Low-carbon building carbon emission optimization system

Abstract

The invention discloses a low-carbon building carbon emission optimization system, which relates to the technical field of building energy conservation, and comprises the following steps: collecting energy consumption data and environmental parameters of a building energy system, preprocessing the collected data, extracting data features, and identifying a typical mode of the building energy system; based on the typical mode 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 constraint conditions and objective functions of the multi-objective optimization model; and generating an energy optimization scheduling strategy and a carbon capture control scheme execution optimization scheduling strategy considering carbon emission intensity according to an objective function of the multi-objective model, collecting an abnormal condition execution recovery strategy, and performing effect evaluation and strategy dynamic adjustment in real time. According to the method, economy, environmental protection and reliability of the system are taken into consideration, effective control over carbon emission is achieved through the improved multi-objective optimization algorithm, and the operation reliability and adaptability of the system are enhanced.

Description

Technical Field

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

[0002] With the increasing requirements for energy conservation and emission reduction in buildings, building energy systems are developing in the direction of diversification and low carbonization. At present, building energy systems usually include multiple 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 are beginning to adopt carbon capture technology. However, due to the intermittent and uncertain nature of renewable energy and the dynamic characteristics of building load demand, traditional building energy scheduling methods are difficult to achieve coordinated optimization of multiple energy forms.

[0003] In the existing technology, most building energy optimization scheduling methods only consider economic goals and lack effective control over carbon emissions; even the methods that consider carbon emission factors often treat carbon capture devices as independent systems and fail to achieve overall coordinated optimization with the building energy system. In addition, when faced with system operation abnormalities, the existing methods lack a complete processing mechanism, which can easily lead to decreased system efficiency and increased carbon emissions. Therefore, it is urgent to develop a building multi-energy coordinated optimization scheduling method that can comprehensively consider economy, environmental protection, and reliability to achieve low-carbon and efficient operation of building energy systems. 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 intermittent and uncertain nature of renewable energy and the dynamic characteristics of building load demand, traditional building energy scheduling methods are difficult to achieve coordinated optimization of multiple energy forms.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides a method for optimizing carbon emissions of low-carbon buildings, which includes: collecting energy consumption data and environmental parameters of the 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 forecasting, load demand forecasting and carbon emission forecasting, and determining the constraints and objective function of the multi-objective optimization model; generating an energy optimization scheduling strategy and a carbon capture control scheme that consider carbon emission intensity according to the objective function of the multi-objective model, executing the optimization scheduling strategy, collecting abnormal situations to execute the recovery strategy, and performing real-time effect evaluation and dynamic adjustment of the strategy.

[0007] 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, is the cluster center, 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; repeat the above steps until convergence, and get Typical load modes include: weekday high load mode, weekday low load mode, weekend mode, holiday mode and special weather mode.

[0008] As a preferred scheme of the low-carbon building carbon emission optimization method described in the present invention, wherein: 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 distribution room, transformer and distribution cabinet; the heating system includes operating parameters of boiler, heat exchange station and pipeline network; the air conditioning system includes operating condition data of chiller, cooling tower and air conditioning terminal; the lighting system includes power consumption and control status of lighting in 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 regional distribution includes environmental parameters of different areas and floors of the building.

[0009] As a preferred solution of the low-carbon building carbon emission optimization method of the present invention, the multi-objective optimization model is constructed by calculating the photovoltaic power generation power, the energy storage system power, the load prediction relative error and the 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 power of the energy storage system is expressed as: ; in, For energy storage systems The output power at the moment, is the energy storage rated power, is the charge state of the energy storage system, is the minimum state of charge; the load prediction relative error is expressed as: ; in, is the relative error of load forecast, is the actual load, is the predicted 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 the photovoltaic power generation, energy storage system power, load forecast relative error and carbon emission intensity, which is 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.

[0010] 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: ; 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 allowed carbon emission intensity; 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, represents the time interval; the efficiency objective function is expressed as:

[0011] ; in, Represents the building energy system in time comprehensive energy utilization efficiency.

[0012] As a preferred solution of the low-carbon building carbon emission optimization method of the present invention, wherein: based on the objective function of the multi-objective model, energy distribution, energy storage scheduling and carbon capture control are considered to construct an optimization scheduling strategy; the basic equation of energy distribution is expressed as: ; in, Indicates The output coefficient of the energy source, Indicates The maximum output of the 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, 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.

[0013] In the 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 the building energy system, and identifying the typical mode of the building energy system based on the collected data; a construction module, based on the typical mode 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 constraints and objective function of the multi-objective optimization model; an optimization processing module for generating an energy optimization scheduling strategy and a carbon capture control scheme that consider carbon emission intensity according to the objective function of the multi-objective model, executing the optimization scheduling strategy, collecting abnormal situations to execute the recovery strategy, and performing real-time effect evaluation and dynamic adjustment of the strategy.

