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The smart grid vision has resulted in many demand side innovations such as nonintrusive load monitoring techniques, residential micro-grids, and demand response programs. Many of these techniques need a detailed residential network model for their research, evaluation, and validation. In response to such a need, this paper presents a sequential Monte Carlo (SMC) simulation platform for modeling and simulating low voltage residential networks. This platform targets the simulation of the quasi-steady-state network condition over an extended period such as 24 h. It consists of two main components. The first is a multiphase network model with power flow, harmonic, and motor starting study capabilities. The second is a load/generation behavior model that establishes the operating characteristics of various loads and generators based on time-of-use probability curves. These two components are combined together through an SMC simulation scheme. Four case studies are presented to demonstrate the applications of the proposed platform. Index Terms-Demand response, low voltage residential networks, microgrids, network simulation, power quality.1949-3029 c 2014 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.Ricardo Torquato (S'11) is currently pursuing the M.Sc. degree in electrical engineering from the University of Campinas, Campinas, Brazil. He is currently a Visiting Student with the University of Alberta, Edmonton, AB, Canada. His research interests include power quality and analysis of distribution systems.Qingxin Shi (S'11) is currently pursuing the M.Sc. degree in electrical engineering from the University of Alberta, Edmonton, AB, Canada.His research interests include power quality and power signaling.Wilsun Xu (M'90-SM'95-F'05) received the Ph.D. degree in electrical engineering from the

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With the increasing penetration of plug‐in electric vehicles (EVs), it has become important for utilities to identify how EV charging will affect their low‐voltage (LV) systems. In this context, EV hosting capacity can be useful to assist utility engineers. However, the appropriate strategy to estimate this index and determine its practical application for utilities is still unclear. In response, this study provides a framework to obtain and apply the hosting capacity information for EVs. Results of analyses are obtained considering the whole universe of a utility. Firstly, a method is developed for estimating this index based only on information readily available to utility engineers. The method is then used in a wide‐scale assessment of EV hosting capacity on 75,550 real LV systems. Quantitative results of this study provide insights into how to manage a system with high EV penetration. It is seen, e.g. that EV charging location is the most important variable to consider when stochastically assessing EV impacts, reducing difficulties to apply this type of solution for practical cases. Practical applications that employ EV hosting capacity statistics are also presented and discussed. Results are shown to be useful not only for utilities but also for regulatory agencies.

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This paper proposes a real-time Energy Management System (EMS) for a low voltage (LV) Microgrid (MG). The system operation consists in solving the Unit Commitment (UC) and Economic Load Dispatch (ELD) simultaneously for 24 hours ahead at every 15-minute period. This operation is formulated as a multi-objective optimization problem where the minimization of operational cost, total emissions and power losses is simultaneously pursued using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In this algorithm, crossover and mutation operators were improved with respect to existing approaches to achieve an adequate characterization of the energy management problem and a good algorithm performance. Simulation studies have outlined that, in fact, the NSGA-II can be used as a real-time optimization tool providing a good-quality Pareto front to operate optimally the MG in a limited time of 15 minutes.Index Terms-Energy management system; economic dispatch; evolutionary algorithm; multi-objective optimization; microgrid; non-dominating sorting genetic algorithm, unit commitment.I.