CN109543235B - Distributed electric heating load modeling system constructed based on parameter library and modeling method thereof - Google Patents
Distributed electric heating load modeling system constructed based on parameter library and modeling method thereof Download PDFInfo
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Abstract
The invention relates to a distributed electric heating load modeling system constructed based on a parameter library and a modeling method thereof, which are characterized in that: the method comprises the steps of obtaining running state time sequence data of the electric heating system in a past period of time by constructing an electric heating load modeling system, fitting initial parameters of a second-order model of electric heating load simplification through a cloud computing system based on actually-measured time sequence data and historical data, and further correcting the initial parameters according to parameters of a historical temperature database to obtain the electric heating load simplification second-order model. The basic principle of electric heating load modeling for identifying a second-order differential equation by utilizing measured parameters is determined, and a modeling method and a system architecture which can be realized by a computer are provided. The invention can provide an accurate model about the electric heating load, obtain an approximate actual simulation result and improve the accuracy of the electric heating load model.
Description
Technical Field
The invention belongs to electric heating, and relates to a distributed electric heating load modeling system constructed based on a parameter library and a modeling method thereof.
Background
With the continuous promotion of clean heating, the proportion of electric heating in the power grid in northern areas is increased year by year. Taking the Changchun city as an example, the accumulated installed capacity of the electric heating in 2017 is 290MW, the heating area is 317 ten thousand square meters, and the total heating amount of the whole city is 3.08%, and the rapid development situation is presented. The distributed electric heating occupies an important proportion in electric heating load. The electric heating load has thermal inertia, can gather the adjusting resources of the electric heating load on the premise of not influencing the comfort of users, and has important value for improving the safe and economic operation of a high-proportion renewable energy power system.
An electric heating load model is constructed, and the basis that the electric heating load meets the economic and flexible operation of an electric power system is provided. However, in practical engineering application, the electric heating load model mostly directly utilizes the first-order equivalent thermodynamic model of the air conditioning load, the air conditioning load is intensively used in the southern region to meet the requirement of refrigeration in summer, the indoor and outdoor environment, the comfort requirement range and the physical operation characteristics of the electric heating load model are greatly different from those of the electric heating load in the northern region, the simple first-order model is difficult to accurately reflect the operation characteristics of the electric heating load, the model error is extremely large, and the accuracy is difficult to meet the requirement of the electric heating load in practical engineering. Meanwhile, the existing model also lacks the identification and analysis of historical data of the electric heating system, and the model parameters lack the accuracy.
Therefore, the patent provides a more accurate distributed electric heating load simplified second-order time sequence model constructed based on a parameter library and a modeling method thereof.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the defects of the prior art are overcome, the accurate distributed electric heating load modeling system constructed based on the parameter library and the modeling method thereof are provided, the simulation result close to the actual result can be obtained, and the accuracy of the electric heating load model is improved.
One of the schemes adopted by the invention for solving the technical problems is as follows: the distributed electric heating load modeling system is characterized by further comprising a data acquisition unit, wherein the data acquisition unit is arranged in a heating room and is in wireless signal connection with the cloud computing service unit and used for transmitting acquired voltage, current and temperature data to the cloud computing service unit, and the comprehensive scheduling control unit is in signal connection with the data acquisition unit and the cloud computing service unit respectively and is used for receiving a computing result output by the cloud computing service unit, outputting a control signal according to the computing result and controlling a temperature controller of the data acquisition unit.
The structure of the data acquisition unit is as follows: it includes centralized controller, wireless voltmeter, temperature controller, control signal encoder and control signal transmitter, the centralized controller equally divide do not with wireless voltmeter temperature controller, cloud computing service unit's data calculate the part with control signal transmitter wireless signal connects, the control signal encoder equally divide do not with comprehensive dispatch control system and control signal transmitter signal connection.
