CN109284576B - Distributed electric heating load scheduling method based on measured data and modeling system thereof - Google Patents
Distributed electric heating load scheduling method based on measured data and modeling system thereof Download PDFInfo
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Abstract
The invention relates to a distributed electric heating load dispatching method based on measured data and a modeling system thereof, which are characterized in that: the method comprises the steps of obtaining running state time sequence data of an electric heating system in the past period by constructing an electric heating load modeling system, fitting an electric heating load simplified second-order model and a model error margin function thereof through a cloud computing system based on the actually measured time sequence data, and optimizing and dispatching the distributed electric heating load by taking the flexible dispatching requirement of the electric heating system as a target. The basic principle of electric heating load dispatching by using measured parameters is determined, and dispatching steps and system architecture which can be realized by a computer are provided. The invention can provide an accurate and practical electric heating load dispatching model, and improves the accuracy and practicability of the electric heating load dispatching process.
Description
Technical Field
The invention belongs to electric heating, and relates to a distributed electric heating load dispatching method based on measured data and a modeling system thereof.
Background
With the continuous promotion of clean heating, the proportion of electric heating in the power grid in northern areas is improved year by year. Taking vinca as an example, in 2017, the electric heating accumulated installed capacity is 290MW, the heating area is 317 kilo square meters, and the electric heating accumulated installed capacity accounts for 3.08% of the total heating amount in the whole city, and the electric heating accumulated installed capacity presents a rapid development situation. Wherein, distributed electric heating occupies an important proportion in electric heating load. The electric heating load has thermal inertia, can gather the regulation resources 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.
And the electric heating load dispatching is based on an electric heating load model and uses the comfort range of a user as constraint, thereby meeting the requirements of economic and flexible operation of an electric power system. However, in practical engineering application, the electric heating load model mostly directly utilizes an air conditioning load first-order equivalent thermodynamic model, and the air conditioning load is intensively used in a southern area for meeting the refrigerating requirement in summer, and the indoor and outdoor environment, the comfort degree requirement range and the physical operation characteristics of the electric heating load model are all greatly different from those of the electric heating load in a northern area. At the same time, the influence of the user comfort range constraint of the model accuracy is not considered.
Therefore, the patent provides a more accurate distributed electric heating load dispatching method based on the identification of measured parameters.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the defects of the prior art are overcome, the accurate distributed electric heating load dispatching method based on measured data and the modeling system thereof are provided, the simulation result close to reality can be obtained, and the accuracy of electric heating load dispatching is improved.
One of the solutions of the present invention for solving the technical problems is: the utility model provides a distributed electric heating load modeling system based on actual measurement data, it includes cloud calculation service unit and comprehensive dispatch control unit, its characterized in that still includes data acquisition unit, data acquisition unit is arranged in the heating room, data acquisition unit with cloud calculation service unit wireless signal connects for the data transmission of voltage, electric current, the temperature of gathering is to cloud calculation service unit, comprehensive dispatch control unit all is connected with data acquisition unit and cloud calculation service unit signal respectively, is used for receiving the calculation result of cloud calculation service unit output, and output control signal according to the calculation result, control data acquisition unit's temperature controller.
The structure of the data acquisition unit is as follows: the intelligent control system comprises a centralized controller, a wireless current voltmeter, a temperature controller, a control signal encoder and a control signal transmitter, wherein the centralized controller is respectively connected with the wireless current voltmeter, the temperature controller, a data calculation part of a cloud calculation service unit and the control signal transmitter through wireless signals, and the control signal encoder is respectively connected with a comprehensive dispatching control unit and the control signal transmitter through signals.
