Coupling optimization method and device for combined cooling heating and power and renewable energy in comprehensive smart energy system, and storage medium

Abstract

The invention provides a coupling optimization method and device for combined cooling heating and power and renewable energy in a comprehensive smart energy system and a storage medium, relates to the field of energy and power engineering, and solves the technical problem that the combined cooling heating and power and the renewable energy are difficult to carry out adaptive constraint and output optimization. The method comprises the steps of obtaining target data of a plurality of time period regions in the future, constructing an energy power generation prediction model, and obtaining the power generation amount of renewable energy in a plurality of time periods in the future based on the target data and the energy power generation prediction model; demand data of combined cooling, heating and power supply in a plurality of time periods in the future are obtained, and relation factors between the generating capacity of the gas internal combustion engine and the heat production value and the refrigeration value of the gas internal combustion engine are determined; determining the output value of the gas internal combustion engine through the relation factor, the generating capacity of the renewable energy and the required electric quantity, and regulating the operation of the gas internal combustion engine based on the output value; according to the application, the output of the gas internal combustion engine can be preferably minimized, so that the natural gas consumption and the carbon emission are reduced to the maximum extent.

Description

Technical Field

[0001] This application relates to the field of energy and power engineering, and in particular to a method, device and storage medium for the coupling optimization of combined cooling, heating and power and renewable energy in an integrated smart energy system. Background Technology

[0002] An integrated smart energy system is an energy management system that integrates multiple energy forms and energy utilization technologies to achieve efficient energy production, transmission, storage, and use. Combined cooling, heating, and power (CCHP) is an energy cascade utilization system that uses natural gas as the main fuel, generates electricity through equipment such as gas turbines, internal combustion engines, or micro-gas internal combustion engines, and utilizes the waste heat generated during the power generation process for heating or cooling.

[0003] Currently, in most integrated smart energy systems, the methods for combining cooling, heating, and power (CCHP) and renewable energy sources make it difficult to adaptively constrain and optimize the output of CCHP and renewable energy sources. This results in an inability to effectively reduce the fuel consumption of internal combustion engines and increases the operating cost of integrated smart energy systems.

[0004] Therefore, the present invention provides a method, device and storage medium for optimizing the coupling of combined cooling, heating and power (CCHP) and renewable energy in an integrated smart energy system to solve the above-mentioned technical problems. Summary of the Invention

[0005] This application provides a method, device, and storage medium for the coupling optimization of combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system, which solves the technical problem that existing technologies in integrated smart energy systems struggle to adaptively constrain and optimize the output of CCHP and renewable energy.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for optimizing the coupling of combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system is provided, including: The target data for several future time periods is obtained, and an energy generation prediction model is constructed. Based on the target data and the energy generation prediction model, the power generation of renewable energy in the future time periods is obtained. The target data includes solar radiation intensity, temperature, wind speed, and wind direction. To obtain demand data for combined cooling, heating and power (CCHP) over several future time periods, and to determine the relationship factors between the power generation of gas-fired internal combustion engines and their heat and cooling output values; the demand data includes demand for electricity, heat, and cooling. The output value of the gas internal combustion engine is determined by relational factors, the power generation of renewable energy, and the power demand, and the operation of the gas internal combustion engine is adjusted based on the output value.

[0007] In conjunction with the first aspect mentioned above, one possible implementation involves acquiring target data for several future time periods, including: The next day is divided into several time periods by manual planning, and the solar radiation intensity, temperature, wind speed and wind direction of the area in the next few time periods are obtained based on the weather forecast platform; the size of the area is selected manually, and it can be the area managed by the integrated smart energy system.

[0008] In conjunction with the first aspect mentioned above, one possible implementation involves constructing an energy generation prediction model, including: Extract solar radiation intensity, temperature, wind speed, and wind direction for several time periods from historical power generation data, as well as the actual renewable energy power generation from solar and wind power for those time periods; The solar radiation intensity, temperature, wind speed, wind direction, and renewable energy power generation over several time periods are integrated into several sets of training and validation data according to the time period number. The training data is used to train the artificial intelligence model, and the validation data is used to validate the trained artificial intelligence model. The artificial intelligence model is then adjusted based on the validation results. The final result is an energy power generation prediction model with solar radiation intensity, temperature, wind speed, and wind direction over a time period as input and renewable energy power generation over that time period as output. The artificial intelligence model is a nonlinear regression model based on a neural network, implemented using a BP neural network structure and / or an RBF neural network structure.

[0009] In conjunction with the first aspect mentioned above, one possible implementation involves obtaining the renewable energy generation capacity for several future time periods based on target data and an energy generation forecasting model, including: Solar radiation intensity, temperature, wind speed, and wind direction are extracted sequentially for several future time periods. Based on these time periods, the solar radiation intensity, temperature, wind speed, and wind direction are input into the energy power generation prediction model to obtain the power generation of renewable energy for the corresponding time periods.

