Regional building carbon emission optimization system and method

Abstract

The invention discloses a regional building carbon emission optimization system and method, and belongs to the technical field of building carbon emission optimization, and the system comprises a data collection module, an evaluation module, a model construction module, an energy storage planning module, a target setting module and a solving module. The specific process is as follows: step 1, electricity price information from different regions and power characteristics and use frequencies of various types of electrical equipment are collected, and the total electricity charge is determined; 2, determining an average carbon emission coefficient of each type of equipment based on a life cycle evaluation algorithm; 3, considering the cost structure difference and importance of different types of electrical equipment, and constructing an electricity-carbon coupling cost model; 4, considering the virtual energy storage of the building, and calculating the carbon emission calculation and operation cost of the low-carbon building; 5, establishing a building operation optimization model; 6, solving the building operation optimization model by adopting a genetic algorithm, and obtaining an optimal solution through multiple iterations; according to the method provided by the invention, the operation efficiency is improved, and the energy waste is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission optimization in buildings, and in particular relates to a regional building carbon emission optimization system and method. Background Art

[0002] With the increasing global attention to climate change, the carbon emission problem in the construction industry has become increasingly prominent. Buildings consume a large amount of energy and generate a large amount of carbon emissions throughout their life cycle, including construction, operation, and demolition. Among them, the energy consumption and carbon emissions during the building operation stage are particularly prominent, mainly from the use of energy such as electricity and gas.

[0003] Due to differences in factors such as electricity prices and climate conditions, the energy costs and carbon emission situations of buildings in different regions vary greatly. For example, in some regions, electricity mainly comes from coal-fired power generation, with a relatively high carbon emission coefficient; while in other regions, there may be more renewable energy power generation, and the carbon emission coefficient is relatively low. At the same time, the usage frequencies and power characteristics of different types of electrical equipment in buildings are also different, which will all affect the electricity-carbon coupling cost of buildings.

[0004] In addition, the energy storage situation inside buildings is also of great significance for energy management and carbon emission optimization. Traditional building energy storage is often limited to physical energy storage devices, but buildings themselves also have the potential for virtual energy storage, such as using building structures to store cold / heat. However, how to accurately define the virtual energy storage capacity, simulate the energy flow, and estimate the carbon emissions throughout the life cycle is an urgent problem to be solved.

[0005] Moreover, in order to optimize building carbon emissions, reasonable building operation optimization strategies need to be formulated. This involves the balance of multiple objectives, such as the costs of purchasing electricity and gas, operation and maintenance costs, renewable energy curtailment costs, and electricity-carbon coupling costs. At the same time, it is necessary to ensure that physical laws and regulatory requirements are followed during the optimization process, select appropriate solution algorithms, and verify the accuracy of the model. In summary, developing a regional building carbon emission optimization system and method has important practical significance. Summary of the Invention

[0006] The purpose of the present invention is to provide a regional building carbon emission optimization system and method, which can accurately evaluate the building energy cost and carbon emission situation, and by establishing an accurate calculation model, reasonably utilizing the virtual energy storage of buildings, and formulating a scientific building operation optimization strategy, thereby reducing the carbon emissions and energy costs of buildings and improving the operation efficiency of the building energy system.

[0007] To achieve the above object, the present invention provides a regional building carbon emission optimization system, including a data acquisition module, an evaluation module, a model construction module, an energy storage planning module, a target setting module, and a solution module;

[0008] Data acquisition module: Responsible for collecting data such as electricity price information, climate conditions, power characteristics, and usage frequencies of electrical equipment in different regions, providing basic data for subsequent calculations and analyses;

[0009] Evaluation module: Adopts the life cycle assessment (LCA) algorithm to evaluate the carbon emission coefficients of each energy-consuming unit in the building;

[0010] Model construction module: Constructs an electricity-carbon coupling cost model and a building operation optimization model;

[0011] Energy storage planning module: Defines the virtual energy storage capacity of the building according to the dynamic programming method, and optimizes the configuration of the cold / heat storage medium;

[0012] Goal setting module: Adopts a multi-objective optimization algorithm to set the objective function for building operation optimization and add constraints to it;

[0013] Solution module: Adopts a genetic algorithm to solve the optimal solution of the objective function.

