Comprehensive energy system control method based on carbon emission index optimization calculation

Abstract

The invention relates to an integrated energy system control method based on carbon emission index optimization calculation, and the method comprises the steps: carrying out the prediction of the power demand of a time period t through an AI model based on the historical power data of a plurality of time periods before the time period t of a user in a region; an optimization function of the overall carbon emission performance is constructed, the optimization target is that the carbon emission cost is minimum, and the carbon emission index in the region is determined according to the optimal solution of the optimization function; and judging whether the optimal solution of the optimization function is greater than zero or not, and controlling energy production and supply of the integrated energy system according to positive and negative of the optimal solution. According to the method, the carbon emission index in the region is determined according to the optimal solution of the optimal mathematical model, energy production and supply of the comprehensive energy system are controlled, and intelligent and refined multi-energy supply and multi-system emission reduction control are achieved.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence, and specifically relates to a comprehensive energy system control method based on carbon emission index optimization calculation. Background Art

[0002] Integrated energy systems play a vital role in the modern energy sector. This system achieves optimal allocation and efficient use of energy by integrating multiple energy generation and consumption technologies. In the context of the global pursuit of a low-carbon economy, integrated energy systems have shown great potential, especially in reducing carbon emissions and improving energy efficiency.

[0003] The core value of the integrated energy system lies in its ability to convert and coordinate between different energy forms, such as electricity, heat, gas, etc. These energy sources can come from a variety of different power generation methods, including gas power generation, oil power generation, nuclear power generation, renewable energy power generation, etc. Each energy technology has its unique advantages and limitations. Through system integration, the efficiency and cost-effectiveness of various energy sources can be optimized while reducing the overall environmental impact.

[0004] In terms of gas-fired power generation and oil-fired power generation, although they are both fossil fuel power generation methods, their carbon emission potential and environmental impact are very different. Gas-fired power generation usually uses natural gas as fuel, which has relatively low carbon emissions, cleaner combustion and higher efficiency. In contrast, oil-fired power generation produces more carbon emissions due to the combustion of diesel or heavy oil. Therefore, in an integrated energy system, how to choose the appropriate power generation method based on actual energy demand and environmental policies is the key to achieving sustainable energy development. However, although under certain specific conditions, oil-fired power generation may be lower in cost than gas-fired power generation, the overall trend is that the cost-effectiveness and environmental benefits of natural gas power generation are gradually exceeding oil-fired power generation, especially when considering long-term operations and environmental impacts. Therefore, there is a certain conflict between environmental protection and economy in the field of power generation.

[0005] In addition, the integrated energy system is not limited to a single energy production, but also includes energy storage and transmission technology. For example, battery storage technology and thermal energy storage technology can store excess energy during low energy demand periods and release it during peak hours to balance supply and demand. At the same time, the application of smart grid technology can further optimize the distribution and use of energy and improve the response speed and reliability of the system.

[0006] Carbon emission control technology is also an integral part of the integrated energy system. By adopting advanced emission control technologies, such as Carbon Capture and Storage (CCS) technology, carbon emissions in the power generation process can be significantly reduced. Combined with the flexible scheduling and intelligent management of the integrated energy system, the dual goals of environmental protection and energy security can be effectively achieved.

[0007] In general, the integrated energy system can not only improve energy efficiency by integrating various energy forms and technologies, but also effectively reduce carbon emissions and combat global climate change. The research and development and application of this system are of great significance to promoting the sustainable development of the energy industry. With the advancement of technology and policy support, the integrated energy system will play a more core role in the global energy field in the future.

[0008] In the application of existing technologies, although the integrated energy system has shown its potential for energy conversion and carbon emission reduction in theory and some practices, there is indeed a lack of specific solutions for fully realizing and expanding these systems to effectively reduce regional carbon emissions. Based on this, the present invention proposes a control method for an integrated energy system based on the optimization calculation of the carbon emission index, aiming to effectively solve this problem. Summary of the invention

[0009] In view of the defects existing in the prior art, the purpose of the present invention is to provide a comprehensive energy system control method based on the optimization calculation of the carbon emission index, use the AI ​​model to predict the electricity demand of all users in the region, characterize the carbon emissions of the comprehensive energy system, and then determine the gas-fired power generation, oil-fired power generation and heating supply of the comprehensive energy system based on the optimization calculation of the carbon emission index. While supplying multiple energy sources, the carbon emission value of the entire region is controlled, thereby realizing intelligent and refined multi-energy supply and multi-system emission reduction control.