[0010] In conjunction with the first aspect mentioned above, one possible implementation involves obtaining demand data for combined cooling, heating, and power (CCHP) over several future time periods, including: The demand database extracts the electricity, heat, and cooling demand of the integrated smart energy system for various time periods in the future. The demand database includes the basic electricity, heat, and cooling demand required to meet the integrated smart energy system, as well as the additional electricity, heat, and cooling demand caused by various pre-set activities within the integrated smart energy system. The basic and additional electricity demands are added together to obtain the electricity demand, the heat demand is added together to obtain the heat value, and the cooling demand is added together to obtain the cooling demand.

[0011] In conjunction with the first aspect above, in one possible implementation, the factors determining the relationship between the power generation of a gas-fired internal combustion engine and the heat and cooling values ​​of the engine include: Extract the heat and cooling values ​​generated per unit of electricity produced by a gas-fired internal combustion engine within the past n days from the historical monitoring database; where n is manually set and can be 20. The heat production values ​​are integrated into data group one, and the cooling values ​​are integrated into data group two. The variance of data group one is obtained. When the variance is not greater than the heat production judgment variance, the average value of the heat production values ​​in data group one is calculated to obtain the relationship factor between the power generation of the gas internal combustion engine and the heat production value of the gas internal combustion engine. When the variance is greater than the heat production judgment variance, the heat production value in data group one with the largest difference from the average value of the heat production value is removed. The variance of the remaining heat production values ​​in data group one is recalculated and the variance judgment is performed again until the variance is not greater than the heat production judgment variance. The average value of the remaining heat production values ​​in data group one is then calculated to obtain the relationship factor between the power generation of the gas internal combustion engine and the heat production value of the gas internal combustion engine. Obtain the variance of the second data set. When the variance is not greater than the refrigeration judgment variance, calculate the average value of the refrigeration values ​​in the second data set to obtain the relationship factor between the power generation of the gas internal combustion engine and the refrigeration value of the gas internal combustion engine. When the variance is greater than the refrigeration judgment variance, remove the refrigeration value in the second data set that differs the most from the average refrigeration value. Recalculate the variance of the remaining refrigeration values ​​in the second data set and re-judge the variance until the variance is not greater than the refrigeration judgment variance. Calculate the average value of the remaining refrigeration values ​​in the second data set to obtain the relationship factor between the power generation of the gas internal combustion engine and the refrigeration value of the gas internal combustion engine. The heat generation judgment variance and the refrigeration judgment variance are both obtained through empirical settings.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the output value of a gas-fired internal combustion engine is determined by relational factors, renewable energy generation, and electricity demand, including: Extract the electricity demand for the next time period Required heat and required cooling capacity And the relationship between the power generation of a gas-fired internal combustion engine and the heat output of the gas-fired internal combustion engine. The relationship between the power generation of a gas-fired internal combustion engine and the cooling capacity of the engine. ; The required electricity is calculated using formula (1). Required heat and required cooling capacity Apply constraints; The calculation formula (1) is: ; In the formula, For renewable energy generation, This refers to the electrical output of a gas-fired internal combustion engine, which is required to meet the system's heating, cooling, and electrical demands. This output is accompanied by a fixed proportion of heat generation. and cooling capacity ,Right now , This step is to find the minimum ; for The proportion of direct power supply, and The value range is [0,1]; for The proportion of heat converted into electricity through electric heating equipment, such as electric boilers, and The value range is [0,1]; for The proportion of cooling capacity converted into cooling capacity through electric refrigeration equipment, such as electric chillers, and The value range is [0,1]; By transforming the constraints of calculation formula (1), we obtain calculation formula (2): ; In the formula, if ,need Supplemental power supply, if , The value is 0; if ,need Supplemental power supply, if , The value is 0; if ,need Supplemental power supply, if , The value is 0; Perform calculation on formula (2) , , The lower bound transformation yields the calculation formula (3): ; Simplifying equation (3) yields equation (4): ; Transpose and solve The lower bound: ; The above derivation is based on " In extreme scenarios where "the demand gap for cooling, heating, and electricity needs to be filled simultaneously," however, in reality, if a certain type of demand has already been met by the incidental output of a gas-fired internal combustion engine (such as...), then... ),but No additional requirements are needed for this type of demand. In this case, the inequality needs to be corrected, and the demand gap term is defined by formula (5): ; In the formula, To meet the electricity demand gap, Heat demand gap, Cold demand gap; The output value of the gas internal combustion engine is obtained by combining the calculation formulas (4) and (5). As shown in calculation formula (6): ; That is, the output value of a gas internal combustion engine. The minimum possible value is: .

[0013] In conjunction with the first aspect above, one possible implementation involves regulating the operation of a gas-fired internal combustion engine based on its output value, including: Extract the output value of the gas internal combustion engine, and adjust the power generation value of the gas internal combustion engine in the next time period based on the output value.

[0014] Secondly, a device for optimizing the coupling of combined cooling, heating and power (CCHP) and renewable energy in an integrated smart energy system is provided, comprising: a communication unit and a processing unit; The communication unit is used to acquire target data for a region over several future time periods and demand data for combined cooling, heating, and power (CCHP) over several future time periods. The target data includes solar radiation intensity, temperature, wind speed, and wind direction. The demand data includes demanded electricity, demanded heat, and demanded cooling capacity. The processing unit is used to construct an energy power generation prediction model, obtain the power generation of renewable energy in several future time periods based on the target data and the energy power generation prediction model; determine the relationship factors between the power generation of the gas internal combustion engine and the heat production and cooling value of the gas internal combustion engine; determine the output value of the gas internal combustion engine through the relationship factors, the power generation of renewable energy and the power demand, and regulate the operation of the gas internal combustion engine based on the output value.

