A low-carbon building carbon emission optimization system

Abstract

The present invention discloses a low-carbon building carbon emission optimization system, which relates to the field of building energy-saving technologies. It includes collecting the energy consumption data and environmental parameters of the building energy system, preprocessing the collected data, extracting data features, and identifying the typical patterns of the building energy system; based on the typical patterns of the building energy system, constructing a multi-objective optimization model including renewable energy power generation prediction, load demand prediction, and carbon emission prediction, determining the constraint conditions and objective function of the multi-objective optimization model; generating an energy optimization scheduling strategy considering carbon emission intensity and a carbon capture control scheme according to the objective function of the multi-objective optimization model, executing the optimization scheduling strategy, collecting abnormal situations and executing recovery strategies, and conducting effect evaluation and strategy dynamic adjustment in real time. The present invention achieves the balance of the economy, environmental protection, and reliability of the system, effectively controls carbon emissions through an improved multi-objective optimization algorithm, and enhances the operation reliability and adaptability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy conservation, and particularly to a low-carbon building carbon emission optimization system. Background Art

[0002] With the continuous improvement of the requirements for building energy conservation and emission reduction, the building energy system is developing towards diversification and low-carbonization. At present, the building energy system usually includes various energy forms such as photovoltaic power generation, energy storage devices, and conventional energy equipment. At the same time, in order to reduce carbon emissions, more and more buildings have started to adopt carbon capture technology. However, due to the intermittency and uncertainty of renewable energy, as well as the dynamic change characteristics of building load demand, traditional building energy scheduling methods are difficult to achieve the coordinated optimization of various energy forms.

[0003] In the prior art, most building energy optimization scheduling methods only consider economic objectives and lack effective control of carbon emissions; even for the methods that consider carbon emission factors, they often treat the carbon capture device as an independent system and fail to achieve overall coordinated optimization with the building energy system. In addition, the existing methods lack a perfect processing mechanism when facing system operation anomalies, which easily leads to a decrease in system efficiency and an increase in carbon emissions. Therefore, there is an urgent need to develop a building multi-energy coordinated optimization scheduling method that can comprehensively consider economy, environmental protection, and reliability to achieve the low-carbon and efficient operation of the building energy system. Summary of the Invention

[0004] In view of the problems existing in the existing low-carbon building carbon emission optimization methods, the present invention proposes a low-carbon building carbon emission optimization system.

[0005] Therefore, the problem to be solved by the present invention is that due to the intermittency and uncertainty of renewable energy and the dynamic change characteristics of building load demand, traditional building energy scheduling methods are difficult to achieve the coordinated optimization of various energy forms.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for optimizing low-carbon building carbon emissions, which includes: collecting energy consumption data and environmental parameters of a building energy system, preprocessing the collected data, extracting data features, and identifying typical patterns of the building energy system; based on the typical patterns of the building energy system, constructing a multi-objective optimization model including renewable energy power generation prediction, load demand prediction, and carbon emission prediction, and determining the constraint conditions and objective function of the multi-objective optimization model; according to the objective function of the multi-objective optimization model, performing collaborative optimization through three dimensions of energy allocation, energy storage scheduling, and carbon capture control to generate an energy optimization scheduling strategy considering carbon emission intensity, and executing corresponding recovery strategies for abnormal situations such as sudden drop in photovoltaic power, sharp increase in load, and decrease in carbon capture efficiency, and evaluating the control effect and dynamically adjusting the strategy in real time.

[0008] The preprocessing includes data cleaning and standardization processing to obtain standardized data; according to the preprocessed data, extracting the time series features of the data and identifying the typical patterns of the data, expressed as:

[0009] ;

[0010] Among them, is a data point, is the clustering center of class is the objective function of clustering, is the total number of data samples, is the preset number of clusters, represents the distance from the data point to the clustering center; by initially randomly selecting clustering centers, calculating the distance from each data point to each clustering center, and assigning the data point to the nearest clustering center, and recalculating the clustering center:

[0011] ;

[0012] Among them, is the number of samples in the th class, represents that the data point belongs to the th class; repeating the above steps until convergence, obtaining typical load patterns, including: high load pattern on weekdays, low load pattern on weekdays, weekend pattern, holiday pattern, and special weather pattern.

