Abstract
The expected growth of Distributed Generation (DG) penetration in distribution systems will fundamentally alter both planning and operating procedures of Distribution Network Operators (DNO). This means that distribution networks can no longer be considered as a passive appendage to the transmission network and should be explored actively to take full advantage of the capabilities of DG units available and avoid technical problems (such as line overloading or poor voltage profiles) resulting from massive integration of this type of sources. Presently, when the capacity of the generation, transmission and distribution systems is exceeded, the traditional utility response is expanding or reinforcing existing circuits through large investments in power transformers, substations or distribution feeders. However, in some situations such as in congested metropolitan areas these actions can have prohibitive costs or simply be impossible due to space restrictions, for instance. Although current investment costs of many solutions for energy storage remain extremely high, recent developments and advances in both energy storage technologies and power electronic interfaces are opening new doors to the inclusion of Energy Storage Systems (ESS) as a potentially viable solution for modern power applications, including their use in distribution network planning and operation. This paper presents a heuristic approach for siting and sizing of ESS in distribution networks in order to maximize the capacity of DG that can be integrated in the grid without bringing technical problems to network operation. The proposed methodology enables a technical and economical comparison between a strategy based on ESS deployment and exploitation and typical traditional DNO grid reinforcement strategies. Several technologies for ESS were considered, each one with different costs and technical characteristics. The proposed methodology was validated using a real Portuguese Medium Voltage (MV) distribution network.

Abstract
The implementation of innovative network solutions to allow for an increase of micro-generation and demand side integration are highly dependent on building scale and cost effective infrastructures, where sharing of responsibilities among different stakeholders is the basis for a proper implementation of an active distribution network concept. In order to integrate large amounts of small and micro-generation (µG) units that exploit different power sources (with high or no intermittency) together with controllable loads and storage devices it is necessary to develop flexible management and control solutions where responsibilities for the system operation are shared between Distribution System Operators (DSO), customers and Distributed Generation (DG) units according to a regulatory environment. The incentives provided to the different parties (stakeholders) may have a major impact on their decision to adopt Micro-grid and Multi Micro-grid concepts. Therefore, an identification of the benefits and costs associated with these concepts adoption is needed in order to recognize the real value of their deployment. Moreover, only by proper allocation of these costs and benefits, each stakeholder affected may receive the right incentive to opt for Micro-grid and Multi Micro-grid solutions. The DSO is interested in maximizing profits, by minimizing the capital and operational costs related to the distribution service (for instance, loss reduction and reinforcement costs) and at the same time achieving the performance goals imposed by the regulators [1]. Micro-generation developers make decision considering capital and operational expenditures, the connection costs, and use of system charges. Incomes from energy selling (which may include RES incentives) are the main goals (benefits) for the µG investors. Therefore, one of the priorities of regulators is to ensure that these benefits are perceived by µG developers. Finally, customers may be asked to share with the DSO the responsibility for an increased reliable system and for having the possibility to reduce their bills when exploiting the best market options offered by different traders. This paper presents a detailed identification and characterization of the different benefits and costs for each stakeholder involved. Some of these benefits can be identified and allocated directly to a certain stakeholder, while others need to be shared among a large group of players, involving the society in general (for example CO2 emissions reduction, job creation).

Abstract
From the studies developed so far, it is a general consensus that Electric Vehicles (EV), when properly managed, can provide many benefits to the grid operation. In the power systems of islands the potential benefits may be even larger. The case of S. Miguel Island, in the Azorean archipelago, may be one of such cases. This island achieves typically an annual peak power of 75 MW and a valley slightly higher than 30 MW. Currently, around 75% of its installed capacity is formed by fuel units, 22% by geothermal units and the rest by small hydro units. Yet, there are numerous unexplored endogenous resources in this place, especially geothermal and wind power, which cannot be used due to technical restrictions. Geothermal is limited by the valley load as the involved technology is not suited for load following, even with very small ramp rates. Wind power requires sufficient conventional spinning reserve to be safely integrated due to the variability of the wind resource. High EV integration, with an adequate charging management, would then increase base load allowing further geothermal and a reduced need for conventional spinning reserves. This paper evaluates the benefits of the presence of EV as controllable loads performing frequency control in a scenario with abundant wind resource availability, where a sudden loss of wind power production over a short period of time occurs. Ultimately, this work will show that S. Miguel power system would benefit from the presence of EV. A comparison with the conventional approach considering EV as regular loads will also be performed for benchmarking purposes.

