Energy management method for intelligent building micro-grid

Abstract

The invention belongs to the technical field of building energy management, and particularly relates to an energy management method for an intelligent building micro-grid. A multi-target dynamic optimization intelligent building energy flow model is constructed by integrating renewable energy sources, namely a building roof wind-solar complementary generator set, a high-capacity energy storage battery, a 5G base station load and an intelligent power grid interaction interface; according to a real-time power grid carbon emission factor and an energy supply and demand state, a layered optimization strategy is adopted to dynamically adjust electricity purchasing, electricity selling and energy storage charging and discharging strategies, and carbon emission minimization is achieved; based on a real-time power grid carbon emission factor and an energy supply and demand state, a layered optimization strategy is adopted to dynamically adjust electricity purchasing, electricity selling and energy storage charging and discharging strategies, and carbon emission minimization is achieved. According to the invention, an efficient and extensible solution is provided for collaborative energy saving of the intelligent building and the communication base station, and the realization of a carbon neutralization target in the building field is facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of building energy management, and in particular relates to an energy management method for a smart building microgrid. Background Art

[0002] Smart buildings, also known as intelligent buildings, aim to integrate traditional architecture with modern communication technologies and intelligent terminals, achieving an organic combination of structural, system, service, and management elements to ensure a safe, convenient, energy-efficient, and healthy living and working environment. For a period after the concept of smart buildings was introduced in 1984, smart building research primarily focused on building automation systems, using intelligent terminals to provide comprehensive, unmanned management of building security, lighting, ionization, drainage, and other systems. As the concepts of 5G and the Internet of Things mature, and as the most common scenarios for modern human interaction, new demands are being placed on the intelligence of buildings. Today, smart buildings are no longer limited to automated building control; they are committed to building complex digital intelligent systems that integrate multiple information subsystems.

[0003] With the increasing prevalence of renewable energy, small-scale renewable energy generation and distribution solutions (known as microgrids) are becoming a major building development trend, and achieving carbon neutrality is on the agenda. It is foreseeable that in the near future, energy management (EM) subsystems will become a core system in smart buildings, building energy-interactive entities based on smart buildings and playing a significant role in reducing building utility expenses and overall carbon emissions.

[0004] The world is experiencing rapid urbanization. As the construction industry continues to grow, the overall building area is rapidly increasing. Energy demand and the corresponding carbon emissions are also increasing year by year. It is imperative that buildings achieve carbon neutrality as quickly and as widely as possible.

[0005] 5G base station power consumption, generally speaking, is not a significant factor in current society. However, with the rapid development of 5G, 5G base station power consumption will account for 2.1% of total electricity consumption. In terms of total electricity consumption, the energy consumed by communication between smart devices in smart buildings will become a significant component of building power consumption.

[0006] Based on this trend of base station building integration, this invention constructs a multi-objective dynamic optimization model by integrating rooftop wind-solar hybrid generator sets (RES), large-capacity energy storage batteries, 5G base station loads, and smart grid interaction interfaces. Based on real-time grid carbon emission factors and energy supply and demand status, a hierarchical optimization strategy is used to dynamically adjust electricity purchase and sales, as well as energy storage charging and discharging strategies, to minimize carbon emissions. This invention provides an efficient and scalable solution for the coordinated energy conservation of smart buildings and communication base stations, helping to promote the goal of carbon neutrality in the construction sector. Summary of the Invention

[0007] The purpose of the present invention is to provide an energy management method for smart building microgrids to solve the problems of low renewable energy utilization and insufficient grid carbon emission optimization in existing building energy management, and to provide an efficient and scalable solution for the collaborative energy saving of smart buildings and communication base stations.

