A building energy low-carbon management method for tracking carbon emission factors of a power grid

Abstract

This invention discloses a method for low-carbon management of building energy consumption by tracking grid carbon emission factors. The method includes: collecting data on photovoltaic (PV) power generation equipment and air conditioning systems of the target building, as well as real-time power generation, air conditioning load, grid electricity purchase and sale data, and electric vehicle power consumption; modeling the output power of the PV power generation equipment and the air conditioning power; obtaining the dynamic carbon emission factor curve of the target building; constructing the operating cost and carbon emissions of the target building; constructing the objective function and constraints accordingly, including electric vehicle capacity constraints, tie-line power transmission constraints, power balance constraints, and carbon emission constraints; and using a particle swarm optimization algorithm to iteratively seek the optimal solution. This invention enables buildings to rationally utilize resources, thereby effectively reducing carbon emissions from building electricity consumption and improving the direct economic benefits of buildings.

Description

Technical Field

[0001] This invention relates to the field of building power planning, and specifically to a method for low-carbon management of building energy consumption by tracking carbon emission factors from the power grid. Background Technology

[0002] With the increasing severity of global climate change, reducing greenhouse gas emissions has become a focus of attention for countries worldwide. As a significant source of energy consumption and carbon emissions, the low-carbon transformation and optimized control of building energy systems are crucial for achieving dual-carbon goals. In modern industrial parks, photovoltaic power generation systems, as a representative of clean energy, are widely used in buildings, providing renewable energy support. Simultaneously, the widespread adoption of electric vehicle charging facilities and the high energy consumption of air conditioning systems are placing higher demands on building energy systems.

[0003] However, current building energy control focuses primarily on economic efficiency or supply-demand balance, lacking real-time tracking and dynamic optimization of carbon emissions. Existing technologies typically use fixed values ​​or annual averages to obtain carbon emission factors, failing to reflect real-time changes in the grid's carbon emission intensity. This leads to inaccurate carbon emission calculations and hinders truly low-carbon operation. Furthermore, insufficient coordination and control among various energy-consuming devices within buildings (such as photovoltaics, air conditioning, and electric vehicles) often fails to simultaneously meet user energy needs while maintaining both low-carbon and economic efficiency.

[0004] Therefore, there is an urgent need for a building energy management method that can track the dynamic carbon emission factors of the power grid, coordinate various energy-consuming devices, and achieve both low-carbon and economic optimization while meeting electricity demand. Summary of the Invention

[0005] The purpose of this invention is to provide a method for low-carbon management of building energy consumption by tracking the carbon emission factors of the power grid. This method addresses the two problems of high carbon emissions and high consumption caused by current building electricity consumption. Based on the carbon emission amount of the dynamic carbon emission factor curve, this invention greatly reduces the carbon emissions generated by buildings and lowers consumption while meeting users' electricity needs as much as possible. It also promotes the rational allocation of building resources and is of great significance for dual carbon emission reduction.

[0006] To achieve the above functions, this invention designs a method for low-carbon management of building energy consumption by tracking the carbon emission factors of the power grid, and executes the following steps S1-S5 to complete the multi-objective optimization control of the target building's electricity consumption:

[0007] Step S1: Collect data on photovoltaic power generation equipment and air conditioning of the target building, as well as real-time power generation of photovoltaic power generation equipment, air conditioning load, power grid purchase and sale data, and electric vehicle power consumption; model the output power of photovoltaic power generation equipment and air conditioning power based on the photovoltaic conversion efficiency, photovoltaic panel temperature, air conditioning load, and indoor and outdoor temperatures.

[0008] Step S2: Obtain the dynamic carbon emission factor curve of the target building from the power grid company's dispatch system;

[0009] Step S3: Based on the power grid purchase and sale data, electric vehicle power consumption, and the power output of photovoltaic power generation equipment and air conditioning power obtained from the modeling in Step S1, and combined with the dynamic carbon emission factor curve of the target building obtained in Step S2, construct the operating cost and carbon emissions of the target building.

