Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Therefore, accurately modeling this machine is a demanding task. Several models have been proposed, where magnetic saturation is often addressed, without considering hysteresis effect. In the proposed model, the SRM magnetization characteristics are generated through the Jiles-Atherton (J-A) hysteresis model. Thus, instead of a post-processing inclusion, static hysteresis is considered in simulation. This is the model main attribute, which is discussed in detail. This may contribute for a better understanding of hysteresis impact over the SRM operation, limited to static modes. Simulation results are also presented and discussed.

Abstract

Abstract
This work deals with the design of a smart balancing system for e-mobility applications. Low-cost bi-directional DC/DC converters, based on cell-to-cell shared energy transfer configuration, are used to connect battery cells to the balancing bus, which also includes a supercapacitor bank. This system can be seen as a hybrid battery management system (HBMS), since, in addition to traditional BMS features, it also enables hybridization of batteries and supercapacitors. A convex optimization problem is formulated to control the HBMS, focusing on the minimization of energy losses, while considering safety and balancing constraints. Simulation results demonstrate that, in comparison with state-of-the-art BMS solutions, the proposed HBMS reduces energy losses in up to 15%.

Abstract
The integration of Renewable Energy Sources, mainly in distribution grids, has been changing the paradigm of power systems operation. This constitutes an opportunity to make use of flexible energy resources to support DSOs and TSOs on network operation activities. The amount of total flexibility available in the system can be quantified starting from lower voltage levels considering the implementation of demand response schemes. This work is focused on the development of an expeditious methodology to assess the total flexibility of low voltage consumers and aggregate it by means of a bottom-up approach until reaching the transmission network nodes.

Abstract
Several challenges arrive with electrical power restructuring, liberalized electricity markets emerge, aiming to improve the system's efficiency while offering new economic solutions. Privatization and liberalization of previously nationally owned systems are examples of the transformations that have been applied. Microgrids and smart grids emerge and new business models able to cope with new opportunities start being developed. New types of players appear, allowing aggregating a diversity of entities, e. g. generation, storage, electric vehicles, and consumers, Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players to facilitate their participation in the electricity markets. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. The paper proposes a normalization method that supports a clustering methodology for the remuneration and tariffs definition. This model uses a clustering algorithm, applied on normalized load values, the value of the micro production, generated in the bus associated to the same load, was subtracted from the value of the consumption of that load. This calculation is performed in a real smart grid on buses with associated micro production. This allows the creation of sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to players characteristics.

Abstract
The increasing use and development of renewable energy sources and distributed generation, brought several changes to the power system operation. Electricity markets worldwide are complex and dynamic environments with very particular characteristics, resulting from their restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players' benefits. This paper proposes a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, applied to load values, submitted to different types of normalization process, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage units.