The second scheme adopted by the invention for solving the technical problems is as follows: a distributed electric heating load modeling method based on parameter library construction is characterized in that: the specific contents are as follows:
1) Establishing an electric heating load simplified second-order time sequence model:
wherein, t time; t is in (t) is the indoor ambient temperature at time t; t is out (t) is the outdoor temperature at time t; p (t) is electric heating power at the moment t; c 1 Equivalent air heat capacity; c 2 Parameter wall equivalent heat capacity; r parameter wall equivalent thermal resistance; t is 0 The indoor temperature at the starting time; k, d, g model scaling coefficients;
2) Measuring and collecting the operation data of the electric heating system in the Sc period;
(1) measuring and storing the indoor temperature T of 1,2, …, s … and Sc period by taking Delta T as a sampling period in-m Outdoor temperature T out-m Power P m Time series data of (1), data of each periodThe total number of groups or total time step is l 1 ,l 2 ,…,l s …,l Sc Wherein Sc represents the current period;
the time period represents the time period of the electric heating switch state, and the switch is continuously turned on or off for one time period;
(2) updating the indoor temperature T with the period of delta T in Outdoor temperature T out A historical database of time series data of power P, the data amount of each group totaling
(3) Calculating the equivalent thermal resistance R of the wall body
Calculating the parameter wall equivalent thermal resistance R according to the formula (2) as follows:
in the formula: is T in-ave (s) averaging the indoor ambient temperature over an s-th time period; t is out-ave (s) average outdoor temperature over time period s; io(s) is the state of the electric heating switch in the s-th period (0 represents closed, and 1 represents open); p (s, t) is the electric heating power at the s time interval t; n is the total sampling time interval number;
3) Fitting model parameters;
(1) using measured room temperature T during period 1 in Outdoor temperature T out Power P time series data fitting C 1 (1)，
C 2 (1) D (1), g (1), the fitting equation is as follows:
an objective function:
in the formula: l is the number of measured temperature data;
constraint conditions are as follows:
in the formula, C 1 (1)，C 2 (1) Respectively a fitting equivalent air heat capacity and a parameter wall equivalent heat capacity in a 1 st time period, and d (1) and g (1) are respectively fitting model proportionality coefficients in the 1 st time period;
(2) using measured indoor temperature T in the Sc-th period in Outdoor temperature T out Equivalent air heat capacity C obtained by time sequence data and history fitting of power P 1 Parameter wall equivalent heat capacity C 2 Model proportionality coefficients d and g, fitting model proportionality coefficient k, the fitting formula is:
an objective function:
in the formula: l is the number of measured temperature data;
constraint conditions are as follows:
in the formula, C 1 (1)，C 2 (1) Respectively a fitting equivalent air heat capacity and a parameter wall equivalent heat capacity in a 1 st time period, and d (1) and g (1) are respectively a fitting model proportional coefficient in the 1 st time period;
(3) obtaining the initial equivalent air heat capacity C of the Sc +1 time period from the step 3) (2) 1 Parameter wall equivalent heat capacity C 2 Model proportionality coefficients d, g and k;
4) Updating a model parameter library;
(1) determining the position of a database to be updated according to the outdoor temperature average Tout-average (S) in the S-th time period;
the position of the database is an acquired temperature interval of electric heating, the temperature interval is represented by a letter I, a serial number is represented by I, and I = I;
(2) and updating the model proportionality coefficient k of the model parameter library of the interval I, wherein the formula is as follows:
in the formula, k (Sc-1), k (Sc) respectively represent the model proportionality coefficient k which is not updated in the current Sc-th period and the updated model proportionality coefficient k. α, β represent update weight coefficients, respectively;
(3) obtaining a model scale coefficient of the model parameter library updated in the Sc period from the step 4) (2);
5) Obtaining model parameters of a Sc +1 time period;
searching a model proportionality coefficient parameter k of the model parameter library in the future Sc +1 th period by using the updated model proportionality coefficient of the model parameter library obtained in the step 4) according to the average outdoor temperature in the future Sc +1 th period;
6) And obtaining the electric heating load simplified second-order time sequence model in the Sc +1 time period, which is shown in a formula (1).
The working process of the invention is as follows: the method comprises the steps that a temperature controller and a wireless current voltmeter transmit measured temperature, voltage and current data to a centralized controller in a heating room through a zig-zag-bee wireless transmission technology, the centralized controller is in signal connection with a cloud computing service unit through an Ethernet to transmit the data, the cloud computing service unit stores the data, then a distributed electric heating load simplified second-order time sequence model constructed based on a parameter base is constructed according to the modeling method, the constructed distributed electric heating load simplified second-order time sequence model constructed based on the parameter base is used for computing received measured data, then a computing result is transmitted to a comprehensive scheduling control unit, a control command is sent out through a comprehensive scheduling control system, a control signal encoder encodes the data, a control signal is sent to the centralized controller through a control signal transmitter, and the centralized controller is used for controlling the on and off of electric heating.
