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 language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - 2024 |
Event | 2024 IEEE Kansas Power and Energy Conference (KPEC) - Manhattan, Kansas Duration: 25 Apr 2024 → 26 Apr 2024 |
Conference
Conference | 2024 IEEE Kansas Power and Energy Conference (KPEC) |
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City | Manhattan, Kansas |
Period | 25/04/24 → 26/04/24 |
NREL Publication Number
- NREL/CP-5D00-91855
Keywords
- congestion management
- demand forecasting
- EVs
- model predictive control
- PVs
- smart charging