The second scheme for solving the technical problem is as follows: a distributed electric heating load dispatching method based on measured data is characterized in that: the method comprises the steps of acquiring the time sequence data of the running state of the electric heating system, constructing an electric heating load simplified second-order model, constructing a model error margin function, and optimally scheduling the distributed electric heating load, and specifically comprises the following steps:
1) Acquiring operation state time sequence data of an electric heating system;
(1) measuring and storing indoor temperature T in 1,2, …, s … and Sc time period by taking DeltaT as sampling period in-m Outdoor temperature T out-m Power P m The total number of groups of data or the total time step of each period is l respectively 1 ,l 2 ,…,l s …,l Sc Wherein Sc is the current period;
the time period represents the time period of the state of an electric heating switch, and the continuous on or continuous off of the switch is a time period;
(2) with DeltaT as period, the indoor temperature T is updated in Outdoor temperature T out A history database of time series data of power P;
(3) calculating equivalent thermal resistance R of parameter wall
Calculating the equivalent thermal resistance R of the parameter wall body according to the formula (1) as follows:
wherein: t (T) in-ave (s) is the average indoor ambient temperature for the s-th period; t (T) out-ave (s) is an s-th period average outdoor temperature; io(s) is an electric heating switch state of the s-th period, 0 represents off, and 1 represents on; p (s, t) is the electric heating power at the time of the s-th period t; n is the total sampling period number calculated;
2) Constructing an electric heating load simplified second-order time sequence model;
(1) using measured indoor temperature T of period 1 in Outdoor temperature T out Power P time series data fitting C 1 (1)，C 2 (1) D (1), g (1), fitting formula is as follows:
wherein: l is the number of the measured temperature data,
wherein C is 1 (1)，C 2 (1) The fitting equivalent heat capacity and the parameter wall body equivalent heat capacity are respectively the fitting equivalent heat capacity of the 1 st period, d (1), g (1) are respectively the fitting model proportionality coefficients of the 1 st period;
(2) using measured Sc-th period indoor temperature T in Outdoor temperature T out Time sequence data of power P and equivalent air heat capacity C obtained by history fitting 1 Equivalent heat capacity C of parameter wall 2 Model proportionality coefficients d and g, fitting model proportionality coefficient k, and fitting formula is:
wherein: l is the number of the measured temperature data,
wherein C is 1 (1)，C 2 (1) The fitting equivalent air heat capacity and the parameter wall body equivalent heat capacity are respectively the 1 st time period, d (1), g (1) are respectively the fitting model proportionality coefficient of the 1 st time period;
(3) obtaining initial equivalent air heat capacity C of the future Sc+1 period from (1) and (2) of step 2) 1 Equivalent heat capacity C of parameter wall 2 Model scaling coefficients d, g and k;
(4) from the continuously updated history of past m periods, a correction equation is determined by data fitting:
k＝α·T out-ave (8)
wherein alpha is a correction coefficient, obtained by fitting historical data, T out-ave As the average outdoor temperature of the air is,
(5) and correcting the parameter k according to a correction equation and a correction coefficient alpha, wherein the formula is as follows:
k＝k+α·(T out-ave-f (Sc+1)-T out-ave (Sc)) (9)
wherein T is out-ave (Sc) a Sc-th period average outdoor temperature; t (T) out-ave-f (sc+1) predicted sc+1 th period average outdoor temperature;
(6) according to the finally obtained parameter wall equivalent thermal resistance R of the future Sc+1 period, equivalent air heat capacity C 1 Equivalent heat capacity C of parameter wall 2 The model scaling coefficients d, g and k obtain the electric heating load simplified second-order time sequence model of Sc+1 period as follows:
wherein, t is time; t (T) in (t) is the indoor ambient temperature at time t; t (T) out (t) is the outdoor temperature at time t; p (t) is the electric heating power at the moment t; c (C) 1 Equivalent air heat capacity; c (C) 2 Equivalent wall heat capacity; r wall equivalent thermal resistance; t (T) 0 Indoor temperature at the starting moment; k, d, g model scaling factor;
3) Constructing a model error margin function;
(1) calculating the indoor temperature T of the model according to 1,2, …, s … and Sc segment fitting prediction in-f (s, T) and measured indoor temperature data T in-m Error DeltaT between (s, T) in (database is established) and a probability distribution function of the indoor temperature prediction fitting error is calculated:
F(ΔT in )＝P(ΔT in >T) (11)
(2) according to the probability distribution, determining a distributed electric heating load adjustable margin, see formula (12):
ΔTm＝F -1 (ε) (12)
wherein epsilon is a temperature out-of-limit probability index expected (input) by a scheduler;
4) Optimizing and dispatching the distributed electric heating load;
in order to meet the flexible scheduling requirement of the power system, according to the scheduling target delta p obj (t) optimizing and dispatching the electric heating load through a dispatching system:
objective function:
Min：(Δp obj (t)-P)(Δp obj (t)-P) (13)
constraint conditions:
indoor temperature constraint: t (T) min (t)+ΔTm＜T in (t)＜T max (t)-ΔTm (14)
Equivalent thermodynamic model:
the flexible scheduling requirement of the power system comprises waste wind power consumption, wind power error and load error tracking and peak shaving.