The beneficial effects of the invention are: the target object of the constructed model and the modeling method thereof is winter heating in northern areas, a distributed electric heating load model can be constructed aiming at indoor and outdoor environments, comfort degree demand range and physical operation characteristics in winter in northern large areas, the economic and flexible operation requirements of electric heating loads of the northern areas for adapting to a power system are met, the defects that the operation characteristics of the electric heating loads are difficult to accurately reflect, the model error is extremely large, the accuracy is difficult to meet the requirements of the electric heating loads in actual engineering by directly utilizing an air-conditioning load first-order equivalent thermodynamic model are overcome, the problems that the existing model is lack of identification and analysis of historical data of the electric heating system, and the model parameters lack of accuracy are solved, and the modeling method has the advantages of accuracy and approximate actual simulation result.
Drawings
FIG. 1 is a flow chart of distributed electric heating load modeling constructed based on a parameter library according to the present invention;
FIG. 2 is a schematic structural diagram of a modeling system of the present invention.
In the figure: the system comprises a cloud computing service unit 1, a centralized controller 2, an electric heating system 3, a wireless current voltmeter 4, a temperature controller 5, a control signal transmitter 6, a control signal encoder 7 and a comprehensive scheduling control unit 8.
Detailed Description
The present invention will be further described with reference to the following examples.
Referring to fig. 1-2, in the embodiment, the distributed electric heating load modeling system constructed based on the parameter library and the modeling method thereof in the embodiment include a cloud computing service unit 1, a comprehensive scheduling control unit 8, and a data acquisition unit, wherein the data acquisition unit is in wireless signal connection with the cloud computing service unit 1 and is used for transmitting acquired voltage, current and temperature data to the cloud computing service unit 1, and the comprehensive scheduling control unit 8 is in signal connection with the data acquisition unit and the cloud computing service unit 1 respectively and is used for receiving a computing result output by the cloud computing service unit 1, outputting a control signal according to the computing result, and controlling a temperature controller 5 of the data acquisition unit.
The structure of the data acquisition unit is as follows: it includes centralized control ware 2, wireless voltmeter 4, temperature controller 5, control signal encoder 7 and control signal transmitter 6, centralized control ware 2 equally divide respectively with wireless voltmeter 4 temperature controller 5, cloud calculate service unit 1's data calculation part with 6 wireless signal of control signal transmitter connects, control signal encoder 7 equally divide respectively with synthesize dispatch control unit 8 and 6 signal connection of control signal transmitter.
The application software of the embodiment is the prior art.
The embodiment is manufactured by adopting the prior art, and the centralized controller 2, the wireless current voltmeter 4, the temperature controller 5, the control signal encoder 7 and the control signal emitter 6 are all commercial products in the prior art.
The process of constructing the electric heating load simplification second-order time sequence model in the embodiment is specifically as follows:
1) Establishing an electric heating load simplified second-order time sequence model:
wherein, t time; t is in (t) is the indoor ambient temperature at time t; t is out (t) is the outdoor temperature at time t; p (t) is electric heating power at the moment t; c 1 Equivalent air heat capacity; c 2 Parameter wall equivalent heat capacity; r parameter wall equivalent thermal resistance; t is a unit of 0 The indoor temperature at the starting time; k, d, g model proportionality coefficients;
2) Measuring and collecting the operation data of the electric heating system in the Sc period;
(1) measuring and storing the indoor temperature T of 1,2, …, s … and Sc period by taking Delta T as a sampling period in-m Outdoor temperature T out-m Power P m The total number of data groups or total time step of each time interval is l 1 ,l 2 ,…,l s …,l Sc Wherein Sc represents the current period;
the time period represents the time period of the electric heating switch state, and the switch is continuously turned on or off for one time period;
