Air conditioning load modeling method considering user comfort

Abstract

The application relates to the field of digital modeling of household air conditioners, and discloses an air conditioner load modeling method considering user comfort, which comprises the following steps: (1) reading real-time voltage, current, active power and reactive power data of air conditioner operation according to an intelligent socket, and transmitting the data to a home energy management system terminal in real time; (2) identifying the maximum impact power of the air conditioner as a constraint condition for scheduling of the home energy management; (3) drawing a curve of the terminal changing with time according to the received data, and performing segmented polynomial fitting on the active power curve of the air conditioner; (4) inputting air conditioner room parameters to model the air conditioner room; and (5) according to a comfort interval, combining the air conditioner room model and a normal operation model of the air conditioner, calculating a scheduling interval of the air conditioner, and realizing scheduling of the air conditioner by the system. The application is suitable for different household application scenarios, and has great significance for energy saving and environment protection.

Description

Technical Field

[0001] This invention belongs to the field of power system load modeling technology, and in particular relates to an air conditioning load modeling method that takes into account user comfort. Background Technology

[0002] For decades, energy consumption in residential and public buildings has been increasing year by year, with air conditioning energy consumption being a major component of this energy consumption. How to model and study air conditioners in steady-state operation to reduce their power consumption without altering their structure is a significant and worthy area of ​​research.

[0003] When an air conditioner is heating, the gas is pressurized by the compressor, becoming a high-temperature gas. This gas then enters the heat exchanger in the indoor unit, also known as the condenser. The refrigerant liquefies in the condenser, becoming a liquid that heats the indoor air, thus raising the room temperature. When the air conditioner is cooling, the refrigerant passes through the compressor in a high-temperature, high-pressure gaseous state. After being cooled by the condenser, it enters a dryer for dehumidification and filtration. Then, it passes through an expansion valve to reduce its pressure. The treated refrigerant exchanges heat with the air drawn into the evaporator, blowing out cool air. The refrigerant then enters the compressor cycle, thus continuously cooling the air conditioner. As one of the most commonly used high-power loads, air conditioners exhibit different operating characteristics in cooling, heating, and various modes.

[0004] Modeling the load characteristics of air conditioners has been practiced for some time, with current research focusing on static, dynamic, start-up, and locked-rotor characteristics. Static characteristics consider the relationship between air conditioner power and voltage changes. However, household / factory voltages are generally stable around 210V-230V, with minimal fluctuations, so static characteristics should not be considered when studying air conditioners operating without faults. Dynamic characteristics consider voltage fluctuations during operation, which is also unnecessary for studying air conditioners operating without faults. Locked-rotor characteristics are used in low-voltage scenarios and are unsuitable for studying normal air conditioner operation. Start-up characteristics study the voltage and current waveforms and start-up time of the air conditioner. The maximum surge power generated in the short period after start-up is a crucial constraint for Home Energy Management Systems (HEMS). HEMS schedules various flexible household loads to minimize total household electricity costs under Time-of-Use Pricing (TOU) / Real-time Pricing (RTP).

[0005] The emerging trend of smart homes provides an opportunity for intelligent electricity use by home users. By installing intelligent data acquisition and communication modules in various electrical devices on the power consumption side, intelligent dispatch terminals can obtain a large amount of data information, enabling the monitoring, analysis, and control of the operating status of electrical devices, thus realizing the intelligence and networking of these devices. Various intelligent electrical devices acquire data through sensors and form a network for collaborative interaction through wireless communication technology, becoming the basic unit for intelligent home electricity dispatch. HEMS will become an indispensable component of future smart grids and smart homes, playing a vital role in maximizing the consumption of renewable energy, intelligent electricity use on the user side, and stable grid operation.

[0006] Air conditioners are a crucial household appliance for HEMS (Home Appliance Management System) scheduling, and modeling them is of great significance for the accuracy and efficiency of HEMS. Furthermore, research on air conditioner load models needs to consider their application within HEMS; therefore, modeling air conditioner power, air-conditioned rooms, and user comfort is of significant research importance. Summary of the Invention

[0007] This invention addresses the shortcomings of existing technologies by providing an air conditioning load modeling method that considers user comfort, solving the problem of insufficient modeling of stable air conditioning operation, and providing strong support for HEMS to achieve refined scheduling.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] An air conditioning load modeling method that considers user comfort includes the following steps:

[0010] Step 1: Read the real-time voltage, current, and active power data of the air conditioner using a smart socket or AC sampling meter, and transmit the data to the home energy management system terminal in real time; calculate the reactive power and power factor based on the voltage, current, and active power.