[0013] As a preferred embodiment of the low-carbon building carbon emission optimization method of the present invention, wherein: the data sources of the building energy system include the power distribution system, heating system, air conditioning system, lighting system, and renewable energy system; the power distribution system includes the power parameters of the power distribution room, transformer, and power distribution cabinet; the heating system includes the operating parameters of the boiler, heat exchange station, and pipe network; the air conditioning system includes the operating conditions of the chiller, cooling tower, and air conditioning terminal; the lighting system includes the lighting electricity consumption and control status of each area; the renewable energy system includes the real-time data of the photovoltaic panels and energy storage devices; the environmental parameters include the outdoor environment, indoor environment, and regional distribution; the outdoor environment includes temperature, humidity, illumination, wind speed, and air pressure; the indoor environment includes temperature, humidity, CO2 concentration, and PM2.5; the regional distribution includes the environmental parameters of different areas and floors of the building.

[0014] As a preferred embodiment of the low-carbon building carbon emission optimization method of the present invention, wherein: the multi-objective optimization model is constructed by calculating the photovoltaic power generation, energy storage system power, relative error of load prediction, and carbon emission intensity; the photovoltaic power generation is expressed as:

[0015] ;

[0016] Wherein, is the photovoltaic power generation, is the photovoltaic conversion efficiency, is the area of the photovoltaic panel, is the solar irradiance, is the temperature coefficient, is the temperature of the battery panel, is the standard temperature; the energy storage system power is expressed as:

[0017] ;

[0018] Wherein, is the output power of the energy storage system at time, is the rated power of the energy storage, is the state of charge of the energy storage system, is the minimum state of charge; the relative error of load prediction is expressed as:

[0019] ;

[0020] Wherein, is the relative error of load prediction, is the actual load, is the predicted load; the carbon emission intensity is expressed as:

[0021] ;

[0022] in, To standardize carbon emission intensity, For the Energy power, For the Carbon emission factors of energy sources, is the total installed capacity; a multi-objective optimization model is constructed based on the photovoltaic power generation, energy storage system power, load forecast relative error and carbon emission intensity, which is expressed as:

[0023] ;

[0024] in, For the comprehensive optimization index of the system, is the load forecast error penalty factor, is the carbon emission weight coefficient, The maximum load of the system.

[0025] As a preferred solution of the low-carbon building carbon emission optimization method of the present invention, wherein: based on the multi-objective optimization model, the constraint conditions and the objective function are determined, and the constraint conditions include power balance constraints, energy storage system constraints and carbon emission constraints; the power balance constraints are expressed as:

[0026] ;

[0027] in, For the Conventional energy output, To generate power for photovoltaic power, For energy storage systems The output power at the moment, Indicates the actual system load. is the transmission and distribution loss rate; the energy storage system constraint is expressed as:

[0028] ;

[0029] in, for Energy storage charge state at all times, For charging efficiency, is the discharge efficiency, is the rated capacity of the energy storage system, is the time step; the carbon emission constraint is expressed as:

[0030] ;

[0031] in, For the Carbon emission factors of energy sources, is the maximum allowable carbon emission intensity; the objective function is expressed as:

[0032] ;

[0033] where, represents the economic objective function, represents the environmental protection objective function, represents the efficiency objective function, , , are weight coefficients; the economic objective function is expressed as:

[0034] ;

[0035] where, is the unit cost of the th type of energy, is the operating cost of the energy storage system, is the carbon emission cost, is the carbon price; the environmental protection objective function is expressed as:

[0036] ;

[0037] where, represents the time interval; the efficiency objective function is expressed as:

[0038] ;

[0039] ;

[0040] where, represents the overall energy utilization efficiency of the building energy system at time .

