Estimating the aggregated available capacity for vehicle to grid services using deep learning and Nonlinear Autoregressive Neural Network

Noğay H. S.

SUSTAINABLE ENERGY GRIDS & NETWORKS, cilt.29, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.segan.2021.100590
  • Dergi Adı: SUSTAINABLE ENERGY GRIDS & NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Anahtar Kelimeler: Vehicle-to-grid V2G, Deep learning, LSTM network, Machine learning, NAR, LITHIUM-ION BATTERIES, SHORT-TERM-MEMORY, ELECTRIC VEHICLES, ENERGY-STORAGE, LOAD, OPERATIONS, VIABILITY, MODEL
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

In order for vehicle-to-grid (V2G) services to participate in the power and energy market, they must provide as much aggregated capacity as the market needs. In order to provide this capacity, the population of electric vehicle batteries is used. For this participation it is necessary to estimate the available capacity, which makes it possible to reliably distribute the existing reserves in the future. In this study, Long Short Term Memory (LSTM) and Nonlinear Autoregressive Neural Network (NAR) were used and developed to predict the next 59 h aggregated available capacity (AAC) of a small fleet of 7 electric vehicles for a ten-day travel adapted from 72 real driving records. Market activities were simulated to include the delivery of reserves to meet the needs and included in the dataset. The ability of the developed LSTM deep learning network and NAR machine learning networks to successfully adapt their predictions to such market events has been demonstrated. The authors highlight the conclusion that this capability is critical to the viability and success of future V2G services by supporting multiple market events. (C) 2021 Elsevier Ltd. All rights reserved.