CN108006809B - Intelligent control navigation method for cooling and heating of distributed energy station - Google Patents
Intelligent control navigation method for cooling and heating of distributed energy station Download PDFInfo
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- CN108006809B CN108006809B CN201711232877.3A CN201711232877A CN108006809B CN 108006809 B CN108006809 B CN 108006809B CN 201711232877 A CN201711232877 A CN 201711232877A CN 108006809 B CN108006809 B CN 108006809B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D19/00—Details
- F24D19/10—Arrangement or mounting of control or safety devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B49/00—Arrangement or mounting of control or safety devices
Abstract
The invention discloses an intelligent control navigation method for cooling and heating of a distributed energy station, which comprises the following steps: s1, determining an output temperature curve, and automatically acquiring the heating and refrigerating target temperature required by a user according to the output temperature curve; s2, determining an environment temperature curve, and automatically predicting the environment temperature of the area where the user is located by using the environment temperature curve; and S3, automatically and intelligently controlling cooling and heating of the energy station according to the difference between the heating and cooling target temperature required by the user and the corresponding ambient temperature. The invention can accurately control the external output energy, improves the control precision, reduces the energy loss, realizes the operation automation, can automatically switch to manual operation or automatic operation during the operation, and finally can realize the unattended operation of the distributed energy station.
Description
Technical Field
The invention relates to an intelligent control navigation method for cooling and heating of a distributed energy station, and belongs to the technical field of cooling and heating control of energy systems.
Background
The existing distributed energy station outputs electric power and heating power (heating, refrigerating and the like), the power load control is set by an operator or given by power grid dispatching, a control system automatically finishes the control, and the technology is mature and perfect; however, the heating power (heating, cooling, etc.) control mainly depends on the experience of operators or sets target values manually to automatically operate, so that the heating temperature fluctuation is large, and energy waste and accidents are caused. The biggest difficulty that heat supply cannot be automatically controlled is that currently output heat reaches a heat user after 10 minutes to 2 hours, and a feedback signal cannot be adjusted at all, and only empirical heat supply can be performed according to the current ambient temperature, so that heat supply errors are large.
Disclosure of Invention
The invention aims to provide an intelligent control navigation method for cooling and heating of a distributed energy station, which can accurately control the output energy, improve the control precision, reduce the energy loss, realize the operation automation, automatically switch to manual operation or automatic operation during the operation and finally realize the unattended operation of the distributed energy station.
In order to solve the technical problems, the invention adopts the following technical scheme: a distributed energy station cooling and heating intelligent control navigation method comprises the following steps:
s1, determining an output temperature curve, and automatically acquiring the heating and refrigerating target temperature required by a user according to the output temperature curve;
s2, determining an environment temperature curve, and automatically predicting the environment temperature of the area where the user is located by using the environment temperature curve;
and S3, automatically and intelligently controlling cooling and heating of the energy station according to the difference between the heating and cooling target temperature required by the user and the corresponding ambient temperature.
Preferably, the output temperature profile in step S1 includes: the solar energy heating and refrigerating system comprises a constant temperature mode, a heating mode, a refrigerating mode, a holiday energy supply mode, an office mode (weekends and holidays all the year round), and a public exhibition mode curve, so that the heating or refrigerating requirements of different objects in different climates can be met.
Preferably, the determining the output temperature curve comprises the following steps; firstly, automatically judging the nature of any day in the year by an astronomical calculation formula, wherein the nature comprises working days, holidays, seasons and climates; secondly, determining an output temperature curve according to a heat supply or refrigeration object, local longitude and latitude basic data of a heat supply station and environmental standard basic data of each country;
wherein, the astronomical calculation formula is as follows:
in the formula, ω: sunrise and sunset time angles of the sun, namely the solar time angle when the sun is positioned on the horizon, wherein the solar time angle is related to time, and positive and negative values represent morning and evening; phi: the global latitude of the position of the heat supply station; n: on the nth day of the year, 1 month and 1 day, n is 1, and so on.
Once the latitude and the time of the earth are input, the astronomical calculation formula is activated, the formula is suitable for 3017 years, and the applicability of the astronomical calculation formula ensures that the time error in 1000 years does not exceed 10 minutes; therefore, the annual temperature output curves can be automatically given according to different national standards by inputting the standards of different countries, and the schemes of refrigeration in summer and heating in winter are automatically judged and finished; by utilizing the output temperature curve, classified energy supply can be realized for buildings of different properties such as houses, hotels, hospitals, cinemas, shops, office buildings, laboratory buildings, kindergartens, schools, libraries, auditoriums, canteens, gymnasiums and the like. The heating and refrigerating target temperature required by the user is automatically obtained through the output temperature curve, so that the heating target temperature required by the user can be efficiently and automatically obtained, and the objective and accurate heating target temperature is obtained.
Preferably, in step S2, the determining the environmental temperature curve includes the following steps:
s21, collecting local meteorological data (such as available from three aspects, respectively purchasing from local meteorological departments; self-measuring collected data; meteorological data published on the network or the meteorological department);
s22, fitting the meteorological data to obtain a corresponding meteorological formula, namely obtaining an ambient temperature curve; (for example, the environmental temperature curve can be a 6 th power function curve, thereby ensuring that the temperature precision is controlled within the range of 0.02 degree)
And S23, putting the weather formula into a database by taking the longitude and latitude as a label.
The environmental temperature of the area where the user is located is automatically predicted through a mathematical model formula derived according to local meteorological data, so that the calculation workload is greatly simplified, and complicated calculation and data calling processes are avoided.
