Abstract
This paper describes a novel Electric Vehicle (EV) charging management system which was designed to control the EV load considering simultaneously the EV owners requirements and the electrical network technical limitations. The system was developed to be integrated with existing commercial equipment for smart grids, such as distribution transformer controllers, SCADA systems and Electrical Vehicles Charging Stations. The performance of the smart charging system was evaluated using a typical Portuguese low voltage network as test case, where several EV were assumed to exist. The results obtained prove the effectiveness of the system, as it allowed charging all the EV according to their owners' preferences without increasing the network peak load or creating voltages or overload problems.

Abstract
This paper presents an analysis of the impacts of photovoltaics and storage units for self-consumption in the day-ahead spot prices. A methodology is proposed, to access these impacts in the Iberian electricity market for 2015, 2020 and 2030.

Abstract
The GReSBAS project (2016-2019) aims to enable the active participation of buildings in DR programs through gamified competition between building owners. In case of large buildings, gamified competition can be established within the building for its occupants, for instance having different floors of the building competing between them. This approach will allow building owners to reduce electricity costs and increase energy efficiency by motivating/rewarding building occupants for participating in DR programs. The concepts and tools developed under GReSBAS will be tested in two demonstration sites: a corporate building in Portugal and a residential building in Turkey. This paper presents the Portuguese demonstration site and describes how the energy consumption, temperature and building occupancy data will be collected, processed and used by the tools developed in GReSBAS.

Abstract
This work addresses the possibility of integrating Electric Vehicles (EV) in the Automatic Generation Control (AGC) operation for provision of secondary reserves in interconnected systems, contributing to reduce the requirements for conventional secondary reserves in systems with large scale integration of renewable sources. A test system was used to assess the performance of EV as secondary reserve providers, when facing the loss of conventional generation as well as the loss of a large amount of wind power production.

Abstract
This paper proposes a two-stage stochastic optimization model to support an aggregator of prosumers in the definition of bids for the day-ahead energy and secondary reserve markets. The aggregator optimizes the prosumers' flexibility with the objective of minimizing the net cost of buying and selling energy and secondary reserve in both day-ahead and real-time market stages. The uncertainties of the renewable generation, consumption, outdoor temperature, prosumers' preferences, and house occupancy are modeled through a set of scenarios. For a case study of 1000 prosumers, the results show that the proposed bidding strategy reduces the costs of both aggregator and prosumers by 40% compared to a bidding strategy typically used by retailers.

Abstract
Optimizing the participation of a large number of prosumers in the electricity markets is a challenging problem, especially for portfolios with thousands or millions of flexible resources. To address this problem, this paper proposes a cluster-based optimization approach to support an aggregator in the definition of demand and supply bids for the day-ahead energy market. This approach consists of two steps. In the first step, the aggregated flexibility of the entire portfolio is computed by a centroid-based clustering algorithm. In the second step, the supply and demand bids are defined by an optimization model that can assume the form of a deterministic or a two-stage stochastic problem. A case study of 10,000 prosumers from the Iberian market is used to evaluate and compare the performance of the bidding optimization models with and without pre-clustering. The numerical results show that the optimized bidding strategies outperform an inflexible strategy by more than 20% of cost savings. The centroid-based clustering algorithm reduces effectively the execution times of the bidding optimization problems, without affecting the quality of the energy bids.