Monte Carlo simulations of the classical two-dimensional discrete frustrated ϕ4\phi ^4 model

The classical two-dimensional discrete frustrated $\phi ^4$ model is studied by Monte Carlo simulations. The correlation function is obtained for two values of a parameter $d$ that determines the frustration in the model. The ground state is a ferro-phase for $d=-0.35$ and a commensurate phase with period N=6 for $d=-0.45$. Mean field predicts that at higher temperature the system enters a para-phase via an incommensurate state, in both cases. Monte Carlo data for $d=-0.45$ show two phase transitions with a floating-incommensurate phase between them. The phase transition at higher temperature is of the Kosterlitz-Thouless type. Analysis of the data for $d=-0.35$ shows only a single phase transition between the floating-fluid phase and the ferro-phase within the numerical error.Comment: 5 figures, submitted to the European Physical Journal

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

Correlated adatom trimer on metal surface: A continuous time quantum Monte Carlo study

The problem of three interacting Kondo impurities is solved within a numerically exact continuous time quantum Monte Carlo scheme. A suppression of the Kondo resonance by interatomic exchange interactions for different cluster geometries is investigated. It is shown that a drastic difference between the Heisenberg and Ising cases appears for antiferromagnetically coupled adatoms. The effects of magnetic frustrations in the adatom trimer are investigated, and possible connections with available experimental data are discussed.Comment: 4 pages, 4 figure

Optimal actuator/sensor selection through dynamic output feedback

© 2016 IEEE. This paper is devoted to the problem of optimal selection of a subset of available actuators/sensors through a multi-channel H22 dynamic output feedback controller for continuous linear time invariant systems. Incorporating two extra terms for penalizing the number of actuators and sensors into the optimization objective function, we develop an iterative process to identify the favorable row/column-wise sparse DOF gains. Employing the identified structure, we solve the constructed row/column structured multi-channel H22 DOF problem in order to derive a gain that exploits optimum number of sensors/actuators by which the closed-loop stability is maintained and the performance degradation of the closed-loop system is restricted. Through an example we demonstrate the remarkable performance and broad applicability of the proposed 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

A framework for optimal actuator/sensor selection in a control system

© 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group. When dealing with large-scale systems, manual selection of a subset of components (sensors/actuators), or equivalently identification of a favourable structure for the controller, that guarantees a certain closed-loop performance, is not very feasible. This paper is dedicated to the problem of concurrent optimal selection of actuators/sensors which can equivalently be considered as the structure identification for the controller. In the context of a multi-channel H 2 dynamic output feedback controller synthesis, we formulate and analyse a framework in which we incorporate two extra terms for penalising the number of actuators and sensors into the variational formulations of controller synthesis problems in order to induce a favourable controller structure. We then develop an explicit scheme as well as an iterative process for the purpose of dealing with the multi-objective problem of controller structure and control law co-design. It is also stressed that the immediate application of the proposed approach lies within the fault accommodation stage of a fault tolerant control scheme. By two numerical examples, we demonstrate the remarkable performance of the proposed approach