[0010] Step S4: Based on the operating cost and carbon emissions of the target building, construct the objective function and corresponding constraints, including electric vehicle capacity constraints, tie-line power transmission constraints, power balance constraints, and carbon emission constraints.

[0011] Step S5: Use the particle swarm optimization algorithm to iterate and seek the optimal solution to complete the multi-objective optimization control of the target building's electricity consumption.

[0012] As a preferred technical solution of the present invention: the photovoltaic power generation equipment is modeled in step S1 as follows:

[0013] ;

[0014] in, This represents the electrical power output of the photovoltaic panels in a photovoltaic system, expressed in units of... ; This indicates the photoelectric conversion efficiency of the photovoltaic panel; express Solar radiation intensity at a given time, in units of ; Represents the power temperature coefficient, with units of . , This represents the outdoor temperature at time t, in units of... ; This represents the surface temperature of a photovoltaic panel module under standard conditions, in units of... ;

[0015] The air conditioner model is as follows:

[0016] ;

[0017] ;

[0018] ;

[0019] in, Indicates the air conditioner power. For cooling / heating load, The energy efficiency ratio of air conditioning equipment. Indoor temperature, Outdoor temperature The heat conversion coefficient, The initial indoor temperature. Let be the indoor temperature at time i. Let be the indoor temperature at time i-1. Let be the outdoor temperature at time i. This refers to the degree to which indoor temperature responds to changes in outdoor temperature. The degree to which air conditioner power affects indoor temperature changes; Represents the sign function:

[0020] ;

[0021] Where x represents the sign function Input data.

[0022] As a preferred embodiment of the present invention, the operating cost of the target building constructed in step S3 is as follows:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] in, This represents the operating cost of the target building. This represents the total cost of purchasing and selling electricity for the target building. , These are the maintenance cost of a single operation of the photovoltaic power generation equipment and the output power of the photovoltaic power generation equipment, respectively. , These represent the electricity sales and purchase power of the target building and the main power grid at time t, respectively. , These represent the purchase and sale costs of the target building and the main power grid at time t, respectively. , These represent the purchase and sale prices of electricity for the target building and the main power grid at time t, respectively.

[0028] The carbon emissions calculated in step S3 are as follows:

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] in, Indicates carbon emissions. , , These are carbon emissions from building air conditioning, photovoltaic power generation, and electric vehicles during time period t. , , These are the carbon emission factors for air-conditioned photovoltaic electric vehicles; , , These are the power ratings for air conditioners, photovoltaic systems, and electric vehicles, respectively.

[0034] As a preferred embodiment of the present invention, the objective function constructed in step S4 is as follows:

[0035] ;

[0036] in, Represented as fitness value, To optimize the weighting of target building operating costs, To optimize the weighting of carbon emissions from target buildings; This represents the operating cost of the target building. This indicates carbon emissions.

[0037] As a preferred embodiment of the present invention, the electric vehicle capacity constraint conditions constructed in step S4 are as follows:

[0038] ;

[0039] in, For electric vehicle capacity, , These represent the lower and upper limits of electric vehicle capacity, respectively.

[0040] The power transmission constraints of the constructed tie lines are as follows:

[0041] ;

[0042] in, To transmit power to the tie line, , These are the lower and upper limits of the transmission power of the tie line, respectively.

[0043] The established power balance constraints are as follows:

[0044] ;

[0045] in, For electrical load power, For photovoltaic power, For air conditioner power, For electric vehicle power, To transmit power to the tie line;

[0046] The constructed carbon emission constraints are as follows:

[0047] ;

[0048] in, Carbon emissions from building air conditioning For photovoltaic carbon emissions, For carbon emissions from electric vehicles, This is the upper limit for carbon emissions.

[0049] The present invention also designs a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for low-carbon control of building energy consumption by tracking grid carbon emission factors.

[0050] The present invention also designs a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for low-carbon management of building energy consumption by tracking grid carbon emission factors.

[0051] Beneficial effects: Compared with the prior art, the advantages of the present invention include:

[0052] 1. By coordinating and optimizing the control of various energy-consuming devices such as photovoltaic power generation, air conditioning systems, and electric vehicle charging, this invention can rationally allocate various resources within a building, maximize the utilization of clean energy such as photovoltaics, reduce dependence on the main power grid, and improve the overall operating efficiency of the building's energy system.

