Power grid new energy consumption capability evaluation device

Abstract

The invention discloses a power grid new energy consumption capability evaluation device in the field of power grid evaluation, which comprises a data acquisition module, a data processing module, an evaluation model module and a result display module, and is characterized in that the data acquisition module is used for acquiring operation data of new energy power generation equipment and a power grid in real time; the data processing module is used for preprocessing, mining and analyzing the data acquired by the data acquisition module; according to the invention, an advanced data processing algorithm and an artificial intelligence evaluation model are adopted, a large amount of data can be rapidly processed, rapid evaluation of the new energy consumption capability of the power grid is realized, the evaluation efficiency is greatly improved, and the requirements of real-time operation and planning of the power grid are met; and strict preprocessing and deep analysis are carried out on the data, and an optimized evaluation model is combined, so that the accuracy and reliability of an evaluation result are effectively improved, and powerful support is provided for power grid planning and operation decision making.

Description

Technical Field

[0001] The present invention relates to the field of power grid assessment, and more particularly to an assessment device for the new energy consumption capacity of a power grid. Background Art

[0002] Against the backdrop of the world's active response to climate change and the practice of the concept of sustainable development, the demand for clean energy has witnessed an explosive growth. New energy sources such as solar, wind, and hydro energy, with their clean and renewable characteristics, are increasingly occupying a larger proportion in the power grid. Taking China as an example, in recent years, the installed capacities of wind power and photovoltaic power generation have been continuously climbing, and the status of new energy in the energy structure has become increasingly crucial.

[0003] However, new energy power generation has inherent characteristics such as intermittency, volatility, and randomness. For example, wind power generation is significantly affected by changes in wind speed and direction, while solar power generation depends on light intensity and duration. Such unstable power generation characteristics have posed unprecedented challenges to the stable operation of the power grid and power balance. When the power generation power of new energy fluctuates significantly, key indicators such as the voltage and frequency of the power grid are extremely likely to deviate from the normal range, which may trigger power grid failures and seriously threaten the safety of the power grid.

[0004] Accurately assessing the new energy consumption capacity of the power grid is extremely urgent. On the one hand, this is a necessary prerequisite for ensuring the safe and stable operation of the power grid. Only by clearly understanding the capacity boundary of the power grid to accommodate new energy can the power generation output of new energy be reasonably arranged in actual operation to avoid power grid imbalance caused by excessive new energy access. On the other hand, for the planning and construction of the power system, accurate assessment of the new energy consumption capacity is the cornerstone of scientific decision-making. Through assessment, the future construction direction and key points of the power grid in terms of new energy access can be clarified, and transmission lines, energy storage facilities, etc. can be reasonably laid out to enhance the carrying capacity of the power grid for new energy. At the same time, in the field of energy policy formulation, the assessment results of the new energy consumption capacity also provide data support for the scientificity and effectiveness of policies, helping to formulate more practical and guiding new energy development policies.

[0005] However, the existing assessment methods and devices for the new energy consumption capacity of the power grid have many deficiencies. Some assessment methods are computationally complex and require a large amount of human and time costs, resulting in low assessment efficiency and being unable to meet the rapidly changing power grid operation requirements. The data acquisition and processing capabilities of some assessment devices are limited, making it difficult to comprehensively and accurately obtain and analyze key information such as new energy power generation data and power grid load data, thus affecting the accuracy and reliability of the assessment results. In addition, the existing devices lack sufficient flexibility and adaptability when dealing with different types of new energy and complex power grid structures.

[0006] Therefore, those skilled in the art have provided an assessment device for the new energy consumption capacity of the power grid to solve the problems raised in the above background art. Summary of the Invention

[0007] The object of the present invention is to provide an evaluation device for the new energy consumption capacity of the power grid to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: An evaluation device for the new energy consumption capacity of the power grid includes a data acquisition module, a data processing module, an evaluation model module, and a result display module. The data acquisition module is used to collect the operation data of new energy power generation equipment and the power grid in real time. The data processing module preprocesses, mines, and analyzes the data collected by the data acquisition module. The evaluation model module evaluates the new energy consumption capacity of the power grid based on artificial intelligence and machine learning algorithms according to the processed data. The result display module displays the evaluation result to the user in an intuitive manner. The data acquisition module adopts a distributed sensor network, including an external data acquisition module and an internal data acquisition module. The external data acquisition module includes a light detection module, a wind speed detection module, and a water flow speed detection module. The internal data acquisition module includes a power sensor, a voltage sensor, an current sensor, a load sensor, and a frequency sensor. The data acquisition module uses 5G communication technology to achieve real-time data transmission. The data preprocessing of the data collected by the data processing module includes data cleaning, filtering, and interpolation operations. The data cleaning algorithm is used to remove noise, outliers, and missing values in the collected data. Interpolation methods, filtering methods, and other technologies are used to repair and correct the missing data and abnormal data to ensure the integrity and accuracy of the data. At the same time, the data is standardized, and different types and magnitudes of data are converted into a unified standard format for subsequent analysis and calculation.

