A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles. (15th April 2016) - He, Lifu; Yang, Jun; Yan, Jun; Tang, Yufei; He, Haibo
Highlights: A bi-layer scheme is proposed to optimize EVs charging and discharging behavior. The charging loads allocation in both temporal and spatial domain is optimized. Both the transmission system level and distribution system level are considered. The uncertainty and volatility of wind power are considered by using the scenarios. Method to evaluate the performance of proposed scheme is elaborated and demonstrated. Abstract: Electric vehicle (EV) is a promising, environmental friendly technique for its potential to reduce the using of fossil fuels. Massive EVs pose both opportunities and challenges for power systems, especially with the growing amount of wind-power integration. This paper investigates the problem of collaborative optimization scheduling of generators, EVs and wind power. A novel bi-layer optimization of transmission and distribution system is proposed to solve the scheduling problem of EVs charging and discharging load from respective time and space domain in the presence of wind-power. The upper layer optimization in transmission grid coordinates EVs with thermal generators, base load, with the consideration of wind power, to optimize load periods of EVs in the time domain. The lower layer optimization in distribution grid then spatially schedules the location of EVs load. Based on a power system benchmark with a 10-unit transmission grid and an IEEE 33-bus distribution grid, the performance of the proposed bi-layer optimization strategy is evaluated. The impacts of electricity price profile, EVs penetration and EVs load location are analyzed. Simulation results show that the proposed bi-layer optimization strategy can accommodate wind power and improve both the economics of grid operation and benefits of EV users by scheduling EVs charging and discharging temporally and spatially. Also, the results have shown that the location of EVs charging and discharging load is critical for the distribution network planning.