Abstract
This paper provides a general overview of the initial developments in the REIVE project (Smart Grids with Electric Vehicles). The main focus of the project is on smart grid infrastructures for large scale integration of EV and micro-generation units. It is a natural evolution of the InovGrid project promoted by the EDP Distribuição - the Portuguese Distribution Network Operator - and allows the development of seminal concepts and enabling technological developments within the Smart Grid paradigm. This paper presents the management and control architecture developed to allow electric vehicle integration in smart grid operation. Additionally, it presents the major impacts in distribution grids of the simultaneous deployment of electric vehicles, micro-generation and smart grid technologies.

Abstract
This paper describes the main results of the MERGE project relative to Electric Vehicle (EV) charging strategies and the impacts of EV integration on the steady-state grid operation. MERGE is a €4.5m, collaborative research project supported by the European Commission's Seventh Framework Programme (FP7). The consortium includes utilities, regulators, commercial organisations and universities with interests in the power generation, automotive, electronic commerce and hybrid and electric vehicle sectors across the entire European Union (EU). One of the MERGE project missions is to evaluate the impacts that EV will have on EU electric power systems, exploring EV and SmartGrid/MicroGrid simultaneous deployment, together with renewable energy increase, to achieve CO 2 emission reduction through the identification of enabling technologies and advanced control approaches. The work presented proposes three charging strategies, dumb charging, multiple price tariffs and smart charging, and uses EV integration scenarios of adherence to these charging schemes. The resulting scenarios are tested using an algorithm coded with Python and using PSS/E, created within the MERGE framework to study EU grids steady-state behaviour. Additionally, the critical mass of EV adherence to smart charging schemes that brings positive impacts to the distribution grids operation was also evaluated.

Abstract
This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economical situation.

Abstract
This paper discusses a novel load management strategy for the optimal use of renewable energy in systems with wind turbines, a battery bank, and a diesel generator. Using predictions concerning wind speed and power, controllable loads are used to minimize the energy supplied by the diesel generator and battery bank, subject to constraints imposed by the user's behavior and duty cycle of the appliances. We analyzed a small hybrid power system in Zaragoza, Spain, and the results showed load management strategy allowed improvement in the wind power use by shifting controllable loads to wind power peaks, increasing the state of the charge in the battery bank, and reducing the diesel generator operating time, when compared to a case without load management.

Abstract
The intermittence of the renewable sources due to its unpredictability increases the instability of the actual grid and energy supply. Besides, in a deregulated and competitive framework, producers and consumers require short-term forecasting tools to derive their bidding strategies to the electricity market. This paper proposes a novel hybrid computational tool, based on a combination of evolutionary particle swarm optimization with an adaptive-network-based fuzzy inference system, for wind power forecasting and electricity prices forecasting in the short-term. The results from two real-world case studies are presented, in order to illustrate the proficiency of the proposed computational tool.

Abstract
The integration of larger and larger amounts of wind power is a major target of the European Union, however it represents a challenge for power system planning and operation. The paper analyses stability aspects concerning the operation of Multi-Terminal HVDC networks connecting offshore wind farms to the AC systems. Modelling issues are tackled, relevant to control schemes needed for a secure operation of the overall AC-DC system in case of contingencies both on the AC side and on the DC side. First, power flow control principles are described for the "backbone" HVDC grid topology (consisting of point-to-point connections between offshore wind farms and mainland grid, linked by a DC connection). Second, dynamic converter models suitable to investigate electromechanical transients are illustrated and some stability issues connected to the network performance under contingencies/disturbances are pointed out. The need both to survive severe disturbances and to provide ancillary services calls for the adoption of advanced control schemes. Some simulations are described to illustrate the behaviour of the mixed AC-DC network under contingencies concerning both faults on DC cables and faults on AC lines. The work has been carried out within Working Package 5 of EU co-founded Project TWENTIES.