4,620 research outputs found
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
Minimizing the impact of EV charging on the electricity distribution network
The main objective of this paper is to design electric vehicle (EV) charging
policies which minimize the impact of charging on the electricity distribution
network (DN). More precisely, the considered cost function results from a
linear combination of two parts: a cost with memory and a memoryless cost. In
this paper, the first component is identified to be the transformer ageing
while the second one corresponds to distribution Joule losses. First, we
formulate the problem as a non-trivial discrete-time optimal control problem
with finite time horizon. It is non-trivial because of the presence of
saturation constraints and a non-quadratic cost. It turns out that the system
state, which is the transformer hot-spot (HS) temperature here, can be
expressed as a function of the sequence of control variables; the cost function
is then seen to be convex in the control for typical values for the model
parameters. The problem of interest thus becomes a standard optimization
problem. While the corresponding problem can be solved by using available
numerical routines, three distributed charging policies are provided. The
motivation is threefold: to decrease the computational complexity; to model the
important scenario where the charging profile is chosen by the EV itself; to
circumvent the allocation problem which arises with the proposed formulation.
Remarkably, the performance loss induced by decentralization is verified to be
small through simulations. Numerical results show the importance of the choice
of the charging policies. For instance, the gain in terms of transformer
lifetime can be very significant when implementing advanced charging policies
instead of plug-and-charge policies. The impact of the accuracy of the non-EV
demand forecasting is equally assessed.Comment: 6 pages, 3 figures, keywords: electric vehicle charging, electricity
distribution network, optimal control, distributed policies, game theor
Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid
A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage
Optimal Charging of Electric Vehicles in Smart Grid: Characterization and Valley-Filling Algorithms
Electric vehicles (EVs) offer an attractive long-term solution to reduce the
dependence on fossil fuel and greenhouse gas emission. However, a fleet of EVs
with different EV battery charging rate constraints, that is distributed across
a smart power grid network requires a coordinated charging schedule to minimize
the power generation and EV charging costs. In this paper, we study a joint
optimal power flow (OPF) and EV charging problem that augments the OPF problem
with charging EVs over time. While the OPF problem is generally nonconvex and
nonsmooth, it is shown recently that the OPF problem can be solved optimally
for most practical power networks using its convex dual problem. Building on
this zero duality gap result, we study a nested optimization approach to
decompose the joint OPF and EV charging problem. We characterize the optimal
offline EV charging schedule to be a valley-filling profile, which allows us to
develop an optimal offline algorithm with computational complexity that is
significantly lower than centralized interior point solvers. Furthermore, we
propose a decentralized online algorithm that dynamically tracks the
valley-filling profile. Our algorithms are evaluated on the IEEE 14 bus system,
and the simulations show that the online algorithm performs almost near
optimality ( relative difference from the offline optimal solution) under
different settings.Comment: This paper is temporarily withdrawn in preparation for journal
submissio
Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems
Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management
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