The working process of the invention is as follows: the temperature controller and the 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 connected with a cloud computing service unit through an Ethernet to transmit the data, the cloud computing service unit stores the data, then the modeling method is used for constructing the distributed electric heating load simplified second-order time sequence model based on actual measurement parameter identification, the distributed electric heating load simplified second-order time sequence model based on actual measurement parameter identification is used for calculating the received actual measurement data, the calculation result is transmitted to a comprehensive dispatching control unit, a control command is sent through the comprehensive dispatching control system, the control signal encoder is used for encoding, then a control signal transmitter is used for sending a control signal to the centralized controller, and the central controller is used for controlling the switching of an electric heater through the central controller.
The beneficial effects of the invention are as follows: the method has the advantages that a target object is winter heating in northern areas, a distributed electric heating load model can be constructed according to indoor and outdoor environments, comfort level demand ranges and physical operation characteristics in winter in northern areas, the requirements of electric heating loads in northern areas for adapting to economical and flexible operation of an electric system are met, the defects that the operation characteristics of the electric heating loads are difficult to accurately reflect, the model errors are extremely large, the accuracy is difficult to meet the requirements of electric heating load dispatching and the influence of user comfort range constraint without considering the model accuracy are overcome, the problems that the existing model lacks identification and analysis of historical data of the electric heating system, and the model parameters lack accuracy are overcome, and the method has the advantages of accurate modeling and accurate and practical dispatching.
Drawings
FIG. 1 is a flow chart of a distributed electric heating load dispatching method based on measured data of the invention;
FIG. 2 is a schematic diagram of a modeling system according to the present invention.
In the figure: the system comprises a cloud computing service unit 1, a centralized controller 2, an electric heating unit 3, a wireless current voltmeter 4, a temperature controller 5, a control signal transmitter 6, a control signal encoder 7 and a comprehensive dispatching control unit 8.
Detailed Description
The invention is further illustrated below with reference to examples.
