An optimization method and system for building energy management and control considering indoor heat transfer

Abstract

The present invention discloses an optimization method and system for building energy management considering indoor heat transfer. First, a third-order thermal parameter model of the building is established according to the dynamic heat transfer process between rooms, and then a central air-conditioning operation model including a chiller energy consumption model, a variable-frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model is constructed. The present invention realizes the function of improving the green and low-carbon operation effect of the building and taking into account the user comfort. Moreover, through the constructed building energy management optimization model, on the basis of considering the uncertainty of photovoltaic power generation and rigid loads, it can take into account the diverse thermal comfort requirements of different rooms and the building's daily operation cost, and use deep strategic gradient decision-making for multi-objective optimization to achieve the optimal low-carbon operation of the building. It not only considers the room heat transfer effect, but also promotes the comfortable, green and low-carbon operation of the building, ensures the building energy management effect, and is suitable for being widely promoted and used.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy control optimization, and particularly relates to a building energy control optimization method and system considering indoor heat transfer. Background Art

[0002] The carbon emissions of buildings account for a large proportion of the national energy consumption. The carbon emissions during the building operation stage account for more than half of the carbon emissions in the whole life cycle of the building. Therefore, how to formulate an efficient building low-carbon energy use control strategy is an effective strategy to promote the green and low-carbon operation of buildings.

[0003] At present, most of the building energy control optimizations consider the building as a whole for regulation, which easily ignores the different thermal comfort requirements of each room in the building. At the same time, the difference in the cooling area of each room also leads to different energy use demands. Factors such as room orientation and heat radiation reception further increase the temperature change difference between rooms. Not only will there be a significant dynamic heat transfer phenomenon between rooms, but also the dynamic heat transfer process between rooms will affect the cooling strategies of two adjacent rooms. At the same time, it is difficult for conventional optimization methods to handle the complex spatio-temporal coupling relationship of the building energy system when solving building optimization problems. This is very time-consuming in the calculation process when the solution space is very large, and it will also lead to an insoluble contradiction between the requirements of multi-objective optimization and fast solution. Therefore, it is necessary to design a building energy control optimization method and system considering indoor heat transfer. Summary of the Invention

[0004] The object of the present invention is to overcome the deficiencies of the prior art and, in order to better and effectively solve the problem that the current energy management and control optimization of buildings often considers the building as a whole for regulation, which easily leads to the neglect of the different thermal comfort requirements of each room in the building. At the same time, the difference in the cooling area of each room also results in different energy consumption demands. Moreover, factors such as room orientation and heat radiation reception further increase the temperature change difference between rooms. This not only causes a significant dynamic heat transfer phenomenon between rooms but also makes the dynamic heat transfer process between rooms affect the cooling strategies of adjacent two rooms. Meanwhile, when conventional optimization methods solve building optimization problems, it is difficult to handle the complex spatio-temporal coupling relationship of the building energy system. When the solution space is very large, the calculation process is very time-consuming, and it also leads to the problem that it is difficult to resolve the contradiction between the requirements of multi-objective optimization and fast solution. The present invention provides a building energy management and control optimization method and system considering indoor heat transfer, which realizes the functions of improving the green and low-carbon operation effect of the building and taking into account the user comfort. And through the constructed building energy management and control optimization model, on the basis of considering the uncertainty of photovoltaic power generation and rigid load, it can take into account the diverse thermal comfort requirements of different rooms and the daily operation cost of the building, and use deep strategic gradient decision-making for multi-objective optimization solution to achieve the optimal low-carbon operation of the building. It not only considers the room heat transfer effect but also promotes the comfortable, green and low-carbon operation of the building, and ensures the building energy management and control effect.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A building energy management and control optimization method considering indoor heat transfer, comprising the following steps:

[0007] Step A: Establish a building three-order thermal parameter model according to the dynamic heat transfer process between rooms;

[0008] Step B: Construct a central air-conditioning operation model including a chiller energy consumption model, a variable-frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model;

[0009] Step C: Based on the building three-order thermal parameter model and the central air-conditioning operation model, establish a building energy management and control optimization model according to the building operation cost and thermal comfort;

[0010] Step D: Use the building energy management and control optimization model to optimize the building energy management and control to obtain a building energy management and control optimization decision;

[0011] Step E: Modify the building energy management and control optimization decision according to the uncertainty of building photovoltaic power generation and rigid load and obtain the modified building energy management and control optimization decision, thereby completing the building energy management and control optimization operation.

