| The proposal of the carbon peaking and carbon neutrality goals means that energy conservation and emission reduction will be one of the important tasks of China’s economic development for a long time in the future.The electric power industry is the single industry with the largest proportion of carbon emissions in China,and demand side management(DSM)is an important measure to promote energy conservation and emission reduction of electric power system.Heating,ventilation and air-conditioning system(HVAC)is an ideal device for participating in demand side management.Direct expansion air-conditioning system(DX A/C)is widely used in small and medium-sized buildings,with great potential and value in demand side management.However,the existing research on air conditioning system DSM control,including energy-saving control and demand response control,have the problems of high development cost and poor application universality,which are not suitable facing the actual situation of DX A/C system.Moreover,most research on air conditioning system DSM control only constrain user thermal comfort by single temperature control,which not only cannot well guarantee user thermal comfort level,but also cannot exert the full energy-saving potential of air-conditioning system,and limits the optimization space of control algorithms.Additionally,on how to achieve simultaneous temperature and humidity control using DX A/C system,the weights based fuzzy logic control algorithm(WBFLCA)based on software-based decoupling lacks the guidance of general rule for weights setting work,leading to a high application difficulty.Under this background,this work conducts a comprehensive study on the demand side management control of DX A/C system in terms of both energy-saving and demand response,the main research contents and conclusions are as follows:1)First,in order to improve the simultaneous temperature and humidity control ability of DX A/C system,this work comprehensively carries out experimental research on 16 basic weights distribution combinations of WBFLCA,and selects 4 basic weights distribution combinations with excellent control performance according to control sensitivity and stability.Qualitative analysis of the difference of control performance among different weights distribution combinations is also carried out.General rule of weights setting is summarized,which greatly reduces the application difficulty of simultaneous temperature and humidity control using DX A/C system.2)Based on the successful implementation of simultaneous temperature and humidity control using DX A/C system,a generalized energy-saving control algorithm for DX A/C system based on room steady-state thermal characteristics is developed.The algorithm can recognize the steady-state thermal characteristics of the target room through online self-learning and independently screen out controllable,comfortable and energy-saving temperature and humidity set points.Verification experiments of the algorithm with different focuses are carried out on an experimental DX A/C system and a market available split-type air conditioner.The verification experiment on the experimental DX A/C system compares the effects of different values of the importance factor in the cost function on control performance.The results show that it can save 23.3% more energy when takes 0 than when takes 1,with indoor thermal comfort level still within an acceptable range.The verification experiment on the split-type air conditioner compares the control performance of the energy-saving control algorithm for 6 different loads under the same importance factor value.The results show that compared with the control group with fixed temperature and humidity set points,the energy-saving control algorithm achieves a maximum of more than 32% and an average of 19% energy-saving effect.Moreover,the proposed algorithm can make the “uncontrollable” load “controllable”.The experimental results also prove that the energy-saving control algorithm can be practically applied in market available DX A/C platforms.3)Continuing the idea of using air conditioner terminal to perform room targeted online self-learning modeling,a fast online self-learning algorithm for room dynamic thermal modeling based on least squares regression is proposed.The algorithm is verified in Simulink environment with a gray-box model of a small office as the target room.Based on the indoor temperature and humidity data collected by the sensors of the DX A/C system for 2 hours and the outdoor weather data obtained through Internet of Things(Io T),a fast reconstruction of room dynamic thermal characteristics is realized,providing a basis for subsequent development of the demand response control algorithm.4)A generalized demand response control algorithm based on deep reinforcement learning for DX A/C system is designed.Using the dynamic self-learned model obtained by the online selflearning algorithm,combined with historical weather data and electricity price information,a demand response control algorithm for the target room can be trained using deep reinforcement learning methods.The control performance of the demand response control algorithm on indoor temperature,humidity,PMV of the target room and the corresponding energy consumption and operating electricity costs are tested in Simulink environment and compared with a single temperature control algorithm and the energy-saving control algorithm.The simulation results show that compared with the commonly used single temperature control algorithm,the energysaving control algorithm achieves a 20.53% reduction in energy consumption and a 21.23%saving in electricity costs throughout a day with better indoor thermal comfort control performance,and compared with the energy-saving control algorithm,the demand response control algorithm further reduces energy consumption by 7% and operating electricity costs by6.69% with similar indoor thermal comfort level.A comprehensive analysis is conducted of the reasons for this further optimization effect. |
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