925 research outputs found
Multiple matrix rank constrained optimization for optimal power flow over large scale transmission networks
© Copyright 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. The optimal power flow (OPF) problem for power transmission networks is an NP-hard optimization problem with numerous quadratic equality and indefinite quadratic inequality constraints on bus voltages. The existing nonlinear solvers often fail in yielding a feasible solution. In this paper, we follow our previously developed nonsmooth optimization approach to address this difficult large-scale OPF problem, which is an iterative process to generate a sequence of improved solutions that converge to an optimal solution. Each iteration calls an SDP of a moderate dimension. Intensive simulations for OPF over networks with a large number of buses are provided to demonstrate the efficiency of our approach
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
Using EPECs to model bilevel games in restructured electricity markets with locational prices
CWPE0619 (EPRG0602) Xinmin Hu and Daniel Ralph (Feb 2006) Using EPECs to model bilevel games in restructured electricity markets with locational prices We study a bilevel noncooperative game-theoretic model of electricity markets with locational marginal prices. Each player faces a bilevel optimization problem that we remodel as a mathematical program with equilibrium constraints, MPEC. This gives an EPEC, equilibrium problem with equilibrium constraints. We establish sufficient conditions for existence of pure strategy Nash equilibria for this class of bilevel games and give some applications. We show by examples the effect of network transmission limits, i.e. congestion, on existence of equilibria. Then we study, for more general EPECs, the weaker pure strategy concepts of local Nash and Nash stationary equilibria. We model the latter via complementarity problems, CPs. Finally, we present numerical examples of methods that attempt to find local Nash or Nash stationary equilibria of randomly generated electricity market games. The CP solver PATH is found to be rather effective in this context
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
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
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