CN109827310B - Resident air conditioner load cluster die type establishing method - Google Patents
Resident air conditioner load cluster die type establishing method Download PDFInfo
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Abstract
The invention discloses a resident air conditioner load group aggregation model establishing method, which is characterized in that under the support of non-invasive technology, through acquiring the historical operating data and outdoor temperature of the resident air conditioner load, and combining the relevant characteristics of the air conditioner load operation, a resident air conditioner load group aggregation model is established, and the parameter identification method is used for acquiring the air conditioner load aggregation model parameters, thereby greatly reducing the calculated amount and not relating to the privacy of users.
Description
Technical Field
The invention relates to a resident air conditioner load cluster die type establishing method, and belongs to the field of intelligent power utilization of an electric power system.
Background
The energy internet and the intelligent power grid are developed and fused with an energy technology and an information technology, indirect uncertain distributed energy large-scale access can be supported, and higher requirements are provided for stable operation of the power grid; on the other hand, the rapid development of economy and the continuous improvement of the living standard of residents lead the peak load to continuously rise, thus causing short-term power shortage and causing great influence on the safe and economic operation of a power system. Aiming at the problems of power system stability caused by rapid load increase and large-scale access of fluctuating energy, the demand response technology is gradually developed.
The buildings to which the air conditioning loads of residents belong have heat storage capacity, and the number of the buildings is increasing in recent years, so that the buildings become important demand response resources. However, the rated power of a single air conditioner load is small, and the control of the single air conditioner load cannot produce an effect on a power grid, so that a large number of residential air conditioner loads need to be aggregated and uniformly controlled. At present, a large amount of research is carried out on the basis of a single resident air conditioner load model to obtain an air conditioner load group aggregation model, the method needs to obtain the attribute of a building to which each air conditioner belongs, even invades the privacy of a user, and the calculation amount is huge.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a method for establishing a resident air conditioner load cluster model and identifying parameters, which reduces the calculated amount and protects the privacy of users.
The technical scheme is as follows: the invention adopts the technical scheme that a resident air conditioner load cluster die type establishing method comprises the following steps:
1) establishing a residential air-conditioning load group aggregation model;
2) acquiring historical operating power data of the load of the resident air conditioner and outdoor temperature at corresponding moment;
3) according to the residential air conditioner load group aggregation model, the average value is calculated to serve as the final adiabatic parameter of the residential air conditioner load aggregation model by using the historical power data and the outdoor temperature data;
4) evaluating an average indoor temperature change curve according to a residential air conditioner load group aggregation model, and screening out multiple groups of historical data corresponding to the average indoor temperature change;
5) and 4) solving heat storage parameters corresponding to the heat storage characteristics of the air-conditioning load model under different historical data according to the indoor temperature historical data and the outdoor temperature in the step 4), and solving an average value as a final heat capacity parameter of the residential air-conditioning load aggregation model.
And 6) selecting multiple groups of data to modify and verify the residential air-conditioning load aggregation model by using historical air-conditioning load data according to the residential air-conditioning load aggregation model obtained by identification.
The dynamic change equation of the indoor temperature according to the thermodynamic theory in the step 1) is as follows:
wherein i is a room ordinal number; t isaIs the outdoor temperature in units; of, TiRoom i indoor temperature, in units; ciHeat capacity for room i in kWh/deg.c units; pi(t) the operating power of the air conditioner in the room i at the moment t, wherein the unit kW is; eta is the air-conditioning energy efficiency ratio; giIs an adiabatic parameter, unit kW/DEG C, and during normal operation, the indoor temperature is maintained at a set temperature TsetNearby, i.e. [ T ]set-/2,Tset+/2]；
Based on the operation characteristics of single air conditioner load, a resident air conditioner load group aggregation model is established as follows:
wherein, TaThe temperature is outdoor temperature, unit ℃, T (t) is average indoor temperature at the moment t, unit ℃, C is polymerization heat capacity parameter of the load of the resident air conditioner, and unit kWh/DEG C; p (t) is the polymerization power of the residential air conditioners at the moment t, unit kW and eta are the energy efficiency ratio of the air conditioners, and G is the polymerization adiabatic parameter of the residential air conditioner load, unit kW/DEG C;
dividing both sides of formula (2) by eta at the same time, and reacting
Then formula (2) is converted into:
and 2) identifying and acquiring multiple groups of historical power data of the air conditioner load based on a non-invasive technology in the step 2), and acquiring outdoor temperature data at corresponding time through a weather data system.
In the step 3), a plurality of groups of adiabatic parameters for describing adiabatic characteristics in the air-conditioning load polymerization model are obtained through linear fitting calculation
Finally, the average value is obtained and used as the final heat insulation parameter of the residential air conditioner load aggregation model
Where L denotes the load data group number, and L denotes the total data group number.