[0041] As a preferred solution of the low-carbon building carbon emission optimization method described in the present invention, wherein: based on the objective function, an optimization scheduling strategy is constructed by respectively considering energy allocation, energy storage scheduling, and carbon capture control; the basic equation of the energy allocation is expressed as:

[0042] ;

[0043] where, represents the output coefficient of the th type of energy, represents the maximum output of the th type of energy; the equation of the energy storage scheduling is expressed as:

[0044] ;

[0045] where, is the output power of the energy storage system at moment, is the deviation between the load and the predicted value, is the electricity price signal; The equation of the carbon capture control is expressed as:

[0046] ;

[0047] wherein, represents the carbon capture amount at moment,

[0048]

[0049]

[0050] In a second aspect, an embodiment of the present invention provides a low-carbon building carbon emission optimization system, which includes a collection module for collecting energy consumption data and environmental parameters of a building energy system, and identifying typical patterns of the building energy system according to the collected data; a construction module for constructing a multi-objective optimization model including renewable energy power generation prediction, load demand prediction and carbon emission prediction based on the typical patterns of the building energy system, and determining the constraint conditions and objective functions of the multi-objective optimization model; an optimization processing module for generating an energy optimization scheduling strategy considering carbon emission intensity through collaborative optimization in three dimensions of energy allocation, energy storage scheduling and carbon capture control according to the objective function of the multi-objective optimization model, and executing corresponding recovery strategies for abnormal situations such as sudden drop in photovoltaic power, sharp increase in load, and decrease in carbon capture efficiency, and evaluating the control effect and dynamically adjusting the strategy in real time. As a preferred solution of the low-carbon building carbon emission optimization system of the present invention, wherein: the collection module collects the operation data and environmental parameters of the building energy system in real time, including renewable energy power generation power, energy consumption equipment load, energy storage system status, carbon capture device operation parameters, and environmental temperature, humidity, and light data, and identifies the typical operation mode of the system through data analysis, providing a reliable modeling basis and characteristic data support for the construction module; the construction module establishes a mathematical model including renewable energy power generation prediction, load demand prediction and carbon emission prediction based on the data provided by the collection module, forms a multi-objective optimization model by setting constraint conditions and optimization objectives, dynamically updates parameters according to the real-time data of the collection module, maintains the accuracy of the model, and provides a scientific decision-making basis for the optimization processing module; the optimization processing module generates specific energy scheduling strategies and carbon capture control schemes based on the multi-objective optimization model of the construction module, continuously receives the real-time data of the collection module during the execution process for effect evaluation, adjusts the strategy in time when abnormal situations occur, and feeds back the operation results to the collection module to form a closed-loop optimization control.In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned low-carbon building carbon emission optimization method is implemented.

[0051] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by a processor, any step of the above-mentioned low-carbon building carbon emission optimization method is implemented.

[0052] The beneficial effects of the present invention are as follows: By constructing a system model considering carbon capture, designing a multi-objective optimization algorithm, and formulating a complete scheduling strategy and exception handling mechanism, the present invention achieves a balance among the economy, environmental protection, and reliability of the system. By adopting an improved multi-objective optimization algorithm and introducing a penalty function and constraint conditions, effective control of carbon emissions is achieved while reducing the operating cost. It not only improves the overall economic efficiency of the system, but also significantly reduces carbon emissions, while enhancing the operating reliability and adaptability of the system, providing a practical and effective solution for the coordinated optimization scheduling of multi-energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0054] Figure 1 It is a flowchart of the low-carbon building carbon emission optimization method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the above-mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0057] Second, the "one embodiment" or "embodiment" mentioned herein refers to specific features, structures, or characteristics that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it an embodiment that is separate or selectively mutually exclusive with other embodiments.

[0058] Embodiment 1

[0059] Referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a low-carbon building carbon emission optimization method, including:

[0060] S1: Collect the energy consumption data and environmental parameters of the building energy system, and identify the typical patterns of the building energy system according to the collected data.

[0061] Collect all data sources in the building energy system, including the power distribution system, heating system, air conditioning system, lighting system, and renewable energy system.

