Abstract
The uncertainties related to when and where Plug-in Electric Vehicles (PEVs) will charge in the future requires the development of stochastic based approaches to identify the corresponding load scenarios. Such tools can be used to enhance existing system operators planning techniques, allowing them to obtain additional knowledge on the impacts of a new type of load, so far unknown or negligible to the power systems, the PEVs battery charging. This chapter presents a tool developed to evaluate the steady state impacts of integrating PEVs in distribution networks. It incorporates several PEV models, allowing estimating their charging impacts in a given network, during a predefined period, when different charging strategies are adopted (non-controlled charging, multiple tariff policies and controlled charging). It uses a stochastic model to simulate PEVs movement in a geographic region and a Monte Carlo method to create different scenarios of PEVs charging. It allows calculating the maximum number of PEVs that can be safely integrated in a given network and the changes provoked by PEVs in the load diagrams, voltage profiles, lines loading and energy losses. Additionally, the tool can also be used to quantify the critical mass (percentage) of PEV owners that need to adhere to controlled charging schemes in order to enable the safe operation of distribution networks. © Springer Science+Business Media Singapore 2015.

Abstract
This work presents a methodology to manage Electric Vehicles (EVs) charging in quasi-real-time, considering the participation of EV aggregators in electricity markets and the technical restrictions of the electricity grid components, controlled by the Distribution System Operator. Two methodologies are presented in this paper to manage EV charging, one to be used by the EV aggregators and the other by the Distribution System Operator (DSO). The methodology developed for the aggregator has as main objective the minimization of the deviation between the energy bought in the market and the energy consumed by EVs. The methodology developed for the DSO allows it to manage the grid and solve operational problems that may appear by controlling EVs charging. A method to generate a synthetic EV data set is used in this work, providing information about EV movement, including the periods when EVs are parked and 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
Using Voltage Source Converter (VSC) based High Voltage Direct Current (HVDC) technologies, in a Multi-terminal dc (MTDC) system, has been envisaged as an attractive solution for connecting the large offshore wind farms that have been planned to meet the EU renewable energy targets. The control and operation of VSC-HVDC technologies comprise a number of challenging tasks, aiming to assure an effective integration of MTDC systems in ac transmission systems. Stability studies play a key role within this framework. Among them, small signal stability analysis is required. Therefore, in this paper, modal analysis is performed for assessing small signal stability, in terms of the electromechanical modes of oscillation, considering the combined AC-MTDC system. Also, this paper evaluates the interest of installing classical Power Systems Stabilizer (PSS) in the onshore VSC stations for providing additional damping to the electromechanical modes of oscillation. Simultaneous tuning is performed for adjusting the parameters of these PSS based controllers. For this purpose, an optimization based approach exploiting Evolutionary Particle Swarm Optimization (EPSO) is proposed. Modal analysis and time domain simulations are performed to evaluate the effectiveness of the proposed solutions.

Abstract
Microgrids have been explored as active cells capable of providing enhanced control schemes towards a more secure, reliable and efficient operation of LV distribution networks or MV networks when aggregated in a multi-microgrid. These structures, within the smart grid concept, rely on a communications infrastructure that introduces uncertainty in the data exchange process. In this paper such uncertainty is evaluated considering a multi-microgrid operating in isolated mode.

Abstract
Microgrids are the basic building cells of a smart grid. They are assumed to be established at the low voltage distribution level, where distributed energy sources, storage devices, controllable loads, and electric vehicles are integrated and need to be properly managed. The microgrid cell is a very flexible system that can be operated connected to the main power network or autonomously, in a controlled and coordinated way. When operating in islanded mode, the MG relies on local energy storage to ensure the balance between generations and loads. However, when operating isolated from the main grid, the MG is more sensitive to power quality issues such as voltage unbalance, caused by the connection of single-phase loads and sources. In order to improve the MG emergency operation conditions, the EV should be envisaged as an active and flexible entity, providing to the MG additional distributed load or storage capacity under the vehicle-to-grid (V2G) concept. This chapter reviews the MG architecture considering EV and focuses on the impact of their active participation on the MG frequency regulation in emergency conditions (namely in islanding operating mode). Voltage unbalance issues during MG autonomous operation and the need for adopting voltage balancing mechanisms in specific power electronic interfaces are also discussed. © Springer India 2014.

Abstract
The large scale integration of electric vehicles and distributed energy resources on low voltage grids might cause serious problems related to, for instance, under/over voltages and line overloading. In order to cope with these problems, this paper presents a multi agent system (MAS) developed to dynamically schedule flexible loads on low voltage grids, preventing operation limit violations. Since different geographical positions of the loads in the grid will cause a different impact on the grid, load flow calculations are used to indicate operation limit violations. The application uses a decentralized algorithm which ensures similar chances of being scheduled to the customer loads using a priority scheme. A case study is carried out on a 70-bus feeder where electric vehicle loads are scheduled to prevent under voltages, showing the applicability of the approach.