current, temperature, internal impedance and operating parameters of the energy storage device, and performing data collection and analysis through a remote monitoring system.

[0020] Preferably, the new energy storage optimization system based on artificial intelligence includes:

[0021] A data collection module, used to collect real-time data from the renewable energy power generation system;

[0022] The prediction module uses deep learning algorithms and machine learning technologies to establish a prediction model to predict power generation and electricity demand in the future;

[0023] An optimization module dynamically adjusts the charging and discharging strategy of the energy storage system according to the prediction model and real-time data;

[0024] Energy efficiency management module, which provides energy efficiency analysis, demand response management and carbon emission monitoring functions;

[0025] Blockchain module, used for peer-to-peer energy transactions between energy storage systems and power grids, ensuring data security and transparency;

[0026] The maintenance module performs predictive maintenance on energy storage equipment and detects and handles potential faults in advance by real-time monitoring of equipment status and operating parameters.

[0027] Preferably, the data collection module obtains power generation, power demand and weather condition data through sensors and IoT devices, and synchronizes with the cloud database to ensure the real-time and integrity of the data.

[0028] The present invention provides a new energy storage optimization method based on artificial intelligence. It has the following beneficial effects:

[0029] 1. The present invention uses a high-precision prediction model established by deep learning and machine learning technology to accurately predict the power generation and electricity demand in the future. Based on these prediction results, the system can dynamically adjust the charging and discharging strategy of the energy storage device to achieve efficient storage and release of energy and reduce energy waste.

[0030] 2. The present invention uses blockchain technology for peer-to-peer energy transactions between energy storage systems and power grids. Through blockchain technology, the data of each energy transaction is recorded in a decentralized distributed ledger, ensuring the immutability and transparency of the data. The smart contract function can automatically execute energy transactions when specific conditions are met, improve transaction efficiency, reduce human intervention, prevent potential cheating and erroneous transactions, and not only enhance user trust, but also promote the development of the new energy market.

[0031] 3. The present invention realizes accurate monitoring of energy storage equipment by real-time monitoring of the operating status and key parameters of energy storage equipment. Combined with the analysis of equipment operation data by machine learning algorithms, the system can predict the remaining service life and potential failures of the equipment, generate maintenance suggestions and early warning notifications, and perform preventive maintenance before failures occur to avoid sudden damage and downtime of the equipment. This predictive maintenance method not only greatly extends the service life of energy storage equipment, but also reduces the economic losses and maintenance costs caused by equipment failures, and improves the overall reliability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the method of the present invention;

[0033] Figure 2 is a system architecture diagram of the present invention; DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Embodiment 1:

[0036] Please refer to the attached Figure 1 -Attached Figure 2 , an embodiment of the present invention provides a new energy storage optimization method based on artificial intelligence, comprising the following steps:

[0037] Step 1: Collect real-time data from renewable energy power generation systems;

[0038] Through sensors and IoT devices, the power generation, power demand and weather condition data of the new energy power generation system are obtained in real time. All data are synchronized to the cloud database to ensure its timeliness and integrity. The accuracy and timeliness of the data are guaranteed to provide a basis for subsequent prediction and optimization.

[0039] Step 2: Use deep learning algorithms and machine learning techniques to build a prediction model to predict power generation and electricity demand;

[0040] Using deep learning algorithms and machine learning techniques, a model is established to predict power generation and electricity demand in the future. The establishment of the prediction model includes the following steps:

[0041] Data preprocessing: Clean, denoise and standardize the collected real-time data to ensure data quality and consistency.

[0042] Feature extraction: Extract key features from the processed data, such as time series features, weather features, and historical electricity consumption data.

[0043] Model training: Using the extracted features, we use algorithms such as time series analysis, regression analysis, support vector machines, and neural networks to train the model and generate a high-precision prediction model.

[0044] Model validation: Evaluate the performance of the forecasting model through cross-validation and historical data backtesting, and make adjustments and optimizations.

[0045] Step 3: Dynamically adjust the charging and discharging strategy of the energy storage system based on the prediction model and real-time data to efficiently store and release energy;

[0046] According to the prediction model and real-time data, the charging and discharging strategy of the energy storage system is dynamically adjusted to achieve efficient storage and release of energy. Specifically, it includes:

[0047] Charging strategy: When power generation exceeds power demand, the excess power is preferentially stored in battery energy storage devices and supercapacitors.