[0062] The power distribution system includes the power parameters of the power distribution room, transformer, and power distribution cabinet; the heating system includes the operation parameters of the boiler, heat exchange station, and pipe network; the air conditioning system includes the operating conditions data of the chiller, cooling tower, and air conditioning terminal; the lighting system includes the lighting electricity consumption and control status of each area; the renewable energy system includes the real-time data of the photovoltaic panels and energy storage devices.

[0063] At the same time, establish a multi-level environmental parameter monitoring network to detect environmental data, including outdoor environment, indoor environment, and regional distribution.

[0064] The outdoor environment includes temperature, humidity, light, wind speed, and air pressure; the indoor environment includes temperature, humidity, CO2 concentration, and PM2.5; the regional distribution includes the environmental parameters of different areas and floors of the building.

[0065] Preprocess the collected data, including data cleaning and standardization processing, to obtain standardized data.

[0066] According to the preprocessed data, extract the time series features of the data and identify the energy usage patterns of the data.

[0067] Among them, by analyzing the load curve characteristics of the data and using Fourier transform to extract the periodic characteristics, the time series characteristics of the data are obtained;

[0068] Based on the pattern classification of the K-means algorithm, identify the typical patterns of the data, expressed as:

[0069] ;

[0070] Among them, is the data point, is The clustering center of the class is the objective function of clustering is the total number of data samples is the preset number of clusters represents the distance from the data point to the clustering center

[0071] By initially randomly selecting clustering centers, calculating the distance from each data point to each clustering center, and assigning the data points to the nearest clustering center, then recalculating the clustering center:

[0072] ;

[0073] wherein is the number of samples in the th class represents the data point belongs to the th class

[0074] Repeat the above steps until convergence, and finally obtain typical load patterns, including: weekday high load pattern, weekday low load pattern, weekend pattern, holiday pattern, and special weather pattern

[0075] By minimizing the objective function , find the optimal clustering result, thereby identifying the typical patterns of building energy use and providing a basis for subsequent energy optimization scheduling

[0076] S2: Based on the typical patterns of the building energy system, construct a multi-objective optimization model including renewable energy power generation prediction, load demand prediction, and carbon emission prediction, and determine the constraint conditions and objective function of the multi-objective optimization model

[0077] Based on the typical patterns of building energy use, obtain the daily load change law, photovoltaic power generation characteristics, energy storage usage pattern, and carbon emission characteristics through cluster analysis

[0078] Determine that the energy supply includes photovoltaic power generation and the building's energy storage system, then according to the characteristics of the photovoltaic power generation curve, construct a photovoltaic power generation power function, expressed as:

[0079] ;

[0080] wherein is the photovoltaic power generation power is the photovoltaic conversion efficiency is the area of the photovoltaic panel is the solar irradiance is the temperature coefficient is the temperature of the battery panel is the standard temperature.

[0081] According to the energy storage usage pattern, construct the power function of the energy storage system, expressed as:

[0082] ;

[0083] where, is the output power of the energy storage system at moment, is the rated power of the energy storage, is the state of charge of the energy storage system, is the minimum state of charge.

[0084] Use the typical patterns obtained by clustering as the prediction benchmark, and calculate the load prediction accuracy through the deviation between the actual load and the predicted value of the typical pattern, expressed as:

[0085] ;

[0086] where, is the relative error of load prediction, is the actual load, is the predicted load.

[0087] According to the carbon emission characteristics of different periods in the typical pattern, considering the composition ratio of various energy sources, predict the carbon emissions, expressed as:

[0088] ;

[0089] where, is the standardized carbon emission intensity, is the power of the th type of energy source, is the carbon emission factor of the th type of energy source, is the total installed capacity.

[0090] Then, construct a multi-objective optimization model based on the photovoltaic power generation, the energy storage system power, the relative error of load prediction, and the carbon emission intensity, expressed as:

[0091] ;

[0092] where, is the comprehensive optimization index of the system, is the penalty factor for load prediction error, is the carbon emission weight coefficient, is the maximum load of the system.