[0008] The energy management method for smart building microgrids provided by this invention integrates renewable energy sources (RES) (such as wind and solar hybrid generators on building rooftops), large-capacity energy storage batteries, 5G base station loads, and smart grid interaction interfaces to build a multi-objective dynamic optimization smart building energy flow model. Based on the real-time grid carbon emission factors and energy supply and demand status, a hierarchical optimization strategy is used to dynamically adjust power purchase, power sales, and energy storage charging and discharging strategies to minimize carbon emissions. The specific steps are as follows:

[0009] (1) Determine the factors for constructing the smart building energy flow model of the shopping mall model:

[0010] This paper constructs an energy flow model between a shopping mall's RES equipment and a smart grid. Factors considered include energy consumption management, with two key elements: first, renewable energy generation and storage devices controlled by smart terminals; and second, a smart grid that allows for energy trading at any time. It assumes that a series of large-capacity battery packs (hereinafter referred to as batteries) serve as energy storage devices within the mall's distribution room. Furthermore, the mall is connected to the smart grid via intelligent electrical boxes, enabling free trading of electricity as long as energy transfer conditions are met, and providing access to carbon emission factors on the grid. Furthermore, considering the mall's large, flat rooftop, which provides abundant wind and solar resources, a hybrid wind and solar generator (RES, hereinafter referred to as the RES device) is installed on the rooftop. Based on these factors, an energy flow model between the mall's RES equipment and the smart grid is designed.

[0011] (2) Mathematically describe the smart building energy flow model of the shopping mall model, including:

[0012] Set parameters such as the power generation of the RES equipment in the mall at the beginning of time slot t and derive the battery energy storage constraints;

[0013] From the perspective of energy transfer, we can understand the relationship between battery energy storage and charge and discharge power, as well as the physical constraints on battery charge and discharge.

[0014] Specifically:

[0015] Let E t is the power generation of the RES equipment in the mall at the beginning of time slot t, and C 0 To express the energy storage of the battery under initial conditions, Ct To represent the energy storage of the battery at the beginning of time slot t. Generally speaking, the battery has an energy storage upper limit C max Considering the battery life requirements, set the lower limit of energy storage for the battery C min Therefore, the battery energy storage constraints are as follows:

[0016]

[0017] The charge and discharge power of the battery at the beginning of time slot t is expressed as when When t is the time when the battery is charged, 0 represents the battery discharge in time slot t. The battery energy storage and charge and discharge power can be described by the following formula:

[0018]

[0019] From the perspective of energy transfer, it can be seen that the charging and discharging of batteries are limited by physical conditions:

[0020]

[0021] In the above formula, P b,min <0, represents the maximum discharge power of the battery; P b,max >0, represents the maximum charging power of the battery.

[0022] (3) Propose an energy management plan based on minimum carbon emissions, including:

[0023] (3.1) Derive the total energy consumption function of the intelligent terminal control at time slot t, describing the carbon emissions of all energy consumption factors controlled by the intelligent terminal at time slot t;

[0024] Specifically:

[0025] The energy sources for the entire shopping mall model are three: the smart grid, the RES power generation equipment within the building, and battery discharge. The energy goes to five destinations: micro base station energy consumption, HVAC energy consumption, lighting energy consumption, battery charging, and macro base station energy consumption. Except for the macro base station, which is directly powered by the grid, the micro base station, HVAC, lighting, and battery charging are all controlled by smart terminals and powered by both the RES equipment within the building and the smart grid. For ease of calculation, the energy consumption of the micro base station and the building are combined and expressed as but represents the total energy consumption of the intelligent terminal control in time slot t, Expressed as:

[0026]

[0027] in, represents the building energy consumption at time t, The total power of the micro base station in time slot t is represented by the function D t To describe the carbon emissions of all energy-consuming factors controlled by the intelligent terminal in time slot t:

[0028]

[0029] in, Represents the total power of the Heterogeneous Cellular Network (HCN).

[0030] (3.2) Analyze the possible behaviors of the smart terminal. Since energy only flows bidirectionally between the battery and the grid, the behavior of the smart terminal is represented as shown in Table 1 below.

[0031] Table 1. Smart terminal behavior patterns

[0032]

[0033] It can be seen that due to the battery charging and discharging power If it is not a value greater than or equal to zero, you can use the formula The symbols are used to describe the energy flow between the smart terminal and the power grid, and the The symbols are used to describe the energy flow between the smart terminal and the battery.