[0012] The aforementioned optimization method for building energy control considering indoor heat transfer, step A, establish a building three-order thermal parameter model according to the dynamic heat transfer process between rooms, and the specific steps are as follows:

[0013] Step A1, calculate the dynamic heat transfer quantity Q between rooms change , as shown in formula (1):

[0014]

[0015] where, T1 is the indoor temperature of the first room, T2 is the indoor temperature of the second room, R ec1 is the equivalent convective heat resistance of the first room, R ec2 is the equivalent convective heat resistance of the second room, T iw is the inner wall temperature, R iw is the equivalent heat resistance of the inner wall;

[0016] Step A2, calculate matrix A and matrix B respectively as shown in formula (2) and formula (3):

[0017]

[0018]

[0019] where, R ec is the convective equivalent heat resistance, R′ ew is half of the equivalent heat resistance of the outer wall, R′ iw is half of the equivalent heat resistance of the inner wall, R gs is the equivalent heat resistance of the window, C ew is the equivalent heat capacity of the outer wall, C iw is the equivalent heat capacity of the inner wall, C in is the equivalent heat capacity of the indoor air;

[0020] Step A3, establish a building three-order thermal parameter model, as shown in formula (4):

[0021]

[0022] where, T ew is the outer wall temperature, T in is the indoor temperature, T out is the outdoor temperature, T adj is the adjacent room temperature, I ew is the absorbed solar radiation on the outer wall, I e is the solar radiation entering the room through the window, I in is the load heat generated in the building, I t dev is the cooling capacity of the HVAC system.

[0023] The aforementioned optimization method for building energy control considering indoor heat transfer, step B, constructs a central air-conditioning operation model including a chiller energy consumption model, a variable-frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model. The specific steps are as follows:

[0024] Step B1, construct a chiller energy consumption model. The specific steps are as follows:

[0025] Step B11, calculate the part load ratio PLR of the chiller chiller , as shown in formula (5):

[0026]

[0027] where Q chiller is the actual refrigeration capacity of the water-cooled chiller, a0, a1, and a2 are all part load correction coefficients, and Q nom is the rated refrigeration capacity of the chiller;

[0028] Step B12, calculate the operating power W of the chiller under rated conditions chiller , as shown in formula (6):

[0029]

[0030]

[0031] where T chiller is the actual refrigeration temperature of the water-cooled chiller, T chws and T cws are the chilled water supply temperature and the cooling water supply temperature respectively, and b0, b1, b2, b3, b4, and b5 are all temperature regression coefficients;

[0032] Step B13, construct a chiller energy consumption model that reflects the relationship between the change in cooling load and the power consumption of the chiller, as shown in formula (7):

[0033] P chiller =W chiller gPLR chiller (7)

[0034] where P chiller is the actual operating power of the chiller, W chiller is the operating power of the chiller under rated conditions, and PLR chiller is the part load ratio of the chiller;

[0035] Step B2, establish a variable-frequency water pump energy consumption model. The variable-frequency water pump adjusts the motor speed by changing the current frequency to regulate the water flow of the pump, and the actual power of the pump is as shown in formula (8):

[0036]

[0037]

[0038] Among them, P pump is the actual power of the water pump, and P pump.nom is the rated power of the water pump, m nom is the rated flow rate of the water pump, m chw is the actual flow rate of the water pump, and PLR pump is the partial load ratio of the water pump; d0, d1, d2, and d3 are all polynomial fitting coefficients;

[0039] Step B3: Construct a cooling tower fan energy consumption model as shown in Formula (9),

[0040]

[0041]

[0042] Among them, P tfan is the power of the cooling tower fan, and P tfan,nom is the rated power of the cooling tower fan; f0, f1, f2, and f3 are all polynomial fitting coefficients, and PLR tfan is the load ratio of the fan, m atfan is the actual air volume of the fan, m atfan,nom is the rated air volume of the fan;

[0043] Step B4: Establish an indoor unit fan energy consumption model as shown in Formula (10),

[0044]

[0045]

[0046] Among them, P cfan,nom represents the rated power of the fan, and P cfan is the actual power of the fan; g0, g1, g2, and g3 are all fitting coefficients of the polynomial model, and PLR cfan is the load ratio of the fan, m acfan,nom is the rated air volume of the fan, m acfan is the actual air volume of the fan.