[0053] 2. During the optimization process, this invention considers the impact of air conditioning power on indoor temperature, sets a comfortable indoor temperature range (e.g., 23℃~26℃), and performs low-carbon optimization while ensuring user comfort, thereby improving the system's practicality and user acceptance.

[0054] 3. This invention constructs mathematical models for various devices such as photovoltaics, air conditioners, and electric vehicles, and introduces various constraints such as power constraints, capacity constraints, and tie-line transmission constraints to ensure that the system achieves optimized control under the premise of safe and stable operation. Attached Figure Description

[0055] Figure 1 This is a flowchart of a method for low-carbon management of building energy consumption that tracks grid carbon emission factors, according to an embodiment of the present invention.

[0056] Figure 2 This is a temperature and air conditioning power fluctuation diagram provided according to an embodiment of the present invention;

[0057] Figure 3 This is a dynamic carbon emission factor curve provided according to an embodiment of the present invention;

[0058] Figure 4 This is a diagram showing the various load powers and power consumption of a building according to an embodiment of the present invention;

[0059] Figure 5 This is an optimization diagram of the objective function solution for carbon emissions and operating costs within a building, provided according to an embodiment of the present invention.

[0060] Figure 6 This is an optimized comparison chart of carbon emissions and operating costs within a building, provided according to an embodiment of the present invention. Detailed Implementation

[0061] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0062] This invention provides a method for low-carbon management of building energy consumption by tracking grid carbon emission factors, referring to... Figure 1 Perform the following steps S1-S5 to complete the multi-objective optimization control of the target building's power consumption:

[0063] Step S1: Collect data on photovoltaic power generation equipment and air conditioning of the target building, as well as real-time power generation of photovoltaic power generation equipment, air conditioning load, power grid purchase and sale data, and electric vehicle power consumption; model the output power of photovoltaic power generation equipment and air conditioning power based on the photovoltaic conversion efficiency, photovoltaic panel temperature, air conditioning load, and indoor and outdoor temperatures.

[0064] The photovoltaic power generation equipment is modeled in step S1 as follows:

[0065] ;

[0066] in, This represents the electrical power output of the photovoltaic panels in a photovoltaic system, expressed in units of... ; This indicates the photoelectric conversion efficiency of the photovoltaic panel, which is set to 16% in this embodiment. express Solar radiation intensity at a given time, in units of ; Represents the power temperature coefficient, with units of . In this embodiment, it is set to -0.0046. This represents the outdoor temperature at time t, in units of... ; This represents the surface temperature of a photovoltaic panel module under standard conditions, in units of... In this embodiment, the value is set to 25.

[0067] The modeling method based on cooling (heating) load calculation is based on the law of conservation of energy, which states that the energy change of a building with air conditioning over any given time period is equal to the difference between the cooling (heating) capacity of the air conditioning and the heat received by the building. From this, an energy change identity for the building can be established, and a recursive formula for indoor temperature change can be derived.

[0068] The air conditioner model is as follows:

[0069] ;

[0070] ;

[0071] ;

[0072] in, Indicates the air conditioner power. For cooling / heating load, The energy efficiency ratio of air conditioning equipment. Indoor temperature, Outdoor temperature The heat conversion coefficient, The initial indoor temperature. Let be the indoor temperature at time i. Let be the indoor temperature at time i-1. Let be the outdoor temperature at time i. This refers to the degree to which indoor temperature responds to changes in outdoor temperature. The degree to which air conditioner power affects indoor temperature changes; Represents the sign function:

[0073] ;

[0074] Where x represents the sign function Input data.

[0075] Temperature and air conditioning power fluctuation diagram (refer to) Figure 2 , Figure 2 Taking user comfort into account, the indoor temperature is controlled at 23 degrees Celsius. ~26 .

[0076] Step S2: Obtain the dynamic carbon emission factor curve of the target building from the power grid company's dispatch system; refer to the dynamic carbon emission factor curve chart. Figure 3 .