[0015] Thirdly, this application provides a storage medium storing instructions that, when executed on a coupling optimization device for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system, cause the CCHP and renewable energy coupling optimization device in the integrated smart energy system to perform the method described in the first aspect and any possible implementation thereof.

[0016] This application provides a method, device, and storage medium for optimizing the coupling of combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system, with the following advantages: 1. This invention acquires target data for several future time periods, constructs an energy generation prediction model, and obtains the renewable energy generation for these periods based on the target data and the prediction model. It also acquires demand data for combined cooling, heating, and power (CCHP) for these periods and determines the relationship factors between the power generation of a gas-fired internal combustion engine and its heat and cooling output. By using these relationship factors, the renewable energy generation, and the demand for electricity, the output value of the gas-fired internal combustion engine is determined. Based on this output value, the operation of the gas-fired internal combustion engine is adjusted. This solves the technical problem in existing integrated smart energy systems where it is difficult to adaptively constrain and optimize the output of CCHP and renewable energy. Furthermore, this invention can reduce the fuel consumption of internal combustion engines.

[0017] 2. This invention constructs a system based on the output value of a gas-fired internal combustion engine. The multi-constraint optimization model with minimization as its objective considers both extreme scenarios of renewable energy gap filling and scenarios dominated by single load demand. It achieves deep coupling and synergistic optimization of renewable energy and combined cooling, heating and power (CCHP) systems, ensuring that the output of gas-fired internal combustion engines is minimized while meeting the demand of CCHP. This minimizes natural gas consumption and carbon emissions, and has significant advantages in improving clean energy consumption, reducing operating costs, improving energy efficiency, and enhancing system stability.

[0018] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0019] Figure 1 A schematic diagram illustrating the steps of a method for optimizing the coupling of combined cooling, heating and power (CCHP) and renewable energy in an integrated smart energy system, as provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a coupling optimization device for combined cooling, heating and power and renewable energy in an integrated smart energy system provided in this application embodiment. Detailed Implementation

[0020] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0021] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0022] like Figure 1 As shown in the embodiment of this application, a coupling optimization method for combined cooling, heating and power (CCHP) and renewable energy in an integrated smart energy system includes: S1: Obtain target data for several future time periods, construct an energy generation prediction model, and obtain the renewable energy generation for several future time periods based on the target data and the energy generation prediction model; among which, the target data includes solar radiation intensity, temperature, wind speed, and wind direction; S2: Obtain demand data for combined cooling, heating and power (CCHP) over several future time periods to determine the relationship factors between the power generation of gas-fired internal combustion engines and their heat and cooling outputs; the demand data includes demanded electricity, heat, and cooling output. S3: Determine the output value of the gas internal combustion engine by using relational factors, renewable energy power generation and demand, and regulate the operation of the gas internal combustion engine based on the output value.

[0023] It is worth noting that the coupling optimization method for combined cooling, heating, and power (CCHP) and renewable energy in the integrated smart energy system provided by this invention achieves efficient collaborative operation and deep optimization of multiple energy systems by constructing a collaborative architecture of renewable energy analysis, CCHP analysis, and comprehensive processing. Specifically, renewable energy analysis, based on target data such as solar radiation intensity, temperature, wind speed, and wind direction, constructs a high-precision energy generation prediction model to accurately predict renewable energy generation over several future time periods, providing forward-looking data support for system scheduling, effectively improving the renewable energy absorption rate, and reducing wind and solar curtailment. CCHP analysis, by acquiring demand data such as electricity demand, heat demand, and cooling capacity, accurately determines the relationship factors between the power generation of the gas internal combustion engine and its heat and cooling values, achieving dynamic matching of energy output with diverse load demands. Comprehensive processing, based on the above relationship factors, predicted renewable energy power generation, and actual electricity demand, dynamically adjusts the output value of the gas internal combustion engine through a multi-variable collaborative optimization algorithm, minimizing gas consumption and reducing carbon emissions while meeting electricity, heat, and cooling load demands. This method, through multi-data interaction and deep coupling, not only achieves the organic integration of renewable energy with combined cooling, heating, and power (CCHP) systems, improving the overall energy efficiency of the energy system, but also enhances the system's adaptability to load fluctuations and uncertainties in renewable energy output through refined control strategies, ensuring the stability and economy of energy supply. Furthermore, by quantitatively analyzing the characteristics of combined cooling, heating, and power generation from gas-fired internal combustion engines, this invention establishes a mathematical mapping relationship between energy output and load demand, providing a replicable and scalable optimization method for the intelligent scheduling of smart energy systems. It is applicable to various application scenarios such as industrial parks, commercial complexes, and large communities, and is of great significance for promoting energy structure transformation and achieving "dual-carbon" goals.

[0024] In one possible implementation of this application embodiment, the above-mentioned S1 can be implemented by the following S101, S102 and S103, which are described in detail below: S101: Obtain target data for several future time periods, including: The next day is divided into several time periods by manual planning, and the solar radiation intensity, temperature, wind speed and wind direction of the area in the next few time periods are obtained based on the weather forecast platform; the size of the area is selected manually, and it can be the area managed by the integrated smart energy system.

