CN-121977268-A - Dynamic aggregation optimization method and system for air conditioner clusters

CN121977268ACN 121977268 ACN121977268 ACN 121977268ACN-121977268-A

Abstract

The invention relates to the technical field of air-conditioning cluster aggregation, and discloses an air-conditioning cluster dynamic aggregation optimization method and system, wherein the method comprises the steps of maximizing the energy boundary width of an air conditioner at each moment to be an objective function, and establishing an air-conditioning monomer equivalent model by taking an air-conditioning energy track as a constraint condition; according to the thermodynamic model of the air conditioner, a temperature power relation is obtained, a temperature energy relation is obtained according to the power energy relation, an air conditioner monomer equivalent model is solved according to the temperature energy relation, an air conditioner energy boundary is obtained, the air conditioner energy boundaries of each air conditioner are overlapped to obtain a cluster energy boundary of an air conditioner cluster, an aggregate total power curve of the air conditioner cluster is used as an objective function, and a cluster energy boundary and a cluster power boundary of the air conditioner cluster are used as constraint conditions to construct an air conditioner cluster dynamic aggregate model. The invention ensures the comfort of the temperature of the user and realizes the dynamic self-adaption of the environmental change while improving the calculation efficiency of the aggregation model.

Inventors

YE JICHAO
XIA TONG
HUANG HUI
LI ZIYI
XU YONGHAI
ZHANG HANBING
LIU LINPING
LU WU
LI XINCHI
LIU FANGZHOU
HU XINWEI
YOU ZILONG

Assignees

国网浙江省电力有限公司丽水供电公司

Dates

Publication Date: 20260505
Application Date: 20260409

Claims (10)