(2) updating the indoor temperature T with the period of delta T in Outdoor temperature T out A historical database of time series data of power P, the data amount of each group totaling
(3) Calculating the equivalent thermal resistance R of the wall body
Calculating the parameter wall equivalent thermal resistance R according to the formula (2) as follows:
in the formula: is T in-ave (s) averaging the indoor ambient temperature over a period s; t is a unit of out-ave (s) average outdoor temperature over a s-th time period; io(s) is the state of the electric heating switch in the s-th period (0 represents closed, and 1 represents open); p (s, t) is the electric heating power at the time of the s time period t; n is the total sampling time interval number;
3) Fitting model parameters;
(2) using measured indoor temperature T of period 1 in Outdoor temperature T out Time series data fitting C of power P 1 (1)，C 2 (1) D (1), g (1), the fitting equation is as follows:
an objective function:
in the formula: l is the number of measured temperature data;
constraint conditions are as follows:
in the formula, C 1 (1)，C 2 (1) Respectively a fitting equivalent air heat capacity and a parameter wall equivalent heat capacity in a 1 st time period, and d (1) and g (1) are respectively fitting model proportionality coefficients in the 1 st time period;
(2) using measured indoor temperature T in the Sc-th period in Outdoor temperature T out Equivalent air heat capacity C obtained by time sequence data and history fitting of power P 1 Wall equivalent heat capacity C of parameter 2 Model proportionality coefficients d and g, fitting model proportionality coefficient k, the fitting formula is:
an objective function:
in the formula: l is the number of measured temperature data;
constraint conditions are as follows:
in the formula, C 1 (1)，C 2 (1) Respectively a fitting equivalent air heat capacity and a parameter wall equivalent heat capacity in a 1 st time period, and d (1) and g (1) are respectively fitting model proportionality coefficients in the 1 st time period;
(3) obtaining the initial equivalent air heat capacity C of the Sc +1 period from the step 3) (2) 1 Wall equivalent heat capacity C of parameter 2 Model proportionality coefficients d, g and k;
4) Updating a model parameter library;
(1) and determining the position of the database to be updated according to the outdoor temperature average Tout-average (S) in the S-th time period:
the position of the database is an acquired temperature interval of electric heating, the temperature interval is represented by a letter I, a serial number is represented by I, and I = I;
(2) and updating the model proportionality coefficient k of the model parameter library of the interval I, wherein the formula is as follows:
in the formula, k (Sc-1) and k (Sc) respectively represent the model proportionality coefficient k which is not updated in the current Sc period and the updated model proportionality coefficient k. α, β represent update weight coefficients, respectively;
(3) obtaining a model proportionality coefficient of the model parameter library updated in the Sc-th period by the step 4);
5) Obtaining model parameters of the Sc +1 time period;
searching a model proportionality coefficient parameter k of the model parameter library in the future Sc +1 th period by using the updated model proportionality coefficient of the model parameter library obtained in the step 4) according to the average outdoor temperature in the future Sc +1 th period;
6) And obtaining the electric heating load simplified second-order time sequence model in the Sc +1 time period, which is shown in a formula (1).
The working process of the embodiment is as follows: the temperature controller 5 and the radio current voltmeter 4 transmit measured temperature, voltage and current data to the centralized controller 2 in a heating room through a zig-zag-bee wireless transmission technology, the centralized controller 2 is in signal connection with the cloud computing service unit 1 through the Ethernet to transmit the data, the cloud computing service unit 1 stores the data, then a distributed electric heating load simplified second-order time sequence model constructed based on a parameter base is constructed according to the modeling method of the invention, the constructed distributed electric heating load simplified second-order time sequence model constructed based on the parameter base is applied to calculate the received measured data, then the calculation result is transmitted to the comprehensive scheduling control unit 8, a control command is sent through the comprehensive scheduling control unit 8 and is encoded by the control signal encoder 7, then a control signal is sent to the centralized controller 2 by the control signal emitter 6, and the centralized controller 2 acts on the temperature controller 5 to control the on and off of electric heaters.