[0025] It should be noted that the duration of each time period in the artificial division of the future day into several time segments is based on the variation range of solar radiation intensity and wind speed during the future day. For example, if the variation range of solar radiation intensity is B1 and the variation range of wind speed is B2, the standard variation range of solar radiation intensity divided by B1 yields the anomaly rate Y1, and the standard variation range of wind speed divided by B2 yields the anomaly rate Y2. The duration of each time segment is obtained by weighted summing of the anomaly rates Y1 and Y2 and multiplying by a reference duration. The weights in the weighted summation of the anomaly rates Y1 and Y2 are designed based on local solar and wind resources. For example, if the annual electricity generation ratio of local solar and wind resources is 6:4, then the weight of anomaly rate Y1 is 0.6, and the weight of anomaly rate Y2 is 0.4. The reference duration can be 1 minute, and the standard variation range of solar radiation intensity can be the average variation range of solar radiation intensity over historical days, and the standard variation range of wind speed can be the average variation range of wind speed over historical days.

[0026] S102: Construct an energy generation prediction model, including: Extract solar radiation intensity, temperature, wind speed, and wind direction for several time periods from historical power generation data, as well as the actual renewable energy power generation from solar and wind power for those time periods; The solar radiation intensity, temperature, wind speed, wind direction, and renewable energy power generation over several time periods are integrated into several sets of training and validation data according to the time period number. The training data is used to train the artificial intelligence model, and the validation data is used to validate the trained artificial intelligence model. The artificial intelligence model is then adjusted based on the validation results. The final result is an energy power generation prediction model with solar radiation intensity, temperature, wind speed, and wind direction over a time period as input and renewable energy power generation over that time period as output. The artificial intelligence model is a nonlinear regression model based on a neural network, implemented using a BP neural network structure and / or an RBF neural network structure.

[0027] It should be noted that the power generation of renewable energy is the sum of solar power generation and wind power generation. The energy power generation prediction model first analyzes the solar radiation intensity within a time period to obtain solar power generation, then analyzes the wind speed and wind direction to obtain wind power generation, and finally adds the solar power generation and wind power generation to obtain the power generation of renewable energy.

[0028] It should be noted that the energy generation prediction model includes two independent sub-prediction models: 1. Solar power generation prediction sub-model: Using solar radiation intensity and temperature within a time period as the core input features, a BP neural network model (containing two hidden layers with 32 and 16 neurons respectively, and ReLU activation function) is used to output the solar power generation Ps(t) for that time period. 2. Wind power generation prediction sub-model: Taking wind speed and wind direction within a time period as input features, the model is processed by a preset wind direction-effective wind speed conversion formula (e.g., converting the angle θ between the wind direction and the wind turbine hub orientation into the effective wind speed Ve=v×cosθ, where v is the actual wind speed). The result is then input into the RBF neural network model (the radial basis function is a Gaussian function, and the number of centers is determined by K-means clustering) to output the wind power generation Pw(t) for that time period. 3. Add Ps(t) and Pw(t) to obtain the total renewable energy generation during this period: Pt(t) = Ps(t) + Pw(t).

[0029] Specifically, the steps for testing the trained AI model using validation data and adjusting the AI ​​model based on the validation results are as follows: The solar radiation intensity, temperature, wind speed, and wind direction from the test data are input into the trained artificial intelligence model to obtain the corresponding power generation. The power generation is compared with the corresponding power generation in the test data. If the difference between the two is within a threshold (obtained empirically), no parameter adjustment is required, and the next set of test data is tested. If it is not within the threshold, the corresponding parameters are adjusted until the difference between the two is within the threshold, and the next set of test data is tested. When the number of test data in which the power generation difference is within the threshold accounts for 90% or more of the total test data, an energy power generation prediction model is obtained, which takes solar radiation intensity, temperature, wind speed, and wind direction for a time period as input and outputs renewable energy power generation for that time period.

[0030] S103: Based on target data and energy generation forecasting models, the power generation of renewable energy in several future time periods is obtained, including: Solar radiation intensity, temperature, wind speed, and wind direction are extracted sequentially for several future time periods. Based on these time periods, the solar radiation intensity, temperature, wind speed, and wind direction are input into the energy power generation prediction model to obtain the power generation of renewable energy for the corresponding time periods.

[0031] In one possible implementation of this application embodiment, the above-mentioned S2 can be implemented by the following S201 and S202, which are described in detail below: S201: Obtain demand data for combined cooling, heating and power (CCHP) over several future time periods, including: The demand database extracts the electricity, heat, and cooling demand of the integrated smart energy system for various time periods in the future. The demand database includes the basic electricity, heat, and cooling demand required to meet the integrated smart energy system, as well as the additional electricity, heat, and cooling demand caused by various pre-set activities within the integrated smart energy system. The basic and additional electricity demands are added together to obtain the electricity demand, the heat demand is added together to obtain the heat value, and the cooling demand is added together to obtain the cooling demand.