In the foregoing intelligent control navigation method for cooling and heating of a distributed energy station, step S2 further includes:
and when the area where the user is located does not have the environmental temperature curve, predicting the environmental temperature of the area where the user is located by referring to the environmental temperature curves of other cities in the same climate zone.
The northern hemisphere is divided into 14 climate zones, China spans 11 climate zones, and therefore 150 cities in the country are also classified into 11 climate zones, and through a large amount of researches, the inventor finds that the change rules of city temperature curves in the same climate zone are very close to each other, and the environmental temperature of the area where the user is located can be indirectly obtained through the method.
In the foregoing intelligent control navigation method for cooling and heating of a distributed energy station, the step S2 of automatically predicting the ambient temperature of the area where the user is located by using the ambient temperature curve includes the following steps:
firstly, calling an environment temperature curve of a place according to the local longitude and latitude;
secondly, obtaining the ambient temperature at the current time point and the time point of heat supply or refrigeration required by the user according to the ambient temperature curve;
and thirdly, obtaining the ambient temperature of the current time point according to the real-time weather forecast, and correcting the ambient temperature of the time point required by the user for heating or refrigerating by using the difference value between the real-time ambient temperature data of the current time point and the ambient temperature data obtained according to the ambient temperature curve.
By the method, the prediction accuracy of the ambient temperature at the time point when the user requires heating or cooling is improved.
In the foregoing intelligent control navigation method for cooling and heating of a distributed energy station, step S2 further includes: correcting the ambient temperature at the time point of heating or cooling required by the user by using the meteorological correction coefficient; the weather correction coefficient comprises: specific heat ratio of the air to dry air under atmospheric saturation relative humidity, heat loss ratio of corresponding wind level and wind speed and rain and snow coefficient corresponding to rain and snow level.
Preferably, when the relative humidity of the atmosphere exceeds 75%, the temperature correction coefficient is 0% at 0 ℃ or less, 0 to 5 ℃ or more and 1.0% at 5 ℃ or more; when the relative humidity of the atmosphere is lower than 75%, the temperature correction coefficient is 0.
Preferably, the heat loss ratio corresponding to 1-12 grades of wind is respectively as follows: 0.0, 1.01, 1.03, 1.06, 1.08, 1.1, 1.14, 1.18, 1.22, 1.26, 1.3, using said heat loss to correct the ambient temperature at the point in time when the user requests heating or cooling.
Preferably, the rain coefficients corresponding to the first, second and third levels of rainfall are 1.02, 1.03 and 1.05 respectively; the snow coefficients corresponding to the first, second and third snow amounts are 1.03, 1.05 and 1.1 respectively. And correcting the ambient temperature at the time point of heating or cooling required by the user by using the rain coefficient or the snow coefficient.
The meteorological correction calculation data are basic data obtained through theoretical calculation, merging and rounding are carried out on the basis of the basic data, and gradual correction can be carried out according to specific conditions in application.
By the method, the prediction accuracy of the ambient temperature at the time point when the user requires heating or cooling can be further improved.
Preferably, step S3 further includes: and calculating a heat supply time lag constant (the actual time difference from the outlet of the boiler to a user of a medium at a certain temperature) of the unit according to the basic data of the energy station equipment, and regulating and controlling the energy station equipment in advance according to the heat supply time lag constant.
Specifically, the heat supply time lag constant Δ δ tol of the unit is obtained by calculation in the following way:
Δδtol＝δ1+δ2+δ3+δ4+δ5+δ6；
wherein:
wherein, G3: boiler water wall tube thickness, unit: rice; a1: the number of the coefficients 1,
a1 ═ 1/(0.4022+ 0.2297/G3); a2: coefficient 2, a2 ═ (1000-G4)/975, where G4: boiler water wall pipeline inner wall temperature (generally than boiler outlet temperature 50 ~ 100 degrees higher), unit: c, centigrade degree; LN: a logarithmic function.
Delta 2 is the time of primary circulating water from the outlet of the boiler to the inlet of the heat exchanger; δ 2 ═ L2/V2, where L2: primary circulating water pipe length, i.e. the pipe length from the boiler outlet to the plate heat exchanger inlet, unit: rice; v2: circulating water flow rate (the general flow rate is selected in the range of 1-3 m/s), unit: per meter per second;
By the method, the precision of the time point of controlling heating or cooling of the energy station can be improved, so that the temperature required by a user can be reached at the time point required by the user.
Step S3 of the invention further comprises regulating and controlling the energy station equipment according to the feedback of the return water temperature of the heat exchange station and the feedback of the 1% data of the user temperature in the process of heating or refrigerating. Thereby, the temperature control precision of the energy station equipment can be further improved.
