Ricardo Jorge Ferreira

Abstract
Power systems rely on ancillary services (ASs) to ensure system security and stability. Until recently, only the conventional power generation resources connected to the transmission grids were allowed to provide these ASs managed by the transmission system operators (TSOs), while distribution system operators (DSOs) had a more passive role, focused on guaranteeing distribution capacity to bring power to final consumers with enough quality. Now, with the decarbonization, digitalization and decentralization processes of the electrical networks, the growing integration of distributed energy resources (DERs) in distribution grids are displacing conventional generation and increasing the complexity of distribution networks' operation, requiring the implementation of new active and coordinated management strategies between TSOs and DSOs. In this context, DERs are becoming potential new sources of flexibility for both TSOs and DSOs in helping to manage the power system. This paper proposes a systematic characterization of both traditional and potentially new ASs for TSOs, and newly expected DSO local system services to support the new distribution grid operation paradigm, reviewing, in addition, the main TSO-DSO coordination mechanisms.

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
In this paper, a number of possibilities are presented and discussed for the ownership of distributed storage devices (DSD) in a Smart Grid environment. The cases in which the distribution system operator (DSO) has either full control (grid owned storage) or no control whatsoever over the operation of the DSD (independently owned storage) will be differentiated. For each ownership possibility, the technical and regulatory implications are discussed, with analysis and validation of the results being performed on real MV distribution networks, both rural and urban. In order to evaluate each ownership possibility, a number of multi-period optimization models are presented, corresponding to different assumptions in regards to the operation of the DSD. The resulting daily operation strategies are subsequently used as a basis for carrying out distribution reinforcement planning.

Abstract
Many studies addressing the effect of wind power integration strategies on the system adequacy assessment have been made, only concerning the generation point of view and usually disregarding the effect of the transmission network. On the other hand, studies considering the transmission network usually have ignored the effect of wind power integration strategies, focusing only on capturing the time dependent nature of this type of renewable energy source. Therefore, this work presents a chronological Monte Carlo simulation approach that assesses the system adequacy of composite systems (generation and transmission) considering non-dispatchable and dispatchable renewable energy production (wind and hydro, respectively). Case studies involving the IEEE-RTS 79 and modified versions of this system are presented and discussed as didactic examples.

Abstract
This paper presents a generation adequacy evaluation based on analytical calculation, considering electric vehicles. The scenarios used are exploring electric vehicles penetrations in six different European countries, in order to assess their impact on the security of supply. An analytical method is developed to perform this evaluation. Afterwards, a discussion about the accuracy of this methodology is done and the differences between this approach and a flexible Sequential Monte Carlo Simulation are identified © 2011 IEEE.