CN115660325A - Power grid peak regulation capacity quantification method and system - Google Patents
Power grid peak regulation capacity quantification method and system Download PDFInfo
- Publication number
- CN115660325A CN115660325A CN202211228666.3A CN202211228666A CN115660325A CN 115660325 A CN115660325 A CN 115660325A CN 202211228666 A CN202211228666 A CN 202211228666A CN 115660325 A CN115660325 A CN 115660325A
- Authority
- CN
- China
- Prior art keywords
- working medium
- air source
- heat
- temperature
- heat supply
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
The application provides a power grid peak regulation capacity quantification method and a system, the method is used for quantifying the power grid peak regulation capacity of an air source heat pump heating system, the air source heat pump heating system comprises a plurality of air source heat pumps, and an air source is used for heating a heat supply working medium, and the method comprises the following steps: s1: determining a plurality of key parameters influencing the temperature of a heat supply working medium of an air source heat pump heat supply system; s2: establishing a heat supply working medium temperature prediction model based on the key parameters and training by using the historical operation data; s3: and acquiring the quantized power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model. The quantification method and the quantification system provided by the application can extract factors which obviously affect the peak regulation capacity of the air source heat pump heating system according to the operation characteristics of the air source heat pump heating system and set reasonable quantification indexes, so that the capacity of the air source heat pump heating system participating in power grid peak regulation can be accurately quantified.
Description
Technical Field
The application belongs to the technical field of intelligent control of power grids, and further provides a power grid peak regulation capacity quantification method and system, which are used for quantifying and evaluating the power grid peak regulation capacity of an air source heat pump heating system.
Background
The air source heat pump heating system is a novel heating system, and is controlled by a plurality of distributed air source heat pump units to heat hot water for heating in a centralized manner, and then the heat is supplied by a heat medium pipeline to a plurality of heating power inlets or heat users through a circulating water pump in a manner of decentralized short pipe network.
When the heating function is realized, the required power load can be adjusted by controlling the opening or closing of each air source heat pump unit in the air source heat pump heating system, so that the purpose of participating in power grid peak shaving is realized. The demand side is used as a resource equivalent to the supply side to participate in power grid peak shaving, the fluctuation of the power output is guided to be actively tracked, the important goal of the power grid peak shaving is achieved, and the upper-level power department needs to know the adjustability in advance when the air source heat pump heating system with adjustable power participates in demand response regulation.
In the existing regulation mode, an air source heat pump load aggregator generally reports all air source heat pumps which are completely opened or completely closed as maximum up/down regulation capacity, so as to serve as a basis for matching a demand side and a supply side of a higher-level power department. The mode for evaluating the power grid peak regulation capacity of the air source heat pump heating system is greatly different from the actual situation, so that the power department cannot acquire the accurate peak regulation capacity of the power grid, and further the power grid cannot reasonably formulate a peak regulation scheme and distribute peak regulation tasks.
The reason for the above problems is that: the specific operation of participating in the peak regulation of the power grid by utilizing the air source heat pump heating system is to turn on or turn off a certain number of air source heat pumps in the system within a certain time period so as to meet the power regulation requirements of a higher-level electric power department on the air source heat pumps. However, the air source heat pump heating system is a nonlinear complex system, the operation process of the air source heat pump heating system is influenced by various external factors such as outdoor temperature, heating building area, heating pipe network laying and the like, and the primary task is to meet the requirement on indoor temperature regulation.
Therefore, when the peak regulation capacity of the power grid of the air source heat pump heating system is evaluated, a conventional peak regulation capacity evaluation method of the power load cannot be simply used, and factors which have obvious influences on the peak regulation capacity of the air source heat pump heating system are extracted according to the operation characteristics of the air source heat pump heating system so as to improve the accuracy of quantitative evaluation on the peak regulation capacity of the air source heat pump heating system.
Disclosure of Invention
In order to solve the problems in the prior art, the present application aims to provide a method and a system for quantizing the peak shaving capacity of a power grid, which can establish a prediction model of the peak shaving capacity of the power grid based on the actual operation and heat transfer characteristics of an air source heat pump heating system, and quantitatively predict and evaluate the peak shaving capacity of the power grid by designing a proper peak shaving capacity quantization index.
One aspect of the present application provides a method for quantifying peak shaving capability of a power grid, which is used for quantifying the peak shaving capability of the power grid of an air source heat pump heating system, wherein the air source heat pump heating system comprises a plurality of air source heat pumps, and the air source heat pumps are used for heating a heat supply working medium, and the method comprises the following steps:
s1: determining a plurality of key parameters influencing the temperature of a heat supply working medium of an air source heat pump heat supply system;
s2: establishing a heat supply working medium temperature prediction model based on the key parameters and training by using the historical operation data;
s3: and acquiring the quantized power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model.
Further, step S1 comprises the steps of:
s11: a heat supply working medium thermodynamic process model of the air source heat pump heat supply system is established based on the formula (1),
wherein rho is the density of the heat supply working medium, c is the specific heat capacity of the heat supply working medium, and V is the volume (m) of the heat supply working medium 3 ) (ii) a Theta is the temperature of the heat supply working medium, t is time, and phi is the heat variation of the heat supply working medium;
s12: determining a relation between phi and theta based on the equation (2),
wherein phi p For the system to produce heat,. Phi d For the heat dissipation of the system, phi 1 Heating capacity of air source heat pump for starting, phi 2 For heat flow of solar radiation, COP n Number of air source heat pumps to be turned on, P n H is the surface heat convection coefficient, A is the total heat dissipation area, delta t is the time interval, theta out Is the outdoor temperature;
s13: the convective heat transfer coefficient h is approximately characterized by using the formula (3) surface,
wherein N is u Is the Nussel number, l is the characteristic size, and lambda is the heat conductivity coefficient of the heat supply working medium;
s14: determining influence N under the condition of forced convection heat transfer u Factor (2)
influence on N under the condition of natural convection heat transfer u Factor (2)
Wherein Re is Reynolds number, pr is Prandtl number, gr is Gravadaff number, u, v and a are respectively flow velocity, kinematic viscosity and thermal diffusivity of heat supply working medium, and g is weightAcceleration of force, α v Is a coefficient of volume expansion, Δ θ b Is the temperature difference between the wall temperature and the ambient temperature.
S15: based on the formulae (1) to (3) and the influence N u Determines a number of key parameters that affect theta.
Preferably, the key parameters further include upper and lower limits of the temperature of the heat supply working medium.
Preferably, the heat supply working medium temperature prediction model is a BP neural network model.
Preferably, the input quantity of the heat supply working medium temperature prediction model comprises the operation data of the key parameter at t moment and at least 2 time intervals before the t moment; and the output quantity of the heat supply working medium temperature prediction model is the heat supply working medium temperature of the next time interval at the moment t.
Preferably, the quantized grid peak shaving capacity is a down-adjustable quantized value and/or an up-adjustable quantized value of the air source heat pump heating system corresponding to each switch control scheme.
Further, the down-adjustable quantization value corresponding to each switching control scheme is
And the up-adjustable quantization values corresponding to the respective switch control schemes are
Wherein, P down 、t down 、W down Respectively for the down-regulated power, down-regulated time and down-regulated capacity, P, of each switching control scheme up 、t up 、W up Respectively, the adjustable power, the adjustable time and the adjustable capacity, m, of each switching control scheme n Number of air source heat pumps started for each on-off control scheme, m b Number of starting-up units, P, for achieving balance of heat generation and dissipation n Of air-source heat pumpsRated electric power, t start Is the peak shaving start time, t min For the moment when the temperature of the heating medium reaches the lower temperature limit, t max The moment when the temperature of the heat supply working medium reaches the upper temperature limit.
Further, step S3 includes the steps of:
s31: determining a switching control scheme for realizing heat generation and dissipation balance based on actual operation data of the air source heat pump heating system and corresponding m b ；
S32: based on actual operation data of the air source heat pump heating system, tracking the change condition of the temperature of the heating working medium under each switch control scheme by using the heating working medium temperature prediction model, wherein when the temperature of the heating working medium reaches a preset temperature limit value, the switch control scheme is switched to the switch control scheme for realizing balance of heat generation and dissipation, and the switch control scheme is switched to each switch control scheme again after the heat generation and dissipation are balanced again;
s33: and determining the quantized power grid peak regulation capacity of the air source heat pump heating system corresponding to each switch control scheme in real time based on the tracking result of the step S32.
