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CN-121981566-A - Air conditioner comprehensive efficiency quantitative evaluation method based on power grid friendly interaction

CN121981566ACN 121981566 ACN121981566 ACN 121981566ACN-121981566-A

Abstract

The application provides an air conditioner comprehensive efficiency quantitative evaluation method based on power grid friendly interaction, which can be applied to the cross technical field of power system optimization operation and building flexible resource fine management. The method comprises the steps of processing target flexible adjustment potential data of an air conditioner in a demand response period according to a quantitative association model corresponding to each of a plurality of recovery strategies to obtain estimated rebound effect data corresponding to each of the plurality of recovery strategies, wherein the demand response period represents a period for adjusting the temperature of an electric appliance of the air conditioner from a base line temperature based on power saving demands, the target flexible adjustment potential data represents power reduction data generated in the process of adjusting the temperature based on the demand response period, and the target flexible adjustment potential data and the plurality of estimated rebound effect data are used for determining a plurality of estimated load efficiency net values according to differences between the target flexible adjustment potential data and each of the plurality of estimated rebound effect data, wherein the plurality of estimated load efficiency net values are used for determining target recovery strategies from the plurality of recovery strategies so as to execute temperature down-regulation operation for the air conditioner.

Inventors

  • WANG RAN
  • TANG CAIBO
  • LV SHILEI

Assignees

  • 天津大学

Dates

Publication Date
20260505
Application Date
20251208

Claims (10)

  1. 1. The utility model provides a comprehensive efficiency quantization evaluation method of air conditioner based on friendly interaction of electric wire netting, which is characterized in that the method comprises the following steps: Processing target flexible regulation potential data of an air conditioner in a demand response period according to a quantitative association model corresponding to each of a plurality of recovery strategies to obtain estimated rebound effect data corresponding to each of the plurality of recovery strategies, wherein the quantitative association model represents a correlation between flexible regulation potential data and rebound effect data, the demand response period represents a period in which the temperature of an electric appliance of the air conditioner is up-regulated from a base line temperature based on power saving demand, the target flexible regulation potential data represents power reduction data generated in the process of regulating the temperature based on the demand response period, and the estimated rebound effect data represents estimated power increase data generated in the process of down-regulating the temperature of the electric appliance of the air conditioner to the base line temperature after the demand response period; And determining a plurality of estimated load efficiency net values according to the difference value between the target flexibility adjustment potential data and each of the plurality of estimated rebound effect data, wherein the plurality of estimated load efficiency net values are used for determining a target recovery strategy from a plurality of recovery strategies so as to execute temperature down-regulation operation for the air conditioner.
  2. 2. The method of claim 1, wherein the quantized correlation model corresponding to each of the plurality of recovery strategies is determined based on: For any one of a plurality of said recovery strategies, Processing a plurality of sample air conditioner simulation models in a sample air conditioner simulation model set by using a simulation algorithm to obtain sample flexible adjustment potential data and sample rebound effect data corresponding to each of the plurality of sample air conditioner simulation models; Fitting the sample flexible adjustment potential data and the sample rebound effect data to obtain a quantization correlation model corresponding to the recovery strategy; The sample air conditioner simulation model set is constructed according to at least one of a sample building parameter model, a sample meteorological parameter model, a sample air conditioner configuration parameter model or a plurality of sample demand response strategies corresponding to the sample air conditioner and the recovery strategy, wherein the sample building parameter model represents a building environment where the simulated sample air conditioner is located, the sample meteorological parameter model represents a climate condition where the simulated sample air conditioner is located, the sample air conditioner configuration parameter model represents a refrigeration control system inside the simulated sample air conditioner, and the sample demand response strategy represents a control strategy for simulating the temperature up-regulation of an electrical appliance for the sample air conditioner.
  3. 3. The method of claim 2, wherein said fitting the sample compliance accommodation potential data and the sample bounce-back effect data to obtain a quantized correlation model corresponding to the recovery strategy comprises: performing correlation analysis on the sample flexible adjustment potential data and the sample rebound effect data to obtain correlation strength; Under the condition that the correlation strength is greater than or equal to a preset strength threshold value, fitting the sample flexible adjustment potential data and the sample rebound effect data by using a regression algorithm to obtain an initial quantized correlation model; and under the condition that the fitting degree and the significance of the initial quantization associated model meet the preset check conditions, determining the initial quantization associated model as a quantization associated model corresponding to the recovery strategy.
  4. 4. A method according to claim 2 or 3, wherein the sample meteorological parameter model comprises setting differential cold period parameters for a plurality of sample cities, and/or The sample air conditioner configuration parameter model comprises at least one of cold and hot medium transmission and distribution system parameters or differential water chiller parameters set for a plurality of sample cities.
  5. 5. The method of any of claims 2-4, wherein the sample demand response strategy comprises at least one of a sample demand response period parameter, a sample temperature up range parameter, a sample temperature up step size parameter, a sample adjustment time interval step size parameter, or a sample baseline temperature parameter.
  6. 6. The method of any of claims 2-5, wherein the sample building parameter model is constructed based on: sampling and taking values of a plurality of parameters of a plurality of sample building components from a parameter value range by using an inverse transformation algorithm according to probability distribution of each of the parameters of the sample building components aiming at any sample building component in the plurality of sample building components to obtain a plurality of samples; and combining a plurality of samples corresponding to the sample building components to obtain the sample building parameter model.
  7. 7. The method of any of claims 1-6, wherein the plurality of net predicted load performance values are used to determine a target recovery strategy from a plurality of recovery strategies, comprising: Determining a target estimated load performance net value from a plurality of estimated load performance net values according to the sorting result of the estimated load performance net values, and determining a recovery strategy corresponding to the target estimated load performance net value as the target recovery strategy, or Determining estimated load efficiency net values greater than or equal to a preset net value threshold from a plurality of estimated load efficiency net values, obtaining at least one candidate estimated load efficiency net value, and determining the target recovery strategy from recovery strategies corresponding to at least one candidate estimated load efficiency net value according to at least one candidate estimated load efficiency net value.
  8. 8. The method of any one of claims 1-7, wherein the estimated net load performance comprises at least one of an estimated net power dimension performance or an estimated net energy dimension performance, wherein the estimated net power dimension performance characterizes a full cycle load performance assessment of the demand response period and the recovery period in the instantaneous power dimension, the estimated net energy dimension performance characterizes a full cycle load performance assessment of the demand response period and the recovery period in the cumulative power dimension, and/or The target flexible accommodation potential data includes at least one of a target peak load shedding rate that characterizes an upper load shedding capacity in an instantaneous power dimension or a target cumulative load shedding rate that characterizes an upper load shedding capacity in a cumulative power dimension, and/or The estimated rebound effect data comprises at least one of an estimated peak load increase rate or an estimated cumulative load increase rate, the estimated peak load increase rate representing an upper limit of load loss capacity in an instantaneous power dimension, the estimated cumulative load increase rate representing an upper limit of load loss capacity in a cumulative power dimension.
  9. 9. The method of claim 8, wherein, in the case where the net predicted load efficacy comprises the net predicted power dimensional efficacy, the target compliance adjustment potential data comprises the target peak load reduction rate, and the predicted bounce-back effect data comprises the predicted peak load increase rate, the determining a plurality of net predicted load efficacy values based on differences between the target compliance adjustment potential data and each of the plurality of predicted bounce-back effect data comprises: Determining a target peak load reduction rate based on a baseline power and a target load reduction of the demand response period; And determining a plurality of estimated power dimension performance net values according to the difference value between the target peak load reduction rate and each of the plurality of estimated peak load increase rates.
  10. 10. The method of claim 8 or 9, wherein, in a case where the net predicted load performance includes the net predicted energy dimensional performance, the target compliance adjustment potential data includes the target cumulative load reduction rate, and the predicted rebound effect data includes the predicted cumulative load increase rate, determining a plurality of net predicted load performance values based on differences between the target compliance adjustment potential data and each of the plurality of predicted rebound effect data comprises: determining the target accumulated load reduction rate according to the target accumulated load reduction of the demand response period and the accumulated baseline power estimated based on the baseline operation mode; and determining a plurality of estimated energy dimension performance net values according to the difference value between the target accumulated load reduction rate and each of the plurality of estimated accumulated load increase rates.

