Day-ahead scheduling optimization method, device, equipment, medium and product for heat pump heating system

Abstract

The invention discloses a day-ahead scheduling optimization method, device and equipment for a heat pump heating system, a medium and a product, and relates to the field of novel electric heating comprehensive energy systems and intelligent heat supply.The method comprises the steps that according to a user room temperature setting curve, the set ventilation frequency and weather forecast parameters, the heat demand of a user in the next day is predicted; determining a system relative thermal load coefficient; determining a water supply temperature regulation and control predicted value of the next day according to the relative thermal load coefficient of the system; the heat pump heating system input parameters are adjusted into user input parameters, and the user relative heat load coefficient of the next day is predicted according to the user input parameters; the valve opening time proportion of the user in the next day is determined according to the relative heat load coefficient of the user and the water supply temperature regulation and control predicted value; according to the valve opening time proportion and the time-of-use electricity price signal, an optimal tail end regulation and control strategy is formulated, and the optimal time sequence of opening of the tail end regulation and control valve is determined, the problem of response lag is solved, the energy utilization rate is increased, and the operation cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of novel electric-thermal integrated energy systems and intelligent heating, and in particular to a method, device, equipment, medium and product for optimizing the day-ahead scheduling of a heat pump heating system. Background Art

[0002] With the rapid development of smart grid and renewable energy technologies, how to achieve efficient operation and demand response of energy systems has become a core issue. Heat pump heating system is an effective way to achieve electrification of heating and low-carbon flexible operation of electric and thermal integrated systems. However, the existing heat pump heating system has shown obvious deficiencies in dealing with demand fluctuations and balancing power grid supply and demand. The fundamental reason is that the control strategy of traditional heat pump heating system is relatively extensive, mostly based on manual experience and weather adjustment. Due to the thermal inertia and complexity of heat pump heating system, the system has large thermal inertia, lacks energy storage mechanism, and insufficient demand response capability, resulting in delayed response, low energy utilization efficiency and high operating costs. Summary of the invention

[0003] The purpose of this application is to provide a method, device, equipment, medium and product for optimizing the day-ahead scheduling of a heat pump heating system to solve problems such as delayed response, low energy efficiency and high operating costs.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for optimizing day-ahead scheduling of a heat pump heating system, comprising:

[0006] Predict the user's heat demand for the next day based on the user's room temperature setting curve, set ventilation times and weather forecast parameters, and determine the system's relative heat load coefficient;

[0007] Determine the predicted value of water supply temperature control for the next day based on the relative heat load coefficient of the system;

[0008] Adjust the input parameters of the heat pump heating system to the user input parameters, and predict the user relative heat load coefficient for the next day according to the user input parameters; the input parameters of the heat pump heating system include the comprehensive heat transfer coefficient of the external envelope structure, the external surface area of ​​the external envelope structure, the cold air penetration, the room temperature set value for the next day, and the maximum allowable control room temperature deviation;

[0009] Determine the valve opening time ratio of the user in the next day according to the user's relative heat load coefficient and the water supply temperature control prediction value;

[0010] According to the valve opening time ratio and the time-sharing electricity price signal, an optimal terminal control strategy is formulated to determine the optimal timing for opening the terminal control valve; the optimal terminal control strategy is to allow the heat pump heating system to store heat during the low electricity price period and release heat during the peak electricity price period.

[0011] In a second aspect, the present application provides a day-ahead scheduling optimization device for a heat pump heating system, comprising:

[0012] The system relative heat load coefficient determination module is used to predict the user's heat demand for the next day based on the user's room temperature setting curve, the set ventilation times and the weather forecast parameters, and determine the system relative heat load coefficient;

[0013] A water supply temperature control prediction value determination module is used to determine the water supply temperature control prediction value for the next day based on the relative heat load coefficient of the system;

[0014] A user relative heat load coefficient prediction module is used to adjust the heat pump heating system input parameters to user input parameters, and predict the user relative heat load coefficient for the next day according to the user input parameters; the heat pump heating system input parameters include the comprehensive heat transfer coefficient of the outer envelope structure, the outer surface area of ​​the outer envelope structure, the cold air penetration, the room temperature set value for the next day, and the maximum allowable control room temperature deviation;

[0015] A valve opening time ratio determination module, used to determine the valve opening time ratio of the user in the next day according to the relative heat load coefficient of the user and the predicted value of the water supply temperature control;

[0016] The optimal terminal control strategy formulation module is used to formulate the optimal terminal control strategy and determine the optimal timing for opening the terminal control valve according to the valve opening time ratio and the time-sharing electricity price signal; the optimal terminal control strategy is to allow the heat pump heating system to store heat during the low electricity price period and release heat during the peak electricity price period.