[0032] S202: Factors determining the relationship between the power generation of a gas-fired internal combustion engine and its heat and cooling values, including: Extract the heat and cooling values ​​generated per unit of electricity produced by a gas-fired internal combustion engine within the past n days from the historical monitoring database; where n is manually set and can be 20. The heat production values ​​are integrated into data group one, and the cooling values ​​are integrated into data group two. The variance of data group one is obtained. When the variance is not greater than the heat production judgment variance, the average value of the heat production values ​​in data group one is calculated to obtain the relationship factor between the power generation of the gas internal combustion engine and the heat production value of the gas internal combustion engine. When the variance is greater than the heat production judgment variance, the heat production value in data group one with the largest difference from the average value of the heat production value is removed. The variance of the remaining heat production values ​​in data group one is recalculated and the variance judgment is performed again until the variance is not greater than the heat production judgment variance. The average value of the remaining heat production values ​​in data group one is then calculated to obtain the relationship factor between the power generation of the gas internal combustion engine and the heat production value of the gas internal combustion engine. Obtain the variance of the second data set. When the variance is not greater than the refrigeration judgment variance, calculate the average value of the refrigeration values ​​in the second data set to obtain the relationship factor between the power generation of the gas internal combustion engine and the refrigeration value of the gas internal combustion engine. When the variance is greater than the refrigeration judgment variance, remove the refrigeration value in the second data set that differs the most from the average refrigeration value. Recalculate the variance of the remaining refrigeration values ​​in the second data set and re-judge the variance until the variance is not greater than the refrigeration judgment variance. Calculate the average value of the remaining refrigeration values ​​in the second data set to obtain the relationship factor between the power generation of the gas internal combustion engine and the refrigeration value of the gas internal combustion engine. The heat generation judgment variance and the refrigeration judgment variance are both obtained through empirical settings.

[0033] It should be noted that the heat and cooling values ​​generated per unit of electricity generated by the gas internal combustion engine in the historical monitoring database are obtained by dividing the total heat generated by the gas internal combustion engine per unit time after the gas engine starts, and the total heat generated in the heat-generating area and the total cooling value in the cooling area caused by the total heat generated per unit of electricity. The heat generated per unit of electricity is obtained by dividing the total heat generated per unit of electricity, and the cooling value per unit of electricity is obtained by dividing the total cooling value per unit of electricity.

[0034] It should be noted that if, after removing 90% of the heat production value from data set 1, the variance of the remaining heat production value is still greater than the heat production judgment variance, then the average value of the original heat production value of data set 1 will be used as the relationship factor between the power generation of the gas internal combustion engine and the heat production value of the gas internal combustion engine; if, after removing 90% of the heat production value from data set 2, the variance of the remaining cooling value is still greater than the cooling judgment variance, then the average value of the original cooling value of data set 2 will be used as the relationship factor between the power generation of the gas internal combustion engine and the cooling value of the gas internal combustion engine.

[0035] In one possible implementation of this application embodiment, the above-mentioned S3 can be implemented by the following S301 and S302, which are described in detail below: S301: Determine the output value of a gas-fired internal combustion engine by considering relationship factors, renewable energy generation, and electricity demand, including: Extract the electricity demand for the next time period Required heat and required cooling capacity And the relationship between the power generation of a gas-fired internal combustion engine and the heat output of the gas-fired internal combustion engine. The relationship between the power generation of a gas-fired internal combustion engine and the cooling capacity of the engine. ; The required electricity is calculated using formula (1). Required heat and required cooling capacity Apply constraints; The calculation formula (1) is: ; In the formula, For renewable energy generation, This refers to the electrical output of a gas-fired internal combustion engine, which is required to meet the system's heating, cooling, and electrical demands. This output is accompanied by a fixed proportion of heat generation. and cooling capacity ,Right now , This step is to find the minimum ; for The proportion of direct power supply, and The value range is [0,1]; for The proportion of heat converted into electricity through electric heating equipment, such as electric boilers, and The value range is [0,1]; for The proportion of cooling capacity converted into cooling capacity through electric refrigeration equipment, such as electric chillers, and The value range is [0,1]; By transforming the constraints of calculation formula (1), we obtain calculation formula (2): ; In the formula, if ,need Supplemental power supply, if , The value is 0; if ,need Supplemental power supply, if , The value is 0; if ,need Supplemental power supply, if , The value is 0; Perform calculation on formula (2) , , The lower bound transformation yields the calculation formula (3): ; Simplifying equation (3) yields equation (4): ; Transpose and solve The lower bound: ; The above derivation is based on " In extreme scenarios where "the demand gap for cooling, heating, and electricity needs to be filled simultaneously," however, in reality, if a certain type of demand is already met by the incidental output of a gas-fired internal combustion engine, such as... ,but No additional requirements are needed for this type of demand. In this case, the inequality needs to be corrected, and the demand gap term is defined by formula (5): ; In the formula, To meet the electricity demand gap, Heat demand gap, Cold demand gap; The output value of the gas internal combustion engine is obtained by combining the calculation formulas (4) and (5). As shown in calculation formula (6): ; That is, the output value of a gas internal combustion engine. The minimum possible value is: .