[0011] Step 2: Identify the maximum impact power of the air conditioner as a constraint for home energy management scheduling;

[0012] Step 3: The home energy management system plots curves of voltage, current, active power, and reactive power over time based on the received data, and performs piecewise polynomial fitting on the active power curve.

[0013] Step 4: Input the parameters of the air-conditioned room and model the air-conditioned room;

[0014] Step 5: Based on the user-set comfort range, combined with a specific air-conditioned room model and the air conditioner's normal operating power model, calculate the reasonable scheduling range for the air conditioner, thereby enabling the home energy management system to schedule the air conditioner.

[0015] To optimize the above technical solution, the specific measures also include:

[0016] Further, in step 1, the calculation of reactive power and power factor based on voltage, current, and active power specifically involves:

[0017]

[0018] Among them, U, I, P ac Q ac ψ and cosψ represent the voltage, current, active power, reactive power, and power factor of the air conditioner, respectively.

[0019] Furthermore, in step 2, the constraints are specifically as follows:

[0020]

[0021] In the formula, P acpeak P is the maximum impact power of the air conditioner. act Let P be the power of the air conditioner at time t. acN λ is the rated power of the air conditioner. ac This refers to the active power margin that a home energy management system should reserve for scheduling air conditioning.

[0022] Furthermore, in step 3, the piecewise polynomial fitting of the active power curve specifically involves:

[0023] The active power curve is divided into multiple fitting functions:

[0024]

[0025] Among them, P aci Let be the active power function of the air conditioner during the i-th period. For the nth term of the fitting function in the i-th period, a n to a o Both are constants, t iflagl Let t be the leftmost time of the i-th cycle. iflagr The rightmost time of the i-th cycle;

[0026] Calculate the root mean square error (RMSE) between the fitted active power curve and the actual active power curve:

[0027]

[0028] In the formula, m is the total number of measurement points within a certain period, and f(t) j P represents the active power value at the j-th measuring point within a certain period after fitting. acj This represents the actual active power value at the j-th measuring point within this cycle.

[0029] Furthermore, step 4 specifically involves:

[0030] Considering the basic heat conduction process, the thermal dynamics of a house under air conditioning cooling conditions can be expressed by the following formula:

[0031]

[0032] The thermal dynamics of a house under air conditioning heating mode can be expressed by the following formula:

[0033]

[0034] In the formula c air V is the specific heat capacity of air; room ρ is the volume of indoor air. air η is the air density. hot For the heating energy efficiency ratio of an air conditioner; η cold The energy efficiency ratio (EER) of an air conditioner; P ac T represents the active power of the air conditioner. out Outdoor temperature; T room Indoor temperature; R eq The equivalent thermal resistance of the building envelope;

[0035] The formulas for the thermal dynamic process of a house under air conditioning cooling and heating conditions are transformed into state equations as follows:

[0036]

[0037]

[0038] The forward Euler method is used to discretize the state equation in time and iterate repeatedly. Based on the initial indoor temperature, the outdoor temperature forecast over a period of time, and the corresponding air conditioning power, the indoor temperature change during this period is predicted to obtain the real-time temperature Troom(t0).

[0039] Furthermore, step 5 specifically includes:

[0040] Establish a comfort function:

[0041] User dissatisfaction with temperature is expressed as the square of the difference between indoor temperature and comfort temperature:

[0042]

[0043] In the formula, C temp , t For user dissatisfaction with temperature; T r oo m,t Indoor temperature; T set1 and Tset2 These are the upper and lower limits of the user-defined comfortable temperature range;

[0044] According to user settings, the permissible range for room temperature is:

[0045] T set2 ≤T room,t ≤T set1

[0046] By comparing the real-time temperature Troom(t0) with the allowable range of room temperature, the home energy management system schedules the air conditioning operation to control the real-time temperature within the allowable range of room temperature.

[0047] The beneficial effects of this invention are:

[0048] (1) A mathematical model for stable operation of a household air conditioner was established, which can accurately describe the working characteristics of the air conditioner load.

[0049] (2) An air-conditioned room model was established, and a model of the relationship between the air-conditioned room temperature and the air-conditioned output power was established under heating / cooling mode.

[0050] (3) This invention has strong applicability and can be used in conjunction with home energy scheduling. It is of great significance for alleviating grid pressure, protecting the environment, and saving electricity costs. Attached Figure Description

[0051] Figure 1 The flowchart of this invention

[0052] Figure 2 The original waveform of the received air conditioner terminal voltage

[0053] Figure 3 The original waveform of the received air conditioner terminal current

[0054] Figure 4 The received raw waveform of the air conditioner's active power.