Claims (1)
1. A distributed electric heating load modeling system constructed based on a parameter base comprises a cloud computing service unit, a comprehensive scheduling control unit and a data acquisition unit, wherein the data acquisition unit is in wireless signal connection with the cloud computing service unit and is used for transmitting acquired voltage, current and temperature data to the cloud computing service unit; the structure of the data acquisition unit is as follows: the intelligent traffic control system comprises an integrated controller, a wireless current voltmeter, a temperature controller, a control signal encoder and a control signal transmitter, wherein the integrated controller is respectively in wireless signal connection with the wireless current voltmeter, the temperature controller and a data calculation part of a cloud calculation service unit and the control signal transmitter, and the control signal encoder is respectively in signal connection with a comprehensive scheduling control unit and the control signal transmitter; the method is characterized in that: the method further comprises a modeling method, and the specific content is as follows:
1) Establishing an electric heating load simplified second-order time sequence model:
wherein, t time; t is in (t) is the indoor ambient temperature at time t; t is out (t) is the outdoor temperature at time t; p (t) is electric heating power at the moment t; c 1 Equivalent air heat capacity; c 2 Parameter wall equivalent heat capacity; r parameter wall equivalent thermal resistance; t is 0 The indoor temperature at the starting time; k, d, g model proportionality coefficients;
2) Measuring and collecting the operation data of the electric heating system in the Sc period;
(1) measuring and storing the indoor temperature T of 1,2, …, s … and Sc period by taking Delta T as a sampling period in-m Outdoor temperature T out-m Power P m The total number of data groups or the total time step of each time interval is l 1 ,l 2 ,…,l s …,l Sc Wherein Sc represents the current period;
the time period represents the time period of the electric heating switch state, and the switch is continuously turned on or off for one time period;
(2) updating the indoor temperature T with the period of delta T in Outdoor temperature T out A historical database of time series data of power P, the data amount of each group totaling
(3) Calculating the equivalent thermal resistance R of the wall body
Calculating the parameter wall equivalent thermal resistance R according to the formula (2) as follows:
in the formula: is T in-ave (s) averaging the indoor ambient temperature over a period s; t is out-ave (s) average outdoor temperature over time period s; io(s) is the state of the electric heating switch in the s-th time period, 0 represents closing, and 1 represents opening; p (s, t) is the electric heating power at the s time interval t; n is the total sampling time interval number;
3) Fitting model parameters;
(1) using measured room temperature T during period 1 in Outdoor temperature T out Time series data fitting C of power P 1 (1)，
C 2 (1) D (1), g (1), the fitting equation is as follows:
an objective function:
in the formula: l is the number of measured temperature data;
constraint conditions are as follows:
in the formula, C 1 (1)，C 2 (1) Respectively a fitting equivalent air heat capacity and a parameter wall equivalent heat capacity in a 1 st time period, and d (1) and g (1) are respectively a fitting model proportional coefficient in the 1 st time period;
(2) using measured indoor temperature T in the Sc-th period in Outdoor temperature T out Equivalent air heat capacity C obtained by time sequence data and history fitting of power P 1 Wall equivalent heat capacity C of parameter 2 Model proportionality coefficients d and g, fitting model proportionality coefficient k, the fitting formula is:
an objective function:
in the formula: l is the number of measured temperature data;
constraint conditions are as follows:
in the formula, C 1 (1)，C 2 (1) Respectively the fitting equivalent air heat capacity and the parameter wall equivalent heat capacity in the 1 st time period, d (1) and g (1) are respectively the fitting in the 1 st time periodA model scaling factor;
(3) obtaining the initial equivalent air heat capacity C of the Sc +1 period from the step 3) (2) 1 Wall equivalent heat capacity C of parameter 2 Model proportionality coefficients d, g and k;
4) Updating a model parameter library;
(1) determining the position of a database to be updated according to the outdoor temperature average Tout-average (S) in the S-th time period;
the position of the database is an acquired temperature interval of electric heating, the temperature interval is represented by a letter I, a serial number is represented by I, and I = I;
(2) and updating the model proportionality coefficient k of the model parameter library of the section I, wherein the formula is as follows:
in the formula, k (Sc-1) and k (Sc) respectively represent the model proportionality coefficient which is not updated in the current Sc period and the model proportionality coefficient which is updated; α, β represent update weight coefficients, respectively;
(3) obtaining a model proportionality coefficient of the model parameter library updated in the Sc-th period by the step 4);
5) Obtaining model parameters of a Sc +1 time period;
searching a model proportionality coefficient parameter k of the model parameter library in the future Sc +1 th period by using the updated model proportionality coefficient of the model parameter library obtained in the step 4) according to the average outdoor temperature in the future Sc +1 th period;
6) And obtaining the electric heating load simplified second-order time sequence model in the Sc +1 time period, which is shown in a formula (1).
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| CN108256739A (en) * | 2017-12-22 | 2018-07-06 | 国网北京市电力公司 | Load determination method and device applied to electric heating |
| CN108305456A (en) * | 2018-01-26 | 2018-07-20 | 住建互联清洁能源供热采暖技术研究院（北京）有限公司 | Internet+electric heating heating system and cloud platform electric load monitoring method |
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| CN108256739A (en) * | 2017-12-22 | 2018-07-06 | 国网北京市电力公司 | Load determination method and device applied to electric heating |
| CN108305456A (en) * | 2018-01-26 | 2018-07-20 | 住建互联清洁能源供热采暖技术研究院（北京）有限公司 | Internet+electric heating heating system and cloud platform electric load monitoring method |
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