Another aspect of the present application provides a power grid peak regulation capacity quantization system, which is used for quantizing the power grid peak regulation capacity of an air source heat pump heating system, wherein the air source heat pump heating system comprises a plurality of air source heat pumps, and heats a heating medium by using an air source, and the power grid peak regulation capacity quantization system comprises:
the parameter determining unit is used for determining a plurality of key parameters influencing the temperature of a heat supply working medium of the air source heat pump heat supply system; the model training unit is used for establishing a heat supply working medium temperature prediction model based on the key parameters and training by using the historical operating data; and the quantification unit is used for acquiring the quantified power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model.
Further, the parameter determination unit determines the plurality of key parameters by:
firstly, establishing a heat supply working medium thermodynamic process model of an air source heat pump heat supply system based on the formula (1),
wherein rho is the density of the heat supply working medium, c is the specific heat capacity of the heat supply working medium, and V is the volume (m) of the heat supply working medium 3 ) (ii) a Theta is the temperature of the heat supply working medium, t is time, and phi is the heat variation of the heat supply working medium;
secondly, determining the relation between phi and theta based on the formula (2),
wherein phi p For the system to produce heat,. Phi d For the heat dissipation of the system, phi 1 Heating capacity of air source heat pump for starting, phi 2 For heat flow of solar radiation, COP n Number of air source heat pumps to be turned on, P n H is the surface heat convection coefficient, A is the total heat dissipation area, delta t is the time interval, theta out Is the outdoor temperature;
thirdly, the surface convection heat exchange coefficient h of the formula (3) is used for approximate representation,
wherein N is u Is the Nussel number, l is the characteristic size, and lambda is the heat conductivity coefficient of the heat supply working medium;
the fourth step, determining the influence N under the condition of forced convection heat transfer u Factor (2)
influence on N under the condition of natural convection heat transfer u Factor (2)
Wherein Re is Reynolds number, pr is Prandtl number, gr is Gravadaff number, u, v and a are flow velocity, kinematic viscosity and thermal diffusivity of heat supply working medium respectively, g is gravity acceleration, alpha v Is the coefficient of volume expansion, Δ θ b Is the temperature difference between the wall temperature and the ambient temperature.
A fifth step of forming a film on the basis of the formulas (1) to (3) and the influence N u Determines a number of key parameters that affect theta.
Preferably, the key parameters further include upper and lower temperature limits for the preset thermal working medium.
Preferably, the heat supply working medium temperature prediction model is a BP neural network model.
Preferably, the input quantity of the heat supply working medium temperature prediction model comprises operation data of the key parameter at time t and at least 2 time intervals before time t; and the output quantity of the heat supply working medium temperature prediction model is the heat supply working medium temperature of the next time interval at the moment t.
Preferably, the quantized grid peak shaving capacity is a down-adjustable quantized value and/or an up-adjustable quantized value of the air source heat pump heating system corresponding to each switch control scheme.
Further, the down-adjustable quantization value corresponding to each switching control scheme is
And the adjustable quantization values corresponding to the respective switching control schemes are
Wherein, P down 、t down 、W down Separately for each switching control scheme, a down-adjustable power, a down-adjustable time and a down-adjustable capacity, P up 、t up 、W up Respectively, the adjustable power, the adjustable time and the adjustable capacity, m, of each switching control scheme n Number of air source heat pumps started for each on-off control scheme, m b Number of starting-up units, P, for achieving balance of heat generation and dissipation n Rated electric power of the air source heat pump, t start To the peak shaver start time, t min For the moment when the temperature of the heating medium reaches the lower temperature limit, t max The moment when the temperature of the heat supply working medium reaches the upper temperature limit.
Further, the quantification unit obtains the quantified power grid peak regulation capacity of the air source heat pump heating system through the following steps:
firstly, determining a switch control scheme for realizing heat generation and radiation balance and a corresponding m based on actual operation data of the air source heat pump heating system b ；
Secondly, tracking the change situation of the temperature of the heat supply working medium under each switch control scheme by using the heat supply working medium temperature prediction model based on the actual operation data of the air source heat pump heat supply system, wherein the switch control scheme is switched to the switch control scheme for realizing balance of heat generation and dissipation when the temperature of the heat supply working medium reaches a preset temperature limit value, and the switch control scheme is switched to each switch control scheme again after the heat generation and dissipation balance is achieved again;
and thirdly, determining the quantized power grid peak regulation capacity of the air source heat pump heating system corresponding to each switch control scheme in real time based on the tracking result of the heat supply working medium temperature prediction model.
The method and the system for quantizing the power grid peak regulation system provided by the embodiment of the application have the following beneficial effects:
according to the technical scheme, a heat generation and dissipation model is established according to the actual operation characteristics of the air source heat pump heat supply system, a plurality of key parameters which influence the heat generation and dissipation of the system and are easy to detect and obtain are analyzed and extracted, a heat supply working medium temperature prediction model is established on the basis of the key parameters, and the temperature change conditions of the heat supply working medium under different switch control schemes can be accurately predicted;
according to the technical scheme, the method for evaluating the peak shaving capacity of the power grid of the heat supply system directly according to the completely opened or closed states of all air source heat pumps in the system in the prior art is changed, and a reasonable quantized index of the peak shaving capacity of the power grid is established based on the prediction of the temperature change condition of the heat supply working medium and the actual use scene of the heat supply system. By using the quantization method and the quantization system provided by the application, the power grid peak regulation capability can be evaluated in real time according with the actual running state of the power grid peak regulation capability, so that a superior power department can master the accurate peak regulation capability and accurately regulate the peak of the power grid.
Drawings
FIG. 1 is a schematic diagram of the operation of an air source heat pump heating system;
fig. 2 is an implementation flow of a method for quantizing peak shaving capacity of a power grid according to an embodiment of the present application;
FIG. 3 is a basic structure of a BP neural network according to an embodiment of the present application;
FIG. 4 is a schematic diagram of determining an adjustable-up time and an adjustable-down time according to a temperature change of a heating medium according to an embodiment of the present application;
FIG. 5 is a graph of electrical power variations of an air source heat pump based on actual operating data for a particular air source heat pump heating system;
FIG. 6 is a graph of prediction accuracy of a heating medium temperature prediction model constructed and trained using actual operating data for a particular air source heat pump heating system, according to an embodiment of the present application;
FIG. 7 is a graph of prediction accuracy for a heating medium temperature prediction model constructed and trained using actual operating data for another specific air-source heat pump heating system, in accordance with an embodiment of the present application;
FIG. 8 shows the predicted temperature variation of a heating medium for a specific air source heat pump heating system under different on-off control schemes according to an embodiment of the present application;
FIG. 9 is a graphical result of the up-and down-adjustable capabilities corresponding to FIG. 8;
fig. 10 is a system framework schematic diagram of a power grid peak shaving capability quantification system according to an embodiment of the present application.
Detailed Description
Hereinafter, the present application will be further described based on preferred embodiments with reference to the accompanying drawings.
In addition, various components on the drawings are enlarged or reduced for convenience of understanding, but this is not intended to limit the scope of the present application.
Singular references also include plural references and vice versa.
In the description of the embodiments of the present application, it should be noted that, if the terms "upper", "lower", "inner", "outer", etc. are used to indicate an orientation or a positional relationship based on an orientation or a positional relationship shown in the drawings, or an orientation or a positional relationship which is usually placed when a product of the embodiments of the present application is used, it is only for convenience of description and simplification of the description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the present application cannot be construed as being limited. Moreover, the terms first, second, etc. may be used in the description to distinguish between different elements, but these should not be limited by the order of manufacture or by importance to be understood as indicating or implying any particular importance, and their names may differ from their names in the detailed description of the application and the claims.
The terminology used in the description is for the purpose of describing the embodiments of the application and is not intended to be limiting of the application. It is also to be understood that, unless otherwise expressly stated or limited, the terms "disposed," "connected," and "connected" are intended to be open-ended, i.e., may be fixedly connected, detachably connected, or integrally connected; they may be mechanically coupled, directly coupled, indirectly coupled through intervening media, or may be interconnected between two elements. The specific meaning of the above terms in the present application will be specifically understood by those skilled in the art.