In the step 4), the final heat insulation parameter of the residential air conditioner load aggregation model in the step 3) is utilized according to the formula (4)
The estimated indoor temperature T is calculated by the formula (5)ev，
And (3) drawing a curve according to the formula (5), analyzing the fluctuation condition of the indoor temperature curve, and screening out corresponding historical data of the indoor temperature change.
Identifying the indoor temperature T before change according to the indoor temperature evaluation curve obtained by evaluation in the step 4) in the step 5)1After variation of the indoor temperature T2And the transition time t of indoor temperature changetrans；
Respectively calculating the sum of T and T according to the following formula1、T2And ttransCorresponding resident air conditioner load aggregation heat capacity parameter
Finally, the average value is obtained and used as the final heat capacity parameter of the resident air conditioner load aggregation model
Where i represents the load data group number and K represents the number of data groups in which the average indoor temperature changes.
Has the advantages that: according to the method, the resident air-conditioning load group aggregation model is obtained by utilizing the historical operating data and the outdoor data of the resident air-conditioning load group, so that the establishment of a single air-conditioning load model is avoided, and the calculation amount of the traditional air-conditioning aggregation model is reduced. Meanwhile, the invention accurately describes the dynamic relation among the outdoor temperature, the indoor temperature and the air conditioner load group aggregated power, is used for predicting the air conditioner load aggregated power and analyzing the air conditioner load response characteristic, and establishes an air conditioner load demand response control strategy.
Drawings
FIG. 1 is a diagram of a heat transfer model of a single residential air conditioner;
FIG. 2 is a diagram of a heat transfer model of a residential air conditioning load group;
FIG. 3 is an indoor temperature evaluation curve;
FIG. 4 is a graph of adiabatic parameters
Identifying a result;
FIG. 5 is a heat storage parameter
Identifying a result;
fig. 6 is a verification result of the residential air conditioning load group aggregation model.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
Such as
1) And analyzing the load operation characteristics of the single resident air conditioner according to a thermodynamic theory to obtain the air conditioner load group aggregation thermodynamic characteristic, and establishing a resident air conditioner load group aggregation model.
According to the thermodynamic theory, the operation characteristics of the air conditioning load of a single resident are analyzed, the air conditioning load has heat storage and heat dissipation characteristics, as shown in fig. 1, when the air conditioning cold quantity exceeds the heat quantity transmitted into the room from the outside, the indoor temperature is reduced, otherwise, the indoor temperature is increased, and the dynamic change equation of the indoor temperature is as follows:
wherein i is a room ordinal number; t isaIs the outdoor temperature in units; of, TiRoom i indoor temperature, in units; ciHeat capacity for room i in kWh/deg.c units; pi(t) the operating power of the air conditioner in the room i at the moment t, wherein the unit kW is; eta is the air-conditioning energy efficiency ratio; giIs the i adiabatic parameter in kW/deg.C. During normal operation, the indoor temperature is maintained at the set temperature TsetNearby, i.e. [ T ]set-/2,Tset+/2]。
Based on the operating characteristics of the single air-conditioning load, the thermodynamic characteristics of the air-conditioning load group aggregation are obtained, and as shown in fig. 2, a residential air-conditioning load group aggregation model is established as follows:
wherein, TaThe temperature is outdoor temperature, unit ℃, T (t) is average indoor temperature at the moment t, unit ℃, C is polymerization heat capacity parameter of the load of the resident air conditioner, and unit kWh/DEG C; p (t) is the polymerization power of the residential air conditioner at the time t, unit kW and eta are the energy efficiency ratio of the air conditioner, and G is the polymerization adiabatic parameter of the residential air conditioner load, unit kW/DEG C.
Dividing both sides of formula (2) by eta at the same time, and reacting
Then formula (2) is converted into:
2) and acquiring historical operating power data of the residential air conditioner load and outdoor temperature at corresponding moment.
And identifying and acquiring multiple groups of historical power data of air conditioner load based on a non-invasive technology, and acquiring outdoor temperature data at corresponding time through a weather data system.
3) According to the residential air-conditioning load group aggregation model, multiple groups of air-conditioning load historical power data and outdoor temperature data are utilized, and multiple groups of heat insulation parameters describing heat insulation characteristics in the air-conditioning load aggregation model are obtained through linear fitting calculation
Finally, the average value is obtained and used as the final heat insulation parameter of the residential air conditioner load aggregation model
As shown in fig. 4:
where L denotes the load data group number, and L denotes the total data group number.
4) And evaluating an average indoor temperature change curve according to the residential air conditioner load group aggregation model, and selecting multiple groups of historical data corresponding to the average indoor temperature change.
Utilizing the final heat insulation parameter of the residential air conditioner load aggregation model of the formula (4) in the step 3)
The estimated indoor temperature T is calculated by the formula (5)ev，
And (3) drawing a curve according to the formula (5), analyzing the fluctuation condition of the indoor temperature curve, and screening out corresponding historical data of the indoor temperature change.