[0093] Furthermore, determine the constraint conditions according to the multi-objective optimization model, including power balance constraint, energy storage system constraint, and carbon emission constraint;

[0094] The power balance constraint is expressed as:

[0095] ;

[0096] Wherein, is the output of the type of conventional energy, is the output of photovoltaic power generation, is the output power of the energy storage system at time, represents the actual load of the system, is the transmission and distribution loss rate.

[0097] The energy storage system constraint is expressed as:

[0098] ;

[0099] Wherein, is the time energy storage state of charge, is the charging efficiency, is the discharging efficiency, is the rated capacity of the energy storage system, is the time step.

[0100] The carbon emission constraint is expressed as:

[0101] ;

[0102] Wherein, is the type of energy carbon emission factor, is the maximum allowable carbon emission intensity.

[0103] According to the constraint conditions, the comprehensive objective function of multi-objective optimization is determined as:

[0104] ;

[0105] Wherein, represents the economic objective function, represents the environmental protection objective function, represents the efficiency objective function, , , are the weight coefficients.

[0106] Furthermore, the economic objective function reflects the total operating cost of the system, including energy purchase cost, equipment operating cost and carbon emission cost, and is expressed as:

[0107] ;

[0108] Among them, is the unit cost of the th type of energy, is the operating cost of the energy storage system, is the carbon emission cost, is the carbon price.

[0109] Environmental protection objective function reflects the carbon emission intensity of the system, including the carbon emission contributions of various types of energy, and is expressed as:

[0110] ;

[0111] Among them, represents the time interval.

[0112] Efficiency objective function reflects the operating efficiency of the system, considering the energy utilization rate and equipment performance, and is expressed as:

[0113] ;

[0114] ;

[0115] Among them, is the same as the system comprehensive optimization index , when used as the system real-time evaluation index when, is used to monitor the system performance in real time, judge whether the operation strategy needs to be adjusted, and evaluate the current control effect; when it is the parameter of the optimization objective function when, is used as the efficiency objective in multi-objective optimization to calculate the cumulative efficiency during the entire optimization period.

[0116] S3: According to the objective functions of the multi-objective optimization model, collaborative optimization is carried out through three dimensions of energy allocation, energy storage scheduling, and carbon capture control to generate an energy optimization scheduling strategy considering carbon emission intensity, and corresponding recovery strategies are executed for abnormal situations such as sudden drops in photovoltaic power, sharp increases in load, and decreases in carbon capture efficiency, and the control effect and strategy dynamic adjustment are evaluated in real time.

[0117] Based on the objective function, through the energy allocation equation, the participation degree of each type of energy is determined, and the basic energy allocation equation is expressed as:

[0118] ;

[0119] Among them, represents the output coefficient of the th type of energy, represents the maximum output of the th type of energy.

[0120] Furthermore, the charging and discharging strategy of the energy storage system is determined by the energy storage scheduling equation, which is expressed as:

[0121] ;

[0122] in, For energy storage systems The output power at the moment, is the deviation between load and predicted value, The electricity price signal.

[0123] The operation intensity of the carbon capture system is controlled by the carbon capture control equation, which is expressed as:

[0124]

[0125] in, express The amount of carbon captured at each moment, represents the carbon capture efficiency coefficient.

[0126] The optimization problem is constructed by the objective function, and the solution is obtained for each period. , and Form specific scheduling instructions and execute optimized scheduling strategies.

[0127] Observe abnormal situations during the scheduling process. If there is a sudden drop in photovoltaic power, the energy storage system will increase the discharge power at the maximum ramp rate. Conventional energy will gradually increase its output within 10 minutes, and the backup power supply will be started when the energy storage SOC is <30%.

[0128] If photovoltaic power generation exceeds the predicted value, the energy storage system will increase the charging power to the maximum value, and conventional energy will reduce its output in order of economy. When the energy storage SOC>90%, the photovoltaic utilization rate will be reduced.

[0129] If the load growth rate exceeds 5% per minute, the fast response power supply is immediately activated and the energy storage system discharges at maximum power, reducing the carbon capture efficiency to the minimum.

[0130] If the actual load deviates from the predicted value by more than 15%, the energy allocation ratio will be recalculated, the energy storage charging and discharging plan will be adjusted, and the short-term load forecast correction will be initiated.