Compared with the prior art, the invention has the following advantages:
1. the method comprises the steps of automatically acquiring heating and refrigerating target temperatures required by a user by utilizing an output temperature curve, and automatically predicting the ambient temperature of the area where the user is located by utilizing an ambient temperature curve; the intelligent control of cooling and heating can be automatically carried out on the energy station according to the difference value between the heating and cooling target temperature required by the user and the corresponding environment temperature, so that the external output energy can be accurately controlled, the control precision is improved, the energy loss is reduced, the operation automation is realized, the manual operation or the automatic operation can be automatically switched in the operation process, and finally the unattended operation of the distributed energy station can be realized;
2. by utilizing the technology of the invention, the output temperature curve model, the environment temperature curve model and the heat supply time lag constant model are packaged by a singlechip, a PLC or a DCS, and a meteorological module is used as an auxiliary to obtain local basic data of a user in a wired or wireless mode to manufacture the distributed energy intelligent navigator, which is convenient and portable;
3. the invention can be used for distributed energy source stations and heating and heat supply stations; the device can also be used for heating coal, gas and gas steam combined cycle power stations and other energy source stations for generating electricity and supplying heat by renewable energy sources;
4. the invention originally creates an astronomical calculation formula, once the latitude and the time of the earth are input, the formula is activated, the formula is suitable for 3017 years, and the applicability of the astronomical calculation formula ensures that the time error in 1000 years does not exceed 10 minutes. In addition, an output temperature curve is determined by combining an astronomical calculation formula with heat supply or refrigeration objects, local longitude and latitude basic data of a heat supply station and environmental standard basic data of each country; therefore, the annual temperature output curves can be automatically given according to different national standards by inputting the standards of different countries, and the schemes of refrigeration in summer and heating in winter are automatically judged and finished; by utilizing the output temperature curve, classified energy supply can be realized for buildings with different properties such as houses, hotels, hospitals, cinemas, shops, office buildings, experimental buildings, kindergartens, schools, libraries, auditoriums, canteens, gymnasiums and the like, so that the energy station can automatically operate under various working conditions all the year around, and the working area is suitable for all areas from the northern end to the desert river and from the southern end to the southern sand islands in China;
5. the invention also solves two problems, firstly, a heat supply time difference mathematical model (namely a heat supply time lag constant model) is established, secondly, a local meteorological mathematical model (namely an environmental temperature curve model) in the whole year is established, environmental temperature change in a plurality of hours in the future is predicted, and after the current temperature is acquired and corrected, the environmental temperature and the heat supply temperature difference delta t at the next moment are known, heat supply amount correction is carried out in advance, and the heat supply accuracy is greatly improved.
6. By utilizing the technology of the invention, the temperature control precision of the energy station is higher, wherein, when no feedback exists, the temperature control precision of the energy station is within the range of +/-0.5 ℃, and when feedback exists (namely, the feedback is carried out according to the return water temperature of the heat exchange station and the data feedback of 1% of the user temperature), the temperature control precision of the energy station is within the range of +/-0.3 ℃.
Drawings
FIG. 1 is a software model framework diagram;
FIG. 2 is a schematic diagram of an output temperature profile and an ambient temperature profile;
FIG. 3 is a schematic flow diagram of the method of the present invention;
FIG. 4 is a graph of the temperature difference of the heating and the temperature of the daily environment.
The invention is further described with reference to the following figures and detailed description.
Detailed Description
The embodiment of the invention comprises the following steps: an intelligent control navigation method for cooling and heating of a distributed energy station is shown in fig. 3, and comprises the following steps:
s1, determining an output temperature curve, and automatically acquiring the heating and refrigerating target temperature required by a user according to the output temperature curve;
s2, determining an environment temperature curve, and automatically predicting the environment temperature of the area where the user is located by using the environment temperature curve;
and S3, automatically and intelligently controlling cooling and heating of the energy station according to the difference between the heating and cooling target temperature required by the user and the corresponding ambient temperature.
The output temperature profile described in step S1 includes: a constant temperature mode, a heat supply mode, a refrigeration mode, a holiday energy supply mode, an office type mode (weekends and holidays all the year round), a public exhibition type mode curve and the like.
The output temperature profile may be determined by the following method or other methods: firstly, automatically judging the nature of any day in a year through an astronomical calculation formula, wherein the nature comprises working days, holidays, seasons and climate (the applicability of the method ensures that the time error in 1000 years does not exceed 10 minutes); secondly, determining an output temperature curve according to a heat supply or refrigeration object, local longitude and latitude basic data of a heat supply station and environmental standard basic data of each country;
wherein, the astronomical calculation formula is as follows:
in the formula, ω: sunrise and sunset time angles of the sun, namely the solar time angle when the sun is positioned on the horizon, wherein the solar time angle is related to time, and positive and negative values represent morning and evening; phi: the global latitude of the position of the heat supply station; n: on the nth day of the year, 1 month and 1 day, n is 1, and so on.
In step S2, the ambient temperature profile may be determined by the following method or other methods:
s21, collecting local meteorological data (such as available from three aspects, respectively purchasing from local meteorological departments; self-measuring collected data; meteorological data published on the network or the meteorological department);
s22, fitting the meteorological data to obtain a corresponding meteorological formula, namely obtaining an ambient temperature curve; (for example, the environmental temperature curve can be a 6 th power function curve, thereby ensuring that the temperature precision is controlled within the range of 0.02 degree)
And S23, putting the weather formula into a database by taking the longitude and latitude as a label.
Step S2 may further include:
and when the area where the user is located does not have the environmental temperature curve, predicting the environmental temperature of the area where the user is located by referring to the environmental temperature curves of other cities in the same climate zone.
The step of automatically predicting the ambient temperature of the area where the user is located by using the ambient temperature curve in step S2 may include the following steps:
firstly, calling an environment temperature curve of a place according to the local longitude and latitude;
secondly, obtaining the ambient temperature at the current time point and the time point of heat supply or refrigeration required by the user according to the ambient temperature curve;
and thirdly, obtaining the ambient temperature of the current time point according to the real-time weather forecast, and correcting the ambient temperature of the time point required by the user for heating or refrigerating by using the difference value between the real-time ambient temperature data of the current time point and the ambient temperature data obtained according to the ambient temperature curve.