[0010] To achieve the above objectives, the technical solution adopted by the present invention is: a comprehensive energy system control method based on the optimization calculation of the carbon emission index, a comprehensive energy system power generation unit, an energy storage unit, a carbon capture and storage module, a carbon emission monitoring module, a thermal energy system, a heating and cooling system, and a smart energy management system. The smart energy management system is used for the operation control of the comprehensive energy system, and the coordinated operation of the power generation unit, the energy storage module, the thermal energy system, and the heating and cooling system. The carbon capture and storage module is used to capture and store the carbon dioxide generated in the comprehensive energy system.

[0011] The integrated energy system control method comprises the following steps: S1. Divide each day into 24 time periods in units of one hour; S2. Based on the historical electricity consumption data of users in the region in previous time periods, use the AI ​​model to estimate the electricity demand for the next time period Make predictions; S3. Construct an optimization function for overall carbon emission performance. The optimization goal is to minimize the carbon emission cost. Determine the carbon emission index in the region based on the optimal solution of the optimization function. ; S4, determining whether the optimal solution of the optimization function is greater than zero, and controlling the energy production and supply of the integrated energy system according to the positive or negative value of the optimal solution; like , then during this period of time, the power generation of the gas power generation module of the integrated energy system will be reduced, and the power generation of the oil power generation module will be increased to reduce the system cost. like , then during this time period, the power generation of the gas-fired power generation module of the integrated energy system will be increased, and the power generation of the oil-fired power generation module will be reduced.

[0012] Furthermore, the smart energy management system formulates operation plans for power generation units, energy storage units, thermal energy systems, and heating and cooling systems, determines the dispatchable range of distributed power output and conventional loads, demand response loads, unit dispatch costs and expected power consumption plans, the capacity of energy storage units and controllable distributed power sources, dispatch power limits and unit dispatch costs, charge state limits of energy storage units, and remaining capacity at the initial dispatch time.

[0013] Preferably, the power generation unit includes a coal-fired power generation module, a gas-fired power generation module, an oil-fired power generation module, a solar photovoltaic power generation module, a wind power generation module, a hydropower generation module, a nuclear power generation module and a biomass power generation module.

[0014] Preferably, each coal-fired power generation module, gas-fired power generation module, and oil-fired power generation module is equipped with a carbon capture and storage module to capture and store carbon dioxide generated during coal-fired power generation, gas-fired power generation, and oil-fired power generation.

[0015] Preferably, in step S2, the AI ​​model is a long short-term memory network model LSTM.

[0016] Preferably, in step S3, the optimization function is:

[0017] Optimal solution:

[0018] In the formula is the regional carbon emission index, a , b are carbon emission cost parameters, c is the excess emission factor; It is the cumulative excess carbon emissions in all previous time periods.

[0019] Furthermore, the step S4 specifically includes: i)If and , then during this period of time, the carbon capture and storage module is shut down, the power generation of the gas power generation module of the integrated energy system is reduced, and the power generation of the oil power generation module is increased; ii) If and , the carbon capture and storage module will be shut down during this period, and electricity will be generated according to the power generation agreed in the market on the previous day; iii) If and , then during this time period, the carbon capture and storage module is turned on, the power generation of the gas power generation module of the integrated energy system is increased, and the power generation of the oil power generation module is reduced; iv) If and , then power generation will be carried out during this period according to the power generation amount agreed upon in the market on the previous day.

[0020] The advantages and beneficial effects of the present invention are: 1) The present invention constructs an optimization mathematical model for the overall carbon emission performance of the integrated energy system, determines the carbon emission index in the region according to the optimal solution of the optimization mathematical model, controls the energy production and supply of the integrated energy system, and minimizes the carbon emission cost.

[0021] 2) The present invention equips coal-fired, gas-fired and oil-fired power generation modules with carbon capture and storage modules to capture and store carbon dioxide generated in coal-fired power generation, gas-fired and oil-fired power generation, thereby effectively reducing carbon emissions in the power generation process of the integrated energy system and achieving the dual goals of environmental protection and energy security.

[0022] 3) The present invention uses the LSTM model to accurately predict the electricity demand of users in the region in the next time period, and combines the electricity demand prediction results with the regional carbon emission index determined by the optimization mathematical model, and uses carbon capture and storage technology to accurately control the carbon emissions of the integrated energy system in the next time period, effectively realizing emission reduction control of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] Figure 1 Schematic diagram of an integrated energy system using a smart energy management system.

[0025] Figure 2 Schematic diagram of a comprehensive energy system control method according to an embodiment.

[0026] Figure 3 It is a line graph of the regional carbon emission index for a certain day obtained by solving the optimization function in the embodiment. DETAILED DESCRIPTION

[0027] The integrated energy system of the embodiment is as follows Figure 1 As shown, the system includes a power generation unit, an energy storage unit, a carbon capture and storage module, a carbon emission monitoring module, a thermal energy system, a heating and cooling system, and a smart energy management system; the power generation modules include a solar photovoltaic power generation module, a wind power generation module, a hydropower generation module, a coal-fired power generation module, a gas-fired power generation module, an oil-fired power generation module, a nuclear power generation module, and a biomass power generation module.