[0047] The aforementioned building energy control optimization method considering indoor heat transfer, step C: Based on the building third-order thermal parameter model and the central air-conditioning operation model, establish a building energy control optimization model according to the building operation cost and thermal comfort. The specific steps are as follows,

[0048] Step C1, calculate the total building comfort cost, where the total building comfort cost is the sum of the thermal comfort costs of all rooms, as shown in Equation (11).

[0049]

[0050]

[0051]

[0052] Among them, C total is the total building operation cost, is the cost of purchased electricity or the revenue from selling electricity, is the quantity of purchased electricity, is the price of purchased electricity, is the quantity of sold electricity, is the price of sold electricity, G room (k) is the thermal comfort gain of the k-th room, and PMV(k) is the PMV value of the k-th room;

[0053] Step C2, establish an optimization model for building energy management and control. Specifically, establish an optimization objective P1 that takes into account the optimal comfort of each room and the minimum building operation cost, and the constraint conditions include the central air-conditioning operation model, the thermal comfort model, and the energy storage device model, as shown in Equation (12).

[0054]

[0055] For the aforementioned building energy management and control optimization method considering indoor heat transfer, in Step D, use the building energy management and control optimization model to optimize the building energy management and control, and obtain the building energy management and control optimization decision. Among them, the building energy management and control optimization decision P2 is as shown in Equation (13).

[0056]

[0057] For the aforementioned building energy management and control optimization method considering indoor heat transfer, in Step E, correct the building energy management and control optimization decision according to the uncertainty of building photovoltaic power generation and rigid load, and obtain the corrected building energy management and control optimization decision to complete the building energy management and control optimization operation. The specific process of correcting the building energy management and control optimization decision is as shown in Equation (14).

[0058]

[0059] Among them, ω1 is the weight of building energy cost, and ω2 is the total weight of the total comfort of each room in the building.

[0060] An optimized building energy management and control system considering indoor heat transfer, comprising a building three - order thermal parameter model establishment module, a central air - conditioning operation model construction module, a building energy management and control optimization model establishment module, a building energy management and control optimization module, and an optimization and correction module. The building three - order thermal parameter model establishment module is used to establish a building three - order thermal parameter model according to the dynamic heat transfer process between rooms;

[0061] The central air - conditioning operation model construction module is used to construct a central air - conditioning operation model including a chiller energy consumption model, a variable - frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model;

[0062] The building energy management and control optimization model establishment module is used to establish a building energy management and control optimization model based on the building three - order thermal parameter model and the central air - conditioning operation model and according to the building operation cost and thermal comfort;

[0063] The building energy management and control optimization module is used to optimize the building energy management and control by using the building energy management and control optimization model and obtain an optimized decision for building energy management and control;

[0064] The optimization and correction module is used to correct the optimized decision for building energy management and control according to the uncertainty of building photovoltaic power generation and rigid load and obtain a corrected optimized decision for building energy management and control.

[0065] The beneficial effects of the present invention are as follows: An optimized building energy management and control method and system considering indoor heat transfer according to the present invention first establishes a building three - order thermal parameter model according to the dynamic heat transfer process between rooms, then constructs a central air - conditioning operation model including a chiller energy consumption model, a variable - frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model. Then, based on the building three - order thermal parameter model and the central air - conditioning operation model and according to the building operation cost and thermal comfort, a building energy management and control optimization model is established. Then, the building energy management and control is optimized by using the building energy management and control optimization model and an optimized decision for building energy management and control is obtained. Subsequently, the optimized decision for building energy management and control is corrected according to the uncertainty of building photovoltaic power generation and rigid load and a corrected optimized decision for building energy management and control is obtained, thereby completing the optimized operation of building energy management and control. It effectively realizes the functions that the optimized building energy management and control method and system have the effects of improving the green and low - carbon operation of the building and taking into account the user comfort. And through the constructed building energy management and control optimization model, on the basis of considering the uncertainty of photovoltaic power generation and rigid load, it can take into account the diverse thermal comfort requirements of different rooms and the building daily operation cost and use deep strategic gradient decision - making for multi - objective optimization solution to realize the optimal low - carbon operation of the building. It not only considers the room heat transfer effect, but also promotes the comfortable, green and low - carbon operation of the building and ensures the building energy management and control effect. Description of the Drawings

[0066] Figure 1 It is a flowchart of an optimization method for building energy control considering indoor heat transfer according to the present invention;