Step S2 may further include: correcting the ambient temperature at the time point of heating or cooling required by the user by using the meteorological correction coefficient; the weather correction coefficient comprises: specific heat ratio of the air to dry air under atmospheric saturation relative humidity, heat loss ratio of corresponding wind level and wind speed, rain and snow coefficient corresponding to rain and snow level and the like; for example, when the relative humidity of the atmosphere exceeds 75%, the temperature correction coefficient at 0 ℃ or lower is 0%, the temperature correction coefficient at 0 to 5 ℃ is 0.5%, and the temperature correction coefficient at 5 ℃ or higher is 1.0%; when the relative humidity of the atmosphere is lower than 75%, the temperature correction coefficient is 0; the heat loss ratio corresponding to 1-12 grades of wind is respectively as follows: 0.0, 1.01, 1.03, 1.06, 1.08, 1.1, 1.14, 1.18, 1.22, 1.26 and 1.3, and correcting the ambient temperature at the time point of heating or cooling required by the user by using the heat loss; rain coefficients corresponding to the first, second and third levels of rainfall are 1.02, 1.03 and 1.05 respectively; the snow coefficients corresponding to the first, second and third snow amounts are 1.03, 1.05 and 1.1 respectively. And correcting the ambient temperature at the time point of heating or cooling required by the user by using the rain coefficient or the snow coefficient.
Step S3 may further include: and calculating a heat supply time lag constant of the unit according to the basic data of the energy station equipment, and regulating and controlling the energy station equipment in advance according to the heat supply time lag constant.
The heat supply time lag constant delta tol of the unit can be obtained by calculation in the following mode:
Δδtol＝δ1+δ2+δ3+δ4+δ5+δ6；
wherein:
wherein, G3: boiler water wall tube thickness, unit: rice; a1: the number of the coefficients 1,
a1 ═ 1/(0.4022+ 0.2297/G3); a2: coefficient 2, a2 ═ (1000-G4)/975, where G4: boiler water wall pipeline inner wall temperature (generally than boiler outlet temperature 50 ~ 100 degrees higher), unit: c, centigrade degree; LN: a logarithmic function.
Step S3 may also include regulating and controlling the energy station equipment according to the feedback of the return water temperature of the heat exchange station and the feedback of the 1% data of the user temperature during the heating or cooling process.
Experimental example:
generally, the heat output by the distributed energy station is equal to the heat demand of the user, and the expression of the heat output of the distributed energy station is as follows:
Q1＝q1*m1*(t2-t1)
wherein q1 is the specific heat capacity of the medium, and the unit is kJ/kg; m1 is energy station medium flow (hot water or steam) in kg; t2 and t1 are output medium outlet/inlet temperatures, respectively, in degrees Celsius.
The heat-up expression of the heat consumer is as follows:
wherein q2 is the heat capacity required by the unit area of the heat consumer, and the unit is kJ/m2(ii) a m2 is the heat exchange area of the heat consumer, unit m2(ii) a t2 'and t 1' are the heating temperature of the hot user, the ambient temperature, respectively, in units of ℃.
When Q1 is Q2, the heat supply is balanced, so the boiler knows that the amount of heat supplied is proportional to the difference between the used heat temperature and the ambient temperature. See in detail the heating temperature difference versus the daily ambient temperature curve of fig. 4.
The output temperature curve is determined by an astronomical formula and a sampling standard together and can be various curves such as a constant temperature mode, a heat supply mode, a refrigeration mode, a holiday energy supply mode and the like; the environmental temperature curve is simulated according to local meteorological data, and delta 1 and delta 2 are the current temperature difference and the temperature difference after delta tol time interval respectively. Delta 2 needs to be obtained through calculation, the temperature at the target time is predicted according to the time difference, heat supply is carried out in advance, and when the heat supply amount reaches a user, the heat supply amount is exactly the required heat supply amount.
Fig. 1 shows a platform model framework, and basic data of an output temperature curve model is provided by a group a and a group D, and is input once during installation, wherein the group a is composed of longitude, latitude, time and other data of a place where a distributed energy station belongs, and the group D is composed of heat supply and environmental standard basic data; the 'boiler hysteresis constant model' provides basic data by F group data (consisting of data of a distributed energy station and a heating and refrigerating system); the 'environment temperature curve model' is provided by the B group data, the continuous basic data including the instantaneous atmospheric temperature, humidity and wind speed of the local place are provided by the local weather station, and the C group data are directly taken by the weather model according to the multi-year measurement data provided by the local weather station. The group E data provides more accurate adjustment through the feedback temperature of the user, when the control precision is enough, the group E data can not participate in the adjustment, when a local operation fault occurs and the local heat supply exits, the group E data automatically participates in the adjustment, the operation of the whole heat supply system is stabilized in advance, and the local fault is guaranteed not to influence the whole operation.
As shown in fig. 2, the present invention needs to obtain two output curves (an output temperature curve and an ambient temperature curve) and a boiler time constant (a heating time lag constant of the energy station), and adjusts and controls the energy station to supply heat and cool according to a difference between the output temperature curve and the ambient temperature curve.
Specifically, the method comprises the following steps:
first, output temperature curve
The output temperature profile comprises: constant temperature mode, heating mode, refrigeration mode, festival holiday energy supply mode curve, etc.
The output temperature curve is comprehensively determined according to conditions such as astronomical calculation formulas, heating or refrigerating objects, local longitude and latitude basic data of heating stations, national environmental standard basic data and the like, and different annual temperature output curves (including winter heating and summer refrigerating) are given.
TABLE 1A group input data (one time input)
The method specifically comprises the following steps:
class I: constant temperature mode (no weekend, no holiday all the year round); including apartments, hotels, residential houses, commercial houses, villas, hospitals, shopping malls, cinemas, computing centers, restaurants, cold stores, etc.;
and II: office type mode (weekend, holiday and holiday all year round); including offices, office buildings, laboratory buildings, banks, kindergartens, schools, etc.;
class III: public exhibition type mode (no weekend, no holiday all year round, but one day off each week): museums, libraries, cultural halls, sports halls, auditoriums, and the like.