[0048] Discharge strategy: When electricity demand is higher than power generation, energy storage devices are prioritized for releasing electricity to ensure the stability of power supply.

[0049] Dynamic adjustment: Based on real-time data and forecast results, the charging and discharging strategies are dynamically adjusted to cope with changes in electricity demand and fluctuations in power generation.

[0050] Step 4: Integrate the energy storage optimization method with the energy efficiency management system to provide energy efficiency analysis, demand response management and carbon emission monitoring functions;

[0051] Integrate energy storage optimization methods with energy efficiency management systems to provide energy efficiency analysis, demand response management, and carbon emission monitoring functions. Specifically, it includes:

[0052] Energy efficiency analysis: Displays energy efficiency analysis results and management suggestions through the user interface to help users optimize electricity usage plans and improve energy efficiency.

[0053] Demand response management: Dynamically adjust electricity consumption strategies based on electricity demand and electricity price fluctuations, participate in the grid’s demand response plan, and obtain economic benefits.

[0054] Carbon emission monitoring: Real-time monitoring and recording of carbon emission data during energy use, providing carbon emission reports to help users achieve green energy goals.

[0055] Step 5: Use blockchain technology to conduct peer-to-peer energy transactions between energy storage systems and power grids to ensure data security and transparency;

[0056] Blockchain technology is used to record and verify peer-to-peer energy transactions between energy storage systems and power grids to ensure data security and transparency. Specifically:

[0057] Data recording: Record each energy transaction on the blockchain to ensure the immutability and transparency of transaction data.

[0058] Smart Contract: Supports smart contract functions to automatically execute energy transactions through preset conditions, improving transaction efficiency and security.

[0059] Step 6: Perform predictive maintenance on energy storage equipment to detect and handle potential faults in advance by real-time monitoring of equipment status and operating parameters.

[0060] Carry out predictive maintenance on energy storage equipment, and detect and handle potential failures in advance by real-time monitoring of equipment status and operating parameters. Specifically, it includes:

[0061] Condition monitoring: Real-time monitoring of the voltage, current, temperature, internal impedance and other operating parameters of the energy storage equipment to ensure that the equipment operates in the best condition.

[0062] Fault prediction: Use machine learning algorithms to analyze equipment operation data, predict the remaining service life and potential failures of equipment, and generate maintenance recommendations and early warning notifications.

[0063] Remote monitoring: Data collection and analysis are carried out through the remote monitoring system to achieve remote diagnosis and maintenance of equipment.

[0064] The collection of multi-dimensional real-time data includes obtaining power generation, electricity demand and weather conditions data through sensors and IoT devices.

[0065] Prediction models include: time series analysis, regression analysis, support vector machines and neural networks.

[0066] The energy efficiency management system displays energy efficiency analysis results and management suggestions through the user interface.

[0067] Blockchain technology is used to record and verify energy transactions between energy storage systems and power grids, ensuring the immutability and transparency of transaction data, and supporting smart contract functions to automatically execute energy transactions.

[0068] Blockchain technology is used to record and verify energy transactions between energy storage systems and power grids, ensuring the immutability and transparency of transaction data, and supporting smart contract functions to automatically execute energy transactions. Specifically, it includes:

[0069] Blockchain network: Establish an energy trading network based on blockchain technology to achieve decentralized energy transaction records and verification.

[0070] Smart Contract: Through smart contract technology, automated energy transaction execution can be achieved, improving transaction efficiency and security.

[0071] Data privacy protection: The privacy and security of transaction data are guaranteed through the encryption mechanism of blockchain technology.

[0072] Predictive maintenance involves using machine learning algorithms to analyze equipment operating data, predict the remaining service life and potential failures of equipment, and generate maintenance recommendations and early warning notifications.

[0073] Real-time monitoring includes monitoring the voltage, current, temperature, internal impedance and operating parameters of the energy storage equipment, and data collection and analysis through a remote monitoring system.

[0074] Embodiment 2:

[0075] Please refer to the attached Figure 1 -Attached Figure 2, an embodiment of the present invention provides a new energy storage optimization system based on artificial intelligence, including:

[0076] A data collection module, used to collect real-time data from the renewable energy power generation system;

[0077] The data collection module obtains power generation, power demand and weather condition data through sensors and IoT devices, and synchronizes with the cloud database to ensure the real-time and integrity of the data. Specifically, it includes:

[0078] Sensors: Installed on power generation equipment, energy storage equipment and power consumption equipment to monitor the equipment's operating status and power data in real time.