[0077] Step S3: Based on the power grid purchase and sale data, electric vehicle power consumption, and the power output of photovoltaic power generation equipment and air conditioning power obtained from the modeling in Step S1, and combined with the dynamic carbon emission factor curve of the target building obtained in Step S2, construct the operating cost and carbon emissions of the target building.

[0078] The operating cost of the target building constructed in step S3 is as follows:

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] in, This represents the operating cost of the target building. This represents the total cost of purchasing and selling electricity for the target building. , These are the maintenance cost of a single photovoltaic power generation device (unit: yuan / kWh) and the output power of the photovoltaic power generation device, respectively. , These represent the electricity sales and purchase power of the target building and the main power grid at time t, respectively. , These represent the purchase and sale costs of the target building and the main power grid at time t, respectively. , These represent the purchase and sale prices of electricity for the target building and the main power grid at time t, respectively.

[0084] The carbon emissions calculated in step S3 are as follows:

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] in, Indicates carbon emissions. , , These are carbon emissions from building air conditioning, photovoltaic power generation, and electric vehicles during time period t. , , These are the carbon emission factors for air-conditioned photovoltaic electric vehicles; , , These are the power ratings for air conditioners, photovoltaic systems, and electric vehicles, respectively.

[0090] Reference for various load capacities and power purchases of buildings Figure 4 .

[0091] Step S4: Based on the operating cost and carbon emissions of the target building, construct the objective function and corresponding constraints, including electric vehicle capacity constraints, tie-line power transmission constraints, power balance constraints, and carbon emission constraints.

[0092] The objective function constructed in step S4 is as follows:

[0093] ;

[0094] in, Represented as fitness value, To optimize the weighting of target building operating costs, To optimize the weighting of carbon emissions from target buildings; This represents the operating cost of the target building. This indicates carbon emissions.

[0095] The electric vehicle capacity constraints constructed in step S4 are as follows:

[0096] ;

[0097] in, For electric vehicle capacity, , These represent the lower and upper limits of electric vehicle capacity, respectively.

[0098] The power transmission constraints of the constructed tie lines are as follows:

[0099] ;

[0100] in, To transmit power to the tie line, , These are the lower and upper limits of the transmission power of the tie line, respectively.

[0101] The established power balance constraints are as follows:

[0102] ;

[0103] in, For electrical load power, For photovoltaic power, For air conditioner power, For electric vehicle power, To transmit power to the tie line;

[0104] The constructed carbon emission constraints are as follows:

[0105] ;

[0106] in, Carbon emissions from building air conditioning For photovoltaic carbon emissions, For carbon emissions from electric vehicles, This is the upper limit for carbon emissions.

[0107] The optimization diagram for solving the objective function of carbon emissions and operating costs within a building is shown in the reference diagram. Figure 5 .

[0108] Step S5: Using the particle swarm optimization algorithm, under the condition of satisfying low carbon and economy as much as possible, the building's electricity consumption is continuously iterated to seek the optimal solution and complete the multi-objective optimization control of the target building's electricity consumption.

[0109] A comparative chart showing the optimization of carbon emissions and operating costs within buildings (see reference). Figure 6 .

[0110] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for low-carbon management of building energy consumption by tracking grid carbon emission factors.

[0111] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for low-carbon management of building energy consumption by tracking grid carbon emission factors.

[0112] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for low-carbon management of building energy consumption by tracking carbon emission factors from the power grid, characterized in that, Perform the following steps S1-S5 to complete the multi-objective optimization control of the target building's power consumption: Step S1: Collect data on photovoltaic power generation equipment and air conditioning of the target building, as well as real-time power generation of photovoltaic power generation equipment, air conditioning load, power grid purchase and sale data, and electric vehicle power consumption; model the output power of photovoltaic power generation equipment and air conditioning power based on the photovoltaic conversion efficiency, photovoltaic panel temperature, air conditioning load, and indoor and outdoor temperatures. Step S2: Obtain the dynamic carbon emission factor curve of the target building from the power grid company's dispatch system; Step S3: Based on the power grid purchase and sale data, electric vehicle power consumption, and the power output of photovoltaic power generation equipment and air conditioning power obtained from the modeling in Step S1, and combined with the dynamic carbon emission factor curve of the target building obtained in Step S2, construct the operating cost and carbon emissions of the target building. Step S4: Based on the operating cost and carbon emissions of the target building, construct the objective function and corresponding constraints, including electric vehicle capacity constraints, tie-line power transmission constraints, power balance constraints, and carbon emission constraints. Step S5: Use the particle swarm optimization algorithm to iterate and seek the optimal solution to complete the multi-objective optimization control of the target building's electricity consumption.