The air source heat pump is a high-efficiency energy-saving heating device which takes outdoor air as a heat source and utilizes heat in the air to achieve the effect of heat production. The distributed air source heat pumps are networked and uniformly controlled, and an air source heat pump heating system can be further formed. Fig. 1 shows a schematic diagram of an operation principle of a typical air source heat pump heating system, as shown in fig. 1, the heating system mainly includes a heat source side, a transmission and distribution pipe network, a user side, and the like, wherein the heat source side generally uses a plurality of air source heat pump units with nominal heating capacity of more than 35kW as distributed medium and small heat sources, hot water (i.e., heating working medium) for user heating is centrally prepared, and then the hot water is delivered to a plurality of heating power inlets or heat users through a heat medium pipeline by a circulating water pump in a manner of short pipe network decentralization.
The air source heat pump heating system can participate in power grid peak shaving while realizing centralized heating. When the air source heat pump is used for participating in peak shaving of a power grid, a certain number of air source heat pumps in the system need to be started or closed within a certain time period so as to meet the power requirements of a higher-level power department on the air source heat pumps. However, during peak shaving, the heat change required by the air source heat pump heating system heating building in a short time is not large, the number of the air source heat pumps required to be started is relatively stable, if the number of the operating heat pumps deviates from the heat generation and dissipation balance number of the heating system, not only can the indoor temperature of a user be affected and the comfort level of the user be reduced, but also the time for the heating system to participate in peak shaving is reduced, so that the operation principle of the air source heat pump heating system needs to be analyzed in detail to measure the capacity of the air source heat pump heating system to participate in the peak shaving of the power grid.
Based on the above analysis, the present application provides a method for quantifying a peak shaving capability of a power grid through an embodiment, and fig. 2 shows an implementation flowchart of the method for quantifying a peak shaving capability of a power grid of an air source heat pump heating system according to the embodiment of the present application, where the method for quantifying a peak shaving capability of a power grid includes the following steps as shown in fig. 2:
s1: determining a plurality of key parameters influencing the temperature of a heat supply working medium of an air source heat pump heat supply system;
s2: establishing a heat supply working medium temperature prediction model based on the key parameters and training by using the historical operation data;
s3: and acquiring the quantized power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model.
The steps S1 to S3 are described in detail below with reference to the accompanying drawings and specific embodiments.
Step S1, determining a plurality of key parameters which have obvious influence on the heat supply effect (namely the temperature of a heat supply working medium) by analyzing the operation and heat transfer characteristics of the air source heat pump heat supply system.
At present, a building or a community which takes an air source heat pump as main heating equipment adopts a centralized heating mode which takes an air source heat pump heating system as a basic unit, the air source heat pump heating system adopts a multi-stage heating mode, each stage is connected in series, and the air source heat pump loads in each stage are connected in parallel. In the air source heat pump heating system, a plurality of data transmission units are installed to acquire the operation data of the air source heat pump heating system in real time, and meanwhile, the data are uploaded to the cloud end by using an information communication technology. Under the non-regulation mode, the air source heat pump heating system operates autonomously, and the number of the heat pumps started can be automatically adjusted according to factors such as the ambient temperature and the like, so that heat production and heat dissipation are balanced. In the regulation mode, the air source heat pump heating system can be used for regularly and quantitatively opening or closing a certain number of heat pump loads according to a superior control instruction.
Under the support of modern information technology, a large number of sensors and data transmission units exist in the operation process of the air source heat pump heat supply system, and data can be acquired and uploaded to a cloud terminal in real time. Table 1 shows various data that can be collected during the operation of the air source heat pump heating system at present, including parameters of a heating medium (circulating hot water), environmental parameters, heat generation parameters, overall operation parameters of the system, and the like.
TABLE 1 relevant data that can be collected in real time by air source heat pump heating system
The data collected by the data transmission unit are various, so that a foundation is provided for digital modeling of the air source heat pump heating system, but meanwhile, the data types are too rich, so that another problem is brought to modeling, namely, how to scientifically select the data types and data objects to provide a basis for digital modeling.
In the embodiment of the application, the air source heat pump heating plant and the heating object (namely a cell) are integrated into an integral system, and the integral operation characteristics of the integral system are researched to simplify the analysis process. According to the analysis, the heat supply working medium penetrates through the heat source measuring and transmitting side and the tail end side of the heat supply system to play an important role in the whole heat supply process, and the heat supply effect of the heat supply system can be effectively evaluated by utilizing the temperature of the heat supply working medium, so that the key variable of the temperature of the heat supply working medium can be selected as a research object, and a plurality of key parameters influencing the temperature of the heat supply working medium are determined through historical operation data of the air source heat pump heat supply system.
The process of determining a number of key parameters affecting the heating medium of an air-source heat pump heating system in embodiments of the present application is described in detail below.
Aiming at the temperature change process of a heat supply working medium (generally, circulating hot water), a mathematical expression of the heat supply working medium thermodynamic process as shown in the formula (1) can be established according to a first thermodynamic law:
wherein rho is the density of the heat supply working medium, c is the specific heat capacity of the heat supply working medium, and V is the volume (m) of the heat supply working medium 3 ) (ii) a Theta is the temperature of the heat supply working medium (in the embodiment of the present application, the average value of the water supply temperature and the return water temperature that can be collected by the system in the actual operation process can be selected as the temperature theta of the heat supply working medium), t is time, phi is the heat variation of the heat supply working medium, and phi can be further expressed by the formula (2):
by analyzing the operation mechanism of the air source heat pumpThe volume of the heat supply working medium is the volume of hot water in the heat supply pipeline, the volume can be regarded as a fixed value after the heat supply pipeline network is laid, the density and the specific heat capacity of the heat supply working medium are physical parameters representing the self characteristics of the heat supply working medium and are influenced by the temperature of the working medium, and the total heat change phi of the heat supply working medium is influenced by the heat production phi p And heat dissipation phi d The two parts are composed of the following expression:
Φ＝Φ p -Φ d ，
in particular the heat production amount phi of the heating system p Consists of two parts, namely the heating capacity phi of all air source heat pumps in working state 1 And heat generated by solar radiation phi 2 Namely:
Φ p ＝Φ 1 +Φ 2 ，
wherein phi 1 ＝COP n ·P n ，COP n Number of air source heat pumps to be turned on, P n Is the number of air source heat pumps.
In particular, the heat dissipation capacity Φ of the heating system d It can be expressed in newton's cooling equation:
Φ d ＝hAΔt＝hA(θ-θ out )，
wherein h is the surface heat convection coefficient, A is the total heat dissipation area, Δ t is the time interval, and θ out Is the outdoor temperature.
In the above equation, h is associated with various factors and cannot be directly measured by a sensor or other means, so that further analysis thereof is required. The surface convection heat transfer coefficient h in heat transfer science can be approximately characterized by a similarity criterion number, generally adopting a Nusselt number (Nu number), and the specific expression of the Nusselt number is shown as a formula (3):
wherein, N u Is the Nussel number, l is the characteristic dimension, and lambda is the heat conductivity coefficient of the heat supply working medium.
Because the representation form of Nu number in different heat dissipation processes is different, in the embodiment of this application, according to the heat transfer process, expand the analysis from the forced convection heat transfer of heat supply working medium and indoor environment and the natural convection heat transfer of indoor environment and outdoor environment respectively:
in the first aspect, heat is supplied to the working medium by forced convection heat exchange with the indoor environment.
The forced convection process of the heat supply working medium and the indoor environment belongs to forced heat exchange in a pipe groove, and at the moment, the Nu number in the heat transfer science can be expressed as a function of Reynolds number (Re number) and Prandtl number (Pr number), namely:
wherein Re is Reynolds number, pr is Plantt number, and u, v, and a are flow velocity, kinematic viscosity, and thermal diffusivity of the heat supply working medium respectively.
In a second aspect, natural convective heat transfer between the indoor environment and the outdoor environment.
In the case of natural convection heat transfer between the indoor environment and the outdoor environment, the Nu number is no longer related to the Re number, but is related to the Grashof number (Gr number), i.e., the Nu number during heat dissipation between the indoor environment and the outdoor environment is a function of the Gr number and the Pr number. The Gr number can be characterized as:
wherein g is the acceleration of gravity, alpha v Is the coefficient of volume expansion, Δ θ b Is the temperature difference between the wall temperature and the ambient temperature.
