Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Hence, modeling this machine is a most demanding task. While magnetic saturation is often addressed, hysteresis effect is usually disregarded. In order to include this phenomenon, an SRM drive simulation model was built, where magnetization characteristics are generated through the Jiles-Atherton (J-A) hysteresis model. SRM losses estimation is a challenging task, which demands continuous research efforts. This paper intends to investigate hysteresis impact on SRM copper losses. Due to the machine features, skin and proximity effects are considered. Different steady-state operation scenarios are simulated and compared.

Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Most SRM models address magnetic saturation, but hysteresis effect is usually disregarded. This paper is based on a developed four-quadrant SRM drive simulation model, where magnetization characteristics are generated through the original Jiles-Atherton (J-A) hysteresis model. The main goal is to investigate the hysteresis impact on the SRM drive controller performance. Steady-state operation scenarios are simulated and compared. For the adopted current control strategy (PWM), the results show a significant impact in all drive components, particularly for low speed with high load operation.

Abstract
This work focuses on hybrid balancing systems, a recently-proposed concept that enables balancing of battery cells and hybridization with supercapacitors. To control this system, a model predictive control framework is developed. In addition to distributing the supercapacitor power among the balancing circuits, this framework is also able to minimize state-of-charge and thermal imbalances in the battery cells, as well as energy losses in the balancing circuits. The effectiveness of the proposed approach is verified via numerical simulations. It is shown that, in comparison with state-of-art balancing solutions, the proposed control approach is able to decrease battery stress in up to 9% and the maximum temperature in up to 4.5%.

Abstract
The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.

Abstract
Nowadays, the power quality has become not only an important competitive factor for industrial users but also a crucial factor in the energy efficiency of the facilities. Seen paradoxically, the increase we have seen in the energy efficiency of the electrical loads, leads to the intensive use of power electronics resulting in a very high injection of harmonics in the distribution networks, thus causing a lack in the power quality leading a significant voltage waveform deformation. An electrical network containing a high harmonic content is synonymous of a low-level quality of the distributed energy, resulting in an increase of losses and low power factors, decreasing the energy efficiency of the installations. This paper presents a model that allows predicting the harmonic content present in an electrical installation, depending on the type of loads. Hence, it will be possible to know several qualitative parameters such as the voltage and current total harmonic distortion (THD), which will help to predict and size the mitigation measures to improve the power quality and energy efficiency of the facilities. © 2019 IEEE.

Abstract
The increasing use of renewable energy sources and distributed generation brought deep changes in power systems, namely with the operation of competitive electricity markets. 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. In order to achieve this objective, it is necessary to define tariff structures that benefit or penalize agents according to their behavior. In this paper a method for determining the tariff structures has been proposed, optimized for different load regimes. Daily dynamic tariff structures were defined and proposed, on an hourly basis, 24 hours day-Ahead from the characterization of the typical load profile, the value of the electricity market price and considering the renewable energy production. © 2018 IEEE.