The flexibility of Demand Response (DR) resources in grid operations has become a valuable solution to respond to the several problems brought about by the growth of intermittent renewable generation. However, the flexibility prediction of the DR resources has not yet been fully addressed in the available literature. This paper trained a long short-term memory (LSTM) recurrent neural network to predict the aggregated flexibility of electric vehicles (EVs). The prediction is based on the historical charging behavior of EVs and the DR signal (DS) which is proposed to facilitate prediction and DR management. Both the size and the maintaining time of the aggregated flexibility can be obtained from the prediction results. The accuracy of the flexibility prediction is verified through the simulation of case studies. The simulation results reveal that the size of flexibility changes under different maintaining time. The proposed flexibility prediction method may be of great assistance for DR management as well as the reserve of the gird.
|Title of host publication||Proceedings of 2020 International Top-Level Forum on Engineering Science and Technology Development Strategy and The 5th PURPLE MOUNTAIN FORUM, PMF2020|
|Editors||Yusheng Xue, Yuping Zheng, Anjan Bose|
|Publication status||Published - 2021|
|Event||International Top-Level Forum on Engineering Science and Technology Development Strategy and 5th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control, PMF2020 - Nanjing, China|
Duration: 15 Aug 2020 → 16 Aug 2020
|Conference||International Top-Level Forum on Engineering Science and Technology Development Strategy and 5th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control, PMF2020|
|Period||15/08/2020 → 16/08/2020|
|Series||Lecture Notes in Electrical Engineering|
Bibliographical noteFunding Information:
Funding Information.. This work was supported by the State Key Laboratory of smart grid protection and operation control open project (SGNR0000KJJS1907535).
© 2021, Springer Nature Singapore Pte Ltd.
- Deep learning (DL)
- Demand response (DR)
- Electric vehicles (EVs)
- Load flexibility
- Long short-term memory (LSTM)