[0036] It is worth noting that this invention constructs a system based on the output value of a gas-fired internal combustion engine. A multi-constraint optimization model with minimization as the objective achieves deep coupling and synergistic optimization of renewable energy and combined cooling, heating and power (CCHP) systems. Firstly, this method introduces a multi-path allocation mechanism for renewable energy (…). , , (Parameters), including the power generation of fluctuating renewable energy sources such as wind and solar power. By utilizing multiple dimensions such as direct power supply, electric heating conversion, and electric cooling conversion, the limitations of the single renewable energy consumption model in traditional energy systems are broken through, significantly improving the clean energy consumption rate. Secondly, by establishing a "heat / cooling determined by electricity" coupling relationship for gas internal combustion engines (…),… , ), transforming the output optimization of the tri-generation system into a single variable ( The mathematical programming problem, combined with the demand gap term ( , , The dynamic definition of ) enables precise matching of cooling, heating, and electricity load demands. During the constraint transformation process, through the step-by-step derivation of calculation formulas (1)-(6), the final result is formed. The formula for calculating the lower bound of E ( This formula comprehensively considers both extreme scenarios of renewable energy gap filling and scenarios dominated by single load demand, ensuring that the output of gas-fired internal combustion engines is minimized while meeting the demand for combined cooling, heating, and power (CCHP), thereby minimizing natural gas consumption and carbon emissions. Furthermore, this method... , , The proportional allocation mechanism dynamically adjusts the distribution ratio of renewable energy among electricity, heat, and cooling loads, effectively solving the "source-load mismatch" problem in multi-energy complementary systems; when the demand of a certain load suddenly increases, the demand gap term is used to... Functions are automatically triggered Boundary adjustments are made to ensure system stability. From an economic perspective, minimizing... This directly reduces gas procurement costs, and simultaneously, through the efficient conversion and utilization of renewable energy (such as electric heating and electric cooling), it reduces the operating time of traditional boilers, gas turbines, and other equipment, extending equipment lifespan and reducing maintenance costs. From an energy efficiency perspective, this coupled optimization method achieves cascaded energy utilization, using a fixed ratio to manage the waste heat and cooling generated during the gas internal combustion engine power generation process. , This efficient energy recovery avoids energy waste found in traditional distributed energy systems. From a system flexibility perspective, this model can be optimized on a rolling basis within each scheduling period (e.g., 10 minutes, 20 minutes). This invention adapts to renewable energy forecasting errors and load fluctuations, enhancing the anti-interference capability and dynamic response speed of the integrated smart energy system. Through a combination of mathematical modeling and constraint optimization, this invention constructs a collaborative operation framework for renewable energy and combined cooling, heating, and power (CCHP) systems. It offers significant advantages in improving clean energy consumption, reducing operating costs, increasing energy utilization efficiency, and enhancing system stability, providing a scientific, efficient, and engineerable solution for the optimized scheduling of integrated smart energy systems.

[0037] It should be noted that in a coupled system of combined cooling, heating and power (CCHP) and renewable energy, renewable energy (… The value of ( ) lies in replacing the "active output" of a gas-fired internal combustion engine, but It does not directly generate heat or cooling (it needs to be converted through heat pumps, electric refrigeration equipment, etc.). The system's electricity / heat / cooling requirements are met by two parts: 1. Active output of gas internal combustion engine: Expected electricity production by gas internal combustion engine ( ), and also generate heat ( ) and refrigeration ( ); 2. Renewable energy supplementation: It can be allocated to three scenarios: electricity, heating, and cooling, to fill the demand gap that gas-fired internal combustion engines have not met. , , ).

[0038] In the initial derivation, we assume that " The power / heat / cooling gaps need to be filled simultaneously (i.e.) , , (All are positive), resulting in the formula: ; However, in reality, if the output of a gas-fired internal combustion engine already meets a certain type of demand (such as...), ),but , No additional heat demand is required. In this case, the original formula would overestimate the "demand". The total gap that was filled led to The calculation results are too small and may not even meet the actual needs; “ That is, the sum of the demand gaps ≤ The role of constraints: By introducing , , The "nonnegativity" (i.e.) This constraint automatically excludes "demands already met by the gas internal combustion engine" from... The allocated interference only requires Covering "real, existing gaps". For example: like ,but The constraints are simplified to ; like and The constraints are then further simplified to ,Right now Only the power shortage needs to be replenished.

[0039] This constraint is equivalent to: This ensures that in any Under the given value, Both have sufficient capacity to fill all real gaps and avoid distortion of optimization results caused by "gas internal combustion engines over-satisfying a certain type of demand".

[0040] For example, 1. Abundant renewable energy ( Larger

[0041] like ,but: ; At this time, the gas internal combustion engine does not need to generate electricity. ), It can independently meet the needs of electricity, heat, and cooling; 2. Insufficient renewable energy ( limited) like ,but Determined by the "tightest constraint in electricity / heating / cooling demand": like maximum: , Priority power supply, with any shortfall to be covered by [other suppliers]. Replenish; like maximum: ,even though To supplement heating, the gas-fired internal combustion engine still needs to produce enough electricity. In order to incidentally meet heat demand; like maximum: ,even though To supplement heating, the gas-fired internal combustion engine still needs to produce enough electricity. To incidentally meet the demand for cold.

