2,219 research outputs found
Mixed integer nonlinear programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations
The problem of joint coordination of plug-in electric vehicles (PEVs)
charging and grid power control is to minimize both PEVs charging cost and
energy generation cost while meeting both residential and PEVs' power demands
and suppressing the potential impact of PEVs integration. A bang-bang PEV
charging strategy is adopted to exploit its simple online implementation, which
requires computation of a mixed integer nonlinear programming problem (MINP) in
binary variables of the PEV charging strategy and continuous variables of the
grid voltages. A new solver for this MINP is proposed. Its efficiency is shown
by numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0445
Linearized Asymptotic Stability for Fractional Differential Equations
We prove the theorem of linearized asymptotic stability for fractional
differential equations. More precisely, we show that an equilibrium of a
nonlinear Caputo fractional differential equation is asymptotically stable if
its linearization at the equilibrium is asymptotically stable. As a consequence
we extend Lyapunov's first method to fractional differential equations by
proving that if the spectrum of the linearization is contained in the sector
\{\lambda \in \C : |\arg \lambda| > \frac{\alpha \pi}{2}\} where
denotes the order of the fractional differential equation, then the equilibrium
of the nonlinear fractional differential equation is asymptotically stable
Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations
Next-generation power grids will likely enable concurrent service for
residences and plug-in electric vehicles (PEVs). While the residence power
demand profile is known and thus can be considered inelastic, the PEVs' power
demand is only known after random PEVs' arrivals. PEV charging scheduling aims
at minimizing the potential impact of the massive integration of PEVs into
power grids to save service costs to customers while power control aims at
minimizing the cost of power generation subject to operating constraints and
meeting demand. The present paper develops a model predictive control (MPC)-
based approach to address the joint PEV charging scheduling and power control
to minimize both PEV charging cost and energy generation cost in meeting both
residence and PEV power demands. Unlike in related works, no assumptions are
made about the probability distribution of PEVs' arrivals, the known PEVs'
future demand, or the unlimited charging capacity of PEVs. The proposed
approach is shown to achieve a globally optimal solution. Numerical results for
IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this
approach
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