Energy management system of multi-energy complementary smart energy based on source network load storage

Abstract

The invention belongs to the field of energy management systems, and particularly relates to an energy management system of multi-energy complementary smart energy based on source network load storage. Comprising a historical data acquisition module, a historical data processing module, an energy consumption prediction module, a data monitoring module, a data transmission module, a source network load storage module, a data extraction module, a calculation module, an energy complementation module, a real-time monitoring module, a feedback module, a recording module, a comparison module, an early warning module and an AI algorithm module. And the historical data acquisition module is connected with the historical data processing module and is used for acquiring historical energy consumption data, energy price data, economic indexes and policy data in the region and processing the acquired data through the historical data processing module. And management and reasonable utilization of energy are facilitated.

Description

Technical Field

[0001] This invention relates to the field of energy management system technology, and in particular to an energy management system based on a multi-energy complementary smart energy system with source-grid-load-storage. Background Technology

[0002] Energy is an important material foundation for national economic and social development. China's insufficient resources and energy shortage have become important factors restricting the sustainable development of the national economy. Due to the current extensive economic model, energy utilization efficiency is low and energy consumption is high, resulting in serious energy waste. The energy shortage situation has also put enormous pressure on my country's resource shortage and environmental governance.

[0003] Existing technologies cannot achieve multi-energy complementarity, which is not conducive to energy management and rational utilization. Therefore, we propose an energy management system based on multi-energy complementarity smart energy source with source-grid-load-storage structure to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies, such as the inability to perform multi-energy complementarity, which is detrimental to the management and rational utilization of energy. Therefore, this invention proposes an energy management system based on multi-energy complementary smart energy with source-grid-load-storage structure.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An energy management system based on a multi-energy complementary smart energy system encompassing source-grid-load-storage includes:

[0007] Historical data acquisition module, historical data processing module, energy consumption prediction module, data monitoring module, data transmission module, source-grid-load-storage module, data extraction module, calculation module, energy complementarity module, real-time monitoring module, feedback module, recording module, comparison module, early warning module, AI algorithm module;

[0008] The historical data acquisition module, connected to the historical data processing module, is used to collect historical energy consumption data, energy price data, economic indicators, and policy data within the region, and to process the collected data through the historical data processing module.

[0009] The energy consumption prediction module is connected to the historical data processing module and is used to predict the energy consumption of the region based on the processed data.

[0010] The data monitoring module is used to monitor energy production, energy consumption, and energy storage.

[0011] The data transmission module is used to transmit the data monitored by the data monitoring module;

[0012] The source-network-load-storage module is used to classify and store the data transmitted by the data transmission module.

[0013] The data extraction module is used to extract stored data;

[0014] The calculation module is used to calculate the difference between the data predicted by the energy consumption prediction module and the actual stored data;

[0015] The energy complementarity module is used to make up the difference with other energy sources when the data predicted by the energy consumption prediction module is greater than the actual stored data.

[0016] The real-time monitoring module is used to monitor energy usage after compensation.

[0017] The feedback module is used to provide feedback on the monitored data.

[0018] The recording module is used to record feedback data.

[0019] Preferably, the comparison module is used to compare the supplementary data with the actual amount of energy lacking in the region. When the supplementary data is lower than the actual amount of energy lacking, an early warning module is used to issue an early warning.

[0020] Preferably, the AI ​​algorithm module is used to provide algorithms to the computing module, and the AI ​​algorithms include linear regression, logistic regression, support vector machine, ensemble learning, and neural network.

[0021] Preferably, the energy consumption prediction module is connected to the calculation module, the data monitoring module is connected to the data transmission module, the data transmission module is connected to the source-grid-load-storage module, and the source-grid-load-storage module is connected to the data extraction module.

[0022] Preferably, the data extraction module is connected to the calculation module, the calculation module is connected to the energy complementarity module, the energy complementarity module is connected to the real-time monitoring module, the real-time monitoring module is connected to the feedback module, the feedback module is connected to the recording module, the energy complementarity module is connected to the comparison module, and the comparison module is connected to the early warning module.

[0023] Preferably, the source-grid-load-storage module includes an energy production data unit, which is connected to an energy transmission network unit, which is connected to an energy consumption data unit, and the energy consumption data unit is connected to an energy storage data unit.

[0024] Preferably, the historical data acquisition module includes a consumption data acquisition unit, which is connected to an energy price acquisition unit, which is connected to an economic indicator acquisition unit, and the economic indicator acquisition unit is connected to a policy factor acquisition unit.

[0025] Preferably, the energy consumption prediction module includes a data processing unit, which is connected to a model building unit, which is connected to a model training and optimization unit, and the model training and optimization unit is connected to the energy consumption prediction unit.