Referring to fig. 1-2, embodiment 1, a distributed electric heating load dispatching method based on measured data in this embodiment includes obtaining operation state time sequence data of an electric heating system, constructing a simplified second-order model of electric heating load, constructing a model error margin function, and optimizing dispatching of distributed electric heating load, specifically as follows:
1) Acquiring operation state time sequence data of an electric heating system;
(1) measuring and storing indoor temperature T in 1,2, …, s … and Sc time period by taking DeltaT as sampling period in-m Outdoor temperature T out-m Power P m The total number of groups of data or the total time step of each period is l respectively 1 ,l 2 ,…,l s …,l Sc Wherein Sc is the current period;
the time period represents the time period of the state of an electric heating switch, and the continuous on or continuous off of the switch is a time period;
(2) with DeltaT as period, the indoor temperature T is updated in Outdoor temperature T out A history database of time series data of power P;
(3) calculating equivalent thermal resistance R of parameter wall
Calculating the equivalent thermal resistance R of the parameter wall body according to the formula (1) as follows:
wherein: t (T) in-ave (s) is the average indoor ambient temperature for the s-th period; t (T) out-ave (s) is an s-th period average outdoor temperature; io(s) is an electric heating switch state of the s-th period, 0 represents off, and 1 represents on; p (s, t) is the electric heating power at the time of the s-th period t; n is the total sampling period number calculated;
2) Constructing an electric heating load simplified second-order time sequence model;
(1) using measured indoor temperature T of period 1 in Outdoor temperature T out Power P time series data fitting C 1 (1)，C 2 (1) D (1), g (1), fitting formula is as follows:
wherein: l is the number of the measured temperature data,
wherein C is 1 (1)，C 2 (1) The fitting equivalent heat capacity and the parameter wall body equivalent heat capacity are respectively the fitting equivalent heat capacity of the 1 st period, d (1), g (1) are respectively the fitting model proportionality coefficients of the 1 st period;
(2) using measured Sc-th period indoor temperature T in Outdoor temperature T out Time sequence data of power P and equivalent air heat capacity C obtained by history fitting 1 Equivalent heat capacity C of parameter wall 2 Model proportionality coefficients d and g, fitting model proportionality coefficient k, and fitting formula is:
wherein: l is the number of the measured temperature data,
Wherein C is 1 (1)，C 2 (1) The fitting equivalent air heat capacity and the parameter wall body equivalent heat capacity are respectively the 1 st time period, d (1), g (1) are respectively the fitting model proportionality coefficient of the 1 st time period;
(3) obtaining initial equivalent air heat capacity of future Sc+1 period from (1) and (2) of step 2)C 1 Equivalent heat capacity C of parameter wall 2 Model scaling coefficients d, g and k;
(4) from the continuously updated history of past m periods, a correction equation is determined by data fitting:
k＝α·T out-ave (8)
wherein alpha is a correction coefficient, obtained by fitting historical data, T out-ave As the average outdoor temperature of the air is,
(5) and correcting the parameter k according to a correction equation and a correction coefficient alpha, wherein the formula is as follows:
k＝k+α·(T out-ave-f (Sc+1)-T out-ave (Sc)) (9)
wherein T is out-ave (Sc) a Sc-th period average outdoor temperature; t (T) out-ave-f (sc+1) predicted sc+1 th period average outdoor temperature;
(6) according to the finally obtained parameter wall equivalent thermal resistance R of the future Sc+1 period, equivalent air heat capacity C 1 Equivalent heat capacity C of parameter wall 2 The model scaling coefficients d, g and k obtain the electric heating load simplified second-order time sequence model of Sc+1 period as follows:
wherein, t is time; t (T) in (t) is the indoor ambient temperature at time t; t (T) out (t) is the outdoor temperature at time t; p (t) is the electric heating power at the moment t; c (C) 1 Equivalent air heat capacity; c (C) 2 Equivalent wall heat capacity; r wall equivalent thermal resistance; t (T) 0 Indoor temperature at the starting moment; k, d, g model scaling factor;
3) Constructing a model error margin function;
(1) calculating the indoor temperature T of the model according to 1,2, …, s … and Sc segment fitting prediction in-f (s, T) and measured indoor temperature data T in-m Error DeltaT between (s, T) in (database is established) and a probability distribution function of the indoor temperature prediction fitting error is calculated:
F(ΔT in )＝P(ΔT in >T) (11)
(2) according to the probability distribution, determining a distributed electric heating load adjustable margin, see formula (12):
ΔTm＝F -1 (ε) (12)
wherein epsilon is a temperature out-of-limit probability index expected (input) by a scheduler;
4) Optimizing and dispatching the distributed electric heating load;
in order to meet the flexible scheduling requirement of the power system, according to the scheduling target delta p obj (t) optimizing and dispatching the electric heating load through a dispatching system:
objective function:
Min：(Δp obj (t)-P)(Δp obj (t)-P) (13)
constraint conditions:
indoor temperature constraint: t (T) min (t)+ΔTm＜T in (t)＜T max (t)-ΔTm (14)
Equivalent thermodynamic model:
the flexible scheduling requirement of the power system comprises waste wind power consumption, wind power error and load error tracking and peak shaving.
