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

Li, Yue; Su, Tong; Zhao, Junbo; Yang, Rui

doi:10.1109/KPEC61529.2024.10676202

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

Conference · 18 September 2024

DOI:https://doi.org/10.1109/KPEC61529.2024.10676202· OSTI ID:2476208

Li, Yue; Su, Tong; Zhao, Junbo; Yang, Rui

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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: U.S. Department of Energy (DOE)

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 2476208

Report Number(s):: NREL/CP-5D00-91855; MainId:93633; UUID:9fb6202f-3310-460a-83fc-c602bb0eefb9; MainAdminId:74152

Country of Publication:: United States

Language:: English

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Related Subjects

ADVANCED PROPULSION SYSTEMS
SOLAR ENERGY
EVs
PVs
congestion management
demand forecasting
model predictive control
smart charging

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

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