[0026] Preferably, the data processing unit is used for data preprocessing, including identifying and processing abnormal data, which is achieved through the K-means algorithm based on machine learning. The data samples are divided by calculating the Euclidean distance from the data points to the cluster centers, thereby improving the quality of the data and the accuracy of the prediction.

[0027] Preferably, the model building unit builds a prediction model based on the preprocessed data, and obtains the final energy consumption prediction model through model training and verification.

[0028] The beneficial effects of the energy management system based on multi-energy complementary smart energy source (source-grid-load-storage) described in this invention are as follows:

[0029] The system comprises the following modules: a historical data acquisition module, connected to the historical data processing module, for collecting historical energy consumption data, energy price data, economic indicators, and policy data within the region, and processing the collected data; an energy consumption prediction module, also connected to the historical data processing module, for predicting regional energy consumption based on the processed data; a data monitoring module for monitoring energy production, energy consumption, and energy storage; a data transmission module for transmitting data monitored by the data monitoring module; a source-grid-load-storage module for classifying and storing data transmitted by the data transmission module; a data extraction module for extracting stored data; a calculation module for calculating the difference between the data predicted by the energy consumption prediction module and the actual stored data; and an energy complementarity module for using other energy sources to compensate for the difference when the data predicted by the energy consumption prediction module exceeds the actual stored data.

[0030] This invention can predict regional energy consumption and combine it with energy storage capacity for complementary purposes, which is conducive to the management and rational use of energy. Attached Figure Description

[0031] Figure 1 This is a block diagram of an energy management system based on a multi-energy complementary smart energy source with source-grid-load-storage proposed in this invention.

[0032] Figure 2 This is a block diagram of the source-grid-load-storage module of an energy management system based on a multi-energy complementary smart energy system proposed in this invention.

[0033] Figure 3 This is a block diagram of the transmission channel selection module of an energy management system based on a multi-energy complementary smart energy source with source-grid-load-storage proposed in this invention.

[0034] Figure 4 This is a block diagram of the historical data acquisition module of an energy management system based on a multi-energy complementary smart energy source with source-grid-load-storage proposed in this invention;

[0035] Figure 5 This is a block diagram of the energy consumption prediction module of an energy management system based on a multi-energy complementary smart energy source with source-grid-load-storage proposed in this invention. Detailed Implementation

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0037] Example 1

[0038] Reference Figures 1-5 An energy management system based on a multi-energy complementary smart energy system of source-grid-load-storage includes:

[0039] Historical data acquisition module, historical data processing module, energy consumption prediction module, data monitoring module, data transmission module, source-grid-load-storage module, data extraction module, calculation module, energy complementarity module, real-time monitoring module, feedback module, recording module, comparison module, early warning module, AI algorithm module;

[0040] The historical data acquisition module, connected to the historical data processing module, is used to collect historical energy consumption data, energy price data, economic indicators, and policy data within the region, and to process the collected data through the historical data processing module.

[0041] The energy consumption prediction module is connected to the historical data processing module and is used to predict the energy consumption of the region based on the processed data.

[0042] The data monitoring module is used to monitor energy production, energy consumption, and energy storage.

[0043] The data transmission module is used to transmit the data monitored by the data monitoring module;

[0044] The source-network-load-storage module is used to classify and store the data transmitted by the data transmission module.

[0045] The data extraction module is used to extract stored data;

[0046] The calculation module is used to calculate the difference between the data predicted by the energy consumption prediction module and the actual stored data;

[0047] The energy complementarity module is used to make up the difference with other energy sources when the data predicted by the energy consumption prediction module is greater than the actual stored data.

[0048] The real-time monitoring module is used to monitor energy usage after compensation.

[0049] The feedback module is used to provide feedback on the monitored data.

[0050] The recording module is used to record feedback data.

[0051] In this embodiment, the comparison module is used to compare the supplementary data with the actual amount of energy missing in the region. When the supplementary data is lower than the actual amount of energy missing, the early warning module issues an early warning.

[0052] In this embodiment, the AI ​​algorithm module is used to provide algorithms to the computing module. The AI ​​algorithms include linear regression, logistic regression, support vector machine, ensemble learning, and neural network.

[0053] In this embodiment, the energy consumption prediction module is connected to the calculation module, the data monitoring module is connected to the data transmission module, the data transmission module is connected to the source-grid-load-storage module, and the source-grid-load-storage module is connected to the data extraction module.