[0034] (3.3) Derive the carbon emission factor formula, describe the energy management function realized by the smart terminal through the expression, make the assumption that the shopping mall purchases electricity only to fill its own energy gap, derive the carbon emission expression controlled by the smart terminal, establish the optimization problem, and derive the carbon emission equation. Specifically:

[0035] Assume that at the beginning of time slot t, the carbon emission factor of the smart grid is α t , the carbon emission factor in the battery at this moment is β t (β 0 is the carbon emission factor of the energy stored in the battery under initial conditions). Note that if the battery is discharged, then β t remains unchanged, while the battery is charging, β t It is calculated by the total carbon content of the battery at time t-1 and the carbon content flowing into the battery at time slot t. Calculated by the following formula:

[0036]

[0037] in:

[0038]

[0039] Obviously,

[0040]

[0041] If the shopping mall purchases electricity from the grid, its carbon emissions can be expressed as:

[0042]

[0043] The carbon emission factor of RES equipment production capacity is 0. The carbon emission from electricity sold from RES equipment to the grid can be expressed as:

[0044]

[0045] The carbon emissions from selling electricity from battery storage to the grid can be expressed as:

[0046]

[0047] Combined with the behavior pattern of smart terminals, Written in the following form:

[0048]

[0049] Note that the energy management function implemented by the smart terminal can be described through the above expression:

[0050] By β t <α t When the carbon content of the grid is low, the company buys electricity to meet its own electricity consumption peak (usually also the peak of the carbon content of the grid), thus reducing carbon emissions over a period of time. t <α t By selling electricity when the grid is more carbon-intensive, we can create as much negative carbon emissions as possible. By buying electricity when the grid is less carbon-intensive and storing it in batteries, and selling it when the grid is more carbon-intensive, we can balance the load on the smart grid, reduce high-carbon generation on the grid, and thus reduce the grid's overall carbon emissions.

[0051] The above explanation makes a key assumption: shopping malls purchase electricity only to fill their own energy gap. The energy gap here refers to the energy deficit that the mall will incur within a limited or unlimited time window, as calculated using forecasting algorithms or other methods.

[0052] According to the above assumptions, Simplified to:

[0053]

[0054] The carbon emission expression of intelligent terminal control is obtained, and G t represents the carbon emissions of the entire model at time slot t, which can be expressed as:

[0055]

[0056] Since the global optimal solution is often greater than the sum of the local optimal solutions, we consider the entire scheduling range of intelligent terminal control, that is, the entire time domain to meet the optimal energy efficiency of the HCN network and minimize carbon emissions. This problem can be described as:

[0057]

[0058] st

[0059]

[0060] And there are:

[0061]

[0062] st

[0063]

[0064] Among them, η EE represents the ratio of the minimum target throughput to the total power consumption (i.e., energy efficiency), V t Indicates the density of people in the mall at time t (unit: m 2 / person), T represents the entire time domain, P T2 Represents the micro base station transmit power.

[0065] The objective functions and constraints of the above two optimization problems ((14), (18)) are called carbon emission equations. Due to the large number of assumptions, abstractions, and averaging operations on building energy consumption, the energy efficiency ratio of building electricity is not optimized. Only the energy efficiency ratio of the second-layer HCN is optimized, and the overall energy management is optimized.

[0066] The beneficial effects of the present invention are:

[0067] 1. Significantly reduce carbon emissions: By dynamically optimizing the coordinated scheduling of renewable energy (RES), energy storage batteries and grid power purchases, combined with real-time carbon emission factor control, the average daily carbon emissions of buildings can be reduced compared to traditional energy management solutions, effectively supporting the carbon neutrality goal in the construction sector.

[0068] 2. Improve energy economy: Utilize the two-way trading mechanism of the smart grid to store electricity during low electricity price periods and sell electricity or call on RES electricity during peak periods, thereby reducing the grid's electricity purchase costs and reducing energy consumption waste in communication equipment.