The physical quantity related to the heat dissipation capacity of the heating system can be obtained according to the expression of the similarity criterion numbers, as shown in table 2:
TABLE 2 physical quantities contained in the similarity criterion numbers
As can be seen from table 2, a large number of physical quantities are involved in the above-mentioned similarity criterion numbers, many of which (e.g. dynamic viscosity, thermal diffusivity of the heating medium) are difficult to directly measure. Table 3 lists direct or indirect correspondences of physical quantities implicated by the aforementioned similarity criteria with physical quantities that can be collected in the context of actual engineering applications:
TABLE 3 correlation between physical quantities related to similarity criterion number and measurable physical quantities
In the embodiment of the application, a plurality of key parameters influencing the temperature of the heat supply working medium can be determined through the relationship between the temperature of the heat supply working medium and the heat change established by the formulas (1) to (3) and the corresponding relationship between the factors having key influence on the heat dissipation capacity and the observable physical quantity.
For example, in some preferred embodiments, the temperature θ (t) and the heat generation amount Φ of the heat supply working medium at the current moment can be calculated p Heat dissipation capacity phi d Temperature difference delta theta between indoor environment and outdoor environment b As a key factor for predicting the temperature θ (t + Δ t) of the heat supply working medium at the next time, it is considered that only the physical quantity having the time-varying characteristic among the selected input quantities affects θ (t + Δ t), and the non-time-varying physical quantity is related only to the characteristics of the heat supply system and the building itself, so that only the time-varying physical quantities in table 4 are selected as the key parameters in order to further reduce the number of input variables.
TABLE 4 time-varying characteristics of parameters affecting the temperature of a heating medium
Further, in some preferred embodiments of the present application, considering that the air source heat pump heating system is essentially a temperature control load, the allowable margin of the indoor environment temperature change caused by the on/off of the end user should be mapped to the upper and lower limits of the set temperature of the heating working medium and used as a key parameter influencing the temperature of the heating working medium.
Specifically, an average value of a difference between the total return water temperature and the intelligently set return water temperature may be selected as the temperature control margin, and an upper and lower limit expression shown in the following formula is obtained:
wherein, theta set Is a set value of the temperature of the heat supply working medium,
the return water temperature is set for the intelligence,
the difference between the working medium temperature set value and the intelligent set backwater temperature is obtained,
a lower limit value is set for the temperature of the heat supply working medium,
an upper limit value is set for the temperature of the heat supply working medium,
the temperature control margin.
After determining a plurality of key parameters influencing the temperature of the heat supply working medium through the step S1, a prediction model for predicting the temperature of the heat supply working medium based on the key parameters is further established and trained in the step S2.
In an embodiment of the present application, the heat supply working medium temperature prediction model is a BP neural network model. The BP neural network is a feedforward neural network having an input layer, an output layer, and a plurality of hidden layers, and is called a BP neural network because it updates a weight matrix using a back propagation algorithm to achieve a training effect. FIG. 3 shows the basic structure of a BP neural network, where x m 、y n 、W ij 、V ij The values of the multiple output quantities at the future time can be predicted based on the values of the multiple input quantities at the past time and/or the current time after the training of the BP neural network.
Specifically, in some preferred embodiments of the present application, the input amount of the heat supply working medium temperature prediction model includes the operation data of the key parameter at time t and at least 2 time intervals before time t; and the output quantity of the heat supply working medium temperature prediction model is the heat supply working medium temperature of the next time interval at the moment t. The reason for using the operating data at time t and at least 2 time intervals before time t as input variables is that: in the actual operation process of the air source heat pump heating system, the temperature change of the heating working medium has strong hysteresis with the key parameters and the change of the system power determined by the key parameters, so the time span range of the input quantity needs to be properly prolonged to improve the accuracy of model prediction.
In the actual implementation process, a training set and a test set can be constructed by using actual operation data of the air source heat pump heating system, the heating working medium temperature prediction model is trained through the training set, the prediction accuracy of the prediction model is tested by using the test set, and the tested prediction model can be used in the power grid peak regulation capacity quantification process of the step S3. The above-mentioned embodiment of constructing a training and testing set based on actual data to train and test a neural network model is known to those skilled in the art, and is not described herein again.
And S3, quantifying the power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model.
In the embodiment of the application, the quantized power grid peak shaving capacity is specifically a down-adjustable quantized value and/or an up-adjustable quantized value of the air source heat pump heating system corresponding to each switch control scheme.
In actual power grid dispatching, a power department directly provides load curves participating in the regulation period for each load aggregator, and then the load aggregator controls the governed load to adjust power according to the curves. For the air source heat pump heating system, different switch control schemes can be formed by opening or closing different numbers of air source heat pump units, and correspondingly, the system has different down-adjustable quantitative values and/or up-adjustable quantitative values under different switch control schemes. At present, relevant research on the peak regulation response of an electric power system participated by an air source heat pump is deficient in China, an air source heat pump load aggregator generally reports all air source heat pumps as the maximum up-regulation/down-regulation capacity by directly turning on or turning off all the air source heat pumps, and a scheduling mode mostly adopts task average distribution, so that individual actual conditions cannot be considered, extreme conditions of excessive use or few uses exist, the method is simple and direct, temperature is easily caused to be out of limit, and the comfort level of a user is reduced.
In order to correct the defects of the existing peak regulation capability evaluation mode of the air source heat pump, in the preferred embodiment of the application, a reasonable peak regulation capability quantization index is set according to the actual operation characteristics of the air source heat pump heating system, and the quantization index is predicted by using the heating working medium temperature prediction model. Specifically, each switch control scheme of the system can be adjusted up or down by comparing with the switch control scheme when the heat generation and dissipation balance is realized, meanwhile, because the temperature of the heat supply working medium when the heat generation and dissipation balance is realized can keep the indoor temperature stable, different switch control schemes obviously cause different changes of the temperature of the heat supply working medium, and further cause the indoor temperature to change, when the temperature of the heat supply working medium exceeds an acceptable limit, the peak regulation is finished, and the switch control scheme is reset to the switch control scheme when the heat generation and dissipation balance is realized, so that the indoor temperature is recovered to an acceptable range.
It follows that for an air-source heat pump heating system, the peak shaving capability should include different system powers determined by turning on different numbers of air-source heat pumps (i.e., different on-off control schemes), different heating medium temperature off-limits times resulting therefrom, and a total adjustable capacity determined by both. Thus, in embodiments of the present application, the down-adjustable quantization value corresponding to each switching control scheme is
And the up-adjustable quantization values corresponding to the respective switch control schemes are
Wherein, P down 、t down 、W down Respectively for the down-regulated power, down-regulated time and down-regulated capacity, P, of each switching control scheme up 、t up 、W up Respectively, the adjustable power, the adjustable time and the adjustable capacity, m, of each switching control scheme n Number of air source heat pumps started for each on-off control scheme, m b Number of starting-up units, P, for achieving balance of heat generation and dissipation n Rated electric power of the air source heat pump, t start Is the peak shaving start time, t min For the moment when the temperature of the heating medium reaches the lower temperature limit, t max The moment when the temperature of the heat supply working medium reaches the upper temperature limit.
FIG. 4 shows the determination of the adjustable time t as a function of the temperature change of the heating medium in a particular embodiment up And a down-adjustable time t down In which
The curves are respectively set up curves for the upper limit and the lower limit of the temperature of the heat supply working medium.
Specifically, in the embodiment of the present application, the step S3 further includes the steps of:
s31: determining a switching control scheme for realizing heat generation and dissipation balance based on actual operation data of the air source heat pump heating system and corresponding m b ；
S32: based on actual operation data of the air source heat pump heating system, tracking the change condition of the temperature of the heating working medium under each switch control scheme by using the heating working medium temperature prediction model, wherein when the temperature of the heating working medium reaches a preset temperature limit value, the switch control scheme is switched to the switch control scheme for realizing balance of heat generation and dissipation, and the switch control scheme is switched to each switch control scheme again after the heat generation and dissipation are balanced again;
s33: and determining the quantized power grid peak load regulation capacity of the air source heat pump heating system corresponding to each switch control scheme in real time based on the tracking result of the step S32.