5) Combining the average indoor temperature curve in the step 4), calculating and solving heat storage parameters corresponding to the heat storage characteristics of the air-conditioning load model under different historical data by utilizing historical power data and outdoor temperature, and finally solving the average value as the final heat capacity parameter of the resident air-conditioning load aggregation model
According to the indoor temperature evaluation curve obtained by evaluation, the indoor temperature evaluation curve corresponding to the ith group of historical data is assumed to be as shown in fig. 3. Two horizontal dotted lines in FIG. 3 represent the room temperature T before the change1After variation of the indoor temperature T2. The transition time t of the indoor temperature change is between the twotrans. Combining different historical data to respectively calculate T1、T2And ttransCorresponding resident air conditioner load aggregation heat capacity parameter
The formula is as follows:
finally, the average value is obtained and used as the final heat capacity parameter of the resident air conditioner load aggregation model
Where i represents the load data set number and K represents the number of data sets in which the average indoor temperature has changed, the data sets from step 4). As shown in fig. 5.
And selecting multiple groups of data to modify and verify the residential air-conditioning load aggregation model by using historical air-conditioning load data according to the residential air-conditioning load aggregation model obtained by identification.
Examples of the design
120000 residents in a certain area are conditioned. The value ranges of the air-conditioning load parameters of a single resident are assumed as follows (each air-conditioning load parameter is randomly selected in the value range):
firstly, acquiring the outdoor temperature of the historical data of the air conditioner load by using a Monte Carlo method.
Establishing a resident air conditioner load group aggregation model, identifying and obtaining air conditioner load model parameters by using the method of the invention, and obtaining the following specific results
Finally, the aggregation model obtained by the method is used for evaluating the air conditioner load power, and the air conditioner load power is compared with the air conditioner load power obtained by the Monte Carlo method, as shown in figure 6.
Claims (5)
1. A resident air conditioner load group-gathering mold-closing type establishing method is characterized by comprising the following steps:
1) establishing a residential air-conditioning load group aggregation model;
2) acquiring historical operating power data of the load of the resident air conditioner and outdoor temperature at corresponding moment;
3) according to the residential air conditioner load group aggregation model, the average value is calculated to serve as the final adiabatic parameter of the residential air conditioner load aggregation model by using the historical power data and the outdoor temperature data;
4) evaluating an average indoor temperature change curve according to a residential air conditioner load group aggregation model, and screening out multiple groups of historical data corresponding to the average indoor temperature change;
5) according to the indoor temperature historical data and the outdoor temperature in the step 4), heat storage parameters corresponding to the heat storage characteristics of the air-conditioning load model under different historical data are solved, and an average value is solved to serve as a final heat capacity parameter of the resident air-conditioning load aggregation model, wherein the heat capacity parameter is as follows:
identifying the indoor temperature T before change according to the indoor temperature evaluation curve obtained by evaluation in the step 4)1After variation of the indoor temperature T2And the transition time t of indoor temperature changetrans；
Respectively calculating the sum of T and T according to the following formula1、T2And ttransCorresponding resident air conditioner load polymerization heat capacity parameter Ci：
And finally, calculating an average value as a final heat capacity parameter C of the residential air conditioner load aggregation model:
where i represents the load data group number and K represents the number of data groups in which the average indoor temperature changes.
2. The residential air conditioning load group aggregation model establishing method according to claim 1, further comprising a step 6) of selecting multiple groups of data to modify and verify the residential air conditioning load aggregation model by using historical air conditioning load data according to the identified residential air conditioning load group aggregation model.
3. The residential air conditioning load cluster model building method as claimed in claim 1, wherein in step 2), a plurality of sets of historical power data of the air conditioning load are obtained based on non-intrusive technology identification, and outdoor temperature data of corresponding time is collected through a weather data system.
4. The residential air conditioning load group aggregation model establishment method as claimed in claim 1, wherein the linear fitting calculation in step 3) is used to obtain multiple sets of adiabatic characteristics in the air conditioning load aggregation modelThermal insulation parameter of nature
Finally, the average value is obtained and used as the final heat insulation parameter of the residential air conditioner load aggregation model
Where L denotes the load data group number, and L denotes the total data group number.
5. The residential air conditioning load group aggregation mold closing type establishing method as claimed in claim 1, wherein the final residential air conditioning load aggregation model adiabatic parameter of the formula (4) in the step 3) is used in the step 4)
The estimated indoor temperature T is calculated by the formula (5)ev，
And (3) drawing a curve according to the formula (5), analyzing the fluctuation condition of the indoor temperature curve, and screening out corresponding historical data of the indoor temperature change.
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| CN111896831B (en) * | 2020-08-04 | 2021-07-23 | 山东大学 | Non-invasive comprehensive energy load monitoring method and system |
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| CN106485068B (en) * | 2016-10-08 | 2018-12-28 | 东南大学 | A kind of polymerization modeling method of air conditioner load |
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