[0069] 3. Enhance system reliability: A hierarchical optimization strategy is adopted to prioritize the power supply stability of critical loads (such as 5G base stations). When the power grid fluctuates or RES power generation is insufficient, the energy storage battery intelligently switches the power supply mode to ensure uninterrupted operation of the building's core functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Energy flow model between shopping mall RES equipment and smart grid.

[0071] Figure 2 Energy management models reduce carbon emissions. The upper part of the diagram shows a 50kW wind turbine model, while the lower part shows a 30kW wind turbine model. The horizontal axis measures days, while the vertical axis measures kWh.

[0072] Figure 3 This is a graph showing the relationship between wind turbine power and carbon emissions savings. The horizontal axis unit is kWh; the vertical axis unit is kWh. DETAILED DESCRIPTION

[0073] The present invention is further described below by way of examples with reference to the accompanying drawings.

[0074] Example 1:

[0075] 1. Model building and data selection

[0076] Smart building energy flow model for shopping mall model, see Figure 1 As shown in the figure, a carbon-neutral energy management system based on smart building microgrid is demonstrated. By integrating rooftop wind-solar complementary generator sets (RES), large-capacity energy storage batteries, 5G base station loads and smart grid interaction interfaces, an energy flow model between shopping mall RES equipment and smart grid is proposed.

[0077] The parameter values ​​used in the calculation are shown in the following table:

[0078] Coefficient name Coefficient value and unit Poor battery energy storage 50 Maximum discharge power -10 Maximum charging power 10 Area of ​​single floor of shopping mall <![CDATA[5500m 2 ]]> HCN area <![CDATA[90000m 2 ]]> bandwidth <![CDATA[10 7 Hz]]> Macro base station density <![CDATA[10 -4 m -2 ]]> Road Loss Index 4 Static power of macro base stations 1 Macro base station transmission power <![CDATA[4*10 -2 ]]> Target SIR of macro base station 10dB Target SIR of micro base stations 2dB Static power of micro base stations 50W

[0079] 2. Propose a carbon emission equation

[0080] Step 2-1: Mathematical description of energy model

[0081] The power generation of the RES equipment in the mall at the beginning of time slot t is expressed as C 0 To express the energy storage of the battery under initial conditions, C t To represent the energy storage of the battery at the beginning of time slot t. Generally speaking, the battery has an energy storage upper limit C max Considering the battery life requirements, set the lower limit of energy storage for the battery C min Therefore, the battery energy storage constraints are as follows:

[0082]

[0083] The charge and discharge power of the battery at the beginning of time slot t is expressed as when When t is the time when the battery is charged, 0 represents the battery discharge in time slot t. The battery energy storage and charge and discharge power can be described by the following formula:

[0084]

[0085] From the perspective of energy transfer, it can be seen that the charging and discharging of batteries are limited by physical conditions:

[0086]

[0087] In the above formula, P b,min <0, represents the maximum discharge power of the battery; P b,max >0, represents the maximum charging power of the battery.

[0088] Step 2-2: Derive the carbon emissions of the intelligent terminal control in time slot t

[0089] The energy sources of the entire shopping mall model are three: the smart grid, the RES power generation equipment in the building, and the battery discharge. The energy goes to five destinations: micro base station energy consumption, HVAC energy consumption, lighting energy consumption, battery charging, and macro base station energy consumption. Except for the macro base station, which is directly powered by the grid, the micro base station, HVAC, lighting, and battery charging are all controlled by smart terminals and powered by the RES power generation equipment in the building and the smart grid. For ease of calculation, the energy consumption of the micro base station and the building energy consumption are combined and expressed as but It represents the total energy consumption of the intelligent terminal control in time slot t, Expressed as:

[0090]

[0091] in represents the building energy consumption at time t, Denote the total power of the micro base station in time slot t, using the function D t To describe the carbon emissions of all energy-consuming factors controlled by the intelligent terminal in time slot t:

[0092]

[0093] in Represents the total power of the Heterogeneous Cellular Network (HCN).

