A comprehensive energy management method based on integration of building and communication base station energy

Abstract

The application discloses a kind of integrated energy management methods based on building and communication base station energy integration, belong to building energy consumption management technical field, the method of the present application includes the following steps: obtaining the various characteristic variables of intelligent building and related base station;According to the characteristic variables obtained, calculate input parameter;According to the energy efficiency management method of heterogeneous cellular network, the relationship table between micro base station density and transmitting power is obtained for different human flow, and the total power consumption of base station is obtained;Using CVX tool, the carbon emissions after building energy consumption management are obtained;The carbon emissions without energy consumption management are compared with the carbon emissions after management, and the reduction of carbon emissions is calculated.The present application considers the energy loss of communication base station on the basis of intelligent building energy consumption model, and by modeling the communication network, the energy consumption of base station is integrated into the building energy consumption model, so that more comprehensive carbon emission statistics are obtained.

Description

A comprehensive energy management method based on the integration of energy consumption in buildings and communication base stations Technical Field

[0001] This invention belongs to the field of building energy management technology, specifically relating to an integrated energy management method based on the integration of energy consumption in buildings and communication base stations. Background Technology

[0002] The China Building Energy Conservation Association pointed out in its 2022 China Building Energy Consumption and Carbon Emission Research Report that in 2020, the total carbon emissions from the entire building process in China reached 5.08 billion tons of CO2, accounting for 50.9% of the country's total carbon emissions, making it the primary source of carbon emissions. This also means that energy conservation and emission reduction in buildings are urgently needed.

[0003] With the development and popularization of the Internet of Things (IoT), the concept of smart buildings has received unprecedented attention, becoming an important part of smart cities and reducing carbon emissions. Early smart buildings primarily aimed to achieve automated and unmanned management, such as monitoring lighting, drainage, and security. Today, with the maturity of mobile communication and IoT technologies, more intelligent terminals are being incorporated, giving smart buildings a new and more comprehensive concept: a composite digital intelligent system integrating multiple information subsystems such as building automation, communication automation, and security automation. Such a system possesses strong overall building control capabilities, including energy monitoring and management. Through the interaction between the smart grid and the microgrid formed by energy storage and power generation equipment within the building, it constructs an energy management subsystem, playing a crucial role in reducing building infrastructure costs and overall carbon emissions.

[0004] Furthermore, with the increasing automation capabilities of building communications, communication base stations and building clusters are showing a gradual trend of integration. Especially in the 5G (fifth-generation mobile communication technology) era, traditional networking models are no longer sufficient to meet the numerous demands of wireless communication users in today's IoT environment. To meet the high-speed, low-latency, and high-reliability requirements of today's cellular mobile networks, a new networking approach has emerged—heterogeneous cellular networks (HCN). This involves adding low-power micro base stations on top of traditional macro base stations to increase system throughput, improve network edge coverage, enhance service quality in indoor dead zones, and offload the load from macro base stations. Of course, the increase in the number of base stations also leads to increased power consumption; therefore, power consumption management is necessary. Since micro base stations are deployed around or inside buildings, they can be powered by the building's microgrid, thus integrating their power consumption into the building's overall power consumption and enabling energy management in conjunction with smart buildings, thereby achieving building emission reduction. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a comprehensive smart energy management method based on the integration of building energy consumption and communication base station energy use.

[0006] To solve the above technical problems, the present invention adopts the following technical solution:

[0007] A comprehensive energy management method based on the integration of building energy consumption and communication base station energy use includes the following steps:

[0008] (1) Obtain various characteristic variables of smart buildings and related base stations;

[0009] (2) Calculate the input parameters based on the obtained feature variables;

[0010] (3) Based on the energy efficiency management method of heterogeneous cellular networks, the energy efficiency of the base station is maximized by the population density in the building, thereby obtaining the relationship between the micro base station density and the micro base station transmission power;

[0011] (4) According to the energy flow model of smart buildings, the carbon emissions of buildings are related to the density of micro base stations, the transmission power of micro base stations and the charging and discharging power of energy storage devices. Based on the determined relationship between the density of micro base stations and the transmission power, the carbon emissions of buildings can be minimized by controlling the charging and discharging power of energy storage devices.

[0012] (5) Compare the carbon emissions without energy management with the carbon emissions after management, and record the reduction in carbon emissions.

