Abstract
Large-scale integration of variable Renewable Energy Sources (RES) brings significant challenges to grid operation that require new approaches and tools for distribution system management, particularly concerning voltage control. Therefore, an innovative approach for voltage control at the MV level is presented. It is based on a preventive day-ahead analysis that uses data from load/RES forecasting tools to establish a plan for operation of the different Distributed Energy Resources (DER) for the next day. The approach is formulated as a multi-temporal Optimal Power Flow (OPF) solved by a meta-heuristic, used to tackle complex multi-dimensional problems. The tuning of the meta-heuristic parameters was performed to ensure the robustness of the proposed approach and enhance the performance of the algorithm. It was tested through simulation in a large scale test network with good results.

Abstract
This paper presents a study about the influence of Distributed Energy Resources' (DER) flexibility on the operation of a Medium Voltage (MV) network, in a Smart Grid (SG) environment. An AC multi-temporal Optimal Power Flow (OPF) tool was developed and used to simulate the impact of the DER flexibility (including storage devices, EVs, controllable loads and micro-generation) in distribution network operation. Some simulations are presented, demonstrating the impact that DER flexibility can have on solving operation problems namely in terms of branch loading and voltage limits.

Abstract
Vast increase of renewable energy resources' (RER) share in total electricity production have led to evolving studies regarding different aspects of renewables integration. Other than their effects on network, the electricity markets are also affected by uncertain behavior of RERs in the market place. Hence, new approaches for market clearing are investigated. One of the possible solutions is the deployment of stochastic market clearing. However, the adaption of new market models should consider different market characteristics. As a result, this paper assesses the adaption of stochastic market in a hydro-penetrated system. The co-optimized energy and reserve schedule in the day-ahead time frame is derived using the mixed integer linear programming (MILP). The model is tested with Portuguese electricity market data as a real-case of hydro-penetrated system.

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
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
Demand-side solutions are one of the most important customer-dependent options among innovative smart grid technologies. Flexible loads can be controlled and coordinated in several ways to operate in favor of the grid. Contrary to conventional participators in grid services, responding to grid requests is not the primary objective of the owners of demand-side resources. Therefore, it is a vital task for demand side service operators to provide maximized and reliable participation. However, motivation factors may vary due to demographic characteristics of the society and there are important diversities due to cultural differences of countries. This study investigates consumer expectations, preferences and concerns on demand response (DR) and deployable gamification techniques in Turkey. A detailed survey is conducted with individuals and results are analyzed to evaluate general trends together with distinctive customer patterns.