[0028] like Figure 2 As shown, the integrated energy system control method of the embodiment includes the following steps: S1. Divide each day into 24 time periods in units of one hour; S2. Based on the historical electricity consumption data of users in the region for multiple time periods before time period t, the power demand for time period t is analyzed using the long short-term memory network model LSTM. Make predictions; S3. Construct an optimization function for overall carbon emission performance. The optimization goal is to minimize the carbon emission cost. Determine the carbon emission index in the region based on the optimal solution of the optimization function. ; The optimization function is:

[0029] Optimal solution:

[0030] In the formula is the regional carbon emission index, a , b are carbon emission cost parameters, c is the excess emission factor; It is the cumulative excess carbon emissions in all previous time periods.

[0031] The accumulated excess carbon emissions for all previous time periods can be calculated by the carbon emission quota preset for each hour in the system. , the carbon emission value of the current time period t obtained by real-time monitoring , and the cumulative excess carbon emissions in the previous time period t-1 , calculated based on the following formula:

[0032] S4. Determine whether the optimal solution of the optimization function is greater than zero, based on the positive or negative value of the optimal solution and the threshold Controlling energy production and supply in integrated energy systems, including: i)If and , then during this period of time, the carbon capture and storage module is shut down, the power generation of the gas power generation module of the integrated energy system is reduced, and the power generation of the oil power generation module is increased; Indicates the set operation threshold. ; ii) If and , the carbon capture and storage module will be shut down during this period, and electricity will be generated according to the power generation agreed in the market on the previous day; iii) If and , then during this time period, the carbon capture and storage module is turned on, the power generation of the gas power generation module of the integrated energy system is increased, and the power generation of the oil power generation module is reduced; iv) If and , the carbon capture and storage module will be turned on during this time period and electricity will be generated according to the power generation agreed upon in the market on the previous day.

[0033] Using the long short-term memory network (LSTM) to predict electricity demand based on historical data is an effective method for capturing long-term dependencies in time series data. Specifically, the present invention uses LSTM to predict the electricity demand for the next time period based on the historical electricity consumption data of users in the region in previous multiple time periods through the following steps: 1) Data collection and preprocessing: First, collect historical electricity consumption data, which usually includes time series records of electricity consumption. The steps of data preprocessing include: Data cleaning: remove or fill missing values ​​and handle outliers.

[0034] Feature selection: Select the features that affect electricity consumption according to demand, such as date, time, temperature, holidays, etc.

[0035] Normalization: In order to improve the training efficiency and stability of the LSTM model, the data is normalized or standardized.

[0036] Time series conversion: Convert time series data into a format that can be processed by the LSTM network, and construct a series of inputs and outputs through window sliding. For example, the data of the previous N hours and the electricity consumption at the next time point are used as the input and output of the LSTM network respectively.

[0037] 2) LSTM model design: Design an LSTM network for time series prediction. The LSTM model includes: Input layer: determines the number and shape of input features.

[0038] LSTM layer: Add one or more LSTM layers. The number of units in each layer, i.e., the number of neurons and the number of layers, need to be adjusted according to the complexity of the problem and the amount of data.

[0039] Output layer: Design the output layer according to the requirements of the prediction task. For continuous power consumption prediction, a single neuron output layer can be used with a linear activation function.

[0040] Model configuration: Choose an appropriate loss function such as mean squared error (MSE) and optimizer such as Adam.

[0041] 3) Model training: Use the preprocessed data to train the LSTM model: Batch training: Divide the data into multiple small batches and use the back-propagation algorithm to iteratively update the network weights.

[0042] Validation: Use a portion of the data as a validation set during training to monitor the performance of the model on unseen data to prevent overfitting.

[0043] Model tuning: Adjust model parameters such as learning rate, batch size, number of LSTM layers and units, etc. based on the performance on the validation set.

[0044] 4) Model prediction: After the model training is completed, the test set is used to evaluate the model performance. Common evaluation indicators include mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). When the evaluation result shows that the error is within the set threshold, LSTM can be used to predict the electricity demand for the next time period based on the historical electricity consumption data of users in the area in previous multiple time periods.

[0045] At the same time, the following technical means are used in the embodiments to achieve real-time monitoring of carbon emissions: installing sensors, installing carbon emission sensors in power plants, factories or other emission sources. Sensors can continuously monitor and record CO2 emissions and other greenhouse gas emissions. These sensors can usually accurately measure the carbon content in the flue gas and calculate the corresponding carbon emissions; configuring data acquisition systems: data acquisition systems such as SCADA systems connected to sensors collect and transmit carbon emission data in real time. These systems are highly automated and can process large amounts of data for further analysis; remote monitoring and control: through the remote monitoring system, the operator can monitor and obtain the carbon emissions of each module in the integrated energy system even when they are not on site. These systems are equipped with a network interface to receive and send the carbon emission values ​​of the current time period t in real time, thereby optimizing operations to reduce carbon emissions.