[0013] Furthermore, the characteristic variables mentioned in step (1) include battery energy storage and battery maximum discharge power P. b,min Maximum charging power of the battery P b,max Building single-floor area S, heterogeneous cellular network area S0, bandwidth B, path loss index α, macro base station density λ1, macro base station transmit power P T1 Macro base station static power P C1 Macro base station target signal-to-interference ratio γ1, micro base station static power P C2 The study included 15 characteristic variables, such as the target signal-to-interference ratio (γ2) of micro base stations, the daily change rate of building personnel, and carbon emission factors.

[0014] Furthermore, the input parameters in step (2) include building foot traffic V. t Lighting energy consumption Air conditioning energy consumption and power generation E t 4 parameters;

[0015] Furthermore, the flow of people within the building is modeled as a normal distribution, with the building's pedestrian flow V... t This can be expressed as the following formula:

[0016]

[0017] Where A is the average weekly passenger flow, μ and σ 2 These represent the mean and variance of the daily occupancy rate of the building, respectively.

[0018] Furthermore, the lighting energy consumption The formula is

[0019]

[0020] Among them, P i Where is the average power of indoor lighting fixtures, N is the number of lighting fixtures, MF is the lighting fixture maintenance factor, F is the lighting fixture illuminance, Φ is the luminous flux, CU is the lighting fixture utilization factor, and S is the floor area of ​​a single building floor.

[0021] Furthermore, air conditioning energy consumption for

[0022]

[0023] Where Q0 is the heat dissipation coefficient of the human body, S is the area of ​​the shopping mall, β is the proportionality coefficient between the heat exchange of fresh air and the heat input of the building envelope, K1 and K2 are the thermal conductivity coefficients of the exterior walls and roof, F1 and F2 are the areas of the exterior walls and roof, and Q e H represents the heat dissipation of equipment within the building. t O represents the outdoor temperature at time t. T Temperature requirements within the shopping mall;

[0024] The building's power generation equipment uses wind power, with a wind power generation capacity of E. t In the part that interacts with the smart grid, the Weibull function is used to fit the wind energy obtained by the system, and in conjunction with the wind turbine generator, the expression is:

[0025]

[0026] Where k is the shape parameter and c is the scaling parameter.

[0027] Furthermore, the heterogeneous cellular network energy efficiency management method described in step (3) includes the following steps:

[0028] S1, Substitute the discrete values ​​of micro base station density into the energy efficiency η EE The expression:

[0029]

[0030] in

[0031] S2, Solving the optimization problem using CVX tools.

[0032]

[0033]

[0034] Obtain the optimal micro base station transmit power P under the corresponding base station density λ2. T2 ;

[0035] When the population density is input, the micro base station transmit power and the base station density span a Pareto solution space, in which all solutions satisfy the highest energy efficiency.

[0036] S3, repeat steps S1 and S2 until all discrete values ​​of micro base station density are exhausted.

[0037] S4, obtain the micro base station density λ2 and transmit power P. T2 Correspondence table between them;

[0038] S5, calculate the total power consumption P of the base station under the corresponding traffic flow. S :

[0039]

[0040] Furthermore, the energy flow model of the intelligent building in step (4) includes both energy consumption and generation aspects, with the energy consumption part... The energy consumption includes lighting, air conditioning, base stations, and other electrical equipment. Energy generation sources include smart grid electricity and electricity generated by hybrid wind and solar power generators in buildings. t Charging and discharging of energy storage devices in buildings Generator sets and energy storage devices constitute a building's microgrid. The energy flow within the building consists of the energy consumption difference between the microgrid and the building's internal electrical equipment, and energy trading with the smart grid. If we use symbols to describe the energy flow between smart terminals and the power grid, then building carbon emissions can be expressed as...

[0041]

[0042] The carbon emission description of the entire energy flow model then presents the following problem:

[0043]

[0044]

[0045]

[0046]

[0047] Furthermore, when When energy consumption inside a building exceeds energy production, it needs to purchase electricity from the grid; when When the building's internal energy balance is achieved, there is no need to purchase electricity from the smart grid; when When energy consumption inside a building is less than energy production, there is an energy surplus that can be sold to the smart grid.

[0048] Furthermore, when conducting energy transactions, it is necessary to combine historical electricity price data from the power grid; based on historical electricity prices, the trend of electricity prices in the future can be predicted. When there is a surplus of energy, the excess energy is sold when the electricity price is high.