[0017] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described day-ahead scheduling optimization methods for a heat pump heating system.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for optimizing day-ahead scheduling of a heat pump heating system.

[0019] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for optimizing day-ahead scheduling of a heat pump heating system.

[0020] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0021] The present application predicts the user's heat demand for the next day and determines the system relative heat load coefficient, so that the water supply temperature control prediction value for the next day can be determined based on the system relative heat load coefficient, and adjustments can be made in advance, thereby overcoming the problem of response lag. The user's relative heat load coefficient for the next day is predicted, and the user's valve opening time ratio for the next day is determined based on the user's relative heat load coefficient and the water supply temperature control prediction value. In combination with the time-of-use electricity price signal, the optimal terminal control strategy is formulated, and the optimal timing for opening the terminal control valve is determined, so that the heat pump heating system can store heat during the low electricity price period and release heat during the peak electricity price period, so as to improve energy utilization and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 This is a flow chart of a method for optimizing day-ahead scheduling of a heat pump heating system in one embodiment of the present application;

[0024] Figure 2 A schematic diagram of a day-ahead optimization scheduling device for a heat pump heating system provided in one embodiment of the present application;

[0025] Figure 3 A schematic diagram of a physical model of a virtual simulation platform building provided in an embodiment of the present application;

[0026] Figure 4 A schematic diagram of a flow chart of a method for optimizing day-ahead scheduling of a heat pump heating system provided in one embodiment of the present application;

[0027] Figure 5 A control result diagram of the heating power change on a typical day during the heating period provided by an embodiment of the present application;

[0028] Figure 6 A diagram showing the control result of the power consumption of a heating system provided in an embodiment of the present application;

[0029] Figure 7 This is a graph showing the control results of the electricity cost of a heating system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] With the rapid development and application of heating electrification and electric heat pump heating systems, it is urgent to explore new ideas from different time scales, source and terminal coordination, and electricity-heat comprehensive perspectives, and develop a day-ahead optimization scheduling method and device for heat pump heating systems that can flexibly respond to grid demand response, so as to achieve flexible operation of source-load coordination and heat-electricity interaction, promote the consumption of renewable energy, improve the system energy utilization efficiency and reduce operating costs.

[0033] The present application embodiment provides a method for optimizing the day-ahead scheduling of a heat pump heating system. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the present application embodiment, Figure 1 As shown, the method includes the following steps.

[0034] S1: Predict the user's heat demand for the next day based on the user's room temperature setting curve, set ventilation times and weather forecast parameters, and determine the system's relative heat load coefficient.

[0035] S2: Determine the predicted value of water supply temperature control for the next day based on the relative heat load coefficient of the system.

[0036] S3: Adjust the input parameters of the heat pump heating system to the user input parameters, and predict the user's relative heat load coefficient for the next day based on the user input parameters; the input parameters of the heat pump heating system include the comprehensive heat transfer coefficient of the outer envelope structure, the outer surface area of ​​the outer envelope structure, the cold air penetration, the room temperature set value for the next day, and the maximum allowable control room temperature deviation.

[0037] S4: Determine the valve opening time ratio of the user in the next day according to the relative heat load coefficient of the user and the predicted value of the water supply temperature control.

[0038] S5: According to the valve opening time ratio and the time-sharing electricity price signal, an optimal terminal control strategy is formulated to determine the optimal timing for opening the terminal control valve; the optimal terminal control strategy is to allow the heat pump heating system to store heat during the low electricity price period and release heat during the peak electricity price period.