[0014] The present invention also provides a method for an optimized regional building carbon emission system, including the following steps:

[0015] Step 1: Collect electricity price information, power characteristics, and usage frequencies of various types of electrical equipment from different regions, and determine the total electricity cost;

[0016] Step 2: Determine the average carbon emission coefficient of various types of equipment based on the life cycle assessment algorithm;

[0017] Step 3: Considering the cost structure differences and importance of different types of electrical equipment, construct an electricity-carbon coupling cost model;

[0018] Step 4: Considering the virtual energy storage of the building, calculate the carbon emission measurement and operation cost of a low-carbon building;

[0019] Step 5: Establish a building operation optimization model;

[0020] Step 6: Use a genetic algorithm to solve the building operation optimization model established in Step 5, and obtain the optimal solution through multiple iterations.

[0021] Preferably, the process of collecting electricity price information, power characteristics, and usage frequencies of various types of electrical equipment from different regions and determining the total electricity cost in Step 1 is as follows:

[0022] S11: Collect electricity price information. Through the API interface provided by the power company, obtain the electricity price information in different regions in real time, including peak-valley electricity prices and seasonal electricity prices;

[0023] S12. Collect the power characteristics of the devices. Obtain the rated power, standby power, and operating power of various electrical devices through the technical manuals provided by the device manufacturers or the measured data.

[0024] S13. Collect the usage frequency of the devices: Real-time monitor the usage frequency of various electrical devices through smart meters or sensors, including the daily usage duration and the number of days used per week.

[0025] S14. Calculate the total electricity cost according to the electricity price information, device power characteristics, and usage frequency. The expression is as follows:

[0026]

[0027] In the formula, C total represents the total electricity cost, P ij represents the power of the i-th type of device in the j-th time period, T ij represents the usage duration of the i-th type of device in the j-th time period, R j represents the electricity price in the j-th time period, n represents the total number of devices, and m represents the total length of the time period.

[0028] Preferably, the process of determining the average carbon emission coefficient of various devices based on the life cycle assessment algorithm in step 2 is as follows:

[0029] S21. Determine the device life cycle. Determine the life cycle of various devices according to the technical manuals provided by the device manufacturers or industry standards, including the production, transportation, use, and scrapping stages.

[0030] S22. Calculate the life cycle carbon emissions. Use the life cycle assessment algorithm to calculate the carbon emissions of various devices during the entire life cycle. The calculation expression is as follows:

[0031] E life = E prod + E trans + E use + E dis ;

[0032] In the formula, E life represents the life cycle carbon emissions, E prod represents the carbon emissions during the production stage, E trans represents the carbon emissions during the transportation stage, E use represents the carbon emissions during the use stage, E dis represents the carbon emissions during the scrapping stage;

[0033] S23. Calculate the average carbon emission coefficient of various devices according to the life cycle carbon emissions and the device usage duration. The expression is as follows:

[0034]

[0035] Where, K avg is the average carbon emission coefficient, and T use is the service life of the equipment.

[0036] Preferably, the carbon emissions during the production stage are the carbon emissions generated during the manufacturing process of the equipment, including raw material extraction, processing, and assembly;

[0037] The carbon emissions during the transportation stage are the carbon emissions generated during the transportation of the equipment from the production location to the usage location;

[0038] The carbon emissions during the usage stage are the carbon emissions generated during the usage of the equipment, which come from power consumption or fuel combustion;

[0039] The carbon emissions during the scrapping stage are the carbon emissions generated during the scrapping process of the equipment, including disassembly, recycling, and landfill.

[0040] Preferably, the expression of the electro-carbon coupling cost model constructed in step 3 is as follows:

[0041]

[0042] Where, C couple is the electro-carbon coupling cost, C i is the cost of the i-th type of equipment, W i is the importance weight of the i-th type of equipment, and K avg,i is the average carbon emission coefficient of the i-th type of equipment.