A control curve operating mode may be determined based on the input data, calculating a value T1.
Specifically, a class i constant temperature mode:
t1 ═ T1(τ) (heating)
T1 ═ T2(τ) (refrigeration)
T1: the target temperature of the user (DEG C, the target temperature of the user is kept unchanged); t1(τ): according to the temperature input by the standard, such as t1 ═ 18 ℃ +/-2 ℃ in Beijing; tau is time, time at any point in the day;
the invention can automatically judge the heating or cooling mode according to the environment temperature.
And II: office type mode:
t1 ═ T1(τ) (heating)
T1 ═ T2(τ) (refrigeration)
T1 ═ T3(τ) (weekend)
T1 ═ T4(τ) (weekend)
T1 ═ T5(τ) (holidays)
T1 ═ T6(τ) (holidays)
τ 1 (working hours per day)
τ 2 (off-duty time per day).
Class III: public exhibition mode:
t1 ═ T1(τ) (heating)
T1 ═ T2(τ) (refrigeration)
T1 ═ T3(τ) (holidays)
T1 ═ T4(τ) (holidays)
τ 1 (working hours per day)
τ 2 (off-duty time per day).
Wherein, for heating and cooling mode:
day (from 10 months to 3 months the following year): t1 ═ T1(τ)
Night (from 10 months to 3 months the following year): t1 ═ T2(τ)
Day (from 4 months to 9 months of the next year): t1 ═ T3(τ)
Night (from 4 months to 9 months of the next year): t1 ═ T4(τ)
For heating and cooling modes in holidays:
day (from 10 months to 3 months the following year): t1 ═ T1(τ)
Night (from 10 months to 3 months the following year): t1 ═ T2(τ)
Day (from 4 months to 9 months of the next year): t1 ═ T3(τ)
Night (from 4 months to 9 months of the next year): t1 ═ T4(τ)
Day (friday, six and holidays): t1 ═ T5(τ)
Night (friday, sixty and holidays): t1 ═ T6(τ)
Specifically, a user heating output temperature curve is determined according to an astronomical calculation formula, local longitude and latitude basic data of a heating station and environmental standard basic data of each country; so that automatic heating or cooling is performed under the inputted standard;
wherein, the astronomical calculation formula is as follows:
in the formula, ω: sunrise and sunset time angles of the sun, namely the solar time angle when the sun is positioned on the horizon, wherein the solar time angle is related to time, and positive and negative values represent morning and evening; phi: the global latitude of the position of the heat supply station; n: on the nth day of the year, 1 month and 1 day, n is 1, and so on.
As shown in the following table 2, the data of the 4 curves in fig. 2 are summarized, and the data sources are according to the national heating standards, including the regulations on urban heat supply management, the standards for indoor air quality GB/T18883-2002, the standards for energy saving design of residential buildings in hot summer and cold winter regions, the standards for energy saving design of public buildings, GB 50189-2005, the methods for measuring air temperature in public places, and the like. Each country has different standards and different requirements for different heat consumption units and objects. The heating of urban residents in winter is basically 18 +/-2 ℃, the precision of the invention is two grades, namely +/-1 ℃ and +/-0.5 ℃ without feedback, and +/-0.5 ℃ and +/-0.3 ℃ with feedback.
TABLE 2 different heating user types heating thermometer (D group environmental standard basic data)
The above table is to be perfected. Wherein, the temperature conversion point is determined by the judgment of a solar astronomical formula; the residential category in the table refers to two types of winter heating, annual heating and refrigerating, and the heating and refrigerating needs to be kept constant at any time; the heating and heating mode of hotels is the same as that of houses, but the heating and cooling temperature standards are different; office buildings are determined by the working modes, such as regular work at ordinary times, weekend rest, and different heating and cooling standards at weekend and ordinary times; other classes may be determined on a case by case basis.
Second, ambient temperature curve
According to the measured meteorological data, the heating and cooling environmental curve is determined, and the value T2 is calculated.
Sources of ambient temperature data can vary, but there are basically two categories:
the first type: basic data (group C basic meteorological data) which can be set in a database in advance according to local meteorological data obtained by 20-year statistics and can be used repeatedly every year according to time, the data is based on the meteorological data of the big cities in various regions, because the nearby meteorological trends are basically the same, and in order to simplify the calculation, the invention adopts a calculation formula deduced through the data.
The first type of meteorological data comprises temperature, relative humidity, rain, snow, wind power and the like, the key of the meteorological data is temperature data, for example, a monthly temperature trend graph in a Chinese weather forecast network indicates the highest temperature and the lowest temperature of each day, the temperature of one day can be simulated according to the meteorological law, and the most recent data are used for daily simulation. If the weather forecast data table is as follows: (12 months and 23 days 2016):
TABLE 3
The first category of data acquisition is essentially three, including procurement from local weather departments; self-measuring and collecting data; meteorological data published on the internet or by meteorological departments.
The second type: according to the data (B group measured meteorological data) obtained by weather forecast, the second type data is used for verifying and modifying the first type data, according to the weather forecast information, the data forecast is generally carried out for 7 days, the closer meteorological data is more accurate, the prediction in the range of 2 hours ahead is basically carried out in the actual software analysis, the data at the earlier time is not involved in the calculation, and the current temperature is measured by adopting a meteorological instrument in the experimental example.