[0039] In an exemplary embodiment, based on the user room temperature setting curve and weather forecast data, the traditional algorithm only considers the external temperature factor, and further considers the historical room temperature control deviation feedback correction and the influence of solar radiation, cold wind penetration and other factors on the heat load to predict the system relative heat load coefficient Q for the next day. r,m+1 At the same time, the feedback adjustment factor α is introduced to dynamically adjust according to the difference between the indoor temperature and the set temperature. The smaller the temperature difference, the lower the α value, thereby gradually reducing the heating or cooling output to prevent over-adjustment.

[0040] The relative heat load coefficient calculation formula is:

[0041]

[0042] In the formula, K z Indicates the comprehensive heat transfer coefficient of the external protective structure, W / (m 2 ·K); F represents the outer surface area of ​​the external protective structure, m 2 ;c pa ρ a represents the heat capacity of air, J / (m 3 ·K); G a Indicates the amount of cold air penetration, m 3 ; G' a Indicates the designed cold air penetration, m 3 ;q sol Represents the heat gain from solar radiation, W; q' sol represents the designed solar radiation heat gain, W; t set,m+1 Indicates the room temperature setting value on the m+1th day, ℃; t out,m+1 Indicates the outdoor temperature value on the m+1th day, ℃; t' a Indicates the room temperature design value, ℃; t' out Indicates the design value of outdoor temperature, ℃; t a,m Indicates the measured room temperature value on the mth day, ℃; Δt max It indicates the maximum allowable deviation of controlled room temperature, ℃; α indicates the feedback adjustment factor.

[0043] When the calculated Q r,m+1 <0, take Q r,m+1 =0; when the calculated Q r,m+1 >1+β, take Q r,m+1 =1+β, where β is the surplus coefficient of the heat pump capacity configuration, corresponding to the maximum heating capacity of the heat pump system.

[0044] In an exemplary embodiment, according to the predicted system relative heat load factor Q r,m+1 , determine the predicted value t of water supply temperature control for the next day g,m+1, and put the algorithm into the heat pump water supply temperature actuator. The water supply temperature control prediction formula is as follows:

[0045] t g,m+1 =t est,m+1 +0.5(t' g +t' h -2t set,m+1 )·Q r,m+1 +0.5(t' g -t' h )·Q r,m+1 (3)

[0046] Where, t g,m+1 represents the predicted water supply temperature on day m+1; t' g Indicates the design water supply temperature, °C; t' h Indicates the design return water temperature, ℃.

[0047] In an exemplary embodiment, the heat pump heating system input parameters in formulas (1) and (2) are adjusted to the relevant input parameters corresponding to each user, so that the relative heat load coefficient Q of each user in the next day can be predicted. r-c,m+1 ; Among them, the input parameters of the heat pump heating system include the comprehensive heat transfer coefficient of the outer envelope structure, the outer surface area of ​​the outer envelope structure, the cold air penetration, the room temperature set value for the next day and the maximum allowable control room temperature deviation.

[0048] Further, the adjustment method is: set the parameter input panel on the user room temperature controller at the user end; for example, when predicting the relative heat load of the building, the F used is the outer envelope area of ​​the entire building; and when pre-testing the relative heat load of the user, the F used is the outer envelope area of ​​the user. The calculation formula used is the same, but the original parameters of the entire building are adjusted to the corresponding parameters of each heat user; a building contains multiple heat users.

[0049] In an exemplary embodiment, the valve opening time ratio of the user in the coming day is determined based on the predicted value of the user's relative heat load coefficient and the predicted value of the water supply temperature control.

[0050]

[0051] In an exemplary embodiment, an economically optimal terminal control strategy is formulated by combining the user's valve opening time ratio with the time-of-use electricity price signal.

[0052] The basic principle of the optimal terminal control strategy is that under the condition that the user's valve opening time ratio in the next day is satisfied, the priority of the valve opening time sequence is: low-valley electricity price period > flat-valley electricity price period > peak electricity price period. The specific terminal control strategy is as follows:

[0053] Calculate the total valve opening duration X for the user within the next day, i.e., the valve opening period.

[0054] X = 24 × γ(5)

[0055] Statistically calculate the low - valley period duration A, the total duration of the flat - valley period B, and the total duration of the peak period 24-(A + B) within the next day.

[0056] When 0 < X <= A, let X be considered to be distributed within the low - valley period.