1. The dynamic aggregation optimization method for the air conditioner clusters is characterized by comprising the following steps of: the energy boundary width of the air conditioner at each moment is maximized to be an objective function, and an air conditioner energy track is used as a constraint condition, so that an air conditioner monomer equivalent model is respectively built for each air conditioner in the air conditioner cluster; obtaining a temperature power relation between temperature deviation and air conditioner power deviation according to a thermodynamic model of an air conditioner, and obtaining a temperature energy relation between the temperature deviation and the air conditioner energy deviation according to a power energy relation between the air conditioner power deviation and the air conditioner energy deviation; solving the air conditioner monomer equivalent model according to the temperature energy relation to obtain air conditioner energy boundaries of each air conditioner at each moment, and superposing the air conditioner energy boundaries of each air conditioner at each moment to obtain cluster energy boundaries of an air conditioner cluster; and constructing an air-conditioning cluster dynamic aggregation model by taking an aggregate total power curve of the air-conditioning clusters as an objective function and taking a cluster energy boundary and a cluster power boundary of the air-conditioning clusters as constraint conditions, and carrying out optimal scheduling on the air-conditioning clusters according to a solving result of the air-conditioning cluster dynamic aggregation model.
2. The method for dynamic aggregation optimization of air conditioner clusters according to claim 1, wherein the step of obtaining the temperature power relationship between the temperature deviation and the air conditioner power deviation according to the thermodynamic model of the air conditioner comprises: Performing recursive summation on a first-order thermodynamic model of the air conditioner according to a time sequence to obtain a first temperature power relation between the indoor temperature at the current moment and the air conditioner power at all moments; obtaining a second temperature power relation between the set temperature at the current moment and the reference air conditioner power at all moments according to the first temperature power relation; And obtaining a temperature power relation between the temperature deviation at the current moment and the air conditioner power deviation at all moments according to the first temperature power relation and the second temperature power relation.
3. The method for dynamic aggregation optimization of air conditioning clusters according to claim 2, wherein the step of obtaining the temperature energy relationship between the temperature deviation and the air conditioning energy deviation according to the power energy relationship between the air conditioning power deviation and the air conditioning energy deviation comprises: According to the corresponding relation between the air conditioner power and the air conditioner energy, obtaining the power energy relation between the air conditioner power deviation at the current moment and the air conditioner energy deviation at the adjacent moment, wherein the adjacent moment comprises the current moment and the last moment; and obtaining the temperature energy relation between the temperature deviation at the current moment and the air conditioner energy deviation at all moments according to the temperature power relation and the power energy relation.
4. The method for dynamic aggregation optimization of air conditioner clusters according to claim 3, wherein the step of solving the air conditioner monomer equivalent model according to the temperature energy relation to obtain the air conditioner energy boundary of each air conditioner at each moment comprises the following steps: setting an intermediate derivative variable according to the temperature energy relationship to obtain a first conversion relationship between the intermediate derivative variable and the air conditioner energy deviation and a second conversion relationship between the intermediate derivative variable and the temperature deviation; constructing a final constraint model of the intermediate derivative variable according to the first conversion relation, the second conversion relation, a preset air conditioner power constraint and a preset temperature deviation constraint; performing recursive calculation on the final constraint model according to the environmental temperature prediction data to obtain final constraint boundaries of the intermediate derivative variables at all moments; and converting the final constraint boundary into an air conditioner energy boundary according to the first conversion relation to obtain an optimal solution of the air conditioner monomer equivalent model.
5. The method of claim 4, wherein the step of constructing a final constraint model of the intermediate derivative variable according to the first conversion relationship, the second conversion relationship, a preset air conditioning power constraint and a preset temperature deviation constraint comprises: Constructing an initial constraint model of the intermediate derivative variable according to the first conversion relation and a preset air conditioner power constraint; Constructing a temperature constraint model of the intermediate derivative variable according to the second conversion relation and a preset temperature deviation constraint; And constructing a final constraint model of the intermediate derivative variable according to the intersection of the initial constraint model and the temperature constraint model.
6. The method for dynamic aggregation optimization of air conditioner clusters according to claim 5, wherein the step of constructing the initial constraint model of the intermediate derivative variable according to the first conversion relation and a preset air conditioner power constraint comprises: According to the corresponding relation between the air conditioner power and the air conditioner energy, converting the preset air conditioner power constraint into an air conditioner energy deviation constraint; And converting the air conditioner energy deviation constraint according to the first conversion relation to obtain an initial constraint model of the intermediate derivative variable.
7. The method for dynamic aggregation optimization of air conditioner clusters according to claim 5, wherein the step of constructing the temperature constraint model of the intermediate derivative variable according to the second conversion relation and a preset temperature constraint comprises: setting accumulated temperature deviation constraint according to the temperature deviation track; And constructing a temperature constraint model of the intermediate derivative variable according to the second conversion relation according to the accumulated temperature deviation constraint and the preset temperature deviation constraint.
8. An air conditioning cluster dynamic aggregation optimization system, comprising: the equivalent model construction module is used for respectively constructing air conditioner monomer equivalent models for each air conditioner in the air conditioner cluster by taking the energy boundary width of the air conditioner at each moment as an objective function and taking the energy track of the air conditioner as a constraint condition; The equivalent model solving module is used for obtaining a temperature power relation between the temperature deviation and the air conditioner power deviation according to a thermodynamic model of the air conditioner, and obtaining a temperature energy relation between the temperature deviation and the air conditioner energy deviation according to a power energy relation between the air conditioner power deviation and the air conditioner energy deviation; solving the air conditioner monomer equivalent model according to the temperature energy relation to obtain air conditioner energy boundaries of each air conditioner at each moment, and superposing the air conditioner energy boundaries of each air conditioner at each moment to obtain cluster energy boundaries of an air conditioner cluster; The cluster aggregation optimization module is used for constructing an air conditioner cluster dynamic aggregation model by taking an aggregate total power curve of the air conditioner clusters as an objective function and taking a cluster energy boundary and a cluster power boundary of the air conditioner clusters as constraint conditions, and carrying out optimal scheduling on the air conditioner clusters according to a solving result of the air conditioner cluster dynamic aggregation model.
9. The air conditioner cluster dynamic aggregation optimization system according to claim 8, wherein the equivalent model solving module is further configured to recursively sum first-order thermodynamic models of the air conditioner according to a time sequence to obtain a first temperature power relationship between an indoor temperature at a current time and air conditioner power at all times; obtaining a second temperature power relation between the set temperature at the current moment and the reference air conditioner power at all moments according to the first temperature power relation; And obtaining a temperature power relation between the temperature deviation at the current moment and the air conditioner power deviation at all moments according to the first temperature power relation and the second temperature power relation.
10. The air conditioner cluster dynamic aggregation optimization system according to claim 9, wherein the equivalent model solving module is further configured to obtain a power energy relationship between an air conditioner power deviation at a current time and an air conditioner energy deviation at an adjacent time according to a corresponding relationship between air conditioner power and air conditioner energy, where the adjacent time includes the current time and a previous time; and obtaining the temperature energy relation between the temperature deviation at the current moment and the air conditioner energy deviation at all moments according to the temperature power relation and the power energy relation.

