Abstract
In this paper, a mixed-integer linear programing (MILP) model for the stochastic clearing of electricity markets with probabilistic participants is proposed. It is assumed that the sources of uncertainty in the market comes both from generation and demand side. The wind generating unit and electric vehicle aggregator are the supposed sources of uncertainty in the system. For the compensation of probable deviation of stochastic participants, flexible generation and demand will offer for the reserve activation. The two-stage model takes into account the day-ahead cost as well as the expected balancing costs due to probabilistic behavior of uncertain participants. A scenario-based approach is used to model the probabilistic participants. The proposed model stochastically clears the market and the results discuss the lower costs obtained by incorporating various resources of uncertainty and flexibility in the market.

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
In this article, we present a holistic framework for the integration of electric vehicles (EVs) in electric power systems. Their charging management and control methodologies must be optimized to minimize the negative impact of the charging process on the grid and maximize the benefits that charging controllability may bring to their owners, energy retailers, and system operators. We have assessed the performance of these methods initially through steady-state computational simulations, and then we validated them in a microgrid (MG) laboratory environment.

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.