[0094] Step 2-3: Get the behavior representation of the smart terminal

[0095] In order to obtain the specific form of the abstract function, the present invention will analyze the possible behaviors of the smart terminal. Since energy only flows bidirectionally between the battery and the grid, the behavior of the smart terminal can be expressed as follows: Table 1. Smart terminal behavior pattern

[0096]

[0097]

[0098] It can be seen that due to the battery charging and discharging power is not a value greater than or equal to zero, we can use the formula The symbols are used to describe the energy flow between the smart terminal and the power grid, and the The symbols are used to describe the energy flow between the smart terminal and the battery.

[0099] Agreement:

[0100]

[0101] Step 2-4: Derive the carbon emission factor formula and obtain the carbon emission equation.

[0102] In the present invention, it is assumed that at the beginning of time slot t, the carbon emission factor of the smart grid is α t , β t is the carbon emission factor in the battery at that moment (β 0 is the carbon emission factor of the energy stored in the battery under initial conditions). Note that if the battery is discharged, then β t remains unchanged, while the battery is charging, β t It can be calculated by the total carbon content of the battery at time t-1 and the carbon content flowing into the battery at time slot t. Calculated by the following formula:

[0103]

[0104] Obviously,

[0105]

[0106] If the shopping mall purchases electricity from the grid, its carbon emissions can be expressed as:

[0107]

[0108] The carbon emission factor of RES equipment production capacity is 0. The carbon emission from electricity sold from RES equipment to the grid can be expressed as:

[0109]

[0110] The carbon emissions from selling electricity from battery storage to the grid can be expressed as:

[0111]

[0112] Combined with the behavior pattern of smart terminals, we can Written in the following form:

[0113]

[0114] Note that, through the above expression, we can describe the energy management function implemented by the smart terminal:

[0115] By β t <α t When the carbon content of the grid is low, the company buys electricity to meet its own electricity consumption peak (usually also the peak of the carbon content of the grid), thus reducing carbon emissions over a period of time. t <α t By selling electricity when the grid is more carbon-intensive, we can create as much negative carbon emissions as possible. By buying electricity when the grid is less carbon-intensive and storing it in batteries, and selling it when the grid is more carbon-intensive, we can balance the load on the smart grid, reduce high-carbon generation on the grid, and thus reduce the grid's overall carbon emissions.

[0116] The above explanation makes a key assumption: shopping malls purchase electricity only to fill their own energy gap. The energy gap here refers to the energy deficit that the mall will incur within a limited or unlimited time window, as calculated using forecasting algorithms or other methods.

[0117] According to the above assumptions, Simplified to:

[0118]

[0119] The carbon emission expression of intelligent terminal control is obtained, and G t represents the carbon emissions of the entire model at time slot t, which can be expressed as:

[0120]

[0121] Since the global optimal solution is often greater than the sum of the local optimal solutions, we consider the entire scheduling range of intelligent terminal control, that is, the entire time domain to meet the optimal energy efficiency of the HCN network and minimize carbon emissions. We can describe this problem as:

[0122]

[0123] st

[0124]

[0125] and

[0126]

[0127] st

[0128]

[0129] Among them, η EErepresents the ratio of the minimum target throughput to the total power consumption (i.e., energy efficiency), V t Indicates the density of people in the mall at time t (unit: m 2 / person), T represents the entire time domain, P T2 Represents the micro base station transmit power.

[0130] The objective functions and constraints of the two optimization problems above are called carbon emission equations. Due to numerous assumptions, abstractions, and averaging of building energy consumption, the energy efficiency ratio of building electricity is not optimized. Instead, the energy efficiency ratio of the second-floor HCN is optimized, and overall energy management is optimized.