Further, in some preferred embodiments, all the switch control schemes may be sorted in real time according to the size of the adjustable capacity, the scheme with the largest adjustable capacity is defined as an optimal up-adjusting switch control scheme, and the scheme with the smallest adjustable capacity is defined as an optimal down-adjusting switch control scheme. The adjustable power and the adjustable time under the two switch control schemes are the adjustable power and the adjustable time of the air source heat pump heating system.
By the method, the quantized value of the peak shaving capacity of the air source heat pump heating system participating in the power grid can be updated in real time, so that a higher-level power department can accurately and uniformly acquire the real peak shaving capacity of a demand side, and the formulation of a peak shaving plan and the allocation of peak shaving tasks are more suitable for actual conditions.
Example 1
In the embodiment, the method is used for training a heat supply working medium temperature prediction model and quantifying the peak regulation capacity of a system power grid by adopting real detection data of the operation of a certain air source heat pump heat supply system (hereinafter referred to as system A) provided by a certain air source heat pump load aggregator.
In order to select data capable of reflecting the heat supply characteristics of the air source heat pump heat supply system, the operation data of the air source heat pump heat supply system needs to be analyzed, and the working time interval of the air source heat pump heat supply system in a stable operation state and the time interval of the air source heat pump heat supply system actively adjusting the air source heat pump heat supply system due to the change of the external environment are selected. Fig. 5 shows the variation of the air source heat pump electric power according to the actual operation data of the system in month 2 a of a year.
As can be seen from fig. 5, during the period from 2 months 13 to 2 months 16, the fluctuation of the outdoor environment temperature is large, so the electric power consumed by the system a also fluctuates, and meanwhile, the time of steady-state operation of the system is longer than that of the period from 2 months 20 to 2 months 28, so this part of data has better representativeness, so in this embodiment, 6000 sets of operation data of the system a during the period from 2 months 13 to 2 months 16 (1 set is obtained every 1 minute) are selected for constructing the prediction model of this embodiment.
The time step (i.e., Δ t) is set to 3min, taking into account the balance between data accuracy and quantity. And S1, analyzing the operation data, and finally selecting the number of the started air source heat pumps, the outdoor environment temperature, the total water supply temperature, the total water return temperature and the intelligent set water return temperature as key parameters. The number of the opened air source heat pumps and the outdoor environment temperature can be directly used, and the rest physical quantities related to the temperature of the heat supply working medium still need to be subjected to related data processing.
And then, building and training a heat supply working medium temperature prediction model through the step S2, wherein the concrete building and training conditions are as follows:
(a) Data determination: actual operation data of the air source heat pump heating system, which is used as a data sample for prediction model training, is from 2 months 13 days to 2 months 16 days, is sampled every 1min, 6000 groups of original data are obtained, and after averaging and eliminating jump nodes, a training set is obtained by taking 3 minutes as a step length (delta t), and 2000 groups of data are obtained. The data of two hours of 2.17 days are taken as a verification set in the same method to verify the accuracy of model prediction.
(b) Input-output determination: the actual peak regulation power instruction generally takes 15 minutes as a time interval, and takes 3min as a time interval delta t from the aspects of modeling precision and modeling speed, the number of the started air source heat pumps, the outdoor environment temperature, the heat supply working medium temperature and the heat supply working medium temperature at the moment t, t-delta t and t-2 delta t are set as upper limits, the heat supply working medium temperature is set as lower limit as model input quantity, and the heat supply working medium temperature at the next moment is used as model output quantity, so that a training set and a verification set required by a training model are respectively generated on the basis of the upper limits and the lower limits. Table 5 specifically lists the input amount and the output amount of the heat supply working medium temperature prediction model.
TABLE 5 input and output quantities of heat supply working medium temperature prediction model
(c) Hidden layer node number determination: in the process of training the prediction model, the selection of the number of hidden layer nodes is also very critical, the accuracy is reduced due to the small number of the hidden layer nodes, and the training speed is reduced due to the large number of the hidden layer nodes. In this embodiment, the node number of the hidden layer is evaluated according to an empirical formula. The number of the hidden layer nodes in the empirical formula is generally 2 times that of the input nodes, but is slightly more than that of the input nodes, and in this embodiment, 31 nodes are taken as the number of the hidden layer nodes.
(d) Data preprocessing: and acquiring a standard training set and a standard verification set by adopting a min-max normalization method for a training set and a verification set formed by input data and output data so as to eliminate potential influence of different dimensional data on modeling.
(e) Model training: target error is 10 -4 Maximum training number 2000, minimum gradient 10 -10 And (4) selecting an initial weight immediately, and improving the accuracy of modeling by continuously adjusting the learning rate eta to finish model training.
FIG. 6 shows the prediction accuracy of the heating medium temperature prediction model constructed and trained using actual operating data of System A, and it can be seen that the prediction model can be applied toThe temperature of the heat supply working medium is well predicted, the error between the model predicted working medium temperature and the actual operating working medium temperature is small, and the square root of the error is
Further, according to the above process, historical operation data of the B air source heat pump heat supply system is additionally selected for construction and training of the prediction model, and the prediction accuracy of the obtained prediction model is shown in fig. 7. According to the simulation result, the established heat supply working medium temperature prediction model still has better simulation and tracking on the actual operation process, and can reasonably quantize the peak regulation capacity of the air source heat pump heat supply system.
On the basis of the construction of the heat supply working medium temperature prediction model based on the historical operation data of the system A, different switch control schemes of the system A are formed by changing the number of the starting air source heat pumps, so that the change condition of the working medium temperature of the heat supply system under different switch control schemes is simulated on the basis of actual operation data, when the working medium temperature reaches the limit value due to unbalanced heat generation and dissipation, the switch control scheme is reset to be the switch control scheme corresponding to the balance of the heat generation and dissipation, the adjustable time is calculated at the same time, and the up-adjustable capacity and the down-adjustable capacity of the current switch control scheme are further calculated in real time according to the adjustable time. Fig. 8 shows the variation of the temperature of the heating medium of the system a obtained under different switching control schemes, and the corresponding graphical results of the up-adjustable capacity and the down-adjustable capacity are shown in fig. 9.
As can be seen from fig. 9, when the temperature of the heat supply working medium approaches the temperature control limit value with the passage of time, the switch control scheme returns to the corresponding switch control scheme for realizing the balance of heat generation and dissipation, and the temperature of the heat supply working medium changes in the opposite direction; when the number of the opened air source heat pumps is large, the total power is large, the heat is generated, the temperature of the heat supply working medium is increased continuously, and the increasing rate is increased when the number of the opened air source heat pumps is more; similarly, when the number of the started air source heat pumps is small, the total power is small, the heat production is small, the temperature of the heat supply working medium is continuously reduced, and the reduction rate is higher when the number of the started air source heat pumps is small; the simulation result accords with the thermodynamic process, and the fact that the heat supply working medium temperature prediction model established by the method of the embodiment can effectively simulate the air source heat pump heat supply system in different switch control states is proved.
As can be seen from fig. 9, as the deviation degree between the number of the air source heat pumps that are already turned on and the number of the air source heat pumps that are turned on corresponding to the heat generation and dissipation equilibrium state increases, both the up-adjustment and the down-adjustment result in a lower adjustable capacity. The adjustable power and the adjustable time can be in ideal level only when the difference between the number of the started units and the number corresponding to the balance of heat generation and dissipation is not large, and the adjustable capacity of the heating system is the maximum at the moment.
The present application further provides a power grid peak regulation capacity quantization system through an embodiment, which is used for quantizing the power grid peak regulation capacity of an air source heat pump heating system, wherein the air source heat pump heating system comprises a plurality of air source heat pumps, and heats a heat supply working medium by using an air source, as shown in fig. 10, the power grid peak regulation capacity quantization system comprises:
the parameter determining unit is used for determining a plurality of key parameters influencing the temperature of a heat supply working medium of the air source heat pump heat supply system; the model training unit is used for establishing a heat supply working medium temperature prediction model based on the key parameters and training by using the historical operating data; and the quantization unit is used for acquiring the quantized power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model.