For example, the equation for deriving the annual meteorological temperature in Beijing area (warm temperate zone) is shown in the following table 4:
TABLE 4
In general, meteorological data measurement is carried out by reporting data every 5 minutes, the latest data can be read, and if the dead pixel is to be removed, the data calculation in the last second is still adopted until new data which is qualified in new judgment is read. And (3) calculating the temperature at the time and after prediction (time point calculated according to the time lag constant) by using a calculation formula, and correcting according to the measured new data to obtain the temperature value of the predicted time point.
T2＝y1A(τ)
For another example, according to the monitoring data of the national weather station lasting 57 years, the fitted monthly temperature curve of vinpocetine city is shown in the following table 5:
TABLE 5
After the temperature change curve is simulated, the data can be input into a database, and the data can be called by inputting the local longitude and latitude.
The northern hemisphere is divided into 14 climate zones, China spans 11 climate zones, so 150 cities in the country are also classified into 11 climate zones, through data analysis, the city temperature curve change rules in the same climate zone are very close, and in principle, which city the energy station is located in is determined by adopting local weather fitting data. The urban climate zones in china are shown in table 6.
TABLE 6
In the intelligent control model, the most important meteorological data is the temperature parameter, but the air humidity, the wind speed and the rain and snow have certain influence on the heat supply, so the coefficient correction is carried out on the three meteorological parameters of the air humidity, the wind speed and the rain and snow.
Weather correction factor
The meteorological correction quantity comprises the contents of humidity, air pressure, wind speed, rain, snow, dew point and the like, wherein the humidity, the air pressure and the wind speed are determined by a formula; rain, snow, dew point, etc. are determined by the determined coefficients. Considering the influence of the data on heat supply, wherein the influence is the largest in humidity, wind speed, rain and snow, the compensation only considers the three contents of humidity, wind speed and rain and snow, and the others do not consider any more.
For humidity correction:
the relative humidity is the percentage (%) of the water vapor density contained in the air and the saturated water vapor density corresponding to the temperature, the larger the humidity in the atmosphere is, that is, the more the water vapor in a unit volume is, the water vapor needs to be heated by partial heat, and when the air is heated by the same heat, the original temperature point cannot be reached, so that more heat is needed, and thus more heat needs to be provided. Air humidity greatly affects heating. But atmospheric humidity has only a large effect on refrigeration, whereas heating has a smaller effect. See table 7 below for details:
TABLE 7 ratio of specific heat to dry air at atmospheric saturation relative humidity
| Temperature of | -20 | -10 | 0 | 10 | 20 | 30 | 40 | 50 |
| Specific heat ratio k | 0.72％ | 1.19％ | 2.48％ | 4.78％ | 8.20％ | 15.48％ |
As can be seen from table 7 above, at a temperature of 0 ℃, the amount of heat to be absorbed by the atmospheric saturated water vapor is only 0.72% of the air of the same volume, and at a temperature of 10 ℃, the amount of heat is 1.19%, therefore, when the atmospheric relative humidity exceeds 75%, the temperature correction coefficient is 0% at 0 ℃ or lower, 0.5% at 0 to 5 ℃ or lower, and 1.0% at 5 ℃ or higher; when the relative humidity of the atmosphere is lower than 75%, the temperature correction coefficient is 0%.
The correction method is that Δ t is t1-t2, and the correction is calculated as t '═ 1+ k) × Δ t, (t1 heating set value, outdoor ambient temperature at t2, t' is a temperature difference value, k correction value).
And correcting the wind speed:
the wind speed has a large influence on the heat supply, and the heat supply in the day should be corrected by the average wind speed of the weather forecast in the day.
The wind speed calculation formula is as follows:
af: overall heat-release coefficient, W/(m2.K)
w: wind speed, m/s
The relationship between wind level and wind speed is shown in Table 8 below:
TABLE 8 wind-class and wind-speed relationship table
The heat loss ratio is the heat supply compensation ratio.
The general weather forecast is expressed in wind level, but now more in wind speed.
For rain, snow, dew point correction:
the rain and snow dew point is used as a coefficient for comprehensive analysis, when raining, the rain and snow dew point cannot be snowed, and otherwise, the rain and snow dew point cannot be snowed; rain and snow are classified into three grades, namely light rain, medium rain and heavy rain, and when the rain is above the medium rain, the relative saturation point is considered to be 100 percent, namely the dew point temperature is reached. And determining the rain and snow coefficient by using the three gears, and substituting the coefficient into the formula to compensate the heat supply coefficient.
TABLE 9 rain and snow grade and value-taking table
The rainfall coefficient value is based on the above table, and the value grade and time are based on weather forecast data. The value of the snow quantity coefficient is based on the table, the value grade and the time are based on weather forecast data, and the values are not taken at the same time.
Third, time lag constant for heating and cooling
The time from the instantaneous outlet temperature of the heating boiler to a user is called a heating time lag constant and is expressed by delta tol, and the specific calculation content comprises the following steps: 1) the time from the beginning of combustion at the boiler regulation point to the temperature change at the boiler outlet; 2) the time of the primary circulating water from the outlet of the boiler to the inlet of the heat exchanger; 3) heat exchange time of the heat exchanger; 4) the time from the heat exchanger to the user is the secondary circulating water; 5) the heat exchange time of a user; 6) the temperature change time in the user's room.
Table 10F group heating plant equipment basic data (one-time input)
The total time difference is the sum of 6 heat exchange intervals in seconds.
