Control method for dynamic preheating and demand response of heat pump for smart power grid

Abstract

The invention belongs to the technical field of building heat supply and intelligent power grid demand response control, and particularly relates to an intelligent power grid-oriented heat pump dynamic preheating and demand response control method, which comprises the following steps of: identifying building thermal characteristic parameters, introducing real-time environment variables such as external meteorological parameters and the like and thermal resistance and thermal capacity characteristics of a building, excessive or insufficient preheating caused by only relying on a timer is effectively avoided, and the balance of accurate heat supply and energy conservation is achieved; by adjusting the preheating starting time and the peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or the power grid demand signal, and the peak load is translated or reduced, so that the dual purposes of economic benefits, energy conservation and emission reduction are achieved; and formulating a peak period control strategy and formulating a non-peak period control strategy. According to the control method, actual thermal characteristics and external meteorological conditions of a building are taken as cores, load reduction in the peak period of a power grid and indoor thermal comfort are taken as double targets, and preheating time self-adaption, demand response flexibility and system operation reliability are achieved at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of building heating and intelligent power grid demand response control, and particularly relates to a heat pump dynamic preheating and demand response control method for an intelligent power grid. Background Art

[0002] In the field of building heating, in order to relieve the pressure on the power grid during peak electricity consumption and improve the flexibility of building energy use, buildings are often preheated before the peak period to "store" heat in the building envelope or other heat storage media, so as to minimize the electricity consumption of heating equipment during the peak period. Currently, the typical method is to use a timing control method: users preset the start and stop times of the heat pump through a timer, and the heat pump starts or stops preheating after reaching the time, and cycles within the preset time period. The preheating method based on the timer lacks the ability to adapt to the real-time environment, cannot flexibly adjust the preheating duration according to factors such as real-time outdoor temperature and solar radiation, and is prone to insufficient preheating or over-preheating, resulting in energy waste or inability to ensure indoor comfort; moreover, the start and stop times of the heat pump are difficult to change flexibly with the real-time electricity price, which limits the effective participation of the heat pump in the power grid demand response.

[0003] There are also some technical solutions that combine external data to perform dynamic preheating control on heat pump units. For example, Patent CN202411034341.0 proposes that the heat pump shutdown time and the preheating instruction time are uploaded to the cloud. The cloud uses the 4G Internet of Things to obtain the local historical temperature during the shutdown period and the indoor temperature at the preheating time, then selects a preheating scheme based on the set comparison threshold, and sends the scheme to the heat pump unit for execution. However, the preheating method based on the shutdown duration ignores the dynamic characteristics of the building heat load, triggers preheating only according to the shutdown duration, and only considers the external air temperature, but pays insufficient attention to the heat characteristics of the building itself, which is prone to energy waste or inability to meet the actual heating demand. Moreover, the scheme based only on historical temperature lacks consideration of real-time weather. When the current outdoor temperature or weather conditions differ greatly from the historical data, the preheating control scheme will be overheated or insufficient, and it is difficult to accurately meet the heating demand. Summary of the Invention

[0004] To overcome the technical defects of the existing heat pump unit control methods, such as lack of adaptability to the real-time environment, limitation of the effective participation of the heat pump in the power grid demand response, prone to energy waste, and difficulty in accurately meeting the heating demand, the present invention provides a heat pump dynamic preheating and demand response control method for an intelligent power grid. This control method realizes a control closed-loop around "information perception - calculation and decision - instruction execution", and by dynamically judging the preheating start time and regulating the indoor temperature in stages, while ensuring building thermal comfort, it effectively reduces the peak load of the power grid and improves the economy and flexibility of energy use.

[0005] The present invention provides a heat pump dynamic preheating and demand response control method for a smart grid, and the steps are as follows:

[0006] S1. Identify the building thermal characteristic parameters;

[0007] In the initial operation stage, the user freely controls the heat pump according to their own thermal comfort temperature. The indoor temperature is monitored in real time through a temperature sensor, and the temperature sensor sends the indoor temperature information to the controller. The controller collects and stores the indoor temperature hour by hour, and at the same time obtains the corresponding outdoor meteorological parameters through the controller's network connection. The outdoor meteorological parameters include outdoor air temperature and solar radiation irradiance;

[0008] To identify the thermal resistance R and heat capacity C of the building, first, a set of initial R values and C values need to be assumed, and then the hourly heat supply Q during this time period is calculated according to the heat pump performance curve HVAC ; Subsequently, taking Q HVAC and the outdoor meteorological parameters in the corresponding time period as known disturbances, using the RC model of the building, predict the indoor temperature hour by hour, compare the predicted temperature value with the actual temperature value monitored by the temperature sensor. If the error between the two is within the set range, output the R value and C value. If the error between the two exceeds the set range, use the optimization algorithm to repeatedly iterate and update the R value and C value until the error between the two falls within the set range, and finally output the R value and C value;

[0009] S2. Determine the optimal preheating moment;

[0010] Receive the daily peak power consumption time period and real-time electricity price information of the power grid through the smart meter, generate a demand response signal, and transmit the demand response signal to the controller;

[0011] Let a certain moment t before the peak period and earlier than the actual preheating demand be the initial assumed heat pump startup moment; First, detect the indoor temperature at moment t and regard it as the initial state; Combine the outdoor meteorological data at moment t and calculate Q after the heat pump starts HVAC ; Then, taking Q HVAC and the outdoor meteorological parameters at moment t as known disturbances, use the R value and C value finally obtained in step S1, through the RC model of the building, predict the indoor temperature one time step later, and take the current predicted value as the initial temperature of the next time step; Continue to calculate backward until the start moment of the peak period; If the predicted indoor temperature at the start moment of the peak period is higher than the set thermal comfort upper limit temperature, then adjust moment t backward, that is, postpone starting the heat pump; Repeat the above iterative process. When the predicted indoor temperature at the start moment of the peak period exactly reaches the thermal comfort upper limit temperature, the updated moment t can be determined as the optimal preheating moment;

[0012] S3. Formulate a peak period control strategy;

[0013] When the peak electricity consumption time arrives, the heat pump is turned off first; the indoor temperature is monitored in real time through a temperature sensor. When the indoor temperature drops to the lower limit temperature of thermal comfort, the heat pump is turned on through a controller, and the heat pump is set to operate at the lower limit temperature of thermal comfort;

[0014] S4. Formulate a non-peak period control strategy;

[0015] After the peak electricity consumption time period ends, the indoor set temperature is adjusted to the optimal temperature of thermal comfort. The controller controls the start and stop of the heat pump according to the indoor real-time temperature monitored by the temperature sensor, and the heat pump is set to operate at the optimal temperature of thermal comfort.

[0016] The method of the present invention obtains information such as indoor and outdoor temperatures, solar radiation, and grid demand response signals, and conducts dynamic preheating and peak electricity consumption period regulation on the heat pump unit to achieve the comprehensive goals of energy conservation and improvement of indoor thermal comfort.

[0017] The technical solution provided by the present invention has the following technical effects compared with the prior art:

[0018] First, by introducing real-time environmental variables such as external meteorological parameters and the thermal resistance and heat capacity characteristics of the building itself, the method of the present invention effectively avoids excessive or insufficient preheating caused by relying solely on a timer, and achieves a balance between accurate heating and energy conservation;

[0019] Second, by adjusting the preheating start time and peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or grid demand signal, shift or cut peak loads, thereby achieving the dual goals of economic benefits and energy conservation and emission reduction;

[0020] Third, instead of simply judging whether to preheat and whether to reach the preheating start time based on the shutdown time or simple outdoor air temperature threshold, the thermal characteristics and meteorological conditions are comprehensively considered, making the system have higher flexibility and self-adaptability;

[0021] Fourth, based on the building thermal resistance and heat capacity model, the dynamic heat demand of the building can be estimated more accurately, and the preheating plan is closer to the actual situation, avoiding blind overheating or insufficient preheating; the heat pump control method of the present invention takes the actual thermal characteristics of the building and external meteorological conditions as the core, and takes the peak load reduction of the power grid and indoor thermal comfort as the dual goals, with self-adaptive preheating time, flexibility of demand response and reliability of system operation; compared with the prior art, the present invention has significant advantages in terms of energy conservation, economy and comfort, and has broad prospects for popularization and application. Description of the Drawings

[0022] The drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0024] Figure 1 This is the control system block diagram involved in a heat pump dynamic preheating and demand response control method for a smart grid described in an embodiment of the present invention;

[0025] Figure 2 This is the calculation process of the R value and the C value in step S1 of a heat pump dynamic preheating and demand response control method for a smart grid described in an embodiment of the present invention;

[0026] Figure 3 This is the determination of the optimal preheating time in step S2 of a heat pump dynamic preheating and demand response control method for a smart grid described in an embodiment of the present invention. Detailed implementation manners

[0027] In order to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the following will further describe the solution of the present invention. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0028] Many specific details are set forth in the following description in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present invention, rather than all the embodiments.

[0029] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0030] In a specific embodiment, taking a certain room as the implementation object, a 5R3C simplified physical model (i.e., 5 thermal resistances and 3 heat capacities) is established; at the same time, an air-source heat pump is selected to heat the room. The 5R3C simplified physical model satisfies the energy conservation equation:

[0031]

[0032]

[0033]

[0034]

[0035] Q sol,w = αA w Isol (0 - 5)

[0036] Q sol,tm = f sol,tm ×SHGC×A wd I sol (0 - 6)

[0037] Q sol,in = f sol,in ×SHGC×A wd I sol (0 - 7)

[0038] Where: C w is the heat capacity of the building envelope, J / K; C in is the heat capacity of the indoor air, J / K; C m is the equivalent heat capacity of indoor heat and mass, J / K; R conv,out is the convective heat transfer resistance between the outer surface of the building envelope and the outdoor air, K / W; R conv,in is the convective heat transfer resistance between the inner surface of the building envelope and the indoor air, K / W; R w is the thermal resistance of the building envelope conduction, K / W; R conv,tm is the convective heat transfer resistance between the indoor air and the indoor heat and mass, K / W; R wd is the thermal resistance of the window, K / W; T ow is the temperature on the outside of the building envelope, °C; T iw is the temperature on the inside of the building envelope, °C; T in is the indoor air temperature, °C; T out is the outdoor air temperature, °C; T tm is the temperature of the indoor heat and mass, °C; Q sol,w is the heat generated by solar radiation on the exterior wall, W; Q sol,in is the heat generated by solar radiation passing through the window into the indoor air, W; Q sol,tm is the heat generated by solar radiation on the indoor heat and mass, W; Q HVAC is the heat supplied by the heat pump, J; α is the solar radiation absorption coefficient of the exterior wall surface; A w is the area of the exterior wall, m 2 ; I sol is the solar radiation intensity, W / m 2 ; f sol,in is the heat gain coefficient of solar radiation passing through the window and acting on the indoor air; f sol,tm is the heat gain coefficient of solar radiation passing through the window and acting on indoor objects; SHGC is the solar heat gain coefficient; A wd is the area of the window, m 2 .

[0039] It is known that the performance curve of the air source heat pump is:

[0040] Q HVAC = (0.3415 + 0.0089T out + 0.0001T out 2 ) × Q rated (0 - 8)

[0041] EIR = (2.0391 - 0.0396T out + 0.0005T out 2 ) × EIR rated (0 - 9)

[0042] wherein, T out is the outdoor temperature, K; Q rated is the rated heat supply of the heat pump, J; EIR is the actual energy efficiency ratio of the heat pump; EIR rated is the rated energy efficiency ratio of the heat pump.

[0043] This embodiment discloses a heat pump dynamic preheating and demand response control method for an intelligent power grid. The control system involved in this method includes an intelligent electricity meter, a temperature sensor, a controller, and a heat pump. Among them, the intelligent electricity meter is used to receive information such as the daily peak electricity consumption period and real-time electricity price of the power grid, generate a demand response signal, and transmit this signal to the controller. The temperature controller is used to monitor the indoor air temperature in real time and send the temperature data to the controller. The work of the controller includes: (1) obtaining the hourly outdoor air temperature and solar radiation irradiance through networking, and obtaining the indoor air temperature and demand response signal locally; (2) identifying the thermal characteristic parameters of the building based on an iterative algorithm; (3) predicting the dynamic change of the indoor temperature according to the building thermal characteristic parameters and the heat pump performance curve; (4) calculating and determining the preheating moment and the temperature control strategies during the peak period and the non-peak period; (5) generating and sending the temperature set value and the switch signal to the heat pump. The heat pump is used to receive the control instruction and execute heating.

[0044] The steps of the control method in this embodiment are as follows:

[0045] S1. Identify the thermal characteristic parameters of the building;

[0046] Within the first 3 days of initial operation, the user freely controls the heat pump according to their own thermal comfort temperature, monitors the indoor temperature in real time through the temperature sensor, records the room temperature every hour, the temperature sensor sends the indoor temperature information to the controller, the controller collects and stores the indoor temperature hourly, and at the same time obtains the corresponding outdoor meteorological parameters through networking by the controller. The outdoor meteorological parameters include the outdoor air temperature and the solar radiation irradiance;

[0047] To identify the thermal resistance R and heat capacity C of the building, first, a set of initial R values and C values need to be assumed, and then the hourly heat supply Q during this period is calculated according to the heat pump performance curve HVAC ; Subsequently, with QHVAC The outdoor meteorological parameters within the corresponding time period are known disturbances. Using the RC model of the building, predict the indoor temperature hour by hour. Compare the predicted temperature value with the actual temperature value monitored by the temperature sensor. If the error between the two is within the set range, output the R value and the C value. If the error between the two exceeds the set range, use an optimization algorithm (such as the PSO algorithm, TRA algorithm, or GA algorithm) to repeatedly iterate and update the R value and the C value until the error between the two falls within the set range, and finally output the R value and the C value;

[0048] In this embodiment, the finally output R value and C value are shown in Table 1.

[0049]

[0050] S2. Determine the optimal preheating time;

[0051] Receive the daily peak power consumption time period and real-time electricity price information of the power grid through the smart meter, generate a demand response signal, and transmit the demand response signal to the controller; specifically, the peak power consumption period of the power grid on the current day is 19:00 - 23:00; and assume that the lower limit temperature of thermal comfort is 18 °C, the upper limit temperature of thermal comfort is 24 °C, and the optimal temperature of thermal comfort is 20 °C;

[0052] Let a certain moment t before the start of the peak period and earlier than the actual preheating demand be the initial assumption of the heat pump startup time. First, detect the indoor temperature at moment t and regard it as the initial state; combine the outdoor meteorological data at moment t to calculate Q after the heat pump starts HVAC ; Then, with Q HVAC and the outdoor meteorological parameters at moment t as known disturbances, use the R value and C value finally obtained in step S1, through the RC model of the building, predict the indoor temperature after one time step, and use the current predicted value as the initial temperature of the next time step; continue to calculate backward until the start time of the peak period; if the predicted indoor temperature at the start time of the peak period is higher than the set upper limit temperature of thermal comfort, then adjust moment t backward, that is, postpone the startup of the heat pump; repeat the above iterative process. When the predicted indoor temperature at the start time of the peak period exactly reaches the upper limit temperature of thermal comfort, the updated moment t can be determined as the optimal preheating time; specifically, it is expected that the room temperature reaches 24 °C at 19:00. Assume the heat pump starts at 15:00 through a loop program and predict whether the room temperature at 19:00 is equal to 24 °C; if the predicted room temperature exceeds 24 °C, then postpone the preheating time; if it is less than 24 °C, then advance the preheating time; through repeated iteration, it is most appropriate to start at 18:24: start the heat pump to preheat at this moment, and the room temperature reaches 24.01 °C at 19:00;

[0053] S3. Develop a peak period control strategy;

[0054] When the peak electricity consumption time arrives, first turn off the heat pump; monitor the indoor temperature in real time through the temperature sensor. When the indoor temperature drops to the lower limit temperature of thermal comfort, turn on the heat pump through the controller and set the heat pump to operate at the lower limit temperature of thermal comfort; by implementing this strategy, while ensuring the basic heating demand, the electricity consumption during the peak period is minimized; specifically, turn off the heat pump at 19:00, and when the indoor temperature drops to 18°C, turn on the heat pump;

[0055] S4. Develop a control strategy for off-peak periods;

[0056] After the peak electricity consumption time period ends, adjust the indoor set temperature to the optimal temperature of thermal comfort, that is, 20°C. The controller controls the start and stop of the heat pump according to the real-time indoor temperature monitored by the temperature sensor and sets the heat pump to operate at the optimal temperature of thermal comfort; by implementing this strategy, low-cost electric energy is fully utilized for heating during off-peak and valley electricity price periods, improving the overall economy; in this embodiment, after 23:00, it enters the off-peak and valley periods, and the room temperature setting becomes 20°C, and the building is supplemented with heat during the low electricity price period.

[0057] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Although the foregoing embodiments have been described in detail, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. A heat pump dynamic preheating and demand response control method for smart grid, characterized in that: The steps are: S1. Identify building thermal characteristic parameters; In the initial operation stage, users can freely control the heat pump according to their own thermal comfort temperature, and monitor the indoor temperature in real time through the temperature sensor. The temperature sensor sends the indoor temperature information to the controller, which collects and stores the indoor temperature hour by hour. At the same time, the controller obtains the corresponding outdoor meteorological parameters through the network of the controller, which include outdoor temperature and solar radiation illumination. To identify the thermal resistance R and thermal capacity C of the building, we first need to assume a set of initial R and C values, and then calculate the hourly heat supply Q during the time period based on the heat pump performance curve. HVAC ; followed by Q HVAC The outdoor meteorological parameters in the corresponding time period are known disturbances. The indoor temperature is predicted hourly using the RC model of the building. The predicted temperature value is compared with the actual temperature value monitored by the temperature sensor. If the error between the two is within the set range, the R value and C value are output. If the error between the two exceeds the set range, the R value and C value are repeatedly updated by the optimization algorithm until the error between the two falls within the set range, and finally the R value and C value are output. S2, determining the optimal preheating time; Receive the daily peak power consumption time period and real-time power price information of the power grid through the smart meter, generate a demand response signal, and transmit the demand response signal to the controller; Let a certain time t before the peak period starts and earlier than the actual preheating demand be assumed as the initial heat pump activation time; first, detect the indoor temperature at time t and regard it as the initial state; Combined with the outdoor meteorological data at time t, calculate Q after the heat pump starts HVAC ; Then, with Q HVAC The outdoor meteorological parameters at time t are known disturbances. The R value and C value finally obtained in step S1 are used to predict the indoor temperature after one time step through the RC model of the building, and the current predicted value is used as the initial temperature of the next time step; the calculation is continued until the peak period starts; if the predicted indoor temperature at the beginning of the peak period is higher than the set upper limit of thermal comfort, the time t is adjusted backward, that is, the start of the heat pump is postponed; the above iterative process is repeated, and when the predicted indoor temperature at the beginning of the peak period just reaches the upper limit of thermal comfort, the updated time t can be determined as the optimal preheating time; S3. Develop peak control strategies; When the peak electricity consumption period arrives, the heat pump is turned off first; the indoor temperature is monitored in real time through the temperature sensor, and when the indoor temperature drops to the lower limit of thermal comfort, the heat pump is turned on through the controller and set to operate at the lower limit of thermal comfort; S4. Develop off-peak control strategies; When the peak electricity consumption period ends, the indoor set temperature is adjusted to the optimal temperature for thermal comfort. The controller controls the start and stop of the heat pump according to the real-time indoor temperature monitored by the temperature sensor, and sets the heat pump to operate at the optimal temperature for thermal comfort.

2. A heat pump dynamic preheating and demand response control method for smart grid according to claim 1, characterized in that: The optimization algorithm in step S1 is a PSO algorithm, a TRA algorithm or a GA algorithm.

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