Further, the parameter determination unit determines the plurality of key parameters by:
firstly, establishing a heat supply working medium thermodynamic process model of an air source heat pump heat supply system based on the formula (1),
wherein rho is the density of the heat supply working medium, c is the specific heat capacity of the heat supply working medium, and V is the volume (m) of the heat supply working medium 3 ) (ii) a Theta is the temperature of the heat supply working medium, t is time, and phi is the heat variation of the heat supply working medium;
secondly, determining the relation between phi and theta based on the formula (2),
wherein phi p For the system to produce heat,. Phi d For the heat dissipation of the system, phi 1 Heating capacity of air source heat pump for starting, phi 2 For heat flow of solar radiation, COP n Number of air source heat pumps to be turned on, P n H is the surface heat convection coefficient, A is the total heat dissipation area, delta t is the time interval, theta out Is the outdoor temperature;
thirdly, the surface convection heat exchange coefficient h of the formula (3) is used for approximate representation,
wherein N is u Is the Nussel number, l is the characteristic size, and lambda is the heat conductivity coefficient of the heat supply working medium;
the fourth step, determining the influence N under the condition of forced convection heat transfer u Factor (2)
influence on N under the condition of natural convection heat transfer u Factor (2)
Wherein Re is Reynolds number, pr is Prandtl number, gr is Gravadaff number, u, v and a are flow velocity, kinematic viscosity and thermal diffusivity of heat supply working medium respectively, g is gravity acceleration, alpha v Is a coefficient of volume expansion, Δ θ b Is the temperature difference between the wall temperature and the ambient temperature.
A fifth step of forming a film on the basis of the formulas (1) to (3) and the influence N u Determining the influence ofA number of key parameters for θ.
Preferably, the key parameters further include upper and lower limits for a preset temperature of the thermal working medium.
Preferably, the heat supply working medium temperature prediction model is a BP neural network model.
Preferably, the input quantity of the heat supply working medium temperature prediction model comprises operation data of the key parameter at time t and at least 2 time intervals before time t; and the output quantity of the heat supply working medium temperature prediction model is the heat supply working medium temperature of the next time interval at the moment t.
Preferably, the quantized grid peak shaving capacity is a down-adjustable quantized value and/or an up-adjustable quantized value of the air source heat pump heating system corresponding to each switch control scheme.
Further, the down-adjustable quantization value corresponding to each switching control scheme is
And the up-adjustable quantization values corresponding to the respective switch control schemes are
Wherein, P down 、t down 、W down Respectively for the down-regulated power, down-regulated time and down-regulated capacity, P, of each switching control scheme up 、t up 、W up Respectively, the adjustable power, the adjustable time and the adjustable capacity, m, of each switching control scheme n Number of air source heat pumps started for each on-off control scheme, m b Number of starting-up units, P, for achieving balance of heat generation and dissipation n Rated electric power of the air source heat pump, t start To the peak shaver start time, t min For the moment when the temperature of the heating medium reaches the lower temperature limit, t max The moment when the temperature of the heat supply working medium reaches the upper temperature limit.
Further, the quantification unit obtains the quantified power grid peak regulation capacity of the air source heat pump heating system through the following steps:
firstly, determining a switch control scheme for realizing production and heat dissipation balance and corresponding m based on actual operation data of the air source heat pump heating system b ；
Secondly, based on actual operation data of the air source heat pump heating system, tracking the change condition of the temperature of the heating working medium under each switch control scheme by using the heating working medium temperature prediction model, wherein when the temperature of the heating working medium reaches a preset temperature limit value, the switch control scheme is switched to the switch control scheme for realizing balance of production and heat dissipation, and the switch control scheme is switched to each switch control scheme again after the heat dissipation reaches balance again;
and thirdly, determining the quantized power grid peak regulation capacity of the air source heat pump heating system corresponding to each switch control scheme in real time based on the tracking result of the heat supply working medium temperature prediction model.
While the present invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope thereof as defined in the appended claims.
Claims (16)
1. A power grid peak regulation capacity quantification method is used for quantifying the power grid peak regulation capacity of an air source heat pump heating system, the air source heat pump heating system comprises a plurality of air source heat pumps, and an air source is used for heating a heat supply working medium, and the method is characterized by comprising the following steps:
s1: determining a plurality of key parameters influencing the temperature of a heat supply working medium of an air source heat pump heat supply system;
s2: establishing a heat supply working medium temperature prediction model based on the key parameters and training by using the historical operation data;
s3: and acquiring the quantized power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model.
2. The method for quantizing peak shaving capacity of power grid according to claim 1, wherein step S1 further comprises the steps of:
s11: a heat supply working medium thermodynamic process model of the air source heat pump heat supply system is established based on the formula (1),
wherein rho is the density of the heat supply working medium, c is the specific heat capacity of the heat supply working medium, and V is the volume (m) of the heat supply working medium 3 ) (ii) a Theta is the temperature of the heat supply working medium, t is time, and phi is the heat variation of the heat supply working medium;
s12: determining a relation between phi and theta based on the equation (2),
wherein phi p For the heat production of the system, phi d For the heat dissipation of the system, phi 1 Heating capacity of air source heat pump for starting 2 For heat flow of solar radiation, COP n Number of air source heat pumps to be turned on, P n H is the surface heat convection coefficient, A is the total heat dissipation area, delta t is the time interval, theta out Is the outdoor temperature;
s13: the convective heat transfer coefficient h is approximately characterized by using the formula (3) surface,
wherein N is u Is the Nussel number, l is the characteristic size, and lambda is the heat conductivity coefficient of the heat supply working medium;
s14: determining influence N under the condition of forced convection heat transfer u Factor (2)
influence on N under the condition of natural convection heat transfer u Factor (2)
Wherein Re is Reynolds number, pr is Plantaget number, gr is Gravadaff number, u, v and a are flow velocity, kinematic viscosity and thermal diffusivity of heat supply working medium respectively, g is gravitational acceleration, alpha is v Is the coefficient of volume expansion, Δ θ b Is the temperature difference between the wall temperature and the ambient temperature.
S15: based on the formulae (1) to (3) and the influence N u Determines a number of key parameters that affect theta.
3. The power grid peak shaving capacity quantification method according to claim 2, characterized by:
the key parameters also include upper and lower limits for the temperature of the thermal working medium to be preset.
4. The method for quantifying peak shaving capacity of a power grid according to claim 1, wherein:
the heat supply working medium temperature prediction model is a BP neural network model.
5. The method for quantifying peak shaving capacity of a power grid according to claim 1, wherein:
the input quantity of the heat supply working medium temperature prediction model comprises operation data of the key parameter at the time t and at least 2 time intervals before the time t;
and the output quantity of the heat supply working medium temperature prediction model is the heat supply working medium temperature of the next time interval at the moment t.
6. The power grid peak shaving capacity quantification method according to claim 1, characterized by:
the quantized power grid peak shaving capacity is a down-adjustable quantized value and/or an up-adjustable quantized value of the air source heat pump heating system corresponding to each switch control scheme.
7. The method for quantifying peak shaving capacity of a power grid according to claim 6, wherein:
the down-adjustable quantization values corresponding to the respective switching control schemes are
And the up-adjustable quantization values corresponding to the respective switch control schemes are
Wherein, P down 、t down 、W down Separately for each switching control scheme, a down-adjustable power, a down-adjustable time and a down-adjustable capacity, P up 、t up 、W up Respectively, the adjustable power, the adjustable time and the adjustable capacity, m, of each switching control scheme n Number of air source heat pumps started for each on-off control scheme, m b Number of starting-up units, P, for achieving balance of heat generation and dissipation n Rated electric power of the air source heat pump, t start Is the peak shaving start time, t min For the moment when the temperature of the heating medium reaches the lower temperature limit, t max The moment when the temperature of the heat supply working medium reaches the upper temperature limit.
8. The method for quantifying peak shaving capability of a power grid according to claim 7, wherein step S3 further comprises the steps of:
s31: determining a switching control scheme for realizing heat generation and dissipation balance based on actual operation data of the air source heat pump heating system and corresponding m b ；
S32: based on actual operation data of the air source heat pump heating system, tracking the change condition of the temperature of the heating working medium under each switch control scheme by using the heating working medium temperature prediction model, wherein when the temperature of the heating working medium reaches a preset temperature limit value, the switch control scheme is switched to the switch control scheme for realizing balance of heat generation and dissipation, and the switch control scheme is switched to each switch control scheme again after the heat generation and dissipation are balanced again;
s33: and determining the quantized power grid peak regulation capacity of the air source heat pump heating system corresponding to each switch control scheme in real time based on the tracking result of the step S32.