2. The method for low-carbon management of building energy consumption by tracking grid carbon emission factors according to claim 1, characterized in that, The photovoltaic power generation equipment is modeled in step S1 as follows: ； in, This represents the electrical power output of the photovoltaic panels in a photovoltaic system, expressed in units of... ; This indicates the photoelectric conversion efficiency of the photovoltaic panel; express Solar radiation intensity at a given time, in units of ; Represents the power temperature coefficient, with units of . , This represents the outdoor temperature at time t, in units of... ; This represents the surface temperature of a photovoltaic panel module under standard conditions, in units of... ; The air conditioner model is as follows: ； ； ； in, Indicates the air conditioner power. For cooling / heating load, The energy efficiency ratio of air conditioning equipment. Indoor temperature, Outdoor temperature The heat conversion coefficient, The initial indoor temperature. Let be the indoor temperature at time i. Let be the indoor temperature at time i-1. Let be the outdoor temperature at time i. This refers to the degree to which indoor temperature responds to changes in outdoor temperature. The degree to which air conditioner power affects indoor temperature changes; Represents the sign function: ； Where x represents the sign function Input data.

3. The method for low-carbon management of building energy consumption by tracking grid carbon emission factors according to claim 1, characterized in that, The operating cost of the target building constructed in step S3 is as follows: ； ； ； ； in, This represents the operating cost of the target building. This represents the total cost of purchasing and selling electricity for the target building. , These are the maintenance cost of a single operation of the photovoltaic power generation equipment and the output power of the photovoltaic power generation equipment, respectively. , These represent the electricity sales and purchase power of the target building and the main power grid at time t, respectively. , These represent the purchase and sale costs of the target building and the main power grid at time t, respectively. , These represent the purchase and sale prices of electricity for the target building and the main power grid at time t, respectively. The carbon emissions calculated in step S3 are as follows: ； ； ； ； in, Indicates carbon emissions. , , These are carbon emissions from building air conditioning, photovoltaic power generation, and electric vehicles during time period t. , , These are the carbon emission factors for air-conditioned photovoltaic electric vehicles; , , These are the power ratings for air conditioners, photovoltaic systems, and electric vehicles, respectively.

4. The method for low-carbon management of building energy consumption by tracking grid carbon emission factors according to claim 1, characterized in that, The objective function constructed in step S4 is as follows: ； in, Represented as fitness value, To optimize the weighting of target building operating costs, To optimize the weighting of carbon emissions from target buildings; This represents the operating cost of the target building. This indicates carbon emissions.

5. A method for low-carbon management of building energy consumption by tracking grid carbon emission factors according to claim 1, characterized in that, The electric vehicle capacity constraints constructed in step S4 are as follows: ； in, For electric vehicle capacity, , These represent the lower and upper limits of electric vehicle capacity, respectively. The power transmission constraints of the constructed tie lines are as follows: ； in, To transmit power to the tie line, , These are the lower and upper limits of the transmission power of the tie line, respectively. The established power balance constraints are as follows: ； in, For electrical load power, For photovoltaic power, For air conditioner power, For electric vehicle power, To transmit power to the tie line; The constructed carbon emission constraints are as follows: ； in, Carbon emissions from building air conditioning For photovoltaic carbon emissions, For carbon emissions from electric vehicles, This is the upper limit for carbon emissions.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the building energy low-carbon management method for tracking grid carbon emission factors as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the building energy low-carbon management method for tracking grid carbon emission factors as described in any one of claims 1 to 5.

Images