[0054] In this embodiment, the data extraction module is connected to the calculation module, the calculation module is connected to the energy complementarity module, the energy complementarity module is connected to the real-time monitoring module, the real-time monitoring module is connected to the feedback module, the feedback module is connected to the recording module, the energy complementarity module is connected to the comparison module, and the comparison module is connected to the early warning module.

[0055] In this embodiment, the source-grid-load-storage module includes an energy production data unit, which is connected to an energy transmission network unit. The energy transmission network unit is connected to an energy consumption data unit, and the energy consumption data unit is connected to an energy storage data unit.

[0056] In this embodiment, the historical data acquisition module includes a consumption data acquisition unit, which is connected to an energy price acquisition unit, which is connected to an economic indicator acquisition unit, and the economic indicator acquisition unit is connected to a policy factor acquisition unit.

[0057] In this embodiment, the energy consumption prediction module includes a data processing unit, which is connected to a model building unit, which is connected to a model training and optimization unit, and the model training and optimization unit is connected to the energy consumption prediction unit.

[0058] In this embodiment, the data processing unit is used to preprocess the data, including identifying and processing abnormal data. This is achieved through the K-means algorithm based on machine learning, which divides the data samples by calculating the Euclidean distance from the data points to the cluster centers, thereby improving the quality of the data and the accuracy of the prediction.

[0059] In this embodiment, the model building unit builds a prediction model based on the preprocessed data, and obtains the final energy consumption prediction model through model training and verification.

[0060] Example 2

[0061] An energy management system based on a multi-energy complementary smart energy system encompassing source-grid-load-storage includes:

[0062] Historical data acquisition module, historical data processing module, energy consumption prediction module, data monitoring module, data transmission module, source-grid-load-storage module, data extraction module, calculation module, energy complementarity module, real-time monitoring module, feedback module, recording module, comparison module, early warning module, AI algorithm module, and transmission channel selection module;

[0063] The historical data acquisition module, connected to the historical data processing module, is used to collect historical energy consumption data, energy price data, economic indicators, and policy data within the region, and to process the collected data through the historical data processing module.

[0064] The energy consumption prediction module is connected to the historical data processing module and is used to predict the energy consumption of the region based on the processed data.

[0065] The data monitoring module is used to monitor energy production, energy consumption, and energy storage.

[0066] The data transmission module is used to transmit the data monitored by the data monitoring module;

[0067] The source-network-load-storage module is used to classify and store the data transmitted by the data transmission module.

[0068] The data extraction module is used to extract stored data;

[0069] The calculation module is used to calculate the difference between the data predicted by the energy consumption prediction module and the actual stored data;

[0070] The energy complementarity module is used to make up the difference with other energy sources when the data predicted by the energy consumption prediction module is greater than the actual stored data.

[0071] The real-time monitoring module is used to monitor energy usage after compensation.

[0072] The feedback module is used to provide feedback on the monitored data.

[0073] The recording module is used to record feedback data;

[0074] The transmission channel selection module is used to select the transmission channel of the data transmission module. The transmission channel selection module includes a wired transmission channel unit, a wireless transmission channel unit, a transmission channel selection unit, and a transmission channel confirmation unit.

[0075] In this embodiment, the comparison module is used to compare the supplementary data with the actual amount of energy missing in the region. When the supplementary data is lower than the actual amount of energy missing, the early warning module issues an early warning.

[0076] In this embodiment, the AI ​​algorithm module is used to provide algorithms to the computing module. The AI ​​algorithms include linear regression, logistic regression, support vector machine, ensemble learning, and neural network.

[0077] In this embodiment, the energy consumption prediction module is connected to the calculation module, the data monitoring module is connected to the data transmission module, the data transmission module is connected to the source-grid-load-storage module, and the source-grid-load-storage module is connected to the data extraction module.

[0078] In this embodiment, the data extraction module is connected to the calculation module, the calculation module is connected to the energy complementarity module, the energy complementarity module is connected to the real-time monitoring module, the real-time monitoring module is connected to the feedback module, the feedback module is connected to the recording module, the energy complementarity module is connected to the comparison module, and the comparison module is connected to the early warning module.

[0079] In this embodiment, the source-grid-load-storage module includes an energy production data unit, which is connected to an energy transmission network unit. The energy transmission network unit is connected to an energy consumption data unit, and the energy consumption data unit is connected to an energy storage data unit.

[0080] In this embodiment, the historical data acquisition module includes a consumption data acquisition unit, which is connected to an energy price acquisition unit, which is connected to an economic indicator acquisition unit, and the economic indicator acquisition unit is connected to a policy factor acquisition unit.

[0081] In this embodiment, the energy consumption prediction module includes a data processing unit, which is connected to a model building unit, which is connected to a model training and optimization unit, and the model training and optimization unit is connected to the energy consumption prediction unit.