[0009] As a further solution of the present invention: The light detection module, the wind speed detection module, and the water flow speed detection module are used to detect the new energy power generation environment, and the data processing module uses big data analysis technology to deeply mine and analyze the data collected by the data acquisition module.

[0010] As a further solution of the present invention: The evaluation model module is trained using deep learning algorithms, and the deep learning algorithms include neural network algorithms, convolutional neural network algorithms, or recurrent neural network algorithms. The evaluation model module can update the evaluation model parameters in real time according to the changes in the power grid operation state.

[0011] As a further solution of the present invention: The result display module displays the evaluation result to the user in the form of charts and reports, and the result display module also provides a data comparison function for comparing the evaluation results of different time periods and different regions.

[0012] As a further solution of the present invention: The data processing module has a data storage function and stores the processed data in a local database.

[0013] As a further solution of the present invention: The evaluation device is based on a comprehensive evaluation model of an improved particle swarm optimization algorithm and a deep belief network. The IPSO algorithm is used to optimize the network parameters of the DBN to improve the convergence speed and accuracy of the model. Then, the extracted key feature parameters are used as inputs, and learning and training are carried out through the DBN model to achieve an accurate evaluation of the new energy consumption capacity of the power grid. This model can fully consider factors such as the uncertainty of new energy power generation, the operating constraints of the power grid, and the dynamic changes of the load, improving the comprehensiveness and accuracy of the evaluation, and can adapt to different types of new energy power generation equipment and complex power grid structures.

[0014] As a further solution of the present invention: The data acquisition module adopts a distributed architecture and simultaneously collects data from multiple data sources. When the data processing module processes large-scale power grid data, parallel computing technology and a big data analysis framework are adopted.

[0015] As a further solution of the present invention: When the evaluation model module evaluates a large-scale regional power grid, it is optimized according to the characteristics of the power grid, and real-time update technology is used to adjust the model parameters.

[0016] As a further solution of the present invention: The result display module has an interactive interface and visually and intuitively displays the evaluation results in an easy-to-understand manner. For example, through forms such as charts, maps, and animations, it displays the spatio-temporal distribution of the new energy consumption capacity of the power grid, the evaluation results under different scenarios, and the change trends of risk assessment indicators, etc. Users can flexibly select different display methods and parameters through the interactive interface to conveniently and quickly obtain the required information, and according to the evaluation results, provide decision-making suggestions for the planning, operation, and management of the power system. For example, in terms of power grid planning, give suggestions on the reasonable layout and installed capacity of new energy power generation; in terms of power grid operation, provide optimized dispatching strategies to improve the new energy consumption capacity; in terms of load management, propose measures to cut peaks and fill valleys to reduce the impact of load fluctuations on the new energy consumption of the power grid.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. The present invention adopts advanced data processing algorithms and artificial intelligence evaluation models, can quickly process a large amount of data, realizes the rapid evaluation of the new energy consumption capacity of the power grid, greatly improves the evaluation efficiency, meets the requirements of the real-time operation and planning of the power grid, collects data through high-precision sensors, strictly preprocesses and deeply analyzes the data, and combines with the optimized evaluation model, effectively improving the accuracy and reliability of the evaluation results, providing strong support for power grid planning and operation decision-making.