The modeling system adopted in the embodiment comprises a cloud computing service unit 1 and a comprehensive dispatching control unit 8, and further comprises 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 dispatching control unit 8 is respectively in signal connection with the data acquisition unit and the cloud computing service unit 1 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: the intelligent control system comprises a centralized controller 2, a wireless current voltmeter 4, a temperature controller 5, a control signal encoder 7 and a control signal transmitter 6, wherein the centralized controller 2 is respectively connected with the wireless current voltmeter 4, the temperature controller 5, a data calculation part of a cloud computing service unit 1 and the control signal transmitter 6 in a wireless signal manner, and the control signal encoder 7 is respectively connected with a comprehensive dispatching control unit 8 and the control signal transmitter 6 in a signal manner.
The application software of this embodiment is all the prior art.
The embodiment is manufactured by adopting the prior art, and the centralized controller 2, the wireless ammeter 4, the temperature controller 5, the control signal encoder 7 and the control signal transmitter 6 are all commercial products in the prior art.
The working procedure of this embodiment is: the temperature controller 5 and the wireless 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 connected with the cloud computing service unit 1 through an Ethernet to transmit data, the cloud computing service unit 1 stores the data, then the distributed electric heating load simplified second-order time sequence model constructed based on the parameter library is constructed according to the modeling method of the invention, the received actual measurement data is calculated by applying the constructed distributed electric heating load simplified second-order time sequence model constructed based on the parameter library, the calculation result is transmitted to the comprehensive dispatching control unit 8, a control command is sent through the comprehensive dispatching control unit 8, the control signal encoder 7 is used for encoding, then the control signal transmitter 6 is used for sending a control signal to the centralized controller 2, and the temperature controller 5 is acted on to control the electric heater to switch.
Claims (1)
1. The distributed electric heating load modeling system based on the measured data comprises a cloud computing service unit, a comprehensive dispatching control unit and a data acquisition unit, wherein the data acquisition unit is arranged in a heating room, 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, and the comprehensive dispatching control unit is respectively in signal connection with the data acquisition unit and the cloud computing service unit 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: the intelligent control system comprises a centralized controller, a wireless current voltmeter, a temperature controller, a control signal encoder and a control signal transmitter, wherein the centralized controller is respectively connected with the wireless current voltmeter, the temperature controller, a data calculation part of a cloud computing service unit and the control signal transmitter in a wireless signal manner, and the control signal encoder is respectively connected with a comprehensive dispatching control unit and the control signal transmitter in a signal manner; the method is characterized in that: the electric heating load dispatching method comprises the steps of acquiring the time sequence data of the operation state of the electric heating system, constructing a simplified second-order model of the electric heating load, constructing a model error margin function, and optimally dispatching the distributed electric heating load, and is specifically as follows:
1) Acquiring operation state time sequence data of an electric heating system;
(1) measuring and storing indoor temperature T in 1,2, …, s … and Sc time period by taking DeltaT as sampling period in-m Outdoor temperature T out-m Power P m The total number of groups of data or the total time step of each period is l respectively 1 ,l 2 ,…,l s …,l Sc Wherein Sc is the current period;
the time period represents the time period of the state of an electric heating switch, and the continuous on or continuous off of the switch is a time period;
(2) with DeltaT as period, the indoor temperature T is updated in Outdoor temperature T out A history database of time series data of power P;