[0082] In this embodiment, the data processing unit is used to preprocess the data, including identifying and processing abnormal data. This is achieved through the K-means algorithm based on machine learning, which divides the data samples by calculating the Euclidean distance from the data points to the cluster centers, thereby improving the quality of the data and the accuracy of the prediction.

[0083] In this embodiment, the model building unit builds a prediction model based on the preprocessed data, and obtains the final energy consumption prediction model through model training and verification.

[0084] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An energy management system based on a multi-energy complementary smart energy system with source-grid-load-storage, characterized in that, include: Historical data acquisition module, historical data processing module, energy consumption prediction module, data monitoring module, data transmission module, source-grid-load-storage module, data extraction module, calculation module, energy complementarity module, real-time monitoring module, feedback module, recording module, comparison module, early warning module, AI algorithm module; The historical data acquisition module, connected to the historical data processing module, is used to collect historical energy consumption data, energy price data, economic indicators, and policy data within the region, and to process the collected data through the historical data processing module. The energy consumption prediction module is connected to the historical data processing module and is used to predict the energy consumption of the region based on the processed data. The data monitoring module is used to monitor energy production, energy consumption, and energy storage. The data transmission module is used to transmit the data monitored by the data monitoring module; The source-network-load-storage module is used to classify and store the data transmitted by the data transmission module. The data extraction module is used to extract stored data; The calculation module is used to calculate the difference between the data predicted by the energy consumption prediction module and the actual stored data; The energy complementarity module is used to make up the difference with other energy sources when the data predicted by the energy consumption prediction module is greater than the actual stored data. The real-time monitoring module is used to monitor energy usage after compensation. The feedback module is used to provide feedback on the monitored data. The recording module is used to record feedback data.

2. The energy management system for a multi-energy complementary smart energy system based on source-grid-load-storage as described in claim 1, characterized in that, The comparison module is used to compare the supplementary data with the actual amount of energy lacking in the region. When the supplementary data is lower than the actual amount of energy lacking, an early warning module will issue an early warning.

3. The energy management system for a multi-energy complementary smart energy system based on source-grid-load-storage as described in claim 2, characterized in that, The AI ​​algorithm module is used to provide algorithms to the computing module. The AI ​​algorithms include linear regression, logistic regression, support vector machine, ensemble learning, and neural network.

4. The energy management system for a multi-energy complementary smart energy system based on source-grid-load-storage as described in claim 3, characterized in that, The energy consumption prediction module is connected to the calculation module, the data monitoring module is connected to the data transmission module, the data transmission module is connected to the source-grid-load-storage module, and the source-grid-load-storage module is connected to the data extraction module.

5. The energy management system for a multi-energy complementary smart energy system based on source-grid-load-storage as described in claim 4, characterized in that, The data extraction module is connected to the calculation module, the calculation module is connected to the energy complementarity module, the energy complementarity module is connected to the real-time monitoring module, the real-time monitoring module is connected to the feedback module, the feedback module is connected to the recording module, the energy complementarity module is connected to the comparison module, and the comparison module is connected to the early warning module.

6. The energy management system for a multi-energy complementary smart energy system based on source-grid-load-storage as described in claim 5, characterized in that, The source-grid-load-storage module includes an energy production data unit, which is connected to an energy transmission network unit. The energy transmission network unit is connected to an energy consumption data unit, and the energy consumption data unit is connected to an energy storage data unit.

7. The energy management system for a multi-energy complementary smart energy system based on source-grid-load-storage as described in claim 6, characterized in that, The historical data acquisition module includes a consumption data acquisition unit, which is connected to an energy price acquisition unit, which is connected to an economic indicator acquisition unit, and the economic indicator acquisition unit is connected to a policy factor acquisition unit.

8. The energy management system for a multi-energy complementary smart energy system based on source-grid-load-storage as described in claim 7, characterized in that, The energy consumption prediction module includes a data processing unit, which is connected to a model building unit, which is connected to a model training and optimization unit, and the model training and optimization unit is connected to the energy consumption prediction unit.

9. An energy management system based on a multi-energy complementary smart energy source according to claim 8, characterized in that, The data processing unit is used for data preprocessing, including identifying and processing abnormal data. This is achieved through the K-means algorithm based on machine learning, which divides data samples by calculating the Euclidean distance from data points to cluster centers, thereby improving data quality and prediction accuracy.

10. An energy management system based on a multi-energy complementary smart energy source according to claim 9, characterized in that, The model building unit establishes a prediction model based on the preprocessed data, and obtains the final energy consumption prediction model through model training and verification.

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