[0049] Compared with the prior art, the present invention has the following technical advantages:

[0050] Based on the intelligent building energy consumption model, this invention considers the energy loss of communication base stations. By modeling the communication network, the energy consumption of base stations is integrated into the building energy consumption model, thereby obtaining more comprehensive carbon emission statistics. Attached Figure Description

[0051] Figure 1 is a flowchart of the overall framework of the present invention.

[0052] Figure 2 is a flowchart of the heterogeneous cellular network energy efficiency management method of the present invention. Detailed Implementation

[0053] As shown in Figure 1, the present invention provides an energy consumption management method integrating smart buildings and base stations based on carbon emissions, comprising the following steps:

[0054] S1, obtain various characteristic variables of smart buildings and related base stations:

[0055] The characteristic variables are shown in Table 1;

[0056] Table 1 Feature Variables

[0057]

[0058]

[0059] S2, Calculate the input parameter V based on the obtained feature variables: building pedestrian flow.t Lighting energy consumption Air conditioning energy consumption and power generation E t .

[0060] The flow of people within the building is modeled as a normal distribution, and the building's pedestrian flow parameter V is used. t This can be expressed as the following formula:

[0061]

[0062] Where A is the average weekly passenger flow, μ and σ 2 These represent the mean and variance of the daily occupancy rate of the building, respectively.

[0063] Lighting energy consumption parameters The formula is

[0064]

[0065] Among them, P i Where is the average power of indoor lighting fixtures, N is the number of lighting fixtures, MF is the lighting fixture maintenance factor, F is the lighting fixture illuminance, Φ is the luminous flux, CU is the lighting fixture utilization factor, and S is the floor area of ​​a single building.

[0066] Air conditioning energy consumption parameters for

[0067]

[0068] Where Q0 is the heat dissipation coefficient of the human body, S is the area of ​​the shopping mall, β is the proportionality coefficient between the heat exchange of fresh air and the heat input of the building envelope, K1 and K2 are the thermal conductivity coefficients of the exterior walls and roof, F1 and F2 are the areas of the exterior walls and roof, and Q e H represents the heat dissipation of equipment within the building. t O represents the outdoor temperature at time t. T Temperature requirements for shopping malls.

[0069] The building's power generation equipment uses wind power, with a wind power generation capacity of E. t In the part that interacts with the smart grid, the Weibull function is used to fit the wind energy obtained by the system, and in conjunction with the wind turbine generator, the expression is:

[0070]

[0071] Where k is the shape parameter and c is the scaling parameter.

[0072] S3, based on the energy efficiency management method for heterogeneous cellular networks, for different pedestrian traffic Vt The micro base station density λ2 and transmit power P were obtained. T2 The relationship table is used to obtain the total power consumption P of the base station. S ;

[0073] Energy efficiency management of heterogeneous cellular networks is based on an energy consumption model of heterogeneous cellular networks. The energy consumption in the model is jointly generated by macro base stations and micro base stations in buildings. Since buildings can only control the parameters of micro base stations, the energy consumption of the building is optimized by focusing on the transmit power and base station density of micro base stations. The optimization objective function is the energy efficiency η of the heterogeneous cellular network. EE That is, the minimum target throughput tp of the network. min Total power consumption P of the base station S The ratio of .

[0074]

[0075] in

[0076] The energy efficiency management method for heterogeneous cellular networks is based on factors such as building population density V, micro base station density λ2, and micro base station transmit power P. T2 To maximize the energy efficiency η of micro base stations EE The problem can be expressed in the following form:

[0077]

[0078]

[0079] When the population density is input, the micro base station transmit power and the base station density span a Pareto solution space. All solutions within this space satisfy the condition of maximizing energy efficiency.

[0080] S4. According to the energy flow model of smart buildings, the carbon emissions of buildings are related to the density of micro base stations, the transmission power of micro base stations, and the charging and discharging power of energy storage devices. Based on the determined relationship between the density of micro base stations and the transmission power, the carbon emissions of buildings can be minimized by controlling the charging and discharging power of energy storage devices. Using the CVX tool, the carbon emissions after building energy consumption management can be obtained.