[0067] Figure 2 It is a building room location diagram in the embodiment of the present invention;

[0068] Figure 3 It is an optimization flowchart of building energy control considering indoor heat transfer in the embodiment of the present invention;

[0069] Figure 4 It is a schematic diagram for comparative analysis of indoor temperature changes in rooms considering heat transfer and not considering heat transfer in the embodiment of the present invention;

[0070] Figure 5 It is a schematic diagram of the heat transfer amount between rooms in the embodiment of the present invention;

[0071] Figure 6 It is a schematic diagram of the power of each part of the central air-conditioning internal unit in the embodiment of the present invention;

[0072] Figure 7 It is a schematic diagram of the electric power balance in the embodiment of the present invention. Detailed implementation manners

[0073] The present invention will be further described below in conjunction with the accompanying drawings of the specification.

[0074] As Figure 1 shown, an optimization method and system for building energy control considering indoor heat transfer according to the present invention includes the following steps

[0075] Step A, establish a building third-order thermal parameter model according to the dynamic heat transfer process between rooms. The specific steps are as follows

[0076] Step A1, calculate the dynamic heat transfer amount Q between rooms change , as shown in formula (1):

[0077]

[0078] wherein, T1 is the indoor temperature of the first room, T2 is the indoor temperature of the second room, R ec1 is the equivalent convective heat resistance of the first room, R ec2 is the equivalent convective heat resistance of the second room, T iw is the inner wall temperature, and R iw is the equivalent thermal resistance of the inner wall;

[0079] Step A2, calculate matrix A and matrix B respectively as shown in formula (2) and formula (3)

[0080]

[0081]

[0082] Among them, R ec is the convective equivalent thermal resistance, R′ ew is half of the equivalent thermal resistance of the external wall, R′ iw is half of the equivalent thermal resistance of the internal wall, R gs is the equivalent thermal resistance of the window, C ew is the equivalent heat capacity of the external wall, C iw is the equivalent heat capacity of the internal wall, C in is the equivalent heat capacity of the indoor air;

[0083] Step A3, establish a three-order thermal parameter model of the building, as shown in formula (4),

[0084]

[0085] Among them, T ew is the external wall temperature, T in is the indoor temperature, T out is the outdoor temperature, T adj is the adjacent room temperature, I ew is the absorbed solar radiation of the external wall, I e is the solar radiation entering the room through the window, I in is the load heat generated in the building, is the cooling capacity of the HVAC system.

[0086] Step B, construct a central air-conditioning operation model including a chiller energy consumption model, a variable-frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model. The specific steps are as follows,

[0087] Step B1, construct a chiller energy consumption model. The specific steps are as follows,

[0088] Step B11, calculate the part load ratio PLR of the chiller chiller , as shown in formula (5),

[0089]

[0090] Among them, Q chiller is the actual cooling capacity of the water-cooled chiller, a0, a1, and a2 are all part load correction coefficients, Q nom is the rated cooling capacity of the chiller;

[0091] Step B12, calculate the operating power W of the chiller under rated conditions chiller , as shown in formula (6),

[0092]

[0093]

[0094] Among them, T chiller is the actual refrigeration temperature of the water-cooled unit, T chws and T cws are the chilled water supply temperature and the cooling water supply temperature respectively, and b0, b1, b2, b3, b4 and b5 are all temperature regression coefficients;

[0095] Step B13, construct a chiller energy consumption model that reflects the relationship between the change in cooling load and the power consumption of the chiller, as shown in formula (7),

[0096] P chiller = W chiller gPLR chiller (7)

[0097] Among them, P chiller is the actual operating power of the chiller, W chiller is the operating power of the chiller under rated conditions, and PLR chiller is the part load ratio of the chiller;

[0098] Step B2, establish a variable-frequency pump energy consumption model. The variable-frequency pump adjusts the speed of the motor by changing the current frequency to regulate the water flow of the pump, and the actual power of the pump is as shown in formula (8),

[0099]

[0100]

[0101] Among them, P pump is the actual power of the pump, P pump.nom is the rated power of the pump, m nom is the rated flow of the pump, m chw is the actual flow of the pump, PLR pump is the part load ratio of the pump, and d0, d1, d2 and d3 are all polynomial fitting coefficients;

[0102] Step B3, construct a cooling tower fan energy consumption model, as shown in formula (9),

[0103]

[0104]

[0105] Among them, P tfan is the power of the cooling tower fan, P tfan,nom is the rated power of the cooling tower fan, f0, f1, f2 and f3 are all polynomial fitting coefficients, and PLR tfanis the load rate of the fan, m atfan is the actual air volume of the fan, m atfan,nom is the rated air volume of the fan;

[0106] Step B4, establish the energy consumption model of the indoor unit fan, as shown in formula (10),

[0107]

[0108]

[0109] where P cfan,nom represents the rated power of the fan, P cfan is the actual power of the fan, g0, g1, g2, and g3 are all fitting coefficients of the polynomial model, PLR cfan is the load rate of the fan, m acfan,nom is the rated air volume of the fan, m acfan is the actual air volume of the fan.