[0043] Preferably, in step 4, considering the virtual energy storage of the building, the process of calculating the carbon emissions measurement and operation cost of the low-carbon building is as follows:

[0044] S41. Determine the virtual energy storage capacity. According to the energy management system of the building, determine the capacity of the virtual energy storage, including battery energy storage and thermal energy storage. Among them, battery energy storage is the core component of the virtual energy storage, mainly used to store electric energy and release it when needed to balance the power supply and demand, reduce the peak load and carbon emissions; thermal energy storage is to optimize the use of energy by storing thermal energy (such as hot water, ice thermal storage, phase change materials, etc.), mainly used for heating, cooling, and hot water supply of the building;

[0045] S42. Calculate the carbon emissions of the virtual energy storage according to the charge and discharge efficiency and carbon emission coefficient of the virtual energy storage. The calculation expression is as follows:

[0046]

[0047] Where, E storage is the carbon emissions of the virtual energy storage, E charge,i is the charging carbon emissions of the i-th type of virtual energy storage, n charge,i is the charging efficiency of the i-th type of virtual energy storage, and E discharge,iis the carbon emission during discharging of the i-th type of virtual energy storage, n discharge,i is the discharging efficiency of the i-th type of virtual energy storage;

[0048] S43. Calculate the carbon emission measurement and operation cost of a low-carbon building based on the carbon emission and operation cost of virtual energy storage. The calculation expression is as follows:

[0049] C low-carbon = C couple + E storage ;

[0050] Among them, C low-carbon is the carbon emission measurement and operation cost of a low-carbon building.

[0051] Preferably, the expression of the building operation optimization model established in step 5 is as follows:

[0052] minZ = α × C low-carbon + β × C total ;

[0053] s.t.

[0054]

[0055] Among them, Z represents the optimization objective function, α represents the weight coefficient of the carbon emission measurement and operation cost of a low-carbon building, β represents the weight coefficient of the total electricity cost, P max represents the maximum power limit of the equipment, T max represents the maximum operation time limit of the equipment, E max represents the energy capacity limit of the virtual energy storage.

[0056] Preferably, in step 6, the genetic algorithm is used to solve the building operation optimization model established in step 5. The process of obtaining the optimal solution after multiple iterations is as follows:

[0057] S61. Initialize the population; randomly generate a set of initial solutions as the initial population of the genetic algorithm;

[0058] S62. Calculate the fitness; calculate the fitness value of each individual according to the optimization objective function;

[0059] S63. Selection operation; use the roulette wheel selection method to select individuals with high fitness to enter the next generation;

[0060] S64. Crossover operation; use the single-point crossover method to perform crossover operations on the selected individuals to generate new individuals;

[0061] S65. Mutation operation; use the random mutation method to perform mutation operations on some individuals to increase the diversity of the population;

[0062] S66. Iterative solution; repeat the above steps until the convergence condition is reached, stop the iteration, and obtain the optimal solution.

[0063] Preferably, the convergence condition in S66 is specifically that the change in the fitness value is less than the set threshold ∈, or the maximum number of iterations T is reached max , and the expression is as follows:

[0064]

[0065] In the formula, represents the optimal fitness value in the t-th generation population, represents the optimal fitness value in the (t - 1)-th generation population, ∈ represents the fitness change threshold, and N max represents the maximum number of iterations.

[0066] Therefore, the present invention adopts the above-mentioned regional building carbon emission optimization system and method, and has the following

[0067] beneficial effects:

[0068] (1) By establishing an electric-carbon coupling cost calculation model, the energy cost structure of the building can be accurately evaluated, so as to control the cost targeted, reduce the costs of electricity and gas purchase, operation and maintenance, etc.;

[0069] (2) Comprehensively evaluate the carbon emission coefficients of each energy-consuming unit, fully consider factors such as the virtual energy storage of the building and the carbon footprint of building materials, which helps to accurately calculate and reduce the carbon emissions during the whole life cycle of the building, and conforms to the global low-carbon development trend;

[0070] (3) Establish a reasonable building operation optimization model, use means such as multi-objective optimization and genetic algorithm to optimize the operation time and power of equipment, improve the operation efficiency of the building energy system, and reduce energy waste, such as reducing the phenomenon of curtailment of renewable energy.