[0055] Figure 5 The calculated reactive power waveform of the air conditioner

[0056] Figure 6 The original waveform and fitted waveform of active power

[0057] Figure 7 The original and fitted waveforms of active power within period 1.

[0058] Figure 8 The original and fitted waveforms of active power within period 2. Detailed Implementation

[0059] The invention will now be described in further detail with reference to the accompanying drawings.

[0060] This invention proposes a method for modeling air conditioning load that considers user comfort. The process of this method is as follows: Figure 1 As shown, it specifically includes:

[0061] Step 1: Read the real-time voltage, current, and active power data of the air conditioner using a smart socket or AC sampling meter, and transmit the data to the home energy management system terminal in real time; calculate the reactive power and power factor based on the voltage, current, and active power.

[0062] Measuring equipment for reading air conditioner operation data must have the following characteristics:

[0063] It can read real-time voltage, current, and active power data with errors within a reasonable range; it is equipped with Bluetooth / Wi-Fi / Zigbee modules, enabling the transmission of the above data via communication protocols and ensuring long-term stable operation; the sampling period is less than or equal to 0.001 seconds. The reason for requiring a sampling period less than or equal to 0.001 seconds is that the power surge during air conditioner startup is very brief, typically stabilizing within 0.03 seconds. Active power and other data values ​​are generally larger, allowing for a certain degree of error. Only these three data points are needed because reactive power and power factor can be calculated from these three parameters.

[0064] The voltage, current, active power, and reactive power of the air conditioner can all be displayed in real time on the home energy management system terminal. Reactive power and power factor can be obtained by transmitting only the voltage, current, and active power parameters. The principle is as follows:

[0065]

[0066] Among them, U, I, P ac Q ac ψ and cosψ represent the voltage, current, active power, reactive power, and power factor of the air conditioner, respectively.

[0067] Step 2: Identify the maximum impact power of the air conditioner as a constraint for home energy management scheduling; this step involves reading the maximum impact power of the air conditioner, which can be expressed by the following formula:

[0068]

[0069] In the formula, P acpeak P is the maximum impact power of the air conditioner. act Let P be the power of the air conditioner at time t. acN λ is the rated power of the air conditioner. ac This refers to the active power margin that a home energy management system should reserve for scheduling air conditioning.

[0070] The maximum impact power of an air conditioner can be used as a constraint for home energy management for the following reasons:

[0071] A Home Energy Management System (HEMS) schedules various household flexible loads to minimize total household electricity costs under Time-of-Use Pricing (TOU) / Real-time Pricing (RTP). Because homes of different sizes have strict power requirements, and the ownership rate of various high-power appliances in households is increasing, sufficient startup margins for air conditioners are needed to avoid circuit breaker trips.

[0072] Step 3: The home energy management system plots curves of voltage, current, active power, and reactive power over time based on the received data, and performs piecewise polynomial fitting on the active power curve; the waveforms of the air conditioner's operating voltage, current, active power, and reactive power over a certain period are shown below. Figures 2-5 As shown.

[0073] Air conditioners mainly consist of four major components: condenser, evaporator, compressor, and expansion valve. They also have multiple operating modes such as cooling, heating, dehumidification, defrosting, and electric auxiliary heating, as well as various fan speeds. It is difficult to construct an accurate load characteristic model, and it cannot be applied to the requirements of HEMS.

[0074] User comfort is a key aspect of HEMS (Heated Room Management System), and for air conditioning, comfort is primarily reflected in the temperature of the air-conditioned room. This requires modeling the air-conditioned room and establishing a functional model of the air conditioner's output power. By combining these two models, the relationship between the air conditioner's output power and room temperature can be derived, enabling HEMS to maximize the scheduling of the air conditioning load while ensuring user comfort.

[0075] This invention employs a piecewise polynomial fitting method to represent the power waveform as multiple piecewise fitting functions;

[0076] The fitting process can be described as follows:

[0077]

[0078] Among them, P aci Let be the active power function of the air conditioner during the i-th period. For the nth term of the fitting function in the i-th period, a n to a o Both are constants, t iflagl Let t be the leftmost time of the i-th cycle. iflagr The rightmost time of the i-th cycle;

[0079] To measure the goodness of fit, the root mean square error (RMSE) between the fitted curve and the actual curve is calculated according to formula (4):

[0080]

[0081] In the formula, m is the total number of measurement points within a certain period, and f(t) j P represents the active power value at the j-th measuring point within a certain period after fitting. acj This represents the actual active power value at the j-th measuring point within this cycle.

