Abstract
The scheduling of maintenance actions of generators is not a new problem but gained in recent years a new interest with the advent of electricity markets because inadequate schedules can have a significative impact on the revenues of generation companies. In this paper we report the research on this topic developed during the preparation of the MSc Thesis of the second author. The scheduling problem of generator maintenance actions is formulated as a mixed integer optimization problem in which we aim at minimizing the operation cost along the scheduling period plus a penalty on energy not supplied. This objective function is subjected to a number of constraints detailed in the paper and it includes binary variables to indicate that a generator is in maintenance in a given week. This optimisation problem was solved using Simulated Annealing. Simulated Annealing is a very appealing metaheuristic easily implemented and providing good results in numerous optimization problems. The paper includes results obtained for a Case Study based on a realistic generation system that includes 29 generation groups. This research work was proposed and developed with the collaboration of the third and fourth authors, from EDP Producao, Portugal.

Abstract
This paper presents a model to solve the Generation Expansion Planning (GEP), problem in competitive electricity markets. The developed approach recognizes the presence of several generation agents aiming at maximizing their profits and that the planning environment is influenced by uncertainties affecting the demand, fuel prices, investment and maintenance costs and the electricity price. Several of these variables have interrelations between them turning it important to develop an approach that adequately captures the long-run behavior of electricity markets. In the developed approach we used System Dynamics to capture this behavior and to characterize the evolution of electricity prices and of the demand. Using this information, generation agents can then prepare their individual expansion plans. The resulting individual optimization problems have a mixed integer nature, justifying the use of Genetic Algorithms (GAs). Once individual plans are obtained, they are input once again on the System Dynamics model to update the evolution of the price, of the demand and of the capacity factors. This defines a feedback mechanism between the individual expansion planning problems and the long-term System Dynamics model. This approach can be used by a generation agent to build a robust expansion plan in the sense it can simulate different reactions of the other competitors and also by regulatory or state agencies to investigate the impact of regulatory decisions on the evolution of the generation system. Finally, the paper includes a Case Study to illustrate the use and the results of this approach.

Abstract
This paper presents an impact analysis of communications in frequency and active power control in hierarchic multi-microgrid structures. Since communications can potentially introduce significant difficulties in the operation of electric power systems, particularly in the case of islanded multi-microgrids, an assessment is performed considering delays and losses that control information is likely to experience when traversing the communication infrastructure. Results show that the impact of communication difficulties under most simulated scenarios is reduced, whereas in more demanding scenarios the control structure can have a significant role in minimizing the impact of communication failures.

Abstract
This paper presents a conceptual framework to successfully integrate electric vehicles into electric power systems. The proposed framework covers two different domains: the grid technical operation and the electricity markets environment. All the players involved in both these processes, as well as their activities, are described in detail. Additionally, several simulations are presented in order to illustrate the potential impacts/benefits arising from the electric vehicles grid integration under the referred framework, comprising steady-state and dynamic behavior analysis.

Abstract
Bidirectional and efficient on-board charger systems for electric vehicles is a challenge nowadays. This paper describes a novel power converter system that implements bidirectional flow between the grid and an electric vehicle battery, where a dual active bridge is advantageous. With a bidirectional topology and proper control all the major grid constraints, such as power quality, harmonic rejection, active and reactive power control, and others, can be easily satisfied. A hardware prototype of the power converter is built. Experimental results from this hardware prototype verify the preliminary operation and claims of the V2G power interface.

Abstract
Large deployment of Plug-in Hybrid Electric Vehicles (PHEVs) will put new challenges regarding the power systems operation. The MicroGrid (MG) concept can be exploited to support the progressive integration of PHEVs into the Low Voltage (LV) networks by developing smart charging strategies to manage the PHEVs batteries charging procedures in order to avoid reinforcements in the grid infrastructures. Assuming that a number of PHEVs owners allow managing the batteries charging when their cars are parked, this paper proposes an approach that aims to find suitable individual active power set-points corresponding to the hourly charging rate of each PHEV battery connected to the LV grid. The Evolutionary Particle Swarm Optimization (EPSO) tool is used to find these active power set points. This requires an additional software module to be housed in the MV/LV secondary substation level, called Optimal Power Set-points Calculator (OPSC). © 2011 IEEE.