Specifically, the method comprises the following steps:
δ 1: time lag of combustion
δ 1: time lag of combustion (unit: second)
G3: boiler water wall tube thickness (unit: m);
g4: the temperature of the inner wall of the boiler water wall pipeline is generally 50-100 ℃ higher than the temperature of water at the outlet of the boiler (unit: centigrade);
a1 ═ 1/(0.4022+0.2297/G3) a 1: coefficient of 1
A2 ═ (1000-G4)/975 a 2: coefficient 2
δ1＝-1.8*10^5*G3^2/A1*LN(A2/((1.01+0.26*(1-EXP(-1.7*G3)))*COS(A1^0.5)))
Considering that the wall thickness of the boiler water wall pipeline is 6mm and considering the thermal resistance increased by the pollution on the surface of the pipeline, the temperature of the inner wall of the pipeline is 240 ℃, and the time lag is about 149 seconds.
δ 2: the interval time of the primary circulating water from the outlet of the boiler to the heat exchanger
δ 2: interval of one heat exchange (unit: second)
L2: the length of a primary circulating water pipeline is from the outlet of the boiler to the inlet pipe of the plate heat exchanger (unit: meter);
v2: the flow rate of the circulating water is generally selected to be in the range of 1-3 m/s (unit: per m/s);
δ2＝L2/V2
when the length of the pipeline is 1000 meters and the flow rate of the circulating water is 1.5 meters/second, the delta 2 interval time is 667 seconds.
δ 3: heat exchange interval time of heat exchanger
δ 3: heat exchanger heat exchange interval (unit: second)
The heat exchange interval of the heat exchanger is in direct proportion to the heat exchange temperature difference, and the heat exchange time difference is generally in the range of 10-80 seconds, so that the heat exchange interval can be judged through the temperature difference.
TABLE 11
| Temperature difference of Heat exchange (. degree. C.) | 5～20 | 20～35 | 35～50 | 50～65 | 65～80 | 80～95 |
| Heat transfer time (seconds) | 8 | 16 | 24 | 32 | 40 | 48 |
δ 4: the interval time between the outlet of the heat exchanger and the hot user of the secondary circulating water
δ 4: interval of secondary heat exchange (unit: second)
L4: the length of the secondary circulating water pipeline is the length (unit: meter) from the inlet of the heat exchanger to the inlet of the heat user, if the area of the heat user is wide, the average value of the longest distance and the shortest distance can be selected, and the length of the middle layer is selected on the floor;
v4: the flow rate of the circulating water is generally selected to be in the range of 1-3 m/s (unit: per m/s);
δ4＝L4/V4
when the length of the pipeline is 1200 m and the flow rate of the circulating water is 1.5 m/s, the delta 4 interval time is 800 s.
δ 5: heat exchange interval time of heat user heat exchanger
δ 5: heat exchange interval of user indoor heat exchanger (unit: second)
δ5＝L5/V5
L5: the length of the heat exchange pipeline of the user unit is generally the average value (unit: meter) of local users; v5: the medium flow velocity in the indoor pipeline of the user is unit meter/second.
For example, when the length of the user heat exchanger pipe is 104 meters and the flow speed is 1.5 meters/second, the heat exchange time delta 5 is 156 seconds in total, and the empirical value is 100-300 seconds.
δ 6: interval time of temperature change in hot user room
After the temperature of the indoor heat exchanger changes, the temperature of the center of the house at a position 1.5 meters away from the ground is measured according to the standard, the temperature changes after 90 seconds according to the calculation of convection heat transfer, and the temperature reaches balance within 500 seconds, so that the delta 6 can be selected to be 300 seconds as a calculation reference value.
Δ δ tol: all heat exchange intervals are as follows:
the total time difference Δ δ tol is δ 1+ δ 2+ δ 3+ δ 4+ δ 5+ δ 6
Δ δ tol ═ 149+667+24+800+156+300 ═ 2096 seconds (34.9 minutes)
The one-time actual input data is shown in the following table:
table 12 refrigeration time lag constant input data summary table
Note: the calculated data provided above is used for software checking.
Controlling the final data:
TΔ＝Tnow-Tenvi
Tfinal(τ)＝T1(τ+Δδtol)-T2(τ+Δδtol)+TΔ；
the values in the formula have the following meanings:
t Delta: simulating the difference between the calculated current temperature and the actually measured temperature (note positive and negative values);
tnow, the current temperature is simulated by an environment temperature curve;
tenvi: a temperature curve measured by a meteorological measuring instrument (the current temperature measurement adopts two out of three);
Tfinal(τ): controlling the final value to be taken;
t1(τ + Δ δ tol): the heating and refrigerating temperature values after the delta tol time is prolonged;
t2(τ + Δ δ tol): calculating an environment temperature value after prolonging the delta tol time;
unit: t temperature; τ and δ are times
In the invention, the time interval between the boiler load signal and the control signal is millisecond grade, and the recording point is 1 minute/time; the meteorological signal time interval is in the order of seconds, and the recording point is 5 minutes/time.
Claims (10)
1. The intelligent control navigation method for cooling and heating of the distributed energy station is characterized by comprising the following steps of:
s1, determining an output temperature curve, and automatically acquiring the heating and refrigerating target temperature required by a user according to the output temperature curve; wherein, the step of determining the output temperature curve comprises the following steps; firstly, automatically judging the nature of any day in the year by an astronomical calculation formula, wherein the nature comprises working days, holidays, seasons and climates; secondly, determining an output temperature curve according to a heat supply or refrigeration object, local longitude and latitude basic data of a heat supply station and environmental standard basic data of each country;
wherein, the astronomical calculation formula is as follows:
in the formula, ω: sunrise and sunset hour angles, namely the solar hour angle when the sun is positioned on the horizon, wherein the solar hour angle is related to time, and positive and negative values represent early morning and late evening; phi: the global latitude of the position of the heat supply station; n: on the nth day of the year, 1 month and 1 day, n is 1, and so on;
s2, determining an environment temperature curve, and automatically predicting the environment temperature of the area where the user is located by using the environment temperature curve;
and S3, automatically and intelligently controlling cooling and heating of the energy station according to the difference between the heating and cooling target temperature required by the user and the corresponding ambient temperature.
