This paper presents a holistic framework for electric vehicles integration in electric power systems together with their charging management and control methodologies that allow minimizing the negative impacts in the grid of the charging process and maximize the benefits that charging controllability may bring to their owners, energy retailers and system operators. The performance of these management and control methods will be assessed through steady state computational simulations and then validated in a microgrid laboratory environment.

The Smart Grid vision along with the future deployment of Electric Vehicles presents numerous challenges in terms of grid infrastructure, communication, and control. In this context, Advanced Metering Infrastructure solutions are envisioned to be the active management link between utilities and consumers. This paper presents a survey of potential AMI functionalities particularly developed to foster the large scale deployment of EV in Smart Grids. For this accomplishment, the concepts of Automated Meter Reading, Automatic Meter Management and Smart Metering are revisited. Furthermore, different EV charging approaches are outlined and included in the functionalities under the Vehicle-To-Grid framework. Finally, AMI use cases are described under the Vehicle-to-Home perspective.

a b s t r a c tThe consolidation of smart grids is inevitably related with the development and actual implementation of different functionalities envisioned for future electric grids. This paper presents the major implementations of smart grid projects in Portugal, which resulted from a close collaboration between academia and industry. An overview of the entire development process is presented culminating with the real implementation of the developed concepts. The architectures and functional models are presented as the initial step in defining the management and control functionalities for future smart distribution networks. The intermediate step consists in validating the advances introduced by smart grids. Simulation tools are emphasized considering both electrical and communications aspects. Finally, a laboratory infrastructure implemented to be used as a real test bed and a pilot deployed in a large city are presented in the end. The associated learning has provided relevant information for future developments.

This paper aims at presenting a Home Energy Management System (HEMS) module capable of scheduling electric water heater (EWH) appliances in order to optimize the PV self-consumption. A multi-period optimization model is presented. Laboratory tests were conducted to validate the model and to demonstrate the capability of this HEMS module to address recent challenges of self-consumption in a domestic environment. A commercial EWH device developed by Bosch communicating with the HEMS module is used to perform the tests.I.

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Home energy management systems (HEMSs) are important platforms to allow consumers the use of flexibility in their consumption to optimise the total energy cost. The optimisation procedure embedded in these systems takes advantage of the nature of the existing loads and the generation equipment while complying with user preferences such as air temperature comfort configurations. The complexity in finding the best schedule for the appliances within an acceptable execution time for practical applications is leading not only to the development of different formulations for this optimisation problem, but also to the exploitation of non-deterministic optimisation methods as an alternative to traditional deterministic solvers. This study proposes the use of genetic algorithms (GAs) and the cross-entropy method (CEM) in low-power HEMS to solve a conventional mixedinteger linear programming formulation to optimise the total energy cost. Different scenarios for different countries are considered as well as different types of devices to assess the HEMS operation performance, namely, in terms of outputting fast and feasible schedules for the existing devices and systems. Simulation results in low-power HEMS show that GAs and the CEM can produce comparable solutions with the traditional deterministic solver requiring considerably less execution time.