[0071] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0072] Figure 1 is a schematic structural diagram of a regional building carbon emission optimization system of the present invention;

[0073] Figure 2 is the overall process block diagram of the method of a regional building carbon emission optimization system of the present invention. Detailed Embodiments

[0074] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0075] Please refer to Figure 1 , a regional building carbon emission optimization system, comprising a data collection module, an evaluation module, a model construction module, an energy storage planning module, a target setting module, and a solution module;

[0076] Data collection module: responsible for collecting data such as electricity price information, climate conditions, power characteristics and usage frequencies of electrical equipment in different regions, providing basic data for subsequent calculations and analyses;

[0077] Evaluation module: adopting the life cycle assessment (LCA) algorithm to evaluate the carbon emission coefficients of each energy-consuming unit in the building;

[0078] Model construction module: constructing an electric-carbon coupling cost model and a building operation optimization model;

[0079] Energy storage planning module: defining the virtual energy storage capacity of the building and optimizing the configuration of the chilled / thermal storage medium according to the dynamic programming method;

[0080] Target setting module: adopting a multi-objective optimization algorithm to set the objective function for building operation optimization and adding constraints to it;

[0081] Solution module: adopting a genetic algorithm to solve the optimal solution of the objective function.

[0082] Please refer to Figure 2 , a method for a regional building carbon emission optimization system, comprising the following steps:

[0083] Step 1: Collect electricity price information, power characteristics and usage frequencies of various types of electrical equipment from different regions, and determine the total electricity bill; the specific process is as follows:

[0084] S11: Collect electricity price information, and obtain the electricity price information of different regions in real time through the API interface provided by the power company, including peak-valley electricity prices and seasonal electricity prices;

[0085] S12: Collect equipment power characteristics, and obtain the rated power, standby power and operating power of various types of electrical equipment through the technical manuals provided by the equipment manufacturers or measured data;

[0086] S13: Collect equipment usage frequencies: monitor the usage frequencies of various types of electrical equipment in real time through smart meters or sensors, including daily usage duration and weekly usage days;

[0087] S14. Calculate the total electricity cost based on electricity price information, equipment power characteristics, and usage frequency. The expression is as follows:

[0088]

[0089] In the formula, C total represents the total electricity cost, P ij represents the power of the i-th type of equipment in the j-th time period, T ij represents the usage duration of the i-th type of equipment in the j-th time period, R j represents the electricity price in the j-th time period, n represents the total number of equipment, and m represents the total length of the time period.

[0090] Step 2. Determine the average carbon emission coefficient of each type of equipment based on the life cycle assessment algorithm. The specific process is as follows:

[0091] S21. Determine the equipment life cycle. According to the technical manuals provided by equipment manufacturers or industry standards, determine the life cycles of various types of equipment, including the production, transportation, use, and scrapping stages;

[0092] S22. Calculate the life cycle carbon emissions. Using the life cycle assessment algorithm, calculate the carbon emissions of various types of equipment throughout the life cycle. The calculation expression is as follows:

[0093] E lift = E prod + E trans + E use + E dis ;

[0094] In the formula, E life represents the life cycle carbon emissions, E prod represents the carbon emissions in the production stage, E trans represents the carbon emissions in the transportation stage, E use represents the carbon emissions in the use stage, E dis represents the carbon emissions in the scrapping stage; among them, the carbon emissions in the production stage are the carbon emissions generated during the manufacturing process of the equipment, including raw material extraction, processing, and assembly links; the carbon emissions in the transportation stage are the carbon emissions generated during the transportation of the equipment from the production location to the use location; the carbon emissions in the use stage are the carbon emissions generated during the use of the equipment, which come from electricity consumption or fuel combustion; the carbon emissions in the scrapping stage are the carbon emissions generated during the scrapping process of the equipment, including disassembly, recycling, and landfill.