(3) calculating equivalent thermal resistance R of parameter wall
Calculating the equivalent thermal resistance R of the parameter wall body according to the formula (1) as follows:
wherein: t (T) in-ave (s) is the average indoor ambient temperature for the s-th period; t (T) out-ave (s) is the average outdoor temperature in the s-th periodA degree; io(s) is an electric heating switch state of the s-th period, 0 represents off, and 1 represents on; p (s, t) is the electric heating power at the time of the s-th period t; n is the total sampling period number calculated;
2) Constructing an electric heating load simplified second-order time sequence model;
(1) using measured indoor temperature T of period 1 in Outdoor temperature T out Power P time series data fitting C 1 (1)，C 2 (1) D (1), g (1), fitting formula is as follows:
wherein: l is the number of the measured temperature data,
wherein C is 1 (1)，C 2 (1) The fitting equivalent heat capacity and the parameter wall body equivalent heat capacity are respectively the fitting equivalent heat capacity of the 1 st period, d (1), g (1) are respectively the fitting model proportionality coefficients of the 1 st period;
(2) using measured Sc-th period indoor temperature T in Outdoor temperature T out Time sequence data of power P and equivalent air heat capacity C obtained by history fitting 1 Equivalent heat capacity C of parameter wall 2 Model proportionality coefficients d and g, fitting model proportionality coefficient k, and fitting formula is:
wherein: l is the number of the measured temperature data,
wherein C is 1 (1)，C 2 (1) The fitting equivalent air heat capacity and the parameter wall body equivalent heat capacity are respectively the 1 st time period, d (1), g (1) are respectively the fitting model proportionality coefficient of the 1 st time period;
(3) obtaining initial equivalent air heat capacity C of the future Sc+1 period from (1) and (2) of step 2) 1 Equivalent heat capacity C of parameter wall 2 Model scaling coefficients d, g and k;
(4) from the continuously updated history of past m periods, a correction equation is determined by data fitting:
k＝α·T out-ave (8)
wherein alpha is a correction coefficient, obtained by fitting historical data, T out-ave As the average outdoor temperature of the air is,
(5) and correcting the parameter k according to a correction equation and a correction coefficient alpha, wherein the formula is as follows:
k＝k+α·(T out-ave-f (Sc+1)-T out-ave (Sc))(9)
wherein T is out-ave (Sc) a Sc-th period average outdoor temperature; t (T) out-ave-f (sc+1) predicted sc+1 th period average outdoor temperature;
(6) according to the finally obtained parameter wall equivalent thermal resistance R of the future Sc+1 period, equivalent air heat capacity C 1 Equivalent heat capacity C of parameter wall 2 The model scaling coefficients d, g and k obtain the electric heating load simplified second-order time sequence model of Sc+1 period as follows:
wherein, t is time; t (T) in (t) is the indoor ambient temperature at time t; t (T) out (t) is the outdoor temperature at time t; p (t) is the electric heating power at the moment t; c (C) 1 Equivalent air heat capacity; c (C) 2 Equivalent wall heat capacity; r wall equivalent thermal resistance; t (T) 0 Indoor temperature at the starting moment; k, d, g model scaling factor;
3) Constructing a model error margin function;
(1) calculating the indoor temperature T of the model according to 1,2, …, s … and Sc segment fitting prediction in-f (s, T) and measured indoor temperature data T in-m Error DeltaT between (s, T) in Namely, a database is established, and a probability distribution function of the indoor temperature prediction fitting error is calculated:
F(ΔT in )＝P(ΔT in >T)(11)
(2) according to the probability distribution, determining a distributed electric heating load adjustable margin, see formula (12):
ΔTm＝F -1 (ε)(12)
epsilon is a temperature out-of-limit probability index expected or input by a scheduler;
4) Optimizing and dispatching the distributed electric heating load;
in order to meet the flexible scheduling requirement of the power system, according to the scheduling target delta p obj (t) optimizing and dispatching the electric heating load through a dispatching system:
objective function:
Min：(Δp obj (t)-P)(Δp obj (t)-P)(13)
constraint conditions:
indoor temperature constraint: t (T) min (t)+ΔTm＜T in (t)＜T max (t)-ΔTm(14)
Equivalent thermodynamic model:
the flexible scheduling requirement of the power system comprises waste wind power consumption, wind power error and load error tracking and peak shaving.
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