Abstract
This paper describes an approach to model and to solve the Generation Expansion Planning Problem, GEP, using Genetic Algorithms. This approach was developed in order to help investors in new generation capacity to take decisions regarding new investments. This approach was developed in the scope of the implementation of electricity markets given that they eliminated the traditional centralized planning activities leading to the creation of several generation companies competing to supply the demand. As a result, the generation activity is more risky than in the past and so it becomes important to develop new tools to help decision makers to analyze the investment alternatives, having in mind the possible behavior of the competitors. The developed model aims at maximizing the expected profits that will be obtained by an investor, while it evaluates the reliability and the security of supply and it incorporates uncertainties related with the volatility of electricity prices, with the reliability of generation groups, with the evolution of the demand, and with the operation and investment costs The developed model and the implemented solution algorithm will be applied to a Case Study to illustrate the use of the developed approach to build the expansion plans. © 2013, Springer Science+Business Media Dordrecht.

Abstract
This work presents a methodology to manage Electric Vehicles (EV) charging in quasi-real-time, considering the participation of EV aggregators in electricity markets and the technical restrictions of the electricity grid components, controlled through the distribution system operator. Two methodologies are presented to manage EV charging, one to be used by the EV aggregators and the other by the Distribution System Operators (DSO). The methodology developed for the aggregator has as main objective minimizing the deviation between the energy bought in the market and the energy consumed by EV. The methodology developed for the DSO allows it to manage the grid and solve operational problems that may appear by controlling EV charging. A method to generate a synthetic EV data set is used in this work, which provides information about the EV movement, periods when EV are parked, as well as their energy requirements. This data set is used afterwards to assess the performance of the algorithms developed to manage the EV charging in quasi-real-time.

Abstract
Current electrical distribution systems are facing significant challenges due to the widespread deployment of Distributed Energy Resources (DER), particularly the integration of variable Renewable Energy Sources (RES). This requires a change in the paradigm of distribution grids from a purely passive perspective into fully active networks within the smart grid vision. This new paradigm involves new control and management architectures as well as advanced planning methods and operational tools for distribution systems exploiting a smart metering infrastructure. This infrastructure will enable leveraging data from smart meters and short-term forecasts of load demand and RES in order to manage the distribution system in a more efficient and cost-effective way, thus enabling large scale integration of RES. Future tests to be carried out in a new, state of the art laboratory environment will bring additional added-value to the validation of the proposed concepts and tools.

Abstract
Microgrids are assumed to be established at the low voltage distribution level, where distributed energy sources, storage devices, controllable loads and electric vehicles are integrated in the system and need to be properly managed. The microgrid system is a flexible cell that can be operated connected to the main power network or autonomously, in a controlled and coordinated way. The use of storage devices in microgrids is related to the provision of some form of energy buffering during autonomous operating conditions, in order to balance load and generation. However, frequency variations and limited storage capacity might compromise microgrid autonomous operation. In order to improve microgrid resilience in the moments subsequent to islanding, this paper presents innovative functionalities to run online, which are able to manage microgrid storage considering the integration of electric vehicles and load responsiveness. The effectiveness of the proposed algorithms is validated through extensive numerical simulations.

Abstract
The feasibility of the MicroGrid (MG) concept, as the pathway for integrating Electric Vehicles (EV) and other Distributed energy Resources (DER), has been the focus of several research projects around the world. However, developments have been mainly demonstrated through numerical simulation. Regarding effective smart grid deployment, strong effort is required in demonstration activities, addressing the feasibility of innovative control solutions and the need of specific communication requirements. Therefore, the main objective of this paper is to provide an integrated overview of the laboratorial infrastructure under development at INESC Porto, where it will be possible to conceptualize, implement and test the performance of new control and management concepts for Smart Grid cells. The laboratorial infrastructure integrates two experimental MG, including advanced prototypes for power conditioning units to be used in micro generation applications, batteries for energy storage and a fully controlled bidirectional power converter. Preliminary experimental results and organization of the infrastructure are presented.

Abstract
The development of the microgrid (MG) concept endows distribution networks with increased reliability and resilience and offers an adequate management and control solution for massive deployment of microgeneration and electric vehicles (EVs). Within an MG, local generation can be exploited to launch a local restoration procedure following a blackout. EVs are flexible resources that can also be actively included in the restoration procedure, thus contributing to the improvement of MG operating conditions. The feasibility of MG service restoration, including the active participation of EVs, is demonstrated in this article through extensive numerical simulation and experimentation in a laboratorial setup. © 2007-2011 IEEE.