[0131] If the efficiency of the carbon capture system drops by more than 30%, the output of the coal-fired power unit will be reduced to the minimum technical output and the backup capture unit will be started.

[0132] If extreme weather occurs, the conventional energy backup capacity will be increased to 20% and the energy storage system will be charged in advance to more than 90%.

[0133] If a new abnormal situation occurs, the recovery process shall be immediately suspended, the system status shall be re-evaluated, and a suitable treatment plan shall be selected to ensure the safety and stability of the system.

[0134] In summary, the present invention realizes the consideration of the economy, environmental protection and reliability of the system by constructing a system model considering carbon capture, designing a multi-objective optimization algorithm, and formulating a complete scheduling strategy and abnormal handling mechanism. By introducing a penalty function and constraint conditions, the improved multi-objective optimization algorithm effectively controls carbon emissions while reducing the operating cost; it not only improves the overall economic benefit of the system, but also significantly reduces carbon emissions, and at the same time enhances the operating reliability and adaptability of the system, providing a practical and effective solution for the coordinated optimal scheduling of multi-energy systems. It has strong engineering practicability, can be widely applied to various multi-energy systems with carbon capture, and is of great significance for promoting the low-carbon transformation of the energy system.

[0135] This embodiment further provides a low-carbon building carbon emission optimization system, including:

[0136] A collection module, configured to collect the energy consumption data and environmental parameters of the building energy system, and identify the typical patterns of the building energy system according to the collected data;

[0137] A construction module, based on the typical patterns of the building energy system, constructs a multi-objective optimization model including renewable energy power generation prediction, load demand prediction and carbon emission prediction, and determines the constraint conditions and objective functions of the multi-objective optimization model;

[0138] An optimization processing module, configured to generate an energy optimization scheduling strategy considering carbon emission intensity through collaborative optimization in three dimensions of energy allocation, energy storage scheduling and carbon capture control according to the objective function of the multi-objective optimization model, and execute corresponding recovery strategies for abnormal situations such as sudden drop in photovoltaic power, sharp increase in load, and decrease in carbon capture efficiency, and evaluate the control effect and dynamically adjust the strategy in real time.

[0139] This embodiment also provides a computer device applicable to the low-carbon building carbon emission optimization method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the low-carbon building carbon emission optimization method proposed in the above embodiment.

[0140] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0141] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for optimizing low-carbon building carbon emissions proposed in the above embodiment.

[0142] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0143] Embodiment 2

[0144] This embodiment provides a method for optimizing low-carbon building carbon emissions. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0145] To verify the superiority of the method of the present invention, a typical commercial building is selected for simulation experiments. The building is equipped with a photovoltaic power generation system (300kW), an energy storage system (200kWh), conventional energy equipment, and a carbon capture device. The experiment compares the method of the present invention with traditional scheduling methods and simulates a 24-hour operation cycle.

[0146] The experimental conditions are as follows:

[0147] Ambient temperature: 18 - 32°C;

[0148] Illumination intensity: 0 - 1000 W / m²;

[0149] Energy consumption load: 50 - 500 kW;

[0150] Carbon capture efficiency: 85%;

[0151] Scheduling time interval: 15 min;

[0152] The experimental results are shown in Table 1 as follows:

[0153] Table 1 Experimental comparison results

[0154]

[0155] From the comparison of experimental data, it can be seen that the method of the present invention has significant comprehensive advantages compared with the traditional method. In terms of environmental protection, the daily carbon emissions are reduced from 2100 kg to 1560 kg, a decrease of 25.7%, reflecting the advantages of the present invention in carbon capture control and clean energy utilization; in terms of system efficiency, the comprehensive energy utilization efficiency is increased from 72.5% to 85.8%, and the PV accommodation rate is increased from 78.6% to 92.4%, with increases of 18.3% and 17.6% respectively. This shows that the method of the present invention can better coordinate various energy forms and improve the utilization efficiency of renewable energy.

