Abstract
A fully operational multiterminal dc (MTDC) grid will play a strategic role for mainland ac systems interconnection and to integrate offshore wind farms. The importance of such infrastructure requires its compliance with fault ride through (FRT) capability in case of mainland ac faults. In order to provide FRT capability in MTDC grids, communication-free advanced control functionalities exploiting a set of local control rules at the converter stations and wind turbines are identified. The proposed control functionalities are responsible for mitigating the dc voltage rise effect resulting from the reduction of active power injection into onshore ac systems during grid faults. The proposed strategies envision a fast control of the wind turbine active power output as a function of the dc grid voltage rise and constitute alternative options in order to avoid the use of classical solutions based on the installation of chopper resistors in the MTDC grid. The feasibility and robustness of the proposed strategies are demonstrated and discussed in the paper under different circumstances.

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

Abstract
Current electrical distribution systems are facing significant challenges due to the widespread deployment of Distributed Energy Resources (DER), particularly the integration of variable Renewable Energy Sources (RES). This requires a change in the paradigm of distribution grids from a purely passive perspective into fully active networks within the smart grid vision. This new paradigm involves new control and management architectures as well as advanced planning methods and operational tools for distribution systems exploiting a smart metering infrastructure. This infrastructure will enable leveraging data from smart meters and short-term forecasts of load demand and RES in order to manage the distribution system in a more efficient and cost-effective way, thus enabling large scale integration of RES. Future tests to be carried out in a new, state of the art laboratory environment will bring additional added-value to the validation of the proposed concepts and tools.

Abstract
Microgrids are assumed to be established at the low voltage distribution level, where distributed energy sources, storage devices, controllable loads and electric vehicles are integrated in the system and need to be properly managed. The microgrid system is a flexible cell that can be operated connected to the main power network or autonomously, in a controlled and coordinated way. The use of storage devices in microgrids is related to the provision of some form of energy buffering during autonomous operating conditions, in order to balance load and generation. However, frequency variations and limited storage capacity might compromise microgrid autonomous operation. In order to improve microgrid resilience in the moments subsequent to islanding, this paper presents innovative functionalities to run online, which are able to manage microgrid storage considering the integration of electric vehicles and load responsiveness. The effectiveness of the proposed algorithms is validated through extensive numerical simulations.

Abstract
The future large-scale deployment of electric vehicles (EV) will not only have impact on load growth, but also create opportunities for the electricity sector. Generally, the current methods for security of supply long-term evaluation do not include this new type of load. While the electric components of the generating systems are usually modelled by the Markov process, this paper presents, as its major contribution, an EV model based on the Nonhomogeneous Poisson process, which has been developed in order to better represent the motorized citizen mobility and the EV opportunity to release spinning reserve to electric systems. The simulation procedure lies in combining both Poisson and Markov processes into a sequential Monte Carlo simulation (SMCS) to measure the impact of EV when evaluating the adequacy of generating systems. This evaluation is divided into two complementary concepts: static reserve (generating capacity reserve) and operating capacity reserve. The proposed models are analyzed using a modified version of the IEEE RTS-96 including renewable sources.

Abstract
This paper presents a holistic framework for electric vehicles integration in electric power systems together with their charging management and control methodologies that allow minimizing the negative impacts in the grid of the charging process and maximize the benefits that charging controllability may bring to their owners, energy retailers and system operators. The performance of these management and control methods will be assessed through steady state computational simulations and then validated in a microgrid laboratory environment.