[0082] Figure 6 The original waveform and the fitted waveform of active power are shown. Figure 7 , Figure 8 The original and fitted waveforms of active power within period 1 and period 2 are shown respectively. Figure 6 , 7 Both 8 and 6 were fitted using a 6th-order polynomial, and the resulting parameters are shown in Table 1.

[0083] Table 1. Piecewise and direct fitting data of air conditioner active power over a certain period.

[0084]

[0085] It is evident that piecewise fitting exhibits significant differences, with the root mean square error (RMSE) being much smaller than that of overall fitting. This only reflects 20 minutes of air conditioning operation. As the air conditioning running time increases, overall fitting becomes more difficult and inaccurate. This demonstrates the superiority of piecewise fitting: lower computational cost and more accurate fitting results. The larger RMSE here is due to the power unit being W and the lack of per-unit values ​​in the graph.

[0086] The active power of an air conditioner can be expressed by the following formula:

[0087]

[0088] That is, the active power equation of an air conditioner can be obtained by superimposing the active power equations of each cycle.

[0089] Step 4: Input the air-conditioned room parameters and model the air-conditioned room; the change in indoor temperature is affected by factors such as outdoor temperature, air conditioning power, and the thermal characteristics of the building envelope, and is a complex, nonlinear, and multi-interference thermal dynamic process. From an engineering practical perspective, this paper proposes the following simplified assumptions:

[0090] (1) Assume that the air temperature field inside the building is uniformly distributed and the air temperature inside the building can be represented by the lumped parameter Troom. The air inside the building exchanges heat with the outside through the building's envelope.

[0091] (2) Assuming that the temperature distribution on the inner and outer surfaces of the building is uniform, and ignoring heat conduction along the direction parallel to the surface of the building envelope, the process is simplified to a one-dimensional process along the thickness direction.

[0092] (3) Ignoring the impact of air leakage through gaps in the building, assume that the air conditioning vent is the only vent in the building.

[0093] Considering the basic heat conduction process, the thermal dynamics of a house under air conditioning cooling conditions can be expressed by equation (6):

[0094]

[0095] When the air conditioner is in heating mode, the thermal dynamics of the house can be represented by equation (7):

[0096]

[0097] In the formula c air V is the specific heat capacity of air, kJ / kg·℃; room The volume of indoor air is m 3 ;ρ air air density, kg / m³ 3 η hot For the heating energy efficiency ratio of an air conditioner; η cold The energy efficiency ratio (EER) of an air conditioner; P ac The power rating of the air conditioner is expressed in kW; T. out Outdoor temperature, °C; T room R represents indoor temperature, in °C. eq The equivalent thermal resistance of the building envelope is given in °C / kW.

[0098] Equations (6) and (7) are transformed into state equations as shown in equations (8) and (9):

[0099]

[0100]

[0101] Then, the forward Euler method is used to discretize the state equation in time and iterate repeatedly to facilitate programming calculations. The indoor temperature change during this period can then be predicted based on the initial indoor temperature, the outdoor temperature forecast over a certain time period, and the corresponding air conditioning power, thereby measuring user comfort. The format of the first iteration of the forward Euler method is shown in equations (10) and (11):

[0102]

[0103]

[0104] T room(0) T out(0) Pac(0) These refer to the initial room temperature, the initial outdoor temperature, and the initial power of the air conditioner, respectively. h represents a very small iteration step size. The smaller the step size, the more accurate the prediction of room temperature changes, but the higher the computational requirements. The 2nd, 3rd...nth iterations have the same structure as equations (10) and (11).

[0105] In equations (10) and (11), R eq The equivalent thermal resistance of a building envelope is difficult to measure directly. A very small time interval h is used to measure T. room(0) The temperature value, at this time, in equation (9) besides R eq All other values ​​are known. By substituting the known values, the equivalent thermal resistance R of the building envelope can be calculated. eq This value allows for a detailed description of the temperature model in an air-conditioned room.

[0106] Step 5: Based on the user-set comfort range, combined with a specific air-conditioned room model and the air conditioner's normal operating power model, calculate the reasonable scheduling range for the air conditioner, thereby enabling the home energy management system to schedule the air conditioner.