[0018] 2. The evaluation device of the present invention can adapt to different types of new energy power generation equipment and complex power grid structures. By updating the evaluation model parameters in real time, it can cope with the changes in the power grid operation state, and has strong flexibility and adaptability. The result display module presents the evaluation results in an intuitive way, which is convenient for users to view and analyze. At the same time, it provides a data comparison function, which helps users deeply understand the changes in the new energy consumption capacity of the power grid and provides a convenient user experience for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic structural diagram of the present invention; Figure 2 is a schematic structural diagram of the external data acquisition module in the present invention; Figure 3 is a schematic structural diagram of the internal data acquisition module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to Figures 1 to 3 , in the embodiment of the present invention, an evaluation device for the new energy consumption capacity of a power grid includes a data acquisition module, a data processing module, an evaluation model module, and a result display module. The data acquisition module is used to collect the operation data of new energy power generation equipment and the power grid in real time; the data processing module preprocesses, mines, and analyzes the data collected by the data acquisition module; the evaluation model module evaluates the new energy consumption capacity of the power grid based on artificial intelligence and machine learning algorithms according to the processed data; the result display module displays the evaluation results to the user in an intuitive way; The data acquisition module adopts a distributed sensor network, including an external data acquisition module and an internal data acquisition module. The external data acquisition module includes a light detection module, a wind speed detection module, and a water flow speed detection module. The internal data acquisition module includes a power sensor, a voltage sensor, a current sensor, a load sensor, and a frequency sensor. The data acquisition module uses 5G communication technology to achieve real-time data transmission. The data preprocessing performed by the data processing module on the acquired data includes data cleaning, filtering, and interpolation operations. Data cleaning uses data cleaning algorithms to remove noise, outliers, and missing values in the acquired data. Techniques such as interpolation and filtering are used to repair and correct missing and abnormal data to ensure the integrity and accuracy of the data. At the same time, the data is standardized, converting data of different types and magnitudes into a unified standard format for subsequent analysis and calculation.

[0022] Among them, the light detection module, the wind speed detection module, and the water flow speed detection module are used to detect the new energy power generation environment. The data processing module uses big data analysis technology to deeply mine and analyze the data collected by the data acquisition module.

[0023] Among them, the evaluation model module is trained using deep learning algorithms, including neural network algorithms, convolutional neural network algorithms, or recurrent neural network algorithms. The evaluation model module can update the evaluation model parameters in real time according to changes in the grid operation state.

[0024] Among them, the result display module displays the evaluation results to the user in the form of charts and reports. The result display module also provides a data comparison function for comparing the evaluation results of different time periods and different regions. The data processing module has a data storage function and stores the processed data in a local database.

[0025] Among them, the evaluation device is based on a comprehensive evaluation model of the improved particle swarm optimization algorithm and the deep belief network. The IPSO algorithm is used to optimize the network parameters of the DBN to improve the convergence speed and accuracy of the model. Then, the extracted key feature parameters are used as inputs to learn and train through the DBN model to achieve an accurate evaluation of the grid's new energy consumption capacity. This model can fully consider factors such as the uncertainty of new energy power generation, the operation constraints of the grid, and the dynamic changes of the load, improving the comprehensiveness and accuracy of the evaluation and being able to adapt to different types of new energy power generation equipment and complex grid structures.

[0026] Among them, the data acquisition module adopts a distributed architecture and simultaneously collects data from multiple data sources. When processing large-scale grid data, the data processing module uses parallel computing technology and big data analysis frameworks.

[0027] Among them, when the evaluation model module is used for evaluating a large-scale regional power grid, it is optimized according to the characteristics of the power grid and the model parameters are adjusted by using real-time update technology. The result display module has an interactive interface and visually and understandably visualizes the evaluation results, such as through forms of charts, maps, animations, etc., to display the spatio-temporal distribution of the new energy consumption capacity of the power grid, the evaluation results under different scenarios, and the change trends of risk assessment indicators, etc. Users can flexibly select different display methods and parameters through the interactive interface to conveniently and quickly obtain the required information, and according to the evaluation results, provide decision-making suggestions for the planning, operation and management of the power system. For example, in terms of power grid planning, give suggestions on the reasonable layout and installed capacity of new energy power generation; in terms of power grid operation, provide optimized dispatching strategies to improve the new energy consumption capacity; in terms of load management, propose peak shaving and valley filling measures to reduce the impact of load fluctuations on the new energy consumption of the power grid. Embodiment 1: Evaluation of the New Energy Consumption Capacity of a Small-Scale Power Grid 1. Data collection: In a small-scale power grid, 5 wind power generation devices and 3 solar power generation devices are installed. Power sensors, voltage sensors and current sensors are respectively installed on these new energy power generation devices to collect the output parameters of the power generation devices. At the same time, load sensors, voltage sensors and frequency sensors are installed at the key nodes of the power grid to collect the operation data of the power grid. The data collection module collects data every 1 minute and sends the data to the data processing module through the wireless transmission module.

