EV Forecasting-Based Model Predictive Control for Distribution System Congestion Mitigation

Yue Li, Tong Su, Junbo Zhao, Rui Yang

Research output: Contribution to conferencePaper

Abstract

The uncoordinated charging of electric vehicles (EVs) in time and space brings congestion issues to the distribution network. This paper proposes an EV charging demand forecasting-based model predictive control (MPC) method for distribution system congestion management. To effectively forecast the time-series EV station charging demand, a hybrid forecasting model that integrates the long short-term memory network (LSTM) and Transformer is proposed. The Transformer-LSTM model is trained using a one-year real historical charging dataset of EV stations to forecast future charging demand in 15-minute intervals. This informs the MPC for distribution network congestion management and minimization of PV curtailment. Numerical results carried out on the modified IEEE 123-bus distribution system demonstrate that the proposed method can effectively resolve line congestion issues through EV smart charging and PV curtailment while outperforming other benchmarks.
Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2024
Event2024 IEEE Kansas Power and Energy Conference (KPEC) - Manhattan, Kansas
Duration: 25 Apr 202426 Apr 2024

Conference

Conference2024 IEEE Kansas Power and Energy Conference (KPEC)
CityManhattan, Kansas
Period25/04/2426/04/24

NREL Publication Number

  • NREL/CP-5D00-91855

Keywords

  • congestion management
  • demand forecasting
  • EVs
  • model predictive control
  • PVs
  • smart charging

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