The distributed electric heating load is a demand response resource with great adjustment potential.However,user behavior differences,solar irradiance and other random factors increase the difficulty of establishing accurate distributed electric heating load model.Firstly,combined with specific application scenarios,a distributed electric heating load experimental platform based on climate simulation platform is built to collect the experimental data required for load modeling.Then,aiming at the complex problem of the second-order equivalent thermal parameter model equation of the distributed electric heating load,the model parameter identification method based on the improved particle swarm optimization algorithm is proposed to minimize the error between the simulation temperature and the measured temperature,and the distributed electric heating load model is established.Finally,the load model is optimized by changing the data sampling period.The results show that the proposed parameter identification and modeling optimization method can effectively improve the accuracy of the second-order equivalent thermal parameter model of distributed electric heating load.
维普
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