[0028] 2. Data processing: After receiving the data, the data processing module first performs data cleaning to remove obviously incorrect and abnormal data. Then, a filtering algorithm is used to smooth the data to remove noise interference. For missing data, an interpolation algorithm is used to fill it. The preprocessed data is stored in the local database, and at the same time, big data analysis technology is used to analyze the data to extract information such as the power fluctuation characteristics of new energy power generation and the daily change law of the power grid load.

[0029] 3. Application of the evaluation model: The processed data is input into the evaluation model module, and the evaluation model calculates the new energy consumption capacity of the small-scale power grid according to the input data and the trained parameters. The evaluation model takes into account factors such as the uncertainty of new energy power generation, the power grid load demand, and the power grid line capacity, and performs complex calculations and analyses through deep learning algorithms.

[0030] 4. Result Display: The evaluation results are displayed to the user in the form of charts through the result display module. The user can see the new energy consumption capacity of the small power grid in different time periods, as well as the real-time data comparison between new energy generation and grid load. Through the charts, the user can intuitively understand the new energy consumption situation of the power grid and the impact of new energy generation on the operation of the power grid.

[0031] Embodiment 2: Evaluation of New Energy Consumption Capacity of Large Regional Power Grid 1. Data Collection: For a large regional power grid covering multiple cities and regions, with a large number of new energy generation devices and a complex power grid structure. Various sensors are installed at key locations such as new energy power stations, substations, and transmission lines to form a huge data collection network. The data collection module adopts a distributed architecture, which can collect data from multiple data sources simultaneously and transmit the data to the data processing center through a high-speed communication network.

[0032] 2. Data Processing: The data processing center centrally processes the received data. First, the massive data is stored and managed distributively to ensure data security and scalability. Then, parallel computing technology and big data analysis frameworks are used to quickly process and analyze the data. In the data preprocessing process, more complex data cleaning and anomaly detection algorithms are adopted to handle various complex data problems that may occur in large power grids.

[0033] 3. Evaluation Model Optimization: According to the characteristics of the large regional power grid, the evaluation model is further optimized. In the model training process, more historical data and actual operation cases are added to improve the adaptability of the model to complex power grid structures and operating states. At the same time, real-time update technology is adopted to dynamically adjust the parameters of the evaluation model according to the real-time operation data of the power grid to ensure the accuracy and timeliness of the evaluation results.

[0034] 4. Result Display and Analysis: The evaluation results are displayed to the power grid dispatching personnel and planning departments through a visualization platform. The visualization platform provides various display methods, including Geographic Information System (GIS) map display, dynamic curve display, report display, etc. Through the GIS map, the user can intuitively see the new energy generation distribution and power grid consumption capacity in different regions; the dynamic curve display can reflect the changing trends of new energy generation and grid load in real time; the report display provides detailed evaluation data and analysis results. The user can conduct in-depth analysis based on the displayed results and formulate reasonable power grid operation strategies and new energy development plans.

[0035] Embodiment Test Data 1. Small grid test data: In the small grid test of Embodiment 1, after a period of operation monitoring and evaluation, the following data was obtained: During the peak period of new energy generation, the new energy consumption capacity of the grid reached 80%, and the new energy generation could be effectively integrated into the grid. During the low load period, due to the low load demand of the grid, the new energy consumption capacity decreased to 50%, and some new energy generation needed to be stored through energy storage devices or curtailed. Through the analysis of the evaluation results, it was found that the line capacity of the grid and the configuration of energy storage devices had a great impact on the new energy consumption capacity.

[0036] 2. Large regional grid test data: In the large regional grid test of Embodiment 2, after a long time of operation and evaluation, the following data was obtained: Under normal operating conditions, the average new energy consumption capacity of this regional grid reached 70%. During the period of large-scale new energy generation, by optimizing the grid dispatching and operation mode, the new energy consumption capacity could be increased to 85%. Through the analysis of the evaluation results in different regions, it was found that due to the large grid load demand in economically developed regions, the new energy consumption capacity was relatively high; while in remote areas, due to the weak grid structure, the new energy consumption capacity was relatively low. Based on these test data, the grid planning department can strengthen the grid construction in remote areas targetedly to improve the new energy consumption capacity of the entire regional grid.