[0081] The energy flow model for intelligent buildings includes both energy consumption and generation. Energy consumption component... The energy consumption includes lighting, air conditioning, base stations, and other electrical equipment. Energy generation sources include smart grid electricity and electricity generated by hybrid wind and solar power generators in buildings. t The charging and discharging of energy storage devices (batteries) in buildings Generator sets and energy storage devices constitute a building's microgrid. Energy flow within the building involves the energy consumption difference between the microgrid and the building's electrical-consuming equipment, as well as energy trading with the smart grid. This can be achieved using... If we use symbols to describe the energy flow between smart terminals and the power grid, then building carbon emissions can be expressed as...

[0082]

[0083] when When energy consumption inside a building exceeds energy production, it needs to purchase electricity from the grid; when When the building's internal energy balance is achieved, there is no need to purchase electricity from the smart grid; when When energy consumption inside a building is less than energy production, there is an energy surplus that can be sold to the smart grid.

[0084] When conducting energy transactions, it is necessary to consider historical electricity price data from the power grid. Based on historical electricity prices, the trend of electricity prices in the future can be predicted. When there is a surplus of energy, the excess energy is sold when the electricity price is high.

[0085] The carbon emissions of the entire energy flow model can be described as follows:

[0086]

[0087]

[0088]

[0089]

[0090] Note that its objective variable λ2 has discrete properties and is finite in number. And V within each time slot t... t Given the given information, we can list P where t is the length, λ² is the width, and P is the width. T2 The table contains the information. The parameter sets at each position in the table are Pareto solutions. Therefore, the optimal total power consumption of the base station can be obtained. Thus, the multi-objective optimization problem above is equivalent to a single-objective optimization problem. That is:

[0091]

[0092]

[0093]

[0094]

[0095] Where H(t) is a time-varying variable, and... Irrelevant parameters, by V t The input quantities and constants (including lookup table quantities such as λ²) are represented, and their removal does not affect the calculation if the equation has a solution. If we further assume the boundary condition C for battery energy storage... 0 =C min Since there is no electrical energy initially, the optimization problem can be transformed into a linear programming problem:

[0096]

[0097]

[0098]

[0099] Using the CVX tool, the optimal solution can be obtained, thus yielding the carbon emissions after energy management.

[0100] S5 compares the carbon emissions without building energy management with the carbon emissions after management to calculate the reduction in carbon emissions.

[0101] As shown in Figure 2, the above-mentioned energy efficiency management method for heterogeneous cellular networks includes the following steps:

[0102] S1, Substitute the discrete values ​​of micro base station density into the energy efficiency η EE The expression.

[0103]

[0104] S2, Solving the optimization problem using CVX tools.

[0105]

[0106]

[0107] Obtain the optimal micro base station transmit power P under the corresponding base station density λ2. T2 .

[0108] S3. Repeat steps S1 and S2 until all discrete values ​​of micro base station density are exhausted.

[0109] S4, obtain the micro base station density λ2 and transmit power P. T2 A table showing the correspondence between them.

[0110] S5, calculate the total power consumption P of the base station under the corresponding traffic flow. S :

[0111]

[0112] Based on the intelligent building energy consumption model, this invention considers the energy loss of communication base stations. By modeling the communication network, the energy consumption of base stations is integrated into the building energy consumption model, thereby obtaining more comprehensive carbon emission statistics.

[0113] The above-described technical details and algorithm implementation steps are merely illustrative examples to better illustrate the method proposed in this invention and should not be construed as limiting the invention. Other researchers in the art can make modifications and combinations within the scope of this invention, and these modifications and combinations are still within the protection scope of this invention.

