2 research outputs found
Optimal V2G scheduling of electric vehicles and unit commitment using chemical reaction optimization
An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. In this paper, we propose a new formulation of the joint scheduling of EV and Unit Commitment (UC), called EVUC. Our formulation considers the characteristics of EVs while optimizing the system total running cost. We employ Chemical Reaction Optimization (CRO), a general-purpose optimization algorithm to solve this problem and the simulation results on a widely used set of instances indicate that CRO can effectively optimize this problem. © 2013 IEEE.published_or_final_versio
Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions
To enhance environmental sustainability, many countries will electrify their
transportation systems in their future smart city plans. So the number of
electric vehicles (EVs) running in a city will grow significantly. There are
many ways to re-charge EVs' batteries and charging stations will be considered
as the main source of energy. The locations of charging stations are critical;
they should not only be pervasive enough such that an EV anywhere can easily
access a charging station within its driving range, but also widely spread so
that EVs can cruise around the whole city upon being re-charged. Based on these
new perspectives, we formulate the Electric Vehicle Charging Station Placement
Problem (EVCSPP) in this paper. We prove that the problem is non-deterministic
polynomial-time hard. We also propose four solution methods to tackle EVCSPP
and evaluate their performance on various artificial and practical cases. As
verified by the simulation results, the methods have their own characteristics
and they are suitable for different situations depending on the requirements
for solution quality, algorithmic efficiency, problem size, nature of the
algorithm, and existence of system prerequisite.Comment: Submitted to IEEE Transactions on Smart Grid, revise