[0110] Step C, based on the building three-order thermal parameter model and the central air-conditioning operation model, establish the building energy control and optimization model according to the building operation cost and thermal comfort. The specific steps are as follows,

[0111] Step C1, calculate the total building comfort cost, where the total building comfort cost is the sum of the thermal comfort costs of all rooms, as shown in formula (11),

[0112]

[0113]

[0114]

[0115] where C total is the total building operation cost, is the cost of purchased electricity or the revenue from selling electricity, is the amount of purchased electricity, is the price of purchased electricity, is the amount of electricity sold, is the price of selling electricity, G room (k) is the thermal comfort gain of the kth room, and PMV(k) is the PMV value of the kth room;

[0116] Step C2, establish the building energy control and optimization model, specifically establish the optimization solution objective P1 that takes into account the optimal comfort of each room and the minimum building operation cost, and the constraint conditions include the central air-conditioning operation model, the thermal comfort model, and the energy storage device model, as shown in formula (12),

[0117]

[0118] Step D: Optimize building energy control using the building energy control optimization model to obtain an optimized decision for building energy control, where the optimized decision for building energy control P2 is shown in Formula (13).

[0119]

[0120] Step E: Correct the optimized decision for building energy control according to the uncertainties of building photovoltaic power generation and rigid loads and obtain the corrected optimized decision for building energy control, thus completing the optimization operation of building energy control. The specific process of correcting the optimized decision for building energy control is shown in Formula (14).

[0121]

[0122] Among them, ω1 is the weight of building energy consumption cost, and ω2 is the total weight of the overall comfort of each room in the building.

[0123] A building energy control optimization system considering indoor heat transfer includes a building three-order thermal parameter model establishment module, a central air-conditioning operation model construction module, a building energy control optimization model establishment module, a building energy control optimization module, and an optimization correction module. The building three-order thermal parameter model establishment module is used to establish a building three-order thermal parameter model according to the dynamic heat transfer process between rooms; the central air-conditioning operation model construction module is used to construct a central air-conditioning operation model including a chiller energy consumption model, a variable-frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model; the building energy control optimization model establishment module is used to establish a building energy control optimization model based on the building three-order thermal parameter model and the central air-conditioning operation model and according to building operation costs and thermal comfort; the building energy control optimization module is used to optimize building energy control using the building energy control optimization model and obtain an optimized decision for building energy control; the optimization correction module is used to correct the optimized decision for building energy control according to the uncertainties of building photovoltaic power generation and rigid loads and obtain the corrected optimized decision for building energy control.

[0124] To better illustrate the usage effect of the present invention, a specific embodiment of using the present invention is introduced below.

[0125] The basic parameter settings of this embodiment include: taking a certain building as the calculation scenario, with a total building area of 528.08 m², a height of 7.2 m, and two above-ground floors. The building is equipped with photovoltaic power generation equipment, wind power generation equipment, and energy storage equipment. The building envelope has been energy-efficiently renovated, including the exterior wall, interior wall, and window transmittance, with good thermal insulation characteristics. The main use of this building is for meetings and offices. The area of Room 1 is 72 m², the area of Room 2 is 34.08 m², the area of Room 3 is 21.9 m², the area of Room 4 is 7 m², the area of Room 5 is 15.3 m², the area of Room 6 is 25.2 m², and the positional relationship between the rooms is as Figure 2 shown. The building energy control period is from 8:00 to 9:00, and the optimization time is 15 minutes. The implementation process of this embodiment is as shown in Figure 3.