Claims

1. A comprehensive energy management method based on the integration of building energy consumption and communication base station energy use, characterized in that, Includes the following steps: (1) Obtain various characteristic variables of smart buildings and related base stations; (2) Calculate the input parameters based on the obtained feature variables; (3) Based on the energy efficiency management method of heterogeneous cellular networks, the energy efficiency of the base station is maximized by the population density in the building, thereby obtaining the relationship between the micro base station density and the micro base station transmission power; The heterogeneous cellular network energy efficiency management method described in step (3) includes the following steps: S1, substituting the discrete values ​​of micro base station density into the energy efficiency. The expression: ;in S2, Solve the optimization problem using the CVX tool. Obtain the corresponding base station density Optimal micro base station transmit power When the pedestrian density is input, the micro base station transmit power and the base station density span a Pareto solution space, in which all solutions satisfy the condition of maximum energy efficiency; S3, repeat steps S1 and S2 until all discrete values ​​of the micro base station density are exhaustively enumerated; S4, obtain the micro base station density. With transmission power The correspondence table between them; S5, calculate the total power consumption of the base station under the corresponding traffic flow. ： (4) According to the energy flow model of intelligent buildings, the carbon emissions of buildings are related to the density of micro base stations, the transmission power of micro base stations, and the charging and discharging power of energy storage devices. Based on the determined relationship between the density of micro base stations and the transmission power, the carbon emissions of buildings can be minimized by controlling the charging and discharging power of energy storage devices. The energy flow model of intelligent buildings in step (4) includes two aspects: energy consumption and generation. The energy consumption part The energy consumption includes lighting, air conditioning, base stations, and other electrical equipment. The energy generation portion consists of smart grid electricity and electricity generated by hybrid wind and solar power generators in buildings. Charging and discharging of energy storage devices in buildings ； Generator sets and energy storage devices constitute a building's microgrid. The energy flow within the building consists of the energy consumption difference between the microgrid and the building's internal electrical equipment, and energy trading with the smart grid. [This is achieved using a specific formula / method / approach]. If we use the symbol ] to describe the energy flow between smart terminals and the power grid, then building carbon emissions can be expressed as The carbon emission description of the entire energy flow model then presents the following problem: (5) Compare the carbon emissions without energy management with the carbon emissions after management, and record the reduction in carbon emissions.

2. The integrated energy management method based on the integration of building energy consumption and communication base station energy use as described in claim 1, characterized in that, The characteristic variables mentioned in step (1) include battery energy storage and battery maximum discharge power. Maximum charging power of the battery Single floor area of ​​building heterogeneous cellular network area ,bandwidth Road damage index Macro base station density Macro base station transmission power Macro base station static power Macro base station target signal-to-interference ratio Static power of micro base stations Micro base station target signal-to-interference ratio The study included 15 characteristic variables, such as the daily change rate of building occupancy and carbon emission factors.

3. The integrated energy management method based on the integration of building energy consumption and communication base station energy use as described in claim 1, characterized in that, The input parameters in step (2) include building foot traffic. Lighting energy consumption Air conditioning energy consumption and power generation Four parameters.

4. The integrated energy management method based on the integration of building energy consumption and communication base station energy use as described in claim 3, characterized in that, The flow of people within the building is modeled as a normal distribution model, and the building's pedestrian traffic... This can be expressed as the following formula: ;in The average weekly foot traffic, and These represent the mean and variance of the daily occupancy rate of the building, respectively.

5. The integrated energy management method based on the integration of building energy consumption and communication base station energy use according to claim 3, characterized in that, The energy consumption of lighting The formula is ;in, This represents the average power of the indoor lighting fixtures. For the number of light fixtures, The maintenance factor for lighting fixtures. Φ represents the illuminance of the lamp, and Φ represents the luminous flux. The utilization factor of the lighting fixtures. This refers to the area of ​​a single floor of a building.

6. The integrated energy management method based on the integration of building energy consumption and communication base station energy use according to claim 3, characterized in that, Air conditioning energy consumption for ;in The heat dissipation coefficient of the human body For the area of ​​the shopping mall, This is the ratio coefficient between the amount of heat exchanged by fresh air and the heat input to the building envelope. and The thermal conductivity coefficients of the exterior walls and roof. and This refers to the area of ​​the exterior walls and roof. Heat dissipation from equipment inside the building. This represents the outdoor temperature at time t. To meet the temperature requirements within the shopping mall; the building's power generation equipment uses wind power, and the wind power generation capacity... In the part that interacts with the smart grid, the Weibull function is used to fit the wind energy obtained by the system, and in conjunction with the wind turbine generator, the expression is: ;in, For shape parameters, This is a proportional parameter.

7. The integrated energy management method based on the integration of building energy consumption and communication base station energy use according to claim 1, characterized in that, when When energy consumption inside a building exceeds energy production, it needs to purchase electricity from the grid; when When the building's internal energy balance is achieved, there is no need to purchase electricity from the smart grid; when When energy consumption inside a building is less than energy production, there is an energy surplus that can be sold to the smart grid.

8. The integrated energy management method based on the integration of building energy consumption and communication base station energy use according to claim 1, characterized in that, When conducting energy transactions, it is necessary to combine historical electricity price data from the power grid; based on historical electricity prices, the trend of electricity prices in the future can be predicted. When there is a surplus of energy, the excess energy is sold when the electricity price is high.

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