[0156] In terms of system response performance, the scheduling response time is shortened from 45 seconds to 12 seconds, and the abnormal recovery time is reduced from 8.5 minutes to 2.3 minutes, with improvements of 73.3% and 72.9% respectively, indicating that the present invention has a faster decision-making speed and stronger fault handling ability. In terms of stability, the system operation stability is increased from 85.2% to 96.7%, an increase of 13.5%, reflecting that the method of the present invention can better maintain the stable operation of the system.

[0157] These experimental data comprehensively verify the comprehensive advantages of the present invention in reducing costs, emissions, improving efficiency, enhancing reliability, etc., and prove the practical value of this method in the field of optimal scheduling of building energy systems.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing carbon emissions of low-carbon buildings, characterized by: include: Collect energy consumption data and environmental parameters of building energy systems, pre-process the collected data, extract data features, and identify typical patterns of building energy systems; Based on the typical model of building energy system, a multi-objective optimization model including renewable energy generation forecast, load demand forecast and carbon emission forecast is constructed, and the constraints and objective function of the multi-objective optimization model are determined; According to the objective function of the multi-objective optimization model, the energy distribution, energy storage scheduling and carbon capture control are coordinated and optimized to generate an energy optimization scheduling strategy that takes into account the carbon emission intensity. In addition, corresponding recovery strategies are implemented for abnormal situations such as sudden drop in photovoltaic power, sharp increase in load and decrease in carbon capture efficiency, and control effects and strategy dynamic adjustments are evaluated in real time. The preprocessing includes data cleaning and standardization to obtain standardized data; Based on the preprocessed data, the temporal characteristics of the data are extracted and the typical patterns of the data are identified, which are expressed as: ； in, is the data point, for The cluster center of the class, is the objective function of clustering, is the total number of data samples, is the preset number of clusters, Represents the distance from the data point to the cluster center; By initial random selection Cluster centers are calculated, the distance from each data point to each cluster center is calculated, the data point is assigned to the cluster center with the closest distance, and the cluster center is recalculated: ； in, For the The number of samples in the class, Represents data points Belong to kind; Repeat the above steps until convergence, and we get Typical load modes include: weekday high load mode, weekday low load mode, weekend mode, holiday mode and special weather mode.

2. The low-carbon building carbon emission optimization method according to claim 1, characterized in that: The data sources of the building energy system include power distribution system, heating system, air conditioning system, lighting system and renewable energy system; The power distribution system includes power parameters of the power distribution room, transformer, and distribution cabinet; The heating system includes the operating parameters of the boiler, heat exchange station and pipe network; The air conditioning system includes operating data of the chiller, cooling tower, and air conditioning terminal; The lighting system includes lighting power consumption and control status of each area; The renewable energy system includes real-time data of photovoltaic panels and energy storage devices; The environmental parameters include outdoor environment, indoor environment and regional distribution; The outdoor environment includes temperature, humidity, light, wind speed and air pressure; The indoor environment includes temperature, humidity, CO2 concentration and PM2.5; The zone distribution includes environmental parameters of different zones and floors of the building.

3. The low-carbon building carbon emission optimization method according to claim 1, characterized in that: The multi-objective optimization model is constructed by calculating photovoltaic power generation, energy storage system power, load forecast relative error and carbon emission intensity; The photovoltaic power generation power is expressed as: ； in, is the photovoltaic power generation power, is the photovoltaic conversion efficiency, is the photovoltaic panel area, is the solar irradiance, is the temperature coefficient, is the panel temperature, is the standard temperature; The energy storage system power is expressed as: ； in, For energy storage systems The output power at the moment, is the energy storage rated power, for Energy storage charge state at all times, is the minimum state of charge; The load forecast relative error is expressed as: ； in, is the relative error of load forecast, is the actual load, To predict load; The carbon emission intensity is expressed as: ； in, To standardize carbon emission intensity, For the Energy power, For the Carbon emission factors of energy sources, is the total installed capacity; A multi-objective optimization model is constructed based on photovoltaic power generation, energy storage system power, load forecast relative error and carbon emission intensity, which can be expressed as: ； in, Comprehensive optimization index for the system. is the load forecast error penalty factor, is the carbon emission weight coefficient, The maximum load of the system.