Description

Dynamic aggregation optimization method and system for air conditioner clusters Technical Field The invention relates to the technical field of air-conditioning cluster aggregation, in particular to a dynamic aggregation optimization method and system for air-conditioning clusters. Background The air conditioning load generated by the building heating and refrigerating system has remarkable load flexibility potential because the indoor temperature can be dynamically adjusted in a human comfort zone. When a large number of distributed air conditioning equipment are clustered, the adjustable load capacity of the distributed air conditioning equipment can effectively support renewable energy grid-connected consumption, and the running stability and the energy utilization efficiency of a power grid are improved, so that the distributed air conditioning equipment becomes a research hot spot in the field of energy management. At present, methods for constructing an equivalent aggregation model for an air conditioner cluster are mainly divided into two types, namely an aggregation modeling method based on optimization and an aggregation modeling method without optimization, but the existing methods have certain limitations. On one hand, the optimization-based method obtains an aggregation model by solving a complex optimization problem, but because a large number of constraint conditions of air conditioning equipment are required to be processed simultaneously, the dimension of the optimization problem grows exponentially along with the cluster scale, a large amount of calculation resources are required to be consumed in the solving process, and the real-time scheduling and minute-level response application requirements of a power grid are difficult to adapt, on the other hand, the non-optimization method avoids the calculation complexity of the optimization method, but ignores the dynamic change of the environment temperature in an actual scene, directly causes the great reduction of the model precision, cannot truly reflect the actual adjustment capability of the air conditioning cluster, and cannot ensure that the running tracks of all individual air conditioners strictly meet the user temperature comfort constraint when the aggregation model boundary is constructed, and the risk of violating the constraint exists, so that the user experience is affected. Disclosure of Invention In order to solve the technical problems, the invention provides a dynamic aggregation optimization method and a dynamic aggregation optimization system for an air conditioner cluster. In a first aspect, the present invention provides a dynamic aggregation optimization method for an air conditioner cluster, where the method includes: the energy boundary width of the air conditioner at each moment is maximized to be an objective function, and an air conditioner energy track is used as a constraint condition, so that an air conditioner monomer equivalent model is respectively built for each air conditioner in the air conditioner cluster; obtaining a temperature power relation between temperature deviation and air conditioner power deviation according to a thermodynamic model of an air conditioner, and obtaining a temperature energy relation between the temperature deviation and the air conditioner energy deviation according to a power energy relation between the air conditioner power deviation and the air conditioner energy deviation; solving the air conditioner monomer equivalent model according to the temperature energy relation to obtain air conditioner energy boundaries of each air conditioner at each moment, and superposing the air conditioner energy boundaries of each air conditioner at each moment to obtain cluster energy boundaries of an air conditioner cluster; and constructing an air-conditioning cluster dynamic aggregation model by taking an aggregate total power curve of the air-conditioning clusters as an objective function and taking a cluster energy boundary and a cluster power boundary of the air-conditioning clusters as constraint conditions, and carrying out optimal scheduling on the air-conditioning clusters according to a solving result of the air-conditioning cluster dynamic aggregation model. Further, the step of obtaining the temperature power relation between the temperature deviation and the air conditioner power deviation according to the thermodynamic model of the air conditioner comprises the following steps: Performing recursive summation on a first-order thermodynamic model of the air conditioner according to a time sequence to obtain a first temperature power relation between the indoor temperature at the current moment and the air conditioner power at all moments; obtaining a second temperature power relation between the set temperature at the current moment and the reference air conditioner power at all moments according to the first temperature power relation; And obtaining a temperature power relation be