Adaptive Electric Vehicle Charging Coordination on Distribution Network

• Published in: IEEE transactions on smart grid Vol. 5; no. 6; pp. 2666 - 2675
• Main Authors: Lunci Hua, Wang, Jia, Zhou, Chi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation methods; Demand coordination; distribution grid; electric vehicle (EV); Electric vehicles; Load modeling; Optimization; optimization model; Real-time systems; Scheduling; smart grid; Demand coordination; optimization model; distribution grid; smart grid; electric vehicle (EV)
• DOI: 10.1109/TSG.2014.2336623
• ISSN: 1949-3053

Electric vehicles (EVs) with large battery charging demands may cause detrimental impact on distribution grid stability without EV charging coordination. This paper proposes an on-line adaptive EV charing scheduling (OACS) framework to optimize EV charging schedules and reduce flow limit, voltage magnitude limit, 3-phase voltage imbalance limit, and transformer capacity violations. EV user convenience is considered and EV charging cost is optimized. DC power flow based optimizations is proposed for EV charging scheduling approximation and parallel ac power flow verification is used to verify the scheduling results. Incremental feasibility improvement procedure is further proposed to correct the scheduling discrepancy between dc linear model and the ac model. Experiments are performed on a modified IEEE 34 14.7 kV distribution system with different EV penetration levels to demonstrate performance comparisons between different scheduling schemes. The result shows that our proposed OACS framework optimizes the EV charging coordination problem efficiently.