[0131] 3. Simulate the carbon emission equation proposed by the invention

[0132] We use the CVX toolbox to solve the optimization problem of the carbon emission equation and design a control group without energy management. The control group does not use batteries and maintains the load balance in the microgrid in real time. We compare the carbon emissions output by the energy management system and record the reduction in carbon emissions. The results are as follows: Figure 2 As shown in the figure, both the 50kW and 30kW wind turbines achieve stable energy savings within the time domain. This verifies the feasibility of the management scheme. Note that the energy savings of the 30kW wind turbine remain stable below the energy savings of the 50kW wind turbine. This demonstrates that the more renewable energy resources available in this model, the stronger its energy management capabilities.

[0133] Compare the carbon emissions saved throughout the day with the wind turbine power. Figure 3 As shown. Figure 3 As can be seen, the additional carbon emissions saved by the model are proportional to the wind turbine power. Essentially, the more negative carbon emission resources the model has at its disposal, the more additional benefits it can exchange for these resources. This fully demonstrates that the model is an excellent game model and illustrates the superiority of the present invention.

Claims

1. An energy management method for smart building microgrid, characterized in that: By integrating renewable energy (RES) rooftop wind and solar hybrid generators, large-capacity energy storage batteries, 5G base station loads, and smart grid interaction interfaces, a multi-objective dynamic optimization smart building energy flow model is constructed. Based on real-time grid carbon emission factors and energy supply and demand status, a hierarchical optimization strategy is used to dynamically adjust power purchase, power sales, and energy storage charging and discharging strategies to minimize carbon emissions. The specific steps are as follows: (1) Determine the factors for constructing the smart building energy flow model of the shopping mall model: Construct an energy flow model between the mall's RES equipment and the smart grid. Factors to consider include energy consumption management, which has two aspects: one is the renewable energy generation device and storage device controlled by the smart terminal; the other is the smart grid that allows electricity trading at any time. It is assumed that there are a series of large-capacity battery packs as storage devices in the distribution room inside the mall, hereinafter referred to as batteries. In addition, the mall is connected to the smart grid through an intelligent electrical box, and can freely buy and sell electricity under the premise of meeting the energy transfer conditions, and can read the carbon emission factor on the grid at any time. At the same time, considering the large flat roof area of ​​the mall, which brings a large amount of wind and solar energy resources, a hybrid wind and solar power generator set is installed on the mall roof, which is called RES equipment. Under the above factors, design an energy flow model between the mall's RES equipment and the smart grid. (2) Mathematically describe the smart building energy flow model of the shopping mall model, including: Set parameters such as the power generation of the RES equipment in the mall at the beginning of time slot t and derive the battery energy storage constraints; From the perspective of energy transfer, we can understand the relationship between battery energy storage and charge and discharge power, as well as the physical constraints on battery charge and discharge. (3) Propose an energy management plan based on minimum carbon emissions, including: (3.1) Derive the total energy consumption function of the intelligent terminal control at time slot t, describing the carbon emissions of all energy consumption factors controlled by the intelligent terminal at time slot t; (3.2) Analyze the possible behaviors of the smart terminal. Since energy only flows bidirectionally between the battery and the grid, obtain the behavior representation of the smart terminal. (3.3) Derive the carbon emission factor formula, describe the energy management function realized by the smart terminal through the expression, make the assumption that the shopping mall purchases electricity only to fill its own energy gap, derive the carbon emission expression controlled by the smart terminal, establish the optimization problem, and derive the carbon emission equation.

2. The energy management method for smart building microgrid according to claim 1, characterized in that: The smart building energy flow model of the shopping mall model is mathematically described in step (2), specifically: Let E t is the power generation of the RES equipment in the mall at the beginning of time slot t, and C 0 To express the energy storage of the battery under initial conditions, C t To represent the energy storage of the battery at the beginning of time slot t, assume that the battery has an energy storage upper limit C max and the lower limit of energy storage C min , so the battery energy storage constraints are as follows: The charge and discharge power of the battery at the beginning of time slot t is expressed as when When t is the time when the battery is charged, 0 represents the battery discharge in time slot t; the battery energy storage and charge and discharge power are described by the following formula: From the perspective of energy transfer, the charging and discharging of batteries are limited by physical conditions: Among them, P b,min <0, represents the maximum discharge power of the battery; P b,max >0, represents the maximum charging power of the battery.