[0095] S23. Calculate the average carbon emission coefficient of each type of equipment based on the life cycle carbon emissions and equipment usage duration. The expression is as follows:

[0096]

[0097] In the formula, Kavg is the average carbon emission coefficient, T use is the service life of the equipment.

[0098] Step 3: Considering the cost structure differences and their importance of different types of electrical equipment, construct an electro-carbon coupling cost model; among them, the expression of the constructed electro-carbon coupling cost model is as follows:

[0099]

[0100] In the formula, C couple is the electro-carbon coupling cost, C i is the cost of the i-th type of equipment, W i is the importance weight of the i-th type of equipment, K avg,i is the average carbon emission coefficient of the i-th type of equipment.

[0101] Step 4: Considering the virtual energy storage of the building, calculate the carbon emission measurement and operation cost of the low-carbon building; the specific process is as follows:

[0102] S41: Determine the virtual energy storage capacity. According to the energy management system of the building, determine the capacity of the virtual energy storage, including battery energy storage and thermal energy storage. Among them, battery energy storage is the core component of virtual energy storage, mainly used to store electric energy and release it when needed to balance power supply and demand, reduce peak load and carbon emissions; thermal energy storage realizes the optimal utilization of energy by storing thermal energy (such as hot water, ice thermal storage, phase change materials, etc.), mainly used for heating, cooling and hot water supply of the building;

[0103] S42: According to the charge and discharge efficiency and carbon emission coefficient of the virtual energy storage, calculate the carbon emissions of the virtual energy storage. The calculation expression is as follows:

[0104]

[0105] In the formula, E storage is the carbon emission of the virtual energy storage, E charge,i is the charging carbon emission of the i-th type of virtual energy storage, n charge,i is the charging efficiency of the i-th type of virtual energy storage, E discharge,i is the discharging carbon emission of the i-th type of virtual energy storage, n discharge,i is the discharging efficiency of the i-th type of virtual energy storage;

[0106] S43: According to the carbon emission and operation cost of the virtual energy storage, calculate the carbon emission measurement and operation cost of the low-carbon building. The calculation expression is as follows:

[0107] C low-carbon = C couple + E storage ;

[0108] Among them, C low-carbonFor the calculation of carbon emissions and operating costs of low-carbon buildings.

[0109] Step 5: Establish an optimal building operation model; the specific expression is as follows:

[0110] minZ = α × C low-carbon + β × C total ;

[0111] s.t.

[0112]

[0113] Among them, Z represents the optimization objective function, α represents the weight coefficient of the carbon emission measurement and operation cost of low-carbon buildings, β represents the weight coefficient of the total electricity cost, P max represents the maximum power limit of the equipment, T max represents the maximum operating time limit of the equipment, E max represents the virtual energy storage capacity limit.

[0114] Step 6: Use the genetic algorithm to solve the optimal building operation model established in Step 5, and obtain the optimal solution after multiple iterations; the specific process is as follows:

[0115] S61: Initialize the population; randomly generate a set of initial solutions as the initial population of the genetic algorithm;

[0116] S62: Calculate the fitness; calculate the fitness value of each individual according to the optimization objective function;

[0117] S63: Selection operation; use the roulette wheel selection method to select individuals with high fitness to enter the next generation;

[0118] S64: Crossover operation; use the single-point crossover method to perform crossover operations on the selected individuals to generate new individuals;

[0119] S65: Mutation operation; use the random mutation method to perform mutation operations on some individuals to increase the diversity of the population;

[0120] S66: Iterative solution; repeat the above steps until the convergence condition is reached, stop the iteration, and obtain the optimal solution; among them, the convergence condition is specifically that the change in the fitness value is less than the set threshold ∈, or the maximum number of iterations T max is reached, and the expression is as follows:

[0121]

[0122] In the formula, represents the optimal fitness value in the t-th generation population, represents the optimal fitness value in the (t - 1)-th generation population, ∈ represents the fitness change threshold, N maxIndicates the maximum number of iterations.