[0126] From Figure 4 it can be concluded that due to the different thermal comfort gains of each room, the cooling strategies of each room are different, and the indoor temperature changes of the rooms are also different. The area of Room 1 is the largest, followed by Room 2 with a relatively large area. The indoor temperature fluctuations of these two rooms are relatively gentle. Therefore, the larger the area of the room, the slower the cooling effect of the air conditioner. The areas of Room 4 and Room 5 are relatively small, and it can be seen that the indoor temperature fluctuations of these two rooms are relatively large. Therefore, the smaller the area of the room, the more significant the cooling effect. Room 6 gives a greater thermal comfort gain value than Room 3, making the temperature fluctuations of the two rooms more significant when the room areas are similar. Therefore, the thermal comfort gain has a positive feedback effect on the control of the room temperature. From Figure 4 it can be concluded that the temperature curve of each room plots the temperature curve when the building is considered as a single overall room. When the building is optimized and regulated as a whole, there is no heat transfer between the rooms. Therefore, the temperatures of each room should be the same, and the indoor temperature fluctuations of the rooms are relatively small. Because the overall area of the building is large, the entire building is equivalent to a large room, and the cooling effect of the air conditioner is not significant. At this time, even if the thermal comfort gain is increased, the comfort effect of the building is not obvious and cannot meet the diverse comfort needs of different users in different rooms of the building.

[0127] From Figure 5It can be concluded that room1-room3 represents the heat transfer amount from room 1 to room 3. During the intelligent optimization of the building, room 3 is basically transferring cold to room 1, and room 2 is also basically transferring cold to room 1. Because the area of room 1 is larger and the cooling effect is slow, while the areas of room 2 and 3 are smaller and the cooling effect is fast. Therefore, during the optimization period, room 2 and 3 have a certain auxiliary effect on the cooling of room 1, saving the cooling capacity required by room 1 and thus reducing the central air-conditioning power of room 1. The heat transfer process between room 3 and room 4 is relatively complex, and the two rooms alternately transfer cold to each other. The reason is that the area of room 4 is very small and the cooling effect is very significant. When the set temperature of room 4 is lowered, the temperature drops rapidly and soon becomes lower than that of room 3, so it transfers cold to room 3. Subsequently, when the set temperature is raised, the indoor temperature gradually rises again. When it exceeds room 4, on the contrary, room 4 transfers cold to room 3, and this process alternates repeatedly. The heat exchange between room 2 and room 5 also alternates, but because the area difference between the rooms is small, the alternating process is not particularly significant. For room 5 and room 6, due to the relatively large heat comfort gain of room 6, the temperature of room 6 drops faster at the beginning, and room 5 transfers heat to room 6. At noon, because of the larger room area and window area of room 6, it receives more solar radiation and the temperature rises, so room 6 transfers heat to room 5. As the solar radiation gradually decreases in the afternoon, the temperature of room 6 drops again, and room 5 transfers heat to room 6.

[0128] From Figure 6 The power relationship between the internal units of the central air-conditioning can be obtained. The power consumed by the chiller accounts for most of the central air-conditioning power, followed by the power consumed by the water pump, and the power of the cooling tower fan and the indoor fan is relatively small. At the same time, it can be seen that whether the total power of the central air-conditioning system increases or decreases, a certain proportional relationship still remains among the units.

[0129] From Figure 7The electric power balance relationship between the internal systems of the building energy management and control model can be obtained. For building energy management and control from 9:00 to 18:00 with a control interval of 15 minutes, it can be seen that there is a certain relationship between the photovoltaic power generation and the central air-conditioning power. In the morning, as the photovoltaic power increases and the outdoor temperature gradually rises, in order to reduce the indoor temperature and ensure the comfort of the room, the air-conditioning power also increases, increasing the cooling capacity and enabling the temperature of each room in the building to drop rapidly. At noon, the photovoltaic power generation is at a peak period and the outdoor temperature is also relatively high, so the central air-conditioning power is also relatively large at this time. In the afternoon, as the photovoltaic power gradually decreases and the outdoor temperature drops, and considering the heat preservation characteristics of the building envelope, maintaining the indoor temperature no longer requires a large amount of cooling capacity, so the central air-conditioning power also gradually decreases. It can also be concluded from the figure that the energy storage system plays an important role in building energy management and control. It stores photovoltaic power during periods of sufficient photovoltaic power or low electricity prices, and releases energy during periods of insufficient photovoltaic power or high electricity prices to make up for the building's consumption, fully consuming new energy and saving the building's energy consumption cost.

