Abstract
Uncoordinated Electric Vehicles (EVs) charging can lead to incremental overloads, power losses and voltage fluctuations which are stressful and harmful for the distribution networks. To overcome these consequences, using EVs charging strategies is becoming of tremendous importance. We propose in this paper a new approach aiming at minimizing the EVs charging cost based on the day-ahead electricity price (DAEP) and battery degradation cost subject to the EVs state of charge (SOC) limits, the EVs maximum power charger, the EVs batteries full charging at the end of the charging period and the distribution feeder subscribed power. Besides, to deal with the EVs arrival and departure time uncertainties, Monte Carlo Simulations (MCS) have been applied based on the probability density functions of these parameters, while the EV’s initial SOC uncertainties are estimated based on their daily mileage. Finally, to show the efficiency of the proposed approach, a single phase Low Voltage (LV) distribution network in a residential area has been deployed with an EVs penetration rate of 50% and 100%. In this study, the optimization problem is solved using linear programming method. The results show that the proposed approach allows to reduce the EVs charging cost by 50% and 38% for 100% and 50% of EVs penetration rate respectively compared to uncoordinated EVs charging.
| Original language | English |
|---|---|
| Article number | 100240 |
| Journal | Sustainable Energy, Grids and Networks |
| Volume | 19 |
| DOIs | |
| Publication status | Published - 1 Sept 2019 |
Keywords
- Management policy of Electric Vehicles
- Day-ahead electricity price
- Electric Vehicles
- Initial state of charge
- Linear programming
- Monte Carlo method
- Residential area