Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach with Clustering Technique

Hamidreza Jahangir, Saleh Sadeghi Gougheri, Behzad Vatandoust, Masoud Aliakbar Golkar, Ali Ahmadian, Amin Hajizadeh

Research output: Contribution to journalJournal articleResearchpeer-review

63 Citations (Scopus)
273 Downloads (Pure)

Abstract

Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate clusterspecific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long-and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.
Original languageEnglish
Article number9102300
JournalI E E E Transactions on Smart Grid
Volume11
Issue number6
Pages (from-to)4738-4748
Number of pages11
ISSN1949-3053
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Deep learning
  • classification
  • energy market
  • plug-in electric vehicles
  • travel behavior

Fingerprint

Dive into the research topics of 'Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach with Clustering Technique'. Together they form a unique fingerprint.

Cite this