[0130] In summary, for an optimization method and system for building energy management and control considering indoor heat transfer according to the present invention, first, a building three-order thermal parameter model is established based on the dynamic heat transfer process between rooms, and then a central air-conditioning operation model including a chiller energy consumption model, a variable-frequency water pump energy consumption model, a cooling tower fan energy consumption model, and an indoor unit fan energy consumption model is constructed. Then, based on the building three-order thermal parameter model and the central air-conditioning operation model, and according to the building operation cost and thermal comfort, a building energy management and control optimization model is established. Then, the building energy management and control is optimized using the building energy management and control optimization model to obtain an optimized decision for building energy management and control. Subsequently, the optimized decision for building energy management and control is corrected according to the uncertainty of building photovoltaic power generation and rigid load to obtain a corrected optimized decision for building energy management and control, thereby completing the building energy management and control optimization operation; effectively realizing that the building energy management and control optimization method and system have the function of improving the green and low-carbon operation effect of the building and taking into account the user's comfort, and through the constructed building energy management and control optimization model, it can take into account the diverse thermal comfort requirements of different rooms and the building's daily operation cost on the basis of considering the uncertainty of photovoltaic power generation and rigid load and use deep strategic gradient decision-making for multi-objective optimization to solve, so as to achieve the optimal low-carbon operation of the building. It not only considers the room heat transfer effect, but also promotes the comfortable, green and low-carbon operation of the building, ensuring the building energy management and control effect.

[0131] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A building energy management and control optimization method considering indoor heat transfer, characterized in that: The following steps are included: Step A: Establish a third-order thermal parameter model of the building based on the dynamic heat transfer process between rooms. The specific steps are as follows: Step A1, calculate the dynamic heat transfer Q between rooms change , as shown in formula (1), Among them, T1 is the indoor temperature of the first room, T2 is the indoor temperature of the second room, R ec1 is the equivalent convection thermal resistance of the first room, R ec2 is the equivalent convection thermal resistance of the second room, T iw is the inner wall temperature, R iw is the equivalent thermal resistance of the inner wall; Step A2, calculate matrix A and matrix B as shown in formula (2) and formula (3) respectively, Among them, R ec is the convection equivalent thermal resistance, R′ ew is half of the equivalent thermal resistance of the exterior wall, R′ iw is half of the equivalent thermal resistance of the inner wall, R gs is the equivalent thermal resistance of the window, C ew is the equivalent heat capacity of the exterior wall, C iw is the equivalent heat capacity of the inner wall, C in is the equivalent heat capacity of indoor air; Step A3: Establish a third-order thermal parameter model of the building, as shown in formula (4): Among them, T ew is the external wall temperature, T in is the indoor temperature, T out is the outdoor temperature, T adj is the adjacent room temperature, I ew is the absorbed solar radiation of the exterior wall, I e is the solar radiation entering the room through the windows, I in is the load heat generated in the building, The cooling capacity of the HVAC system; Step B, constructing a central air conditioning operation model including a chiller energy consumption model, a variable frequency water pump energy consumption model, a cooling tower fan energy consumption model and an indoor unit fan energy consumption model; Step C, based on the third-order thermal parameter model of the building and the central air-conditioning operation model, establish a building energy management optimization model according to the building operation cost and thermal comfort; Step D, optimizing the building energy control by using the building energy control optimization model to obtain the building energy control optimization decision; Step E: correct the building energy management optimization decision according to the uncertainty of building photovoltaic power generation and rigid load and obtain the corrected building energy management optimization decision, thereby completing the building energy management optimization operation.