Description

Air conditioner comprehensive efficiency quantitative evaluation method based on power grid friendly interaction Technical Field The application relates to the technical field of intersection of power system optimization operation and building flexible resource refined management, in particular to an air conditioner comprehensive efficiency quantitative evaluation method based on power grid friendly interaction. Background With the high-proportion renewable energy source connected to the power grid, the power supply pressure of the power grid in peak period in summer is remarkably increased, electric appliances such as air conditioners are used as important adjusting resources on the user side, and considerable load reduction potential can be displayed through an on-demand adjusting strategy. However, in the related research, the full-cycle load efficiency net value of the on-demand regulation event cannot be accurately quantified, and further, advanced scientific regulation cannot be performed. Disclosure of Invention In view of the above problems, the application provides a comprehensive performance quantitative evaluation method of an air conditioner based on power grid friendly interaction. According to the first aspect of the application, the comprehensive performance quantitative evaluation method of the air conditioner based on the power grid friendly interaction comprises the steps of processing target flexible regulation potential data of the air conditioner in a demand response period according to quantitative association models corresponding to a plurality of recovery strategies to obtain estimated rebound effect data corresponding to the recovery strategies, wherein the quantitative association models represent correlation between the flexible regulation potential data and the rebound effect data, the demand response period represents a period of time when the temperature of an electric appliance of the air conditioner is up-regulated from a base line temperature based on power saving demands, the target flexible regulation potential data represent power reduction data generated in the process of regulating the temperature based on the demand response period, the estimated rebound effect data represent power increase data generated in the process of estimating the temperature of the electric appliance of the air conditioner down to the base line temperature after the demand response period, and determining a plurality of load performance net values according to differences between the target flexible regulation potential data and the estimated rebound effect data, wherein the estimated load performance net values are used for determining the target recovery strategies from the recovery strategies so as to execute temperature down-regulating operation of the air conditioner. The application provides an air conditioner comprehensive efficiency quantitative evaluation device based on power grid friendly interaction, which comprises an estimation module, an evaluation module and an evaluation module, wherein the estimation module is used for processing target flexible adjustment potential data of an air conditioner in a demand response period according to quantitative association models corresponding to a plurality of recovery strategies to obtain estimated rebound effect data corresponding to the recovery strategies, the quantitative association models represent correlation between the flexible adjustment potential data and the rebound effect data, the demand response period represents a period of time when the temperature of an electric appliance of the air conditioner is up-regulated from a base line temperature based on power saving requirements, the target flexible adjustment potential data represents power reduction data generated in the process of adjusting the temperature based on the demand response period, the estimated rebound effect data represents power increase data generated in the process of estimating the temperature of the electric appliance of the air conditioner down to the base line temperature after the demand response period, and the evaluation module is used for determining a plurality of estimated load efficiency net values according to difference values between the target flexible adjustment potential data and the estimated rebound effect data, and the estimated load efficiency net values are used for determining target recovery strategies from the recovery strategies so as to execute temperature down-regulating operation of the air conditioner. A third aspect of the application provides an electronic device comprising one or more processors and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method. A fourth aspect of the application also provides a computer readable storage medium having stored thereon a computer program or instructions which when executed by a p