[0014] As a preferred solution of the low-carbon building carbon emission optimization system described in the present invention, wherein: 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 environmental temperature, humidity, and light data, and identifies the typical operating mode of the system through data analysis, so as to provide a reliable modeling basis and feature 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 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, continuously receives real-time data from the collection module during execution to evaluate the effect, adjusts the strategy in time when an abnormal situation occurs, and feeds back the operation results to the collection module to form a closed-loop optimization control.

[0015] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein 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.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, 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.

[0017] The beneficial effect of the present invention is that the present invention realizes the balance of 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 exception handling mechanism. By adopting an improved multi-objective optimization algorithm, by introducing penalty functions and constraints, effective control of carbon emissions is achieved while reducing operating costs; it not only improves the overall economic benefits 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 and scheduling of multi-energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 Flowchart of the carbon emission optimization method for low carbon buildings. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1 Reference Figure 1 , which is the first embodiment of the present invention, provides a method for optimizing carbon emissions of low-carbon buildings, including: S1: 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.

[0023] Collect all data sources in the building energy system, including power distribution, heating, air conditioning, lighting, and renewable energy systems.

[0024] The power distribution system includes the power parameters of the distribution room, transformer, and distribution cabinet; the heating system includes the operating parameters of the boiler, heat exchange station, and pipeline network; the air-conditioning system includes the operating data of the chiller, cooling tower, and air-conditioning terminal; the lighting system includes the lighting power consumption and control status of each area; the renewable energy system includes real-time data of photovoltaic panels and energy storage devices.

[0025] At the same time, a multi-level environmental parameter monitoring network is established to detect environmental data, including outdoor environment, indoor environment and regional distribution.

[0026] 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.

[0027] The collected data are preprocessed, including data cleaning and standardization, to obtain standardized data.

[0028] Based on the preprocessed data, the time series characteristics of the data are extracted and the energy usage pattern of the data is identified.

[0029] Among them, the load curve characteristics of the data are analyzed, and the periodic characteristics are extracted by Fourier transform to obtain the time series characteristics of the data; Pattern classification based on the K-means algorithm identifies typical patterns of data, expressed as: ; in, is the data point, is the cluster center, 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.

[0030] 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.

[0031] Repeat the above steps until convergence, and finally get Typical load modes include: weekday high load mode, weekday low load mode, weekend mode, holiday mode and special weather mode.

[0032] By minimizing the objective function , find the optimal clustering results, thereby identifying the typical patterns of building energy use and providing a basis for subsequent energy optimization scheduling.

[0033] S2: 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.

[0034] Based on the typical patterns of building energy use, cluster analysis was used to obtain the daily load variation patterns, photovoltaic power generation characteristics, energy storage usage patterns, and carbon emission characteristics.

[0035] It is determined that the energy supply includes photovoltaic power generation and the building's energy storage system. According to the characteristics of the photovoltaic power generation curve, the photovoltaic power generation power function is constructed, which 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.

[0036] According to the energy storage usage mode, the energy storage system power function is constructed and expressed as: ; in, For energy storage systems The output power at the moment, is the energy storage rated power, is the charge state of the energy storage system, is the minimum state of charge.

[0037] The typical pattern obtained by clustering is used as the prediction benchmark, and the load prediction accuracy is calculated by the deviation between the actual load and the predicted value of the typical pattern, which is expressed as: ; in, is the relative error of load forecast, is the actual load, To predict load.

[0038] According to the carbon emission characteristics of different periods in the typical model, considering the composition ratio of various energy sources, carbon emissions are predicted, 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.

[0039] 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.

[0040] Furthermore, constraints are determined based on the multi-objective optimization model, including 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.

[0041] The energy storage system constraints are 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.

[0042] The carbon emission constraint is expressed as: ; in, For the Carbon emission factors of energy sources, is the maximum carbon emission intensity allowed.

[0043] According to the constraints, the comprehensive objective function of multi-objective optimization is determined as: ; in, represents the economic objective function, represents the environmental protection objective function, represents the efficiency objective function, , , is the weight coefficient.

[0044] Furthermore, the economic objective function Reflects the total cost of system operation, including energy purchase cost, equipment operation cost and carbon emission cost, expressed as: ; in, For the Unit cost of energy, is the operating cost of the energy storage system, is the carbon emission cost, For the carbon price.

[0045] Environmental performance objective function Reflects the carbon emission intensity of the system, including the carbon emission contribution of various energy sources, expressed as: ; in, Indicates a time interval.

[0046] Efficiency objective function Reflects the system operation efficiency, taking into account energy utilization and equipment performance, expressed as: ; in, Comprehensive optimization index of the same system , when used as a real-time evaluation indicator of the system hour, Used to monitor system performance in real time, determine whether the operation strategy needs to be adjusted, and evaluate the current control effect; when the objective function is optimized When the parameters As the efficiency target in multi-objective optimization, it is used to calculate the cumulative efficiency during the entire optimization cycle.