2. A building energy management and optimization method considering indoor heat transfer according to claim 1, characterized in that: Step B, constructing a central air-conditioning operation model including a chiller energy consumption model, a variable frequency water pump energy consumption model, a cooling tower fan energy consumption model and an indoor unit fan energy consumption model. The specific steps are as follows: Step B1, constructing the chiller energy consumption model. The specific steps are as follows: Step B11, calculate the partial load rate PLR ​​of the chiller chiller , as shown in formula (5), Among them, Q chiller is the actual cooling capacity of the water-cooled unit, a0, a1 and a2 are partial load correction coefficients, Q nom is the rated cooling capacity of the chiller; Step B12, calculate the chiller operating power W under rated conditions chiller , as shown in formula (6), Among them, T chiller is the actual cooling temperature of the water cooling unit, T chws and T cws are the chilled water supply temperature and cooling water supply temperature respectively, b0, b1, b2, b3, b4 and b5 are temperature regression coefficients; Step B13, constructing a chiller energy consumption model that reflects the relationship between the cooling load change and the chiller power consumption, as shown in formula (7), P chiller =W chiller ·PLR chiller (7) Among them, P chiller is the actual operating power of the chiller, W chiller PLR is the chiller operating power under rated conditions. chiller is the partial load rate of the chiller; Step B2, establish a variable frequency water pump energy consumption model, where the variable frequency water pump adjusts the motor speed by changing the current frequency to adjust the water flow of the water pump, and the actual power of the water pump is shown in formula (8): Among them, P pump is the actual power of the pump, P pump.nom is the rated power of the pump, m nom Rated flow rate of the pump, m chw is the actual flow rate of the pump, PLR pump is the partial load rate of the water pump, d0, d1, d2 and d3 are all polynomial fitting coefficients; Step B3, construct the cooling tower fan energy consumption model, as shown in formula (9), Among them, P tfan is the power of the cooling tower fan, P tfan,nom is the rated power of the cooling tower fan, f0, f1, f2 and f3 are polynomial fitting coefficients, PLR tfan is the load factor of the fan, m atfan is the actual air volume of the fan, m atfan,nom is the rated air volume of the fan; Step B4, establish the indoor unit fan energy consumption model, as shown in formula (10), Among them, P cfan,nom Indicates the rated power of the fan, P cfan is the actual power of the wind turbine, g0, g1, g2 and g3 are the fitting coefficients of the polynomial model, PLR cfan is the load factor of the fan, m acfan,nom is the rated air volume of the fan, m acfan is the actual air volume of the fan.

3. A building energy management and control optimization method considering indoor heat transfer according to claim 2, characterized in that: Step C: Based on the third-order thermal parameter model of the building and the central air-conditioning operation model, a building energy management optimization model is established according to the building operation cost and thermal comfort. The specific steps are as follows: Step C1, calculate the total building comfort cost, where the total building comfort cost is the cumulative sum of the thermal comfort costs of all rooms, as shown in formula (11), Among them, C total is the total operating cost of the building, The cost of purchasing electricity or the income from selling electricity, The amount of electricity purchased is is the price of purchased electricity, For electricity sales, is the electricity selling price, G room (k) is the thermal comfort gain of the kth room, PMV(k) is the PMV value of the kth room; Step C2, establish a building energy management optimization model, specifically, establish an optimization solution target P1 that takes into account both the optimal comfort of each room and the minimum building operation cost, and the constraints include the central air conditioning operation model, thermal comfort model and energy storage equipment model, as shown in formula (12), 4. A building energy management and control optimization method considering indoor heat transfer according to claim 3, characterized in that: Step D, using the building energy control optimization model to optimize the building energy control, and obtain the building energy control optimization decision, where the building energy control optimization decision P2 is shown in formula (13):

5. A building energy management and control optimization method considering indoor heat transfer according to claim 4, characterized in that: Step E: According to the uncertainty of building photovoltaic power generation and rigid load, the building energy control optimization decision is corrected and the corrected building energy control optimization decision is obtained to complete the building energy control optimization operation. The specific process of correcting the building energy control optimization decision is shown in formula (14): Among them, ω1 is the weight of the building energy cost, and ω2 is the total weight of the total comfort of each room in the building.

6. A building energy management and optimization system considering indoor heat transfer, wherein the optimization process of the building energy management and optimization system is based on the building energy management and optimization method according to any one of claims 1 to 5, characterized in that: It includes a building third-order thermal parameter model establishment module, a central air-conditioning operation model construction module, a building energy management and control optimization model establishment module, a building energy management and control optimization module and an optimization correction module. The building third-order thermal parameter model establishment module is used to establish a building third-order thermal parameter model according to the dynamic heat transfer process between rooms; The central air-conditioning operation model construction module is used to construct a central air-conditioning operation model including a chiller energy consumption model, a variable frequency water pump energy consumption model, a cooling tower fan energy consumption model and an indoor unit fan energy consumption model; The building energy management optimization model establishment module is used to establish a building energy management optimization model based on the building third-order thermal parameter model and the central air-conditioning operation model and according to the building operation cost and thermal comfort; The building energy management and control optimization module is used to optimize the building energy management and control using the building energy management and control optimization model and obtain the building energy management and control optimization decision; The optimization correction module is used to correct the building energy management optimization decision according to the uncertainty of building photovoltaic power generation and rigid load and obtain the corrected building energy management optimization decision.

Images