Name
Abstract | ||
|---|---|---|
From the view point of a charging station (CS), it is important to design a simple, effective and implementable algorithm that reduces the cost, improves the time efficiency and enhances operational stability. Offline algorithms built on global information, in practice, cannot be implemented to achieve the best performance, since current charging rates of existing electric vehicles (EVs) need to be determined in the absence of future information. In the context of a current electricity tariff mechanism, commonly imposed in industry, which additionally charges for peak demand, this paper proposes an online two-stage charging scheduling algorithm (OTCSA) based on observed real time information and historical data to minimize charging cost, reduce charging time, as well as lower the maximum peak power. In the first stage, charging cost is minimized with guarantee to fulfill energy demand of each EV before its departure. The additional cost that penalizes peak demand inherently contributes to flattening the load profile of the CS with the deferrability of EV charging. In the second stage, we squeeze to save more charging time for EVs given the minimal cost. Simulations further validate the three-fold benefits of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCA.2019.8899991 | 2019 IEEE 15th International Conference on Control and Automation (ICCA) |
Keywords | Field | DocType |
Charging station,optimization,electric vehicles,coordinated charging | Automotive engineering,Real-time data,Scheduling (computing),Charging station,Electricity,Load profile,Tariff,Control engineering,Peak demand,Energy demand,Engineering | Conference |
ISSN | ISBN | Citations |
1948-3449 | 978-1-7281-1165-0 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honglin Fan | 1 | 0 | 0.34 |
| Shibo Chen | 2 | 0 | 0.68 |
| Zhenwei Guo | 3 | 2 | 2.39 |
| Pengcheng You | 4 | 0 | 0.34 |
| Zaiyue Yang | 5 | 188 | 15.59 |