[0079] IoT devices: Transmit sensor data to the cloud via wireless networks to enable centralized data management and analysis.

[0080] Data synchronization: Synchronize the data between the cloud database and the local device to ensure real-time update and complete preservation of data.

[0081] The prediction module uses deep learning algorithms and machine learning technologies to establish a prediction model to predict power generation and electricity demand in the future;

[0082] The optimization module dynamically adjusts the charging and discharging strategies of the energy storage system based on the prediction model and real-time data;

[0083] Dynamically adjust the charging and discharging strategies of the energy storage system based on the prediction model and real-time data.

[0084] Energy efficiency management module, which provides energy efficiency analysis, demand response management and carbon emission monitoring functions;

[0085] Blockchain module, used for peer-to-peer energy transactions between energy storage systems and power grids, ensuring data security and transparency;

[0086] The maintenance module performs predictive maintenance on energy storage equipment and detects and handles potential faults in advance by real-time monitoring of equipment status and operating parameters.

[0087] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A new energy storage optimization method based on artificial intelligence, characterized in that: The following steps are involved: Step 1: Collect real-time data from renewable energy power generation systems; Step 2: Use deep learning algorithms and machine learning techniques to build a prediction model to predict power generation and electricity demand; Step 3: Dynamically adjust the charging and discharging strategy of the energy storage system according to the prediction model and real-time data to efficiently store and release energy; Step 4: Integrate the energy storage optimization method with an energy efficiency management system to provide energy efficiency analysis, demand response management, and carbon emission monitoring functions; Step 5: Use blockchain technology to conduct peer-to-peer energy transactions between energy storage systems and power grids to ensure data security and transparency; Step 6: Perform predictive maintenance on energy storage equipment to detect and handle potential faults in advance by real-time monitoring of equipment status and operating parameters.

2. The artificial intelligence-based new energy storage optimization method according to claim 1, characterized in that: The energy storage system includes a battery energy storage device and a supercapacitor.

3. The artificial intelligence-based new energy storage optimization method according to claim 1, characterized in that: The collection of multi-dimensional real-time data includes obtaining power generation, electricity demand and weather condition data through sensors and IoT devices.

4. The new energy storage optimization method based on artificial intelligence according to claim 1 is characterized in that: The prediction model includes: time series analysis, regression analysis, support vector machine and neural network.

5. The artificial intelligence-based new energy storage optimization method according to claim 1, characterized in that: The energy efficiency management system displays energy efficiency analysis results and management suggestions through a user interface.

6. The artificial intelligence-based new energy storage optimization method according to claim 1, characterized in that: The blockchain technology is used to record and verify energy transactions between energy storage systems and power grids, ensure the immutability and transparency of transaction data, and support smart contract functions to automatically execute energy transactions.

7. The artificial intelligence-based new energy storage optimization method according to claim 1, characterized in that: The predictive maintenance includes using machine learning algorithms to analyze equipment operation data, predict the remaining service life and potential failures of the equipment, and generate maintenance recommendations and early warning notifications.

8. The artificial intelligence-based new energy storage optimization method according to claim 1, characterized in that: The real-time monitoring includes monitoring the voltage, current, temperature, internal impedance and operating parameters of the energy storage device, and performing data collection and analysis through a remote monitoring system.

9. A new energy storage optimization system based on artificial intelligence, according to the new energy storage optimization method based on artificial intelligence according to any one of claims 1 to 8, characterized in that: include: A data collection module, used to collect real-time data from the renewable energy power generation system; The prediction module uses deep learning algorithms and machine learning technologies to establish a prediction model to predict power generation and electricity demand in the future; An optimization module dynamically adjusts the charging and discharging strategy of the energy storage system according to the prediction model and real-time data; Energy efficiency management module, which provides energy efficiency analysis, demand response management and carbon emission monitoring functions; Blockchain module, used for peer-to-peer energy transactions between energy storage systems and power grids, ensuring data security and transparency; The maintenance module performs predictive maintenance on energy storage equipment and detects and handles potential faults in advance by real-time monitoring of equipment status and operating parameters.

10. The artificial intelligence-based new energy storage optimization system according to claim 9, characterized in that: The data collection module obtains power generation, power demand and weather condition data through sensors and IoT devices, and synchronizes with the cloud database to ensure the real-time and integrity of the data.

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