[0057] When A < X <= A + B, let the terminal control valve open during the low - valley period, and the valve opening duration during the flat - valley period be X - A.

[0058] When A + B < X <= 24, let the terminal control valve open during the low - valley and flat - valley periods, and the valve opening duration during the peak period be X - A - B.

[0059] In an exemplary instance, to implement the above - mentioned optimization strategy, as Figure 2 shown, the control system consists of room temperature and supply - return water temperature sensors, a small weather station, a supply - water temperature control actuator, and a terminal room - temperature control actuator, and executes the above - mentioned day - ahead scheduling optimization method for the heat - pump heating system through a built - in microprocessor.

[0060] The sensors include multiple subsystems, which are responsible for real - time monitoring of various key parameters: the temperature sensors monitor the indoor temperature and the supply - return water temperature in real time; the small weather station is responsible for collecting outdoor environmental data, including but not limited to outdoor environmental temperature, solar radiation intensity, etc.

[0061] The microprocessor calculates the required supply - water temperature based on the above - mentioned sensor data and Equation (3), sends a supply - water temperature control instruction to the heat pump, and combines the time - of - use electricity price signal, and inputs this information into the actuator. The terminal room - temperature actuator has a built - in control module, which adjusts the valve opening and closing time in combination with the time - of - use electricity price signal and the optimal terminal control strategy.

[0062] The above process ensures that the system realizes the economically optimal heating control while meeting the user's comfort. The system continuously monitors the valve opening and closing, the room - temperature change, and the deviation between the actual operating room - temperature and the set value through the room - temperature automatic feedback mechanism and the source - load collaborative control method. According to the feedback room - temperature control deviation and the meteorological forecast parameters for the next day, combined with the algorithms for relative heat - load coefficient prediction, supply - water temperature prediction, and valve - opening time ratio (i.e., Equations (1), (3), and (4)), the system dynamically adjusts the supply - water temperature and the room - temperature control strategy to ensure that the heat - pump heating system can achieve efficient, intelligent, low - carbon and flexible operation under different load conditions, thereby optimizing the overall energy utilization efficiency and operating cost.

[0063] Relying on the virtual simulation platform system that has been verified through experiments, such as Figure 3As shown, the day-ahead optimization scheduling method for the heat pump heating system proposed in the present invention is applied to obtain the user's heat load transfer effect, peak shaving and valley filling of electricity consumption, and electricity fee regulation simulation results, and the traditional proportional control is used as a benchmark for comparison to verify the application effect of the present application.

[0064] Taking a certain building as an example, the user's room temperature limit range is 18℃~22℃, and the terminal heating equipment is a radiator.

[0065] like Figure 4 As shown, the day-ahead scheduling optimization method for the heat pump heating system of this embodiment includes the following steps:

[0066] Step 1: In the source-side water supply temperature control actuator, an algorithm for predicting the relative heat load coefficient of the system in the next day is built in. On the basis of the traditional algorithm that only considers the outdoor temperature, the feedback correction of the historical room temperature control deviation is further introduced, and the influence of solar radiation, cold air penetration, etc. on the heat load is considered. The sensor network and small weather station collect room temperature data and weather forecast data, and combine the room temperature curve and ventilation times set by the user to predict the relative heat load coefficient Q of the system in the next day. r,m+1 This embodiment is described by taking the water temperature and room temperature control of the heat pump heating system on a typical day in the heating season as an example.

[0067] According to formulas (6) and (7), input the user's room temperature setting value, ventilation frequency, and weather forecast data for the next day to calculate the system relative heat load coefficient Q for the next day. r,m+1 The feedback factor α is introduced to dynamically adjust according to the difference between the indoor temperature and the set temperature. The smaller the temperature difference, the lower the α value, thereby gradually reducing the heating or cooling output.

