Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76214
Title: HELOS : heterogeneous load scheduling for electric vehicle-integrated microgrids
Authors: Li, GX
Wu, D
Hu, JF 
Li, Y
Hossain, MS
Ghoneim, A
Keywords: Energy management
Electric vehicle (EV)
Load scheduling
Microgrid
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on vehicular technology, 2017, v. 66, no. 7, p. 5785-5796 How to cite?
Journal: IEEE transactions on vehicular technology 
Abstract: With increasing concerns about worldwide environmental conditions and rapid development of renewable energy technologies, microgrids have been regarded as a promising solution to reduce the burden of infrastructure-based power systems. However, due to the intrinsically intermittent features of existing renewable energy, along with random residential behavior patterns, unpredictable plugged-in or unplugged actions of electric vehicles (EVs) and the time-varying price of electricity, it is challenging for microgrid operators to efficiently perform load scheduling and energy management. In this paper, we propose an online algorithm to conduct cost-aware scheduling of EV loads and energy supplies for microgrids. We formulate this problem into a stochastic optimization problem with the objective of minimizing the time-average cost of amicrogrid, including the purchase cost of electricity from themain grid, the cost of charging and discharging batteries, renewable harvesting costs, and life-cycle greenhouse-gas emission costs. To solve this problem, the key idea is to exploit the dynamics of the price of electricity to conduct battery charging and discharging operations, renewable energy harvesting, and schedule EVloads properly. Ourmethod is based on the Lyapunov optimization technique, which has low computational complexity and only requires limited prediction of price information. The theoretical analysis of our algorithm confirms that the proposed strategy can achieve optimality with explicit bound. By conducting extensive real-data driven simulations, we demonstrate that our proposed algorithm can achieve much lower cost and be more eco-friendly than other alternative solutions.
URI: http://hdl.handle.net/10397/76214
ISSN: 0018-9545
EISSN: 1939-9359
DOI: 10.1109/TVT.2016.2636874
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