[0047] S3: Generate energy optimization scheduling strategy and carbon capture control scheme considering carbon emission intensity according to the objective function of the multi-objective model to implement the optimization scheduling strategy, collect abnormal situations to implement the recovery strategy, and conduct effect evaluation and dynamic strategy adjustment in real time.

[0048] Based on the objective function, the participation of each energy source is determined through the energy allocation equation. The basic energy allocation equation is expressed as: ; in, Indicates The output coefficient of the energy source, Indicates The maximum output of this type of energy.

[0049] Furthermore, the charging and discharging strategy of the energy storage system is determined by the energy storage scheduling equation, which is expressed as: ; in, For energy storage systems The output power at the moment, is the deviation between load and predicted value, The electricity price signal.

[0050] The operation intensity of the carbon capture system is controlled by the carbon capture control equation, which is expressed as: ; in, express The amount of carbon captured at each moment, represents the carbon capture efficiency coefficient.

[0051] 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.

[0052] 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%.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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%.

[0058] If a new abnormal situation occurs, the recovery process will be immediately suspended, the system status will be re-evaluated, and an appropriate solution will be selected to ensure system security and stability.

[0059] In summary, the present invention achieves a balance between the economy, environmental protection and reliability of the system by constructing a system model that takes carbon capture into consideration, designing a multi-objective optimization algorithm, and formulating a complete scheduling strategy and exception handling mechanism. By adopting an improved multi-objective optimization algorithm, by introducing penalty functions and constraints, effective control of carbon emissions is achieved while reducing operating costs; it not only improves the overall economic benefits 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 and scheduling of multi-energy systems. It has strong engineering practicality and can be promoted and applied to various multi-energy systems containing carbon capture, which is of great significance to promoting the low-carbon transformation of energy systems.

[0060] This embodiment further provides a low-carbon building carbon emission optimization system, including: 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 energy optimization scheduling strategies and carbon capture control schemes that take into account carbon emission intensity according to the objective function of the multi-objective model, execute optimization scheduling strategies, collect abnormal situations to execute recovery strategies, and conduct real-time effect evaluation and dynamic strategy adjustments.

[0061] This embodiment also provides a computer device, which is suitable for 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 computer-executable instructions to implement the low-carbon building carbon emission optimization method proposed in the above embodiment.

[0062] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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 a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0063] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing carbon emissions of low-carbon buildings proposed in the above embodiment is implemented.

[0064] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0065] Example 2 This embodiment provides a method for optimizing carbon emissions of low-carbon buildings. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0066] In order to verify the advantages of the method of the present invention, a typical commercial building was 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 the traditional scheduling method and simulates a 24-hour operation cycle.

[0067] The experimental conditions are: Ambient temperature: 18-32℃; Light intensity: 0-1000W / m²; Energy load: 50-500kW; Carbon capture efficiency: 85%; Scheduling time interval: 15min; The experimental results are shown in Table 1: Table 1 Experimental comparison results

[0068] By comparing the experimental data, it can be seen that the method of the present invention has significant comprehensive advantages over the traditional method. In terms of environmental protection, the daily carbon emissions were reduced from 2100kg to 1560kg, 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 increased from 72.5% to 85.8%, and the photovoltaic absorption rate increased from 78.6% to 92.4%, an increase 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 efficiency of renewable energy use.

[0069] In terms of system response performance, the dispatch response time was shortened from 45 seconds to 12 seconds, and the abnormal recovery time was reduced from 8.5 minutes to 2.3 minutes, which were improved by 73.3% and 72.9% respectively, indicating that the invention has faster decision-making speed and stronger fault handling ability. In terms of stability, the system operation stability was improved from 85.2% to 96.7%, an increase of 13.5%, reflecting that the method of the invention can better maintain the stable operation of the system.

[0070] These experimental data fully verify the comprehensive advantages of the present invention in reducing costs, reducing emissions, improving efficiency, and enhancing reliability, and prove the practical value of this method in the field of optimal scheduling of building energy systems.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in 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; Generate energy optimization scheduling strategy and carbon capture control scheme considering carbon emission intensity according to the objective function of the multi-objective model, execute the optimization scheduling strategy, collect abnormal situations to execute the recovery strategy, and conduct effect evaluation and dynamic strategy adjustment 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, is the cluster center, 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; 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, 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.

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 of the multi-objective model, 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 energy optimization scheduling strategies and carbon capture control schemes that take into account carbon emission intensity according to the objective function of the multi-objective model, execute optimization scheduling strategies, collect abnormal situations to execute recovery strategies, and conduct real-time effect evaluation and dynamic strategy adjustment.

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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