4. The low-carbon building carbon emission optimization method according to claim 3, characterized in that: Determine constraints and objective functions based on the multi-objective optimization model, The constraints include power balance constraints, energy storage system constraints and carbon emission constraints; The power balance constraint is expressed as: ； in, For the Conventional energy output, To generate power for photovoltaic power, For energy storage systems The output power at the moment, Indicates the actual system load. is the transmission and distribution loss rate; The energy storage system constraint is expressed as: ； in, for Energy storage charge state at all times, For charging efficiency, is the discharge efficiency, is the rated capacity of the energy storage system, is the time step; The carbon emission constraint is expressed as: ； in, For the Carbon emission factors of energy sources, is the maximum carbon emission intensity allowed; The objective function is expressed as: ； in, represents the economic objective function, represents the environmental protection objective function, represents the efficiency objective function, , , is the weight coefficient; The economic objective function is expressed as: ； in, For the Unit cost of energy, is the operating cost of the energy storage system, is the carbon emission cost, is the carbon price; The environmental protection objective function is expressed as: ； in, Indicates a time interval; The efficiency objective function is expressed as: ； ； in, Represents the building energy system in time comprehensive energy utilization efficiency.

5. The low-carbon building carbon emission optimization method according to claim 4, characterized in that: Based on the objective function, an optimal scheduling strategy is constructed by considering energy distribution, energy storage scheduling and carbon capture control respectively; The basic equation for energy distribution is expressed as: ； in, Indicates The output coefficient of the energy source, Indicates Maximum output of energy source; The equation for energy storage scheduling is expressed as: ； in, For energy storage systems The output power at the moment, is the deviation between load and predicted value, It is the electricity price signal; The carbon capture control equation is expressed as: ； in, express The amount of carbon captured at each moment, represents the carbon capture efficiency coefficient.

6. A low-carbon building carbon emission optimization system, based on the low-carbon building carbon emission optimization method according to any one of claims 1 to 5, characterized in that: include: A collection module is used to collect energy consumption data and environmental parameters of the building energy system and identify typical patterns of the building energy system based on the collected data; The construction module builds a multi-objective optimization model including renewable energy generation forecast, load demand forecast and carbon emission forecast based on the typical model of building energy system, and determines the constraints and objective function of the multi-objective optimization model; The optimization processing module is used to generate an energy optimization scheduling strategy that takes into account carbon emission intensity through collaborative optimization in three dimensions: energy distribution, energy storage scheduling, and carbon capture control according to the objective function of the multi-objective optimization model, and to execute corresponding recovery strategies for abnormal situations such as sudden drop in photovoltaic power, sharp increase in load, and decrease in carbon capture efficiency, and to evaluate the control effect and dynamic adjustment of the strategy in real time.

7. The low-carbon building carbon emission optimization system according to claim 6, characterized in that: The collection module collects the operating data and environmental parameters of the building energy system in real time, including renewable energy power generation, energy consumption equipment load, energy storage system status, carbon capture device operating parameters, and ambient temperature, humidity, and light data, and identifies the typical operating mode of the system through data analysis, providing a reliable modeling basis and feature data support for the construction module; The construction module establishes a mathematical model including renewable energy generation prediction, load demand prediction and carbon emission prediction based on the data provided by the collection module, forms a multi-objective optimization model by setting constraints and optimization goals, dynamically updates parameters according to the real-time data of the collection module, maintains the accuracy of the model, and provides a scientific decision-making basis for the optimization processing module; The optimization processing module generates a specific energy scheduling strategy and carbon capture control scheme based on the multi-objective optimization model of the construction module. During the execution process, it continuously receives real-time data from the collection module to evaluate the effect. When an abnormal situation occurs, it adjusts the strategy in time and feeds back the operation results to the collection module to form a closed-loop optimization control.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the low-carbon building carbon emission optimization method described in any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the low-carbon building carbon emission optimization method according to any one of claims 1 to 5 are implemented.

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