2021, Volume 7, Issue 8
Engineering >> 2021, Volume 7, Issue 8 doi: 10.1016/j.eng.2021.06.008
Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids
a State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
b Division of Electric Power and Energy Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm 10044, Sweden
c Center for Electric Power and Energy Department of Electrical Engineering, Technical University of Denmark, Kgs Lyngby 2800, Denmark
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Abstract
With the growth of intermittent renewable energy generation in power grids, there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability. The flexibility of demand response (DR) resources has become a valuable solution to this problem. However, existing research indicates that problems on flexibility prediction of DR resources have not been investigated. This study applied the temporal convolution network (TCN)-combined transformer, a deep learning technique to predict the aggregated flexibility of two types of DR resources, that is, electric vehicles (EVs) and domestic hot water system (DHWS). The prediction uses historical power consumption data of these DR resources and DR signals (DS) to facilitate prediction. The prediction can generate the size and maintenance time of the aggregated flexibility. The accuracy of the flexibility prediction results was verified through simulations of case studies. The simulation results show that under different maintenance times, the size of the flexibility changed. The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.
Keywords
Load flexibility ; Electric vehicles ; Domestic hot water system ; Temporal convolution network-combined transformer ; Deep learning
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