[0037] The present invention adopts advanced data processing algorithms and artificial intelligence evaluation models, which can quickly process a large amount of data, realize the rapid evaluation of the new energy consumption capacity of the grid, greatly improve the evaluation efficiency, meet the requirements of the real-time operation and planning of the grid, collect data through high-precision sensors, and perform strict preprocessing and in-depth analysis on the data. Combined with the optimized evaluation model, the accuracy and reliability of the evaluation results are effectively improved, providing strong support for grid planning and operation decision-making.

[0038] The evaluation device can adapt to different types of new energy generation equipment and complex grid structures. By real-time updating the evaluation model parameters, it can cope with the changes in the grid operation state, and has strong flexibility and adaptability. The result display module presents the evaluation results in an intuitive way, which is convenient for users to view and analyze. At the same time, it provides a data comparison function, which helps users deeply understand the changes in the new energy consumption capacity of the grid and provides a convenient user experience.

[0039] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A device for evaluating the new energy consumption capacity of a power grid, comprising a data acquisition module, a data processing module, an evaluation model module and a result display module, characterized in that: The data acquisition module is used to collect the operating data of the new energy power generation equipment and the power grid in real time; the data processing module pre-processes, mines and analyzes the data collected by the data acquisition module; the evaluation model module evaluates the new energy consumption capacity of the power grid based on the processed data based on artificial intelligence and machine learning algorithms; the result display module displays the evaluation results to the user in an intuitive way; The data acquisition module adopts a distributed sensor network, including an external data acquisition module and an internal data acquisition module, wherein the external data acquisition module includes a light detection module, a wind speed detection module and a water flow speed detection module, and the internal data acquisition module includes a power sensor, a voltage sensor, a current sensor, a load sensor and a frequency sensor. The data acquisition module uses 5G communication technology to realize real-time data transmission. The data preprocessing performed by the data processing module on the collected data includes data cleaning, filtering and interpolation operations. Data cleaning uses a data cleaning algorithm to remove noise, outliers and missing values ​​in the collected data, and uses interpolation and filtering techniques to repair and correct missing data and abnormal data to ensure the integrity and accuracy of the data. At the same time, the data is standardized to convert data of different types and magnitudes into a unified standard format to facilitate subsequent analysis and calculation.

2. A device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: The light detection module, wind speed detection module and water flow speed detection module are used to detect the new energy power generation environment, and the data processing module uses big data analysis technology to deeply mine and analyze the data collected by the data acquisition module.

3. A device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: The evaluation model module is trained using a deep learning algorithm, which includes a neural network algorithm, a convolutional neural network algorithm or a recurrent neural network algorithm. The evaluation model module can update the evaluation model parameters in real time according to changes in the operating state of the power grid.

4. A device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: The result display module displays the evaluation results to the user in the form of charts and reports. The result display module also provides a data comparison function for comparing evaluation results of different time periods and different regions.

5. The device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: The data processing module has a data storage function and stores the processed data in a local database.

6. A device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: The evaluation device is based on a comprehensive evaluation model of an improved particle swarm optimization algorithm and a deep belief network, and uses the IPSO algorithm to optimize the network parameters of the DBN to improve the convergence speed and accuracy of the model. Then, the extracted key feature parameters are used as input, and the DBN model is used for learning and training to achieve an accurate evaluation of the power grid's new energy absorption capacity. The model can fully consider the uncertainty of new energy power generation, the operating constraints of the power grid, and the dynamic change factors of the load, improve the comprehensiveness and accuracy of the evaluation, and can adapt to different types of new energy power generation equipment and complex power grid structures.

7. A device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: The data acquisition module adopts a distributed architecture to simultaneously collect data from multiple data sources. The data processing module adopts parallel computing technology and a big data analysis framework when processing large-scale power grid data.

8. The device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: When evaluating a large regional power grid, the evaluation model module is optimized according to the characteristics of the power grid and uses real-time update technology to adjust model parameters.

9. The device for evaluating the new energy consumption capacity of a power grid according to claim 1, characterized in that: The result display module has an interactive interface, which visualizes the evaluation results in an intuitive and easy-to-understand manner, such as through charts, maps, and animations to display the spatiotemporal distribution of the power grid's new energy absorption capacity, the evaluation results under different scenarios, and the changing trends of risk assessment indicators. Users can flexibly select different display methods and parameters through the interactive interface to obtain the required information conveniently and quickly, and provide decision-making recommendations for the planning, operation, and management of the power system based on the evaluation results.

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