[0046] In the embodiment, the regional carbon emission index of 24 time periods of a certain day obtained by solving the optimization function is as follows: Figure 3 shown.

[0047] Through the comprehensive energy system control method based on the optimization calculation of the carbon emission index proposed in the present invention, based on the coordinated cooperation of multiple steps in the method, it is possible to meet the system's power demand while taking into account the system's carbon emission cost and operation and maintenance cost; through the coordination of steps S1-S4, user needs and the system's carbon emission control and cost control needs can be coordinated, and through this intelligent and precise control, effective multi-energy comprehensive function control can be achieved.

Claims

1. A comprehensive energy system control method based on carbon emission index optimization calculation, characterized in that: The integrated energy system includes power generation units, energy storage units, carbon capture and storage modules, carbon emission monitoring modules, thermal energy systems, heating and cooling systems, and smart energy management systems; The power generation unit includes a coal-fired power generation module, a gas-fired power generation module and an oil-fired power generation module; The smart energy management system is used for the operation control of the integrated energy system, as well as the coordinated operation of the power generation unit, energy storage module, thermal energy system, heating and cooling system; The carbon capture and storage module is used to capture and store carbon dioxide generated in the integrated energy system; The integrated energy system control method comprises the following steps: S1. Divide each day into 24 time periods in units of one hour; S2: Based on the historical electricity consumption data of users in the region for multiple time periods before time period t, use the AI ​​model to estimate the electricity demand for time period t Make predictions; S3. Construct an optimization function for overall carbon emission performance. The optimization goal is to minimize the carbon emission cost. Determine the carbon emission index in the region based on the optimal solution of the optimization function. ; S4, determining whether the optimal solution of the optimization function is greater than zero, and controlling the energy production and supply of the integrated energy system according to the positive or negative value of the optimal solution; like , then during this period of time, the power generation of the gas power generation module of the integrated energy system will be reduced, and the power generation of the oil power generation module will be increased to reduce the system cost. like , then during this time period, the power generation of the gas-fired power generation module of the integrated energy system will be increased, and the power generation of the oil-fired power generation module will be reduced.

2. The integrated energy system control method based on carbon emission index optimization calculation according to claim 1 is characterized in that: The smart energy management system formulates operation plans for power generation units, energy storage units, thermal energy systems, and heating and cooling systems, determines the dispatchable range of distributed power output and conventional loads, demand response loads, unit dispatch costs and expected power consumption plans, the capacity of energy storage units and controllable distributed power sources, dispatch power limits and unit dispatch costs, charge state limits of energy storage units, and remaining capacity at the initial dispatch time.

3. The integrated energy system control method based on carbon emission index optimization calculation according to claim 2 is characterized in that: The power generation unit also includes a solar photovoltaic power generation module, a wind power generation module, a hydropower generation module, a nuclear power generation module and a biomass power generation module.

4. The integrated energy system control method based on carbon emission index optimization calculation according to claim 1, 2 or 3, characterized in that: In step S2, the AI ​​model is a long short-term memory network model LSTM.

5. The integrated energy system control method based on carbon emission index optimization calculation according to claim 4 is characterized in that: In step S3, the optimization function is: ； Optimal solution: ； In the formula is the regional carbon emission index, a , b are carbon emission cost parameters, c is the excess emission factor; is the cumulative excess carbon emissions in all time periods before time period t.

6. The integrated energy system control method based on carbon emission index optimization calculation according to claim 5 is characterized in that: Cumulative excess carbon emissions before time period t The calculation formula is: ； In the formula represents the excess amount of accumulated carbon emissions before time period t-1, represents the carbon emission value of time period t obtained by real-time monitoring, Represents the carbon emission quota in time period t.

7. The integrated energy system control method based on carbon emission index optimization calculation according to claim 5 or 6 is characterized in that: The step S4 specifically includes: i)If and , then during this period of time, the carbon capture and storage module is shut down, the power generation of the gas power generation module of the integrated energy system is reduced, and the power generation of the oil power generation module is increased; Indicates the set operation threshold. ; ii) If and , the carbon capture and storage module will be shut down during this period, and electricity will be generated according to the power generation agreed in the market on the previous day; iii) If and , then during this time period, the carbon capture and storage module is turned on, the power generation of the gas power generation module of the integrated energy system is increased, and the power generation of the oil power generation module is reduced; iv) If and , the carbon capture and storage module will be turned on during this time period and electricity will be generated according to the power generation agreed upon in the market on the previous day.

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