3. The energy management method for smart building microgrid according to claim 2, characterized in that: The total energy consumption function of the intelligent terminal control at time slot t is derived as described in step (3.1), which describes the carbon emissions of all energy consumption factors controlled by the intelligent terminal at time slot t; specifically: According to the entire shopping mall model, there are three sources of energy: the smart grid, the RES power generation equipment in the building, and the battery discharge. There are five energy destinations: micro base station energy consumption, HVAC energy consumption, lighting energy consumption, battery charging, and macro base station energy consumption. Except for the macro base station, which is directly powered by the grid, the micro base station, HVAC, lighting, and battery charging are all controlled by smart terminals and powered by the RES equipment in the building and the smart grid. For ease of calculation, the energy consumption of the micro base station and the building energy consumption are combined and expressed as but represents the total energy consumption of the intelligent terminal control in time slot t, Expressed as: in, represents the building energy consumption at time t, Denote the total power of the micro base station in time slot t, using the function D t To describe the carbon emissions of all energy-consuming factors controlled by the intelligent terminal in time slot t: in, Represents the total power of the Heterogeneous Cellular Network (HCN).

4. The energy management method for smart building microgrid according to claim 3 is characterized in that: The behavior of the smart terminal in (step 3.2) is shown in Table 1 below; Table 1. Smart terminal behavior patterns Battery charge and discharge power If the value is not greater than or equal to zero, use the formula The symbols are used to describe the energy flow between the smart terminal and the power grid, and the The symbols are used to describe the energy flow between the smart terminal and the battery.

5. The energy management method for smart building microgrid according to claim 4, characterized in that: The specific operation of step (3.3) is: Assume that at the beginning of time slot t, the carbon emission factor of the smart grid is α t , the carbon emission factor in the battery at this moment is β t , β 0 is the carbon emission factor of energy stored in the battery under initial conditions; The calculation is: in: Obviously there are: If the shopping mall purchases electricity from the grid, its carbon emissions can be expressed as: The carbon emission factor of RES equipment production capacity is 0, and the carbon emissions from electricity sold from RES equipment to the grid are expressed as: The carbon emissions from electricity sales from battery storage to the grid are expressed as: Combined with the behavior pattern of smart terminals, The form is: The above expression can be used to describe the energy management function implemented by the smart terminal: By β t <α t When the carbon content of the grid is low, electricity is purchased to meet the peak demand, thus reducing carbon emissions over a period of time. By β t <α t By selling electricity when the carbon content of the grid is high, the company creates as much negative carbon emissions as possible. By buying electricity when the carbon content of the grid is low and storing it in batteries, the company sells it when the carbon content of the grid is high, balancing the load of the smart grid and reducing high-carbon power generation in the grid, thereby reducing the overall carbon emissions of the grid. Based on this assumption, the mall purchases electricity only to fill its own energy gap. The energy gap here refers to the energy deficit generated by the mall within a limited or unlimited time window calculated by prediction algorithms or other methods. According to the above assumptions, Simplified to: The carbon emission expression of intelligent terminal control is obtained, and G t represents the carbon emissions of the entire model at time slot t, expressed as: Since the global optimal solution is greater than the sum of the local optimal solutions, considering the entire scheduling range of intelligent terminal control, that is, the entire time domain to meet the optimal energy efficiency of the HCN network and minimize carbon emissions, this problem is described as; st And there are: st Among them, η EE It represents the ratio of the minimum target throughput to the total power consumption, i.e., energy efficiency, V t Indicates the density of people in the mall at time t, in m 2 / person, T represents the entire time domain, P T2 Represents the micro base station transmit power; The objective functions and constraints of the above two optimization problems (14) and (18) are called carbon emission equations. Here, the energy efficiency ratio of building electricity is not optimized, but only the energy efficiency ratio of the second-layer HCN is optimized, and the overall energy management is optimized.

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