[0042] S302: Regulating the operation of a gas-fired internal combustion engine based on output value, including: Extract the output value of the gas internal combustion engine, and adjust the power generation value of the gas internal combustion engine in the next time period based on the output value.

[0043] The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, for example, a coupling optimization device for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system, includes at least one of the hardware structures and software modules corresponding to each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware-driven or software-driven manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0044] This application embodiment can divide a coupling optimization device for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system into functional units based on the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents a logical functional division; other division methods may be used in actual implementation.

[0045] When using integrated units, Figure 2 The above embodiment shows a possible structural schematic diagram of a coupling optimization device (referred to as communication device 20) for combined cooling, heating and power and renewable energy in an integrated smart energy system. The communication device 20 includes a processing unit 201 and a communication unit 202, and may also include a storage medium (referred to as storage unit 203). Figure 2 The schematic diagram shown can be used to illustrate the structure of a coupling optimization device for combined cooling, heating and power and renewable energy in an integrated smart energy system involved in the above embodiments.

[0046] when Figure 2The schematic diagram shown illustrates the structure of a coupling optimization device for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system as described in the above embodiments. The processing unit 201 controls and manages the operation of the coupling optimization device for CCHP and renewable energy in the integrated smart energy system. The communication unit 202 facilitates communication between the coupling optimization device for CCHP and renewable energy and other devices in the integrated smart energy system. The storage unit 203 stores the program code and data of the coupling optimization device for CCHP and renewable energy in the integrated smart energy system.

[0047] For example, communication unit 202 is used to acquire target data for a region in the future and demand data for combined cooling, heating and power (CCHP) in the future; the target data includes solar radiation intensity, temperature, wind speed and wind direction; the demand data includes demand for electricity, demand for heat and demand for cooling. Processing unit 201: is used to construct an energy power generation prediction model, obtain the power generation of renewable energy in several future time periods based on target data and the energy power generation prediction model; determine the relationship factors between the power generation of the gas internal combustion engine and the heat production and cooling value of the gas internal combustion engine; determine the output value of the gas internal combustion engine through the relationship factors, the power generation of renewable energy and the power demand, and regulate the operation of the gas internal combustion engine based on the output value.

[0048] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0049] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0050] Some of the data in the above calculation formula are obtained by removing dimensions and taking their numerical values. The calculation formula is a calculation formula that is closest to the real situation, obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the calculation formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

Claims

1. A coupling optimization method for combined cooling, heating and power (CCHP) and renewable energy in an integrated smart energy system, characterized in that, include: The target data for several future time periods is obtained, and an energy generation prediction model is constructed. Based on the target data and the energy generation prediction model, the power generation of renewable energy in the future time periods is obtained. The target data includes solar radiation intensity, temperature, wind speed, and wind direction. To obtain demand data for combined cooling, heating and power (CCHP) over several future time periods, and to determine the relationship factors between the power generation of gas-fired internal combustion engines and their heat and cooling output values; the demand data includes demand for electricity, heat, and cooling. The output value of the gas internal combustion engine is determined by relational factors, the power generation of renewable energy, and the power demand, and the operation of the gas internal combustion engine is adjusted based on the output value.

2. The coupling optimization method for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system according to claim 1, characterized in that, The acquisition of target data for several future time periods includes: The next day is divided into several time periods by manual planning, and the solar radiation intensity, temperature, wind speed and wind direction of the area in the next several time periods are obtained based on the weather forecast platform.

3. The method for optimizing the coupling of combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system according to claim 1, characterized in that, The construction of the energy generation prediction model includes: Extract solar radiation intensity, temperature, wind speed, and wind direction for several time periods from historical power generation data, as well as the actual renewable energy power generation from solar and wind power for those time periods; The solar radiation intensity, temperature, wind speed, wind direction, and renewable energy power generation over several time periods are integrated into several sets of training and validation data according to the time period number. The training data is used to train the artificial intelligence model, and the validation data is used to validate the trained artificial intelligence model. The artificial intelligence model is then adjusted based on the validation results. The final result is an energy power generation prediction model with solar radiation intensity, temperature, wind speed, and wind direction over a time period as input and renewable energy power generation over that time period as output. The artificial intelligence model is a nonlinear regression model based on a neural network, implemented using a BP neural network structure and / or an RBF neural network structure.

4. The coupling optimization method for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system according to claim 3, characterized in that, The renewable energy generation forecast model, based on target data and energy generation prediction, yields the renewable energy generation for several future time periods, including: Solar radiation intensity, temperature, wind speed, and wind direction are extracted sequentially for several future time periods. Based on these time periods, the solar radiation intensity, temperature, wind speed, and wind direction are input into the energy power generation prediction model to obtain the power generation of renewable energy for the corresponding time periods.

5. The coupling optimization method for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system according to claim 1, characterized in that, The acquisition of demand data for combined cooling, heating and power (CCHP) over several future time periods includes: The demand database extracts the electricity, heat, and cooling demand of the integrated smart energy system for various time periods in the future. The demand database includes the basic electricity, heat, and cooling demand required to meet the integrated smart energy system, as well as the additional electricity, heat, and cooling demand caused by various pre-set activities within the integrated smart energy system. The basic and additional electricity demands are added together to obtain the electricity demand, the heat demand is added together to obtain the heat value, and the cooling demand is added together to obtain the cooling demand.