Abstract
The massive interconnection of offshore Wind Farms (WF) brings challenges for the operation of electric grids. The predicted amount of offshore wind power will lead to a smaller ratio of conventional units operating in the system. Thus, the power system will have less capability to provide fast dynamic regulation. Despite of offshore WF being able to inject power on the AC grid through High Voltage Direct Current (HVDC) convertors, they cannot participate on frequency support by the intrinsic decoupling that DC adoption brings. This paper proposes a control methodology, based on local controllers, to enable the participation of offshore WF in primary frequency control. Additionally, enhancements were made on the Wind Energy Converters (WEC) controller to make them capable of emulating inertial behaviour. Tests were performed in a multi-terminal DC network with two off shore wind farms to assess the feasibility and effectiveness of the concept in a communication-free framework.

Abstract
The operation and planning of Low Voltage (LV) and Medium Voltage (MV) distribution networks have been changing over the last decade. Due to the presence of Distributed Generation (DG) and microgeneration, an active role has been attributed to these networks in grid operation. For this accomplishment, different conceptual approaches were developed. In [1], a hierarchical control structure was defined, considering that DG units, onload tap changer transformers, static var compensators and loads can be controlled by a hierarchically higher entity, the Central Autonomous Management Unit (CAMC). The CAMC is also responsible for the management of specific LV networks, the MicroGrids (MG), which in turn have autonomy to manage their loads and microgeneration units through an entity called MicroGrid Central Controller (MGCC). A MV grid with these characteristics plus some storage devices would then be called a Multi-MicroGrid (MMG), being, among other functionalities, able to operate isolated from the upstream network. The recent appearance of a new type of load to the system, the Electric Vehicle (EV), expected to be largely integrated in the electricity grids in the upcoming years, has a great potential for adding controllability to the MMG. In this paper, an EV control droop (see [2]) will be introduced to improve the MMG performance when EVs operate as active elements. EV controllers are then able to receive setpoints from the CAMC and also actively update the droop settings in order to deal with different events that may occur on the MMG, for instance when moving from interconnected to islanded mode of operation. The performance of the MMG with controllable EV will be compared with a MMG without the participation of EV. Additionally, multiple philosophies for setting the droops will be tested, considering that EV may inject power into the grid as storage devices or just act as controllable loads. Simulation results were obtained exploiting a dynamic simulation platform developed using the EUROSTAG and MATLAB environments.

Abstract
The scope of this work is to find the best approach to control advanced inverters used to connect electric vehicles to the grid. Phase-locked Loop (PLL) is a grid voltage phase detection that makes use of an orthogonal voltage to lock the grid phase. This method is suitable for both single and three phase systems, although in single-phase, because they have less information, more advanced systems are required. The easiest way to obtain the orthogonal voltage system is using a transport delay block to introduce a phase shift of 90 degrees with respect to the fundamental frequency of the grid voltage. This method is known as Synchronous Reference Frame PLL (SRF-PLL). The use of inverse Park transformation is also possible. To lower the complexity and increasing the filtering of the output signals, methods using adaptive filters are a good alternative. For this approach, the use of a second order generalized integrator (SOGI) or Adaptive Notch filter combined with PLL, Enhanced PLL (EPLL) and Quadrature PLL (QPLL), leads to satisfactory results. © 2011 INESC Coimbra.