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
This paper proposes a novel method to characterize the availability of household loads to participate in demand response programmes, as well as detailed mathematical models to characterize households loads. The availability of the households results from the flexibility of their controllable loads to increase/reduce consumption. This flexibility is calculated taking into account the comfort levels predefined by the customers and the technical restrictions of the controllable loads. The proposed method was evaluated through a management algorithm developed to perform demand control actions in quasi-real-time, according to the objectives of the distribution system operator or energy aggregator and the availability of the household loads. A scenario with a single household located in a semi-urban area is used to illustrate the application of the algorithm and validate the proposed method.

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
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.

Abstract
The Electric Vehicle (EV) is one source of flexibility to the electric power system. When aggregated by a market agent, it can offer its flexibility in the balancing reserve market. In order to meet this goal, a framework of optimization and forecasting algorithms must designed to cover the different time horizons of the decision process. This paper describes a full framework for EV aggregators participating in different electricity market sessions. This framework is illustrated for the balancing reserve market and the impact of forecasts of different quality for the balancing reserve direction is evaluated. The test case consists in synthetic time series generated from real data for 3000 EV participating in the Iberian electricity market.

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.