[0107] User comfort is determined by indoor temperature, which generally fluctuates within a certain range. This is a soft constraint and can be addressed by adding a penalty term to the objective function. This invention uses the square of the difference between the indoor temperature and the comfortable temperature to represent user dissatisfaction with the temperature:

[0108]

[0109] In the formula, C temp,t For user dissatisfaction with temperature; T room,t Indoor temperature; T set1 and T set2 These are the upper and lower limits of the user-defined comfortable temperature range;

[0110] According to user settings, the permissible range for room temperature is:

[0111] T set2 ≤T room,t ≤T set1 (13)

[0112] In cooling / heating mode, the real-time indoor temperature T is obtained according to equations (10) and (11). room(t0) The home energy management system compares the real-time temperature Troom(t0) with the allowable range of room temperature, and then schedules the air conditioning operation to keep the real-time temperature within the allowable range. Taking heating mode as an example, if T... room(t0) <T set2If the indoor temperature is too low, it indicates that the room temperature is not comfortable. In this case, HEMS needs to increase the air conditioning setting to quickly raise the temperature and reduce user dissatisfaction. Depending on the current electricity price, if it's a period of high electricity prices, the room temperature should be stabilized at T. set2 Near and not less than T set2 If the room temperature is high, the air conditioner can be turned off for a period of time. If it is during a period of low electricity prices, the room temperature can be appropriately increased.

[0113] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for modeling air conditioning load considering user comfort, characterized in that, Includes the following steps: Step 1: Read the real-time voltage, current, and active power data of the air conditioner using a smart socket or AC sampling meter, and transmit this data to the home energy management system terminal in real time; calculate the reactive power and power factor based on the voltage, current, and active power; in Step 1, the calculation of reactive power and power factor based on voltage, current, and active power specifically involves: in, U , I , P ac , Q ac and cosψ These are the voltage, current, active power, reactive power, and power factor of the air conditioner. Step 2: Identify the maximum impact power of the air conditioner as a constraint for home energy management scheduling; in Step 2, the constraint is specifically as follows: In the formula, P acpeak This is the maximum impact power of the air conditioner. P act For air conditioning t Power at any moment P acN This refers to the rated power of the air conditioner. λ ac The active power margin that a home energy management system should reserve for scheduling air conditioning; Step 3: The home energy management system plots curves of voltage, current, active power, and reactive power over time based on the received data, and performs piecewise polynomial fitting on the active power curve; specifically, step 3 involves performing piecewise polynomial fitting on the active power curve as follows: The active power curve is divided into multiple fitting functions: in, P aci For the division of the first i The active power function of the air conditioner over a period of time. For the division of the first i A periodic fitting function n Second item, a n arrive a o Both are constants. t iflagl For the first i The leftmost time of each cycle t iflagr For the first i The rightmost time of each cycle; Calculate the root mean square error between the fitted active power curve and the actual active power curve. RMSE : In the formula, m The total number of measurement points within a certain period. f(t j ) To fit the result within a certain period j Active power values ​​at each measuring point P acj For the first time in this period j The actual active power value at each measuring point; Step 4: Input the air-conditioned room parameters and model the air-conditioned room; Step 4 specifically involves: Considering the basic heat conduction process, the thermal dynamics of a house under air conditioning cooling conditions can be expressed by the following formula: The thermal dynamics of a house under air conditioning heating mode can be expressed by the following formula: In the formula c air The specific heat capacity of air; V room Indoor air volume; ρ air air density; η hot The heating energy efficiency ratio of the air conditioner; η cold The air conditioning cooling energy efficiency ratio; P ac This refers to the active power of the air conditioner. T out Outdoor temperature; T room Indoor temperature; R eq The equivalent thermal resistance of the building envelope; The formulas for the thermal dynamic process of a house under air conditioning cooling and heating conditions are transformed into state equations as follows: The forward Euler method is used to discretize the state equation in time and iterate repeatedly. Based on the initial indoor temperature, the outdoor temperature forecast over a period of time and the corresponding air conditioning power, the indoor temperature change over this period of time is predicted to obtain the real-time temperature Troom(t0). Step 5: Based on the user-defined comfort range, combined with a specific air-conditioned room model and the air conditioner's normal operating power model, calculate the reasonable scheduling range for the air conditioner, thus enabling the home energy management system to schedule the air conditioner. Step 5 specifically involves: Establish a comfort function: User dissatisfaction with temperature is expressed as the square of the difference between indoor temperature and comfort temperature: In the formula, C temp,t This is due to user dissatisfaction with the temperature. T room,t Indoor temperature; T set1 and T set2 These are the upper and lower limits of the user-defined comfortable temperature range; According to user settings, the permissible range for room temperature is: By comparing the real-time temperature Troom(t0) with the allowable range of room temperature, the home energy management system schedules the air conditioning operation to control the real-time temperature within the allowable range of room temperature.

Images