[0123] Therefore, the present invention adopts the above-mentioned regional building carbon emission optimization system and method. By comprehensively collecting electricity price information, equipment power characteristics and usage frequency, an electricity-carbon coupling cost model is constructed. Considering the virtual energy storage of buildings, a building operation optimization model is established, and the genetic algorithm is used to solve it. This method has high practicability and accuracy, can effectively reduce building carbon emissions and operation costs, and provides technical support for the sustainable development of the building industry.

[0124] Finally, 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 they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A regional building carbon emission optimization system, characterized by: It includes data acquisition module, evaluation module, model building module, energy storage planning module, target setting module and solution module; Data collection module: responsible for collecting electricity price information, climate conditions, power characteristics and usage frequency data of electrical equipment in different regions; Evaluation module: Use life cycle assessment algorithm to evaluate the carbon emission coefficient of each energy-consuming unit in the building; Model building module: building electricity-carbon coupling cost model and building operation optimization model; Energy storage planning module: Based on the dynamic programming method, the virtual energy storage capacity of the building is defined and the configuration of cold / heat storage media is optimized; Goal setting module: Use multi-objective optimization algorithm to set the objective function of building operation optimization and add constraints to it; Solution module: Use genetic algorithm to find the optimal solution of the objective function.

2. A method for optimizing regional building carbon emissions as claimed in claim 1, characterized in that: The following steps are involved: Step 1: Collect electricity price information from different regions, power characteristics and usage frequency of various types of electrical equipment, and determine the total electricity cost; Step 2: Determine the average carbon emission coefficient of each type of equipment based on the life cycle assessment algorithm; Step 3: Consider the differences in cost structures and importance of different types of electrical equipment and build an electricity-carbon coupling cost model; Step 4: Considering the virtual energy storage of buildings, calculate the carbon emission and operating costs of low-carbon buildings; Step 5: Establish a building operation optimization model; Step 6: Use genetic algorithm to solve the building operation optimization model established in step 5, and obtain the optimal solution after multiple iterations.

3. The method of a regional building carbon emission optimization system according to claim 2, characterized in that: In step 1, the electricity price information from different regions, the power characteristics and usage frequency of various types of electrical equipment are collected to determine the total electricity cost as follows: S11. Collect electricity price information. Through the API interface provided by the power company, obtain electricity price information in different regions in real time, including peak and valley electricity prices and seasonal electricity prices; S12. Collect equipment power characteristics and obtain the rated power, standby power and operating power of various electrical equipment through technical manuals or measured data provided by equipment manufacturers; S13. Collect equipment usage frequency: Use smart meters or sensors to monitor the usage frequency of various electrical equipment in real time, including daily usage time and weekly usage days; S14. Calculate the total electricity fee based on the electricity price information, equipment power characteristics and usage frequency. The expression is as follows: In the formula, C total represents the total electricity cost, P ij represents the power of the i-th type of equipment in the j-th period, T ij represents the usage time of the i-th type of equipment in the j-th period, R j represents the electricity price in the jth period, n represents the total number of devices, and m represents the total period length.

4. The method of a regional building carbon emission optimization system according to claim 3, characterized in that: The process of determining the average carbon emission coefficient of each type of equipment based on the life cycle assessment algorithm in step 2 is as follows: S21. Determine the equipment life cycle. According to the technical manual or industry standards provided by the equipment manufacturer, determine the life cycle of various types of equipment, including production, transportation, use and scrapping stages; S22. Calculate the life cycle carbon emissions. Use the life cycle assessment algorithm to calculate the carbon emissions of various types of equipment throughout their life cycle. The calculation expression is as follows: AND life =And prod +E trans +E use +E dis ; In the formula, E life represents the life cycle carbon emissions, E prod represents carbon emissions during the production phase, E trans represents the carbon emissions in the transportation stage, E use Indicates carbon emissions during the use phase, E dis Indicates carbon emissions at the end-of-life stage; S23. Calculate the average carbon emission coefficient of each type of equipment based on the life cycle carbon emissions and equipment usage time. The expression is as follows: In the formula, K avg is the average carbon emission coefficient, T use For the service life of the equipment.

