Abstract
This paper proposes a novel Energy Aggregator model responsible for managing the flexibility of low voltage customers in order to reduce electricity costs. The flexibility of the customers is represented by the availability of their controllable loads to reduce/ increase power consumption. The flexible loads are managed according to the customers' preferences and the technical limitations of the flexible loads. The Energy Aggregator model developed includes an algorithm designed to manage customers' flexibility in quasi-real-time, with the objective of minimizing the deviations from the energy bought by the aggregator in the market. A scenario with 30 households located in a semi-urban area is used to illustrate the application of the algorithm and validate the proposed approach.

Abstract
This work proposes an innovative method based on autoencoders to perform state estimation in distribution grids, which has as main advantage the fact of being independent of the network parameters and topology. The method was tested in a real low voltage grid (incorporating smart grid features), under different scenarios of smart meter deployment. Simulations were performed in order to understand the necessary requirements for an accurate distribution grid state estimator and to evaluate the performance of a state estimator based on autoencoders.

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This paper evaluates the benefits of introducing electricity powered vehicles (EV) in one island of the Azores archipelago in Portugal, the island of Flores. A Life Cycle Analysis (LCA) for the road transportation sector including EV was performed and the EV impacts in the electricity grid were evaluated. Two scenarios considering the introduction of EV were considered (Scenarios 2 and 3 with a shift in the car stock in 2050 of 30% and 70% to EV) and compared to the baseline scenario (Scenario 1 with no EV penetration). The results show that, if no alternative solutions are adopted, the road transportation sector LCA energy consumption will increase 58% in 2050, compared to 2009. In the most attractive scenario studied regarding EV integration in Flores, the LCA energy consumption in 2050 decreases by 34% and CO2 emissions by 39%, when comparing with Scenario 1. Moreover, the island's electricity network is ready for EV arrival, at least until 2020. Thereafter, a smart charging scheme should be implemented to manage the vehicles' energy demand according to the network technical limitations and the presence of Renewable Energy Sources (RES) should be reinforced, to decrease the island's dependency on fossil fuels and, consequently, CO2 emissions.

Abstract
This work presents a methodology to manage Electric Vehicles (EVs) charging in quasi-real-time, considering the participation of EV aggregators in electricity markets and the technical restrictions of the electricity grid components, controlled by the Distribution System Operator. Two methodologies are presented in this paper to manage EV charging, one to be used by the EV aggregators and the other by the Distribution System Operator (DSO). The methodology developed for the aggregator has as main objective the minimization of the deviation between the energy bought in the market and the energy consumed by EVs. The methodology developed for the DSO allows it to manage the grid and solve operational problems that may appear by controlling EVs charging. A method to generate a synthetic EV data set is used in this work, providing information about EV movement, including the periods when EVs are parked and 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.