The increasing penetration of Electric Vehicles (EVs) in LV distribution networks can potentially cause voltage quality issues such as voltage unbalance and under-voltage conditions. According to the EV charger characteristics, some strategies can be adopted to mitigate the aforementioned effects. Smart decentralized charging controls seem to be a more practical solution than centralized controls, since there is no need for communication because they rely only on local measurements. The four most relevant decentralized charging strategies, two for single-phase and two for three-phase EV chargers, have been implemented in a typical three-phase four-wire European LV distribution network. Simulations have been carried out for scenarios with single-phase EV chargers, three-phase EV chargers, and a combination of both. Single-phase controls are aimed at under-voltage regulation, while three-phase controls are focused on mitigating voltage unbalance. Results show that the implementation of a decentralized EV charging control is an adequate solution for Distribution System Operators (DSOs) since it improves the reliability and security of the network. Moreover, even though decentralized charging control does not use any communication, the combination of three-phase and single-phase controls is able to mitigate voltage unbalance while preventing the under-voltage condition.

This paper focuses on Hybrid Energy Storage Systems (HESS), consisting of a combination of batteries and Electric Double Layer Capacitors (EDLC), for electric urban busses. The aim of the paper is to develop a methodology to determine the hybridization percentage that allows the electric bus to work with the highest efficiency while reducing battery aging, depending on the chosen topology, control strategy, and driving cycle. Three power electronic topologies are qualitatively analyzed based on different criteria, with the topology selected as the favorite being analyzed in detail. The whole system under study is comprised of the following elements: a battery pack (LiFePO4 batteries), an EDLC pack, up to two DC-DC converters (depending on the topology), and an equivalent load, which behaves as an electric bus drive (including motion resistances and inertia). Mathematical models for the battery, EDLCs, DC-DC converter, and the vehicle itself are developed for this analysis. The methodology presented in this work, as the main scientific contribution, considers performance variation (energy efficiency and battery aging) and hybridization percentage (ratio between batteries and EDLCs, defined in terms of mass), using a power load profile based on standard driving cycles. The results state that there is a hybridization percentage that increases energy efficiency and reduces battery aging, maximizing the economic benefits of the vehicle, for every combination of topology, type of storage device, control strategy, and driving cycle.

Renewable energy is steadily increasing its penetration level in electric power systems. Wind and solar energy have reached a high degree of maturity, and their impacts on the grid are well known. However, this is not the case for emerging sources like wave energy. This work explores the impact of the fluctuating power injected by a wave energy converter on the distribution grid voltage and proposes a strategy for mitigating the induced voltage fluctuations. The paper describes the mechanics of how a fluctuating active power injection leads to grid voltage fluctuations and presents an unbalanced three-phase power flow tool that allows one to quantitatively analyze the voltage evolution at every phase and bus of a distribution grid driven by this power injection. The paper also proposes a corrective action for mitigating the voltage fluctuations that makes use of the hardware resources already available in the wave energy converter, by means of a control strategy on the reactive capability of the grid-side inverter. The use of a STATCOM as additional reactive compensation equipment is also explored. The effectiveness of the proposal is assessed in the IEEE 13-bus test feeder showing that, in some cases, the wave energy converter by itself is able to mitigate the voltage fluctuations that it causes. If not, a STATCOM can provide the extra reactive capability needed.

Voltage sags are power quality disturbances mainly caused by faults with a highly random nature. For this reason, the severity and frequency of occurrence of voltage sags is typically analyzed by means of probabilistic methods, such as Monte Carlo simulations, where different probability distributions are selected to model the uncertain inputs (e.g., fault impedance, fault location, fault rate, etc.). The selection and parameterization of the appropriate probability functions involves many difficulties to ensure that they represent the distribution of input variables in a realistic way and so, many different approaches can be found in literature. This paper studies the sensitivity of voltage sags severity to the uncertainty in the input models in order to focus modeling effort on the input variables that are more critical and effectively have an influence on voltage sags levels, whereas non-influential parameters can be neglected or modeled by means of simplified hypothesis. The method proposed is based on Design of Experiments and Analysis of Variance (ANOVA) and is able to discriminate with a statistic confidence level whether the uncertainty