5. The method of a regional building carbon emission optimization system according to claim 4, characterized in that: Carbon emissions during the production phase are the carbon emissions generated during the manufacturing process of the equipment, including raw material mining, processing and assembly; Carbon emissions during the transportation phase refer to the carbon emissions generated during the transportation of equipment from the production site to the use site; Carbon emissions during the use phase are carbon emissions generated during the use of the equipment, which come from electricity consumption or fuel combustion; Carbon emissions at the end-of-life stage are the carbon emissions generated during the equipment’s end-of-life treatment process, including disassembly, recycling and landfill.

6. The method of a regional building carbon emission optimization system according to claim 5, characterized in that: The expression of the electricity-carbon coupling cost model constructed in step 3 is as follows: In the formula, C couple is the electricity-carbon coupling cost, C i is the cost of the i-th type of equipment, W i is the importance weight of the i-th type of equipment, K avg,i is the average carbon emission coefficient of the i-th type of equipment.

7. The method of a regional building carbon emission optimization system according to claim 6, characterized in that: Considering the virtual energy storage of buildings in step 4, the process of calculating the carbon emission and operating costs of low-carbon buildings is as follows: S41, determining the capacity of virtual energy storage, including battery energy storage and thermal energy storage according to the energy management system of the building; S42. Calculate the carbon emissions of virtual energy storage based on the charging and discharging efficiency and carbon emission coefficient of virtual energy storage. The calculation expression is as follows: In the formula, E storage is the carbon emission of virtual energy storage, E charge,i is the charging carbon emission of the i-th type of virtual energy storage, n charge,i is the charging efficiency of the i-th type of virtual energy storage, E discharge,i is the discharge carbon emission of the i-th type of virtual energy storage, n discharge,i is the discharge efficiency of the i-th type of virtual energy storage; S43. Calculate the carbon emission and operating cost of low-carbon buildings based on the carbon emission and operating cost of virtual energy storage. The calculation expression is as follows: C low-carbon =C couple +E storage ; Among them, C low-carbon Calculate carbon emissions and operating costs for low-carbon buildings.

8. The method of a regional building carbon emission optimization system according to claim 7, characterized in that: The expression of the building operation optimization model established in step 5 is as follows: minZ=α×C low-carbon +β×C total ; st Among them, Z represents the optimization objective function, α represents the weight coefficient of carbon emission calculation and operation cost of low-carbon buildings, β represents the weight coefficient of total electricity cost, and P max Indicates the maximum power limit of the device, T max Indicates the maximum operating time limit of the device, E max Indicates the virtual energy storage energy capacity limit.

9. The method of a regional building carbon emission optimization system according to claim 8, characterized in that: In step 6, a genetic algorithm is used to solve the building operation optimization model established in step 5. The process of obtaining the optimal solution after multiple iterations is as follows: S61, initializing the population; randomly generating a set of initial solutions as the initial population of the genetic algorithm; S62, calculating fitness; calculating the fitness value of each individual according to the optimization objective function; S63, selection operation: using roulette selection method to select individuals with high fitness to enter the next generation; S64, crossover operation: using a single-point crossover method, performing a crossover operation on the selected individuals to generate new individuals; S65, mutation operation: using random mutation method to perform mutation operation on some individuals to increase the diversity of the population; S66, iterative solution; repeat the above steps until the convergence condition is reached, stop the iteration, and obtain the optimal solution.

10. The method of a regional building carbon emission optimization system according to claim 9, characterized in that: The convergence condition in S66 is that the change in fitness value is less than the set threshold ∈, or the maximum number of iterations T is reached. max , the expression is as follows: In the formula, represents the optimal fitness value in the t-th generation population, represents the optimal fitness value in the t-1 generation population, ∈ represents the fitness change threshold, N max Indicates the maximum number of iterations.

Images