992 research outputs found
Optimal Decentralized Protocols for Electric Vehicle Charging
We propose decentralized algorithms for optimally scheduling electric vehicle charging. The algorithms exploit the elasticity and controllability of electric vehicle related loads in order to fill the valleys in electric demand profile. We formulate a global optimization problem whose objective is to impose a generalized notion of valley-filling, study properties of the optimal charging profiles, and give decentralized offline and online algorithms to solve the problem. In each iteration of the proposed algorithms, electric vehicles choose their own charging profiles for the rest horizon according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. The offline algorithms are guaranteed to converge to optimal charging profiles irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles at the expense of a restrictive assumption that all electric vehicles are available for negotiation at the beginning of the planning horizon. The online algorithms relax this assumption by using a scalar prediction of future total charging demand at each time instance and yield near optimal charging profiles. The proposed algorithms need no coordination among the electric vehicles, hence their implementation requires low communication and computation capability. Simulation results are provided to support these results
Continuous-time integral dynamics for Aggregative Game equilibrium seeking
In this paper, we consider continuous-time semi-decentralized dynamics for
the equilibrium computation in a class of aggregative games. Specifically, we
propose a scheme where decentralized projected-gradient dynamics are driven by
an integral control law. To prove global exponential convergence of the
proposed dynamics to an aggregative equilibrium, we adopt a quadratic Lyapunov
function argument. We derive a sufficient condition for global convergence that
we position within the recent literature on aggregative games, and in
particular we show that it improves on established results
Charging Scheduling of Electric Vehicles with Local Renewable Energy under Uncertain Electric Vehicle Arrival and Grid Power Price
In the paper, we consider delay-optimal charging scheduling of the electric
vehicles (EVs) at a charging station with multiple charge points. The charging
station is equipped with renewable energy generation devices and can also buy
energy from power grid. The uncertainty of the EV arrival, the intermittence of
the renewable energy, and the variation of the grid power price are taken into
account and described as independent Markov processes. Meanwhile, the charging
energy for each EV is random. The goal is to minimize the mean waiting time of
EVs under the long term constraint on the cost. We propose queue mapping to
convert the EV queue to the charge demand queue and prove the equivalence
between the minimization of the two queues' average length. Then we focus on
the minimization for the average length of the charge demand queue under long
term cost constraint. We propose a framework of Markov decision process (MDP)
to investigate this scheduling problem. The system state includes the charge
demand queue length, the charge demand arrival, the energy level in the storage
battery of the renewable energy, the renewable energy arrival, and the grid
power price. Additionally the number of charging demands and the allocated
energy from the storage battery compose the two-dimensional policy. We derive
two necessary conditions of the optimal policy. Moreover, we discuss the
reduction of the two-dimensional policy to be the number of charging demands
only. We give the sets of system states for which charging no demand and
charging as many demands as possible are optimal, respectively. Finally we
investigate the proposed radical policy and conservative policy numerically
- …