| Title |
Optimal Electric Vehicle Scheduling Method Using Renewable Energy Forecasting Algorithm in DC Nanogrid |
| Authors |
김준기(Jun-Gi Kim) ; 정일엽(Il-Yop Chung) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.6.808 |
| Keywords |
Electric Vehicle; Optimal Scheduling; Renewable Energy Forecasting; RNN-LSTM; MILP; DC Nanogrid |
| Abstract |
This paper focuses on optimal operation of a DC nanogrid that includes multiple electric vehicles (EV) and photovoltaic (PV) generators. The operator of DC nanogrid optimizes EV charging and discharging schedule to maximize economic benefits by charging more EVs and utilizing more electric power generated by PV while limiting the power imports from the grid. This paper employs the sophisticated forecasting algorithms for solar PV output and load demand in DC nanogrid using RNN(Recurrent Neural Network)-LSTM(Long Short-Term Memory) method. Then, we find the optimal EV charging and discharging schedule with mixed-integer linear programming (MILP) considering various operating concerns for EV owners and the DC nanogrid operator. The details on the overall operation algorithm is described by the definition of objective function as well as designs on equality and inequality constraints. The performance of the proposed method is verified by simulation studies considering multiple EVs and various PV outputs. |