6. The coupling optimization method for combined cooling, heating and power (CCHP) and renewable energy in an integrated smart energy system according to claim 1, characterized in that, The factors determining the relationship between the power generation of a gas-fired internal combustion engine and its heat production and cooling capacity include: Extract the heat and cooling values ​​generated per unit of electricity generated by a gas-fired internal combustion engine within the past n days from the historical monitoring database. The heat production values ​​are integrated into data group one, and the cooling values ​​are integrated into data group two. The variance of data group one is obtained. When the variance is not greater than the heat production judgment variance, the average value of the heat production values ​​in data group one is calculated to obtain the relationship factor between the power generation of the gas internal combustion engine and the heat production value of the gas internal combustion engine. When the variance is greater than the heat production judgment variance, the heat production value in data group one with the largest difference from the average value of the heat production value is removed. The variance of the remaining heat production values ​​in data group one is recalculated and the variance judgment is performed again until the variance is not greater than the heat production judgment variance. The average value of the remaining heat production values ​​in data group one is then calculated to obtain the relationship factor between the power generation of the gas internal combustion engine and the heat production value of the gas internal combustion engine. Obtain the variance of the second data set. When the variance is not greater than the refrigeration judgment variance, calculate the average value of the refrigeration values ​​in the second data set to obtain the relationship factor between the power generation of the gas internal combustion engine and the refrigeration value of the gas internal combustion engine. When the variance is greater than the refrigeration judgment variance, remove the refrigeration value in the second data set that differs the most from the average refrigeration value. Recalculate the variance of the remaining refrigeration values ​​in the second data set and re-judge the variance until the variance is not greater than the refrigeration judgment variance. Calculate the average value of the remaining refrigeration values ​​in the second data set to obtain the relationship factor between the power generation of the gas internal combustion engine and the refrigeration value of the gas internal combustion engine.

7. The coupling optimization method for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system according to claim 1, characterized in that, The determination of the output value of the gas internal combustion engine through relational factors, renewable energy power generation, and electricity demand includes: Extract the electricity demand for the next time period Required heat and required cooling capacity And the relationship between the power generation of a gas-fired internal combustion engine and the heat output of the gas-fired internal combustion engine. The relationship between the power generation of a gas-fired internal combustion engine and the cooling capacity of the engine. ; The required electricity is calculated using formula (1). Required heat and required cooling capacity Apply constraints; The calculation formula (1) is: ； In the formula, For renewable energy generation, This refers to the amount of electricity that a gas-fired internal combustion engine actively produces to meet the system's heating, cooling, and electrical needs; this production also includes a fixed proportion of heat generation. and cooling capacity ,Right now , ; for The proportion of direct power supply, and The value range is [0,1]; for The proportion of heat converted into electricity through electric heating equipment, such as electric boilers, and The value range is [0,1]; for The proportion of cooling capacity converted into cooling capacity through electric refrigeration equipment, such as electric chillers, and The value range is [0,1]; By transforming the constraints of calculation formula (1), we obtain calculation formula (2): ； In the formula, if , The value is 0, if , The value is 0, if , The value is 0; Perform calculation on formula (2) , , The lower bound transformation yields the calculation formula (3): ； Simplifying equation (3) yields equation (4): ； The demand gap term is defined by formula (5): ； In the formula, To meet the electricity demand gap, Heat demand gap, Cold demand gap; The output value of the gas internal combustion engine is obtained by combining the calculation formulas (4) and (5). As shown in calculation formula (6): ； That is, the output value of a gas internal combustion engine. The minimum possible value is: 。 8. The coupling optimization method for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system according to claim 1, characterized in that, The method of regulating the operation of a gas-fired internal combustion engine based on output value includes: Extract the output value of the gas internal combustion engine, and adjust the power generation value of the gas internal combustion engine in the next time period based on the output value.

9. A coupling optimization device for combined cooling, heating and power (CCHP) and renewable energy in an integrated smart energy system, characterized in that, The device includes: a communication unit and a processing unit; The communication unit is used to acquire target data for a region over several future time periods and demand data for combined cooling, heating, and power (CCHP) over several future time periods. The target data includes solar radiation intensity, temperature, wind speed, and wind direction. The demand data includes demanded electricity, demanded heat, and demanded cooling capacity. The processing unit is used to construct an energy power generation prediction model, obtain the power generation of renewable energy in several future time periods based on the target data and the energy power generation prediction model; determine the relationship factors between the power generation of the gas internal combustion engine and the heat production and cooling value of the gas internal combustion engine; determine the output value of the gas internal combustion engine through the relationship factors, the power generation of renewable energy and the power demand, and regulate the operation of the gas internal combustion engine based on the output value.

10. A storage medium, characterized in that, Used to store instructions, which, when running on a coupling optimization device for combined cooling, heating, and power (CCHP) and renewable energy in an integrated smart energy system, cause the coupling optimization device for CCHP and renewable energy in an integrated smart energy system to execute the coupling optimization method for CCHP and renewable energy in an integrated smart energy system as described in any one of claims 1 to 8.

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