2. The distributed energy station cooling and heating intelligent control navigation method according to claim 1, wherein the output temperature curve in step S1 comprises: constant temperature mode, heat supply mode, refrigeration mode, holiday energy supply mode, office mode, public exhibition mode curve.
3. The intelligent navigation method for controlling cooling and heating of a distributed energy station according to claim 1, wherein in step S2, the determining the ambient temperature curve comprises the following steps:
s21, collecting local meteorological data;
s22, fitting the meteorological data to obtain a corresponding meteorological formula, namely obtaining an ambient temperature curve;
and S23, putting the weather formula into a database by taking the longitude and latitude as a label.
4. The distributed energy station cooling and heating intelligent control navigation method according to claim 1, wherein the step S2 further comprises:
and when the area where the user is located does not have the environmental temperature curve, predicting the environmental temperature of the area where the user is located by referring to the environmental temperature curves of other cities in the same climate zone.
5. The intelligent control navigation method for cooling and heating of a distributed energy station according to claim 1, 3 or 4, wherein the step S2 of automatically predicting the ambient temperature of the area where the user is located by using the ambient temperature curve comprises the following steps:
firstly, calling an environment temperature curve of a place according to the local longitude and latitude;
secondly, obtaining the ambient temperature at the current time point and the time point of heat supply or refrigeration required by the user according to the ambient temperature curve;
and thirdly, obtaining the ambient temperature of the current time point according to the real-time weather forecast, and correcting the ambient temperature of the time point required by the user for heating or refrigerating by using the difference value between the real-time ambient temperature data of the current time point and the ambient temperature data obtained according to the ambient temperature curve.
6. The distributed energy station cooling and heating intelligent control navigation method according to claim 1, wherein the step S2 further comprises: correcting the ambient temperature at the time point of heating or cooling required by the user by using the meteorological correction coefficient; the weather correction coefficient comprises: specific heat ratio of the air to dry air under atmospheric saturation relative humidity, heat loss ratio of corresponding wind level and wind speed and rain and snow coefficient corresponding to rain and snow level.
7. The intelligent control navigation method for cooling and heating of the distributed energy station according to claim 6, wherein when the relative humidity of the atmosphere exceeds 75%, the temperature correction coefficient below 0 ℃ is 0%, the temperature correction coefficient between 0 and 5 ℃ is 0.5%, and the temperature correction coefficient above 5 ℃ is 1.0%; when the relative humidity of the atmosphere is lower than 75%, the temperature correction coefficient is 0; the heat loss ratio corresponding to 1-12 grades of wind is respectively as follows: 0.0, 1.01, 1.03, 1.06, 1.08, 1.1, 1.14, 1.18, 1.22, 1.26 and 1.3, and correcting the ambient temperature at the time point of heating or cooling required by the user by using the heat loss; rain coefficients corresponding to the first, second and third levels of rainfall are 1.02, 1.03 and 1.05 respectively; the snow coefficients corresponding to the first, second and third snow amounts are 1.03, 1.05 and 1.1 respectively, and the environmental temperature at the time point of heating or cooling required by the user is corrected by utilizing the rain coefficient or the snow coefficient.
8. The distributed energy station cooling and heating intelligent control navigation method according to claim 1, wherein the step S3 further comprises: and calculating a heat supply time lag constant of the unit according to the basic data of the energy station equipment, and regulating and controlling the energy station equipment in advance according to the heat supply time lag constant.
9. The intelligent control navigation method for cooling and heating of the distributed energy station according to claim 8, wherein the heating time lag constant Δ δ tol of the unit is calculated by the following method:
Δδtol＝δ1+δ2+δ3+δ4+δ5+δ6；
wherein: delta 1 is the time from the beginning of combustion at the regulating point of the boiler to the temperature change at the outlet of the boiler;
wherein, G3: boiler water wall tube thickness, unit: rice; a1: the number of the coefficients 1,
a1 ═ 1/(0.4022+ 0.2297/G3); a2: coefficient 2, a2 ═ (1000-G4)/975, where G4: inner wall temperature of boiler water wall pipeline, unit: c, centigrade degree; LN: a logarithmic function;
delta 2 is the time of primary circulating water from the outlet of the boiler to the inlet of the heat exchanger; δ 2 ═ L2/V2, where L2: primary circulating water pipe length, i.e. the pipe length from the boiler outlet to the plate heat exchanger inlet, unit: rice; v2: circulating water flow rate, unit: per meter per second;
δ 3 is the heat exchange time of the heat exchanger, unit: second;
delta 4 is the time from the heat exchanger to the user for the secondary circulating water; δ 4 — L4/V4; in the formula, L4: the length of the secondary circulating water pipeline, i.e. the length of the pipeline from the heat exchanger inlet to the hot user inlet, unit: rice; v4: circulating water flow rate, unit: per meter per second;
δ 5 is the heat exchange time of the indoor heat exchanger of the user, unit: second;
δ 6 is the temperature change time in the user's room.
10. The intelligent control navigation method for cooling and heating of the distributed energy source station according to claim 1, wherein the step S3 further comprises the step of regulating and controlling energy source station equipment according to the feedback of the return water temperature of the heat exchange station and the feedback of the 1% data of the user temperature in the heating or cooling process.
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