9. The utility model provides a power grid peak regulation ability quantization system for quantify the power grid peak regulation ability of air source heat pump heating system, air source heat pump heating system contains many air source heat pumps, utilizes the air source to heat the working medium that supplies heat, its characterized in that includes:
the parameter determining unit is used for determining a plurality of key parameters influencing the temperature of a heat supply working medium of the air source heat pump heat supply system;
the model training unit is used for establishing a heat supply working medium temperature prediction model based on the key parameters and training by using the historical operating data;
and the quantification unit is used for acquiring the quantified power grid peak regulation capacity of the air source heat pump heating system by using the trained heating working medium temperature prediction model.
10. The system of claim 9, wherein the parameter determination unit determines the plurality of key parameters by:
firstly, establishing a heat supply working medium thermodynamic process model of an air source heat pump heat supply system based on a formula (1),
wherein rho is the density of the heat supply working medium, c is the specific heat capacity of the heat supply working medium, and V is the volume (m) of the heat supply working medium 3 ) (ii) a Theta is the temperature of the heat supply working medium, t is time, and phi is the heat variation of the heat supply working medium;
secondly, determining the relation between phi and theta based on the formula (2),
wherein phi p For the heat production of the system, phi d For the heat dissipation of the system, phi 1 Heating capacity of air source heat pump for starting 2 For heat flow of solar radiation, COP n Number of air source heat pumps to be turned on, P n H is the surface heat convection coefficient, A is the total heat dissipation area, delta t is the time interval, theta out Is the outdoor temperature;
thirdly, the surface convection heat transfer coefficient h of the formula (3) is used for approximate characterization,
wherein N is u Is the Nussel number, l is the characteristic size, and lambda is the heat conductivity coefficient of the heat supply working medium;
the fourth step, determine the influence N under the condition of forced convection heat transfer u Factor (2)
influence on N under the condition of natural convection heat transfer u Factor (2)
Wherein Re is Reynolds number, pr is Plantet number, gr is GravadaffThe number u, v and a are respectively the flow velocity, kinematic viscosity and thermal diffusivity of the heat supply working medium, g is the gravity acceleration, alpha v Is a coefficient of volume expansion, Δ θ b Is the temperature difference between the wall temperature and the ambient temperature.
A fifth step of obtaining the influence N based on the expressions (1) to (3) u Determines a number of key parameters that affect theta.
11. The power grid peak shaving capacity quantification system of claim 10, wherein:
the key parameters also include upper and lower limits for the temperature of the thermal working medium to be preset.
12. The power grid peak shaving capacity quantification system of claim 9, wherein:
the heat supply working medium temperature prediction model is a BP neural network model.
13. The grid peak shaving capacity quantification system of claim 9, wherein:
the input quantity of the heat supply working medium temperature prediction model comprises the operation data of the key parameter at t moment and at least 2 time intervals before the t moment;
and the output quantity of the heat supply working medium temperature prediction model is the heat supply working medium temperature of the next time interval at the moment t.
14. The power grid peak shaving capacity quantification system of claim 9, wherein:
the quantized power grid peak shaving capacity is a down-adjustable quantized value and/or an up-adjustable quantized value of the air source heat pump heating system corresponding to each switch control scheme.
15. The grid peak shaving capacity quantification system of claim 14, wherein:
the down-adjustable quantization value corresponding to each switching control scheme is
And the adjustable quantization values corresponding to the respective switching control schemes are
Wherein, P down 、t down 、W down Respectively for the down-regulated power, down-regulated time and down-regulated capacity, P, of each switching control scheme up 、t up 、W up Respectively, the adjustable power, the adjustable time and the adjustable capacity, m, of each switching control scheme n Number of air source heat pumps started for each on-off control scheme, m b Number of starting-up units, P, for achieving balance of heat generation and dissipation n Rated electric power of the air source heat pump, t start Is the peak shaving start time, t min For the moment when the temperature of the heating medium reaches the lower temperature limit, t max The moment when the temperature of the heat supply working medium reaches the upper temperature limit.
16. The system according to claim 15, wherein the quantification unit obtains the quantified peak shaving capacity of the grid of the air-source heat pump heating system by:
firstly, determining a switch control scheme for realizing heat generation and radiation balance and a corresponding m based on actual operation data of the air source heat pump heating system b ；
Secondly, tracking the change situation of the temperature of the heat supply working medium under each switch control scheme by using the heat supply working medium temperature prediction model based on the actual operation data of the air source heat pump heat supply system, wherein the switch control scheme is switched to the switch control scheme for realizing balance of heat generation and dissipation when the temperature of the heat supply working medium reaches a preset temperature limit value, and the switch control scheme is switched to each switch control scheme again after the heat generation and dissipation balance is achieved again;
and thirdly, determining the quantized power grid peak regulation capacity of the air source heat pump heating system corresponding to each switch control scheme in real time based on the tracking result of the heat supply working medium temperature prediction model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211228666.3A CN115660325B (en) | 2022-10-08 | 2022-10-08 | Power grid peak regulation capacity quantization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211228666.3A CN115660325B (en) | 2022-10-08 | 2022-10-08 | Power grid peak regulation capacity quantization method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115660325A true CN115660325A (en) | 2023-01-31 |
| CN115660325B CN115660325B (en) | 2023-05-30 |
Family
ID=84986899
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211228666.3A Active CN115660325B (en) | 2022-10-08 | 2022-10-08 | Power grid peak regulation capacity quantization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115660325B (en) |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000075903A (en) * | 1998-09-01 | 2000-03-14 | Toshiba Corp | Device and method for constructing operation quantity prediction model and computer-readable recording medium where operation quantity prediction model structuring program is recorded |
| JP2009036415A (en) * | 2007-07-31 | 2009-02-19 | Mayekawa Mfg Co Ltd | Heat pump cycle system using geo-heat |
| CN105202623A (en) * | 2015-09-21 | 2015-12-30 | 国家电网公司 | Method for predicting heating peak modulation capacity of heat supply unit |
| CN106248259A (en) * | 2016-09-28 | 2016-12-21 | 华中科技大学 | A kind of system corrects the dynamic temperature measurement method of hot thermocouple inertial drift |
| CN108319131A (en) * | 2017-12-27 | 2018-07-24 | 国网山东省电力公司电力科学研究院 | Peak load regulation capability assessment method based on data mining |
| CN109193808A (en) * | 2018-11-13 | 2019-01-11 | 国网河北能源技术服务有限公司 | Peak load regulation network energy force prediction method and device |
| CN110160127A (en) * | 2019-05-17 | 2019-08-23 | 烟台锐控自动化控制工程有限公司 | Mist based on fluid heat transferring modeling calculates three-level net for thermal control system |
| CN110906571A (en) * | 2019-11-30 | 2020-03-24 | 华南理工大学 | Solar heat pump hot water system control strategy optimization method based on machine learning |
| CN111047463A (en) * | 2019-12-02 | 2020-04-21 | 国网河北省电力有限公司电力科学研究院 | Peak regulation capacity evaluation method for heat supply unit after heat supply reconstruction by adopting low-temperature waste heat pump |
| CN111429020A (en) * | 2020-04-07 | 2020-07-17 | 中国矿业大学 | Multi-time scale economic scheduling method of electric heating system considering heat storage characteristics of heat supply network |