[0068]

[0069] In the formula, K z Indicates the comprehensive heat transfer coefficient of the external protective structure, W / (m 2 ·K); F represents the outer surface area of ​​the external protective structure, m 2 ;c pa ρ a represents the heat capacity of air, J / (m 3 ·K); G a Indicates the amount of cold air penetration, m 3 ; G' a Indicates the designed cold air penetration, m 3 ;q sol Represents the heat gain from solar radiation, W; q' sol represents the designed solar radiation heat gain, W; t set,m+1 Indicates the room temperature setting value on the m+1th day, ℃; t out,m+1 Indicates the outdoor temperature value on the m+1th day, ℃; t'a Indicates the room temperature design value, ℃; t' out Indicates the design value of outdoor temperature, ℃; t a,m Indicates the measured value of room temperature on the mth day, ℃; Δt max represents the maximum allowable control room temperature deviation, ℃; α represents the feedback adjustment factor.

[0070] When the calculated Q r,m+1 <0, take Q r,m+1 =0; when the calculated Q r,m+1 >1+β, take Q r,m+1 =1+β, where β is the surplus coefficient of the heat pump capacity configuration, corresponding to the maximum heating capacity of the heat pump system. The system heat load coefficient calculated in this embodiment for the next day is 0.605.

[0071] Step 2: The water supply temperature controller also has a built-in water supply temperature control algorithm, namely formula (8), input the design supply and return water temperature, combined with the predicted system relative heat load coefficient Q r,m+1 , determine the predicted value t of water supply temperature control for the next day g,m+1 .

[0072] t g,m+1 =t set,m+1 +0.5(t' g +t' h -2t set,m+1 )·Q r,m+1 +0.5(t' g -t' h )·Q r,m+1 (8)

[0073] In the formula, t g,m+1 represents the predicted water supply temperature on day m+1; t' g Indicates the design water supply temperature, °C; t' h represents the designed return water temperature, °C. The predicted value of the heat pump water supply temperature calculated in this embodiment for the next day is 45 °C.

[0074] Step 3: According to the method in step 1, adjust the input parameters of the heating system in formulas (6) and (7) to the specific input parameters corresponding to each user, so as to achieve the relative heat load coefficient Q of each user in the next day. r-c,m+1 The relative heat load coefficient of a typical user calculated in this embodiment for the next day is 0.435.

[0075] Step 4: A valve opening time ratio prediction algorithm (9) is built into the terminal room temperature control actuator to determine the user's valve opening time ratio for the next day based on the user's relative heat load coefficient prediction value and the water supply temperature prediction value.

[0076]

[0077] In this embodiment, the calculated valve opening time ratio of a typical user for the next day is 0.513.

[0078] Step 5: According to the predicted value of the user's valve opening time ratio and the time-of-use electricity price signal, formulate an economically optimal end control strategy, and regulate the valve through the end room temperature control actuator. The basic principle of the control strategy is to preferentially allocate the valve opening time sequence under the condition of meeting the total valve opening time ratio of the user for the next day, and the order is: off-peak period > flat valley period > peak period. The specific control strategy is as follows:

[0079] Calculate the total valve opening duration X required by the user within the next day. The formula (10) is as follows:

[0080] X = 24 × γ(10)

[0081] Statistically, the duration A of the off-peak period, the total duration B of the flat valley period, and the total duration of the peak period 24 - (A + B) within the next day.

[0082] When 0 < X <= A, all the valve opening periods X are considered to be distributed in the off-peak period.

[0083] When A < X <= A + B, all valves are opened during the off-peak period, and the valve opening duration during the flat valley period is X - A.

[0084] When A + B < X <= 24, all valves are opened during the off-peak period and the flat valley period, and the valve opening duration during the peak period is X - A - B.

[0085] The valve opening time sequence of a typical user for the next day obtained in this embodiment, as well as the corresponding heat load transfer effect, electricity peak shaving and valley filling, and electricity bill regulation results, are as Figure 5-Figure 7 shown. On the premise of ensuring the room temperature thermal comfort range, a 100% peak load transfer rate is achieved. Compared with proportional control, the electricity bill can be saved by about 40.9%. The results of the virtual simulation experiment on the regulation of the heat pump heating system verify the feasibility and control effect of the present invention.

[0086] Based on the same inventive concept, the embodiment of the present application also provides a device for optimizing the day-ahead scheduling of a heat pump heating system for implementing the above-mentioned heat pump heating system day-ahead scheduling optimization method. The implementation solution provided by this device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more of the following embodiments of the device for optimizing the day-ahead scheduling of a heat pump heating system can refer to the limitations on the heat pump heating system day-ahead scheduling optimization method in the above text, and will not be repeated here.

[0087] In an exemplary embodiment, a device for optimizing the day-ahead scheduling of a heat pump heating system is provided, including:

[0088] The system relative heat load coefficient determination module is used to predict the user's heat demand for the next day based on the user's room temperature setting curve, the set ventilation times and the weather forecast parameters, and determine the system relative heat load coefficient.

[0089] The water supply temperature control prediction value determination module is used to determine the water supply temperature control prediction value for the next day based on the relative heat load coefficient of the system.

[0090] The user relative heat load coefficient prediction module is used to adjust the input parameters of the heat pump heating system to the user input parameters, and predict the user relative heat load coefficient for the next day based on the user input parameters; the input parameters of the heat pump heating system include the comprehensive heat transfer coefficient of the outer envelope structure, the outer surface area of ​​the outer envelope structure, the cold air penetration, the room temperature set value for the next day, and the maximum allowable control room temperature deviation.

[0091] The valve opening time ratio determination module is used to determine the user's valve opening time ratio in the next day according to the user's relative heat load coefficient and the water supply temperature control prediction value.

[0092] The optimal terminal control strategy formulation module is used to formulate the optimal terminal control strategy and determine the optimal timing for opening the terminal control valve according to the valve opening time ratio and the time-sharing electricity price signal; the optimal terminal control strategy is to allow the heat pump heating system to store heat during the low electricity price period and release heat during the peak electricity price period.

[0093] This application is based on weather forecast parameters (external temperature, solar radiation, etc.), room temperature control historical deviation and other data, to accurately predict the system and user heating load in the next day. The source controller controls the heat pump water supply temperature according to the predicted value of the system relative load factor, and the terminal controller determines the valve opening time ratio in the next day according to the user's relative load factor, and optimizes the valve on-off time sequence in combination with the time-sharing electricity price signal, thereby realizing the flexible operation of the source-load coordinated electric heating integrated system, reducing operating costs and improving energy efficiency while meeting the user's thermal comfort, and effectively solving the problem of renewable energy consumption. This method not only overcomes the problems of delayed response, low energy efficiency, and high operating costs of traditional heating systems when responding to demand response and electricity market fluctuations, but also makes full use of the large thermal inertia of the building heating system. The proposed control method and device have the advantages of being simple and easy to use and convenient for engineering application, showing good engineering adaptability and promotion value, and having significant economic benefits.

[0094] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store day-ahead scheduling optimization data of a heat pump heating system. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a day-ahead scheduling optimization method for a heat pump heating system is implemented.

[0095] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the computer program.

[0096] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0097] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0098] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric random access memory (Ferroelectric RandomAccess Memory, FRAM), phase change memory (Phase ChangeMemory, PCM), graphene memory, etc. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0099] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0100] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0101] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing the day-ahead scheduling of a heat pump heating system, characterized in that: The day-ahead scheduling optimization method for the heat pump heating system includes: Predicting the user's heat demand for the next day based on the user's room temperature setting curve, set ventilation rate, and weather forecast parameters to determine the system relative heat load coefficient; Determining the predicted value of the supply water temperature regulation for the next day based on the system relative heat load coefficient; Adjusting the input parameters of the heat pump heating system to the user input parameters, and predicting the user relative heat load coefficient for the next day according to the user input parameters; the input parameters of the heat pump heating system include the comprehensive heat transfer coefficient of the peripheral enclosure structure, the outer surface area of the peripheral enclosure structure, the cold air infiltration rate, the room temperature setting value for the next day, and the allowable maximum control room temperature deviation; Determining the valve opening time ratio for the user for the next day according to the user relative heat load coefficient and the predicted value of the supply water temperature regulation; Formulating an optimal terminal regulation strategy according to the valve opening time ratio and the time-of-use electricity price signal to determine the optimal timing of the opening of the terminal regulation valve; the optimal terminal regulation strategy is to store heat during the low electricity price period and release heat during the high electricity price period for the heat pump heating system.

2. The method for optimizing day-ahead scheduling of a heat pump heating system according to claim 1, characterized in that: The system relative heat load factor Q r,m+1 for: Among them, K z represents the comprehensive heat transfer coefficient of the external protective structure; F represents the external surface area of ​​the external protective structure; c pa ρ a represents the heat capacity of air; G a Indicates the amount of cold air penetration; G' a Indicates the designed cold air penetration; q sol Represents the heat gained by solar radiation; q' sol represents the designed solar radiation heat gain; t set,m+1 Indicates the room temperature setting value on the m+1th day; t out,m+1 represents the outdoor temperature value on the m+1th day; t' a represents the room temperature design value; t' out Indicates the design value of outdoor temperature; α represents the feedback regulation factor; t a,m represents the measured value of room temperature on the mth day; Δt max Indicates the maximum allowed controlled room temperature deviation.

3. The method for optimizing day-ahead scheduling of a heat pump heating system according to claim 2, characterized in that: The water supply temperature control prediction value t g,m+1 for: t g,m+1 =t set,m+1 +0.5(t' g +t' h -2t set,m+1 )·Q r,m+1 +0.5(t' g -t' h )·Q r,m+1 ; Among them, t g,m+1 represents the predicted water supply temperature on day m+1; t' g Indicates the design water supply temperature; t' h Indicates the design return water temperature.

4. The method for optimizing day-ahead scheduling of a heat pump heating system according to claim 3, characterized in that: The valve opening time ratio γ is: Among them, Q r-c,m+1 is the user's relative heat load coefficient.

5. The method for optimizing day-ahead scheduling of a heat pump heating system according to claim 1, characterized in that: The basic principle of the optimal terminal regulation strategy is that under the condition constraint of meeting the valve opening time ratio, the priority of the valve opening time sequence is: low electricity price period > flat valley electricity price period > high electricity price period.

6. The method for optimizing day-ahead scheduling of a heat pump heating system according to claim 1, characterized in that: Formulating an optimal terminal regulation strategy according to the valve opening time ratio and the time-of-use electricity price signal to determine the optimal timing of the opening of the terminal regulation valve, specifically including: Determining the total valve opening duration required by the user within the next day according to the valve opening time ratio; Statistical the low valley period duration A, the flat valley period duration B, and the peak period duration C within the next day according to the historical duration of each period; where the low valley period is the low electricity price period, the flat valley period is the flat valley electricity price period, and the peak period is the high electricity price period; A + B + C = 24; When 0 < X ≤ A, let X be distributed in the low valley period; When A < X ≤ A + B, let the terminal regulation valve open in the low valley period, and the valve opening duration in the flat valley period is X - A; When A + B < X ≤ 24, let the terminal regulation valve open in the low valley period and the flat valley period, and the valve opening duration in the peak period is X - A - B.

7. A day-ahead scheduling optimization device for a heat pump heating system, characterized in that: The day-ahead scheduling optimization device for the heat pump heating system includes: A system relative heat load coefficient determination module for predicting the user's heat demand for the next day based on the user's room temperature setting curve, set ventilation rate, and weather forecast parameters to determine the system relative heat load coefficient; A supply water temperature regulation predicted value determination module for determining the predicted value of the supply water temperature regulation for the next day based on the system relative heat load coefficient; A user relative heat load coefficient prediction module for adjusting the input parameters of the heat pump heating system to the user input parameters, and predicting the user relative heat load coefficient for the next day according to the user input parameters; the input parameters of the heat pump heating system include the comprehensive heat transfer coefficient of the peripheral enclosure structure, the outer surface area of the peripheral enclosure structure, the cold air infiltration rate, the room temperature setting value for the next day, and the allowable maximum control room temperature deviation; A valve opening time ratio determination module, used to determine the valve opening time ratio of the user in the next day according to the relative heat load coefficient of the user and the predicted value of the water supply temperature control; The optimal terminal control strategy formulation module is used to formulate the optimal terminal control strategy and determine the optimal timing for opening the terminal control valve according to the valve opening time ratio and the time-sharing electricity price signal; the optimal terminal control strategy is to allow the heat pump heating system to store heat during the low electricity price period and release heat during the peak electricity price period.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the day-ahead scheduling optimization method for a heat pump heating system according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the day-ahead scheduling optimization method for a heat pump heating system according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the day-ahead scheduling optimization method for a heat pump heating system according to any one of claims 1 to 6 is implemented.

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