| US20200233386A1 (en) * | 2017-10-09 | 2020-07-23 | Tsinghua University | Method for calculating control parameters of heating supply power of heating network |
| CN111520808A (en) * | 2020-03-09 | 2020-08-11 | 华电电力科学研究院有限公司 | Complex combined heat and power heating system based on low-temperature waste heat recovery and electric heating load adjusting method |
| CN111578371A (en) * | 2020-05-22 | 2020-08-25 | 浙江大学 | Data-driven accurate regulation and control method for urban centralized heating system |
| CN112348696A (en) * | 2020-10-10 | 2021-02-09 | 湖南大唐先一科技有限公司 | Heat supply unit peak regulation upper limit evaluation method and system based on BP neural network |
| CN112377985A (en) * | 2020-10-10 | 2021-02-19 | 湖南大唐先一科技有限公司 | Heat supply unit peak regulation lower limit evaluation method and system based on feedback neural network |
| CN113531644A (en) * | 2021-08-11 | 2021-10-22 | 山东佐耀智能装备股份有限公司 | Power grid dispatching and peak-shaving system of air source heat pump station |
| CN113606750A (en) * | 2021-10-09 | 2021-11-05 | 山东佐耀科技有限公司 | Peak regulation control instruction optimal distribution method and system for air source heat pump load |
| WO2021238505A1 (en) * | 2020-05-27 | 2021-12-02 | 华北电力大学 | Federated learning-based regional photovoltaic power probability prediction method, and cooperative regulation and control system |
| CN114485244A (en) * | 2022-02-14 | 2022-05-13 | 中国电建集团华东勘测设计研究院有限公司 | Thermal diode, thermal rectifying coating, phase-change heat storage and supply device and heat monitoring method |
| CN114740727A (en) * | 2022-04-25 | 2022-07-12 | 东南大学溧阳研究院 | Air source heat pump dynamic energy efficiency optimization control method under outdoor temperature disturbance condition |
| US20220275981A1 (en) * | 2019-08-02 | 2022-09-01 | Exergyn Ltd. | System and method for supporting sma material and optimising heat transfer in a sma heat pump |
-
2022
- 2022-10-08 CN CN202211228666.3A patent/CN115660325B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000075903A (en) * | 1998-09-01 | 2000-03-14 | Toshiba Corp | Device and method for constructing operation quantity prediction model and computer-readable recording medium where operation quantity prediction model structuring program is recorded |
| JP2009036415A (en) * | 2007-07-31 | 2009-02-19 | Mayekawa Mfg Co Ltd | Heat pump cycle system using geo-heat |
| CN105202623A (en) * | 2015-09-21 | 2015-12-30 | 国家电网公司 | Method for predicting heating peak modulation capacity of heat supply unit |
| CN106248259A (en) * | 2016-09-28 | 2016-12-21 | 华中科技大学 | A kind of system corrects the dynamic temperature measurement method of hot thermocouple inertial drift |
| US20200233386A1 (en) * | 2017-10-09 | 2020-07-23 | Tsinghua University | Method for calculating control parameters of heating supply power of heating network |
| CN108319131A (en) * | 2017-12-27 | 2018-07-24 | 国网山东省电力公司电力科学研究院 | Peak load regulation capability assessment method based on data mining |
| CN109193808A (en) * | 2018-11-13 | 2019-01-11 | 国网河北能源技术服务有限公司 | Peak load regulation network energy force prediction method and device |
| CN110160127A (en) * | 2019-05-17 | 2019-08-23 | 烟台锐控自动化控制工程有限公司 | Mist based on fluid heat transferring modeling calculates three-level net for thermal control system |
| US20220275981A1 (en) * | 2019-08-02 | 2022-09-01 | Exergyn Ltd. | System and method for supporting sma material and optimising heat transfer in a sma heat pump |
| CN110906571A (en) * | 2019-11-30 | 2020-03-24 | 华南理工大学 | Solar heat pump hot water system control strategy optimization method based on machine learning |
| CN111047463A (en) * | 2019-12-02 | 2020-04-21 | 国网河北省电力有限公司电力科学研究院 | Peak regulation capacity evaluation method for heat supply unit after heat supply reconstruction by adopting low-temperature waste heat pump |
| CN111520808A (en) * | 2020-03-09 | 2020-08-11 | 华电电力科学研究院有限公司 | Complex combined heat and power heating system based on low-temperature waste heat recovery and electric heating load adjusting method |
| CN111429020A (en) * | 2020-04-07 | 2020-07-17 | 中国矿业大学 | Multi-time scale economic scheduling method of electric heating system considering heat storage characteristics of heat supply network |
| CN111578371A (en) * | 2020-05-22 | 2020-08-25 | 浙江大学 | Data-driven accurate regulation and control method for urban centralized heating system |
| WO2021238505A1 (en) * | 2020-05-27 | 2021-12-02 | 华北电力大学 | Federated learning-based regional photovoltaic power probability prediction method, and cooperative regulation and control system |
| CN112348696A (en) * | 2020-10-10 | 2021-02-09 | 湖南大唐先一科技有限公司 | Heat supply unit peak regulation upper limit evaluation method and system based on BP neural network |
| CN112377985A (en) * | 2020-10-10 | 2021-02-19 | 湖南大唐先一科技有限公司 | Heat supply unit peak regulation lower limit evaluation method and system based on feedback neural network |
| CN113531644A (en) * | 2021-08-11 | 2021-10-22 | 山东佐耀智能装备股份有限公司 | Power grid dispatching and peak-shaving system of air source heat pump station |
| CN113606750A (en) * | 2021-10-09 | 2021-11-05 | 山东佐耀科技有限公司 | Peak regulation control instruction optimal distribution method and system for air source heat pump load |
| CN114485244A (en) * | 2022-02-14 | 2022-05-13 | 中国电建集团华东勘测设计研究院有限公司 | Thermal diode, thermal rectifying coating, phase-change heat storage and supply device and heat monitoring method |
| CN114740727A (en) * | 2022-04-25 | 2022-07-12 | 东南大学溧阳研究院 | Air source heat pump dynamic energy efficiency optimization control method under outdoor temperature disturbance condition |
Non-Patent Citations (4)
| Title |
|---|
| ZHAO ZAN FENG ETC: ""Laminar mixed convection of large-Prandtl-number in-tube nanofluid flow, Part I Experimental study"" * |
| 吕闯 等: ""太阳能辅助空气源热泵系统供热特性的实验分析"" * |
| 李勇 等: ""基于气象数据的供热机组调峰能力预测方法"" * |
| 李明: ""供热系统节能技术浅析"" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115660325B (en) | 2023-05-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112128841B (en) | Whole-network balance adjusting method based on load prediction and room temperature feedback correction | |
| CN108916986B (en) | Information physical fusion secondary pipe network variable flow hydraulic balance regulation and control method and system | |
| CN109270842B (en) | Bayesian network-based regional heat supply model prediction control system and method | |
| CN107120721B (en) | A kind of central heating dynamic gas candidate compensation method | |
| Ben-Nakhi et al. | Energy conservation in buildings through efficient A/C control using neural networks | |
| CN111550861B (en) | Self-adaptive optimization control method, system and device for heat pump and electric heat storage equipment | |
| CN107025334B (en) | Method and device for establishing overall dynamic model of heat users of central heating system | |
| CN111811016B (en) | Method and system for adjusting hydraulic balance among units of heating unit | |
| CN116187598B (en) | Building-based virtual power plant load prediction method | |
| Delorme-Costil et al. | Forecasting domestic hot water demand in residential house using artificial neural networks | |
| CN114565167B (en) | Dynamic load prediction and regulation method for thermal inlet | |
| CN111473408A (en) | Control method of heat supply control system based on climate compensation | |
| Huang et al. | Model predictive control for energy-efficient buildings: An airport terminal building study | |
| CN112268350B (en) | Air conditioner side load prediction method based on system delay | |
| Wang et al. | Research on intelligent regulation of air conditioning energy saving based on human thermal comfort | |
| CN117091242A (en) | Evaluation method, temperature setting method and system for air conditioner temperature control load cluster | |
| CN115660325B (en) | Power grid peak regulation capacity quantization method and system | |
| CN116307074A (en) | Method for acquiring real thermal data, system and method for constructing neural network model of thermal data, and method for predicting thermal load | |
| CN113078629B (en) | Aggregate power distribution model for cluster temperature control load aggregate power regulation and control and distributed consistency control method | |
| CN113701232A (en) | Heat supply system building-level regulation and control method and system based on temperature diversity analysis | |
| CN114200839A (en) | Office building energy consumption intelligent control model for dynamic monitoring of coupled environmental behaviors | |
| CN207584879U (en) | A kind of heat supply network balance regulation system | |
| CN117073147B (en) | Intelligent air conditioner control method and system for building automatic control | |
| Wang et al. | A Data-driven Control Method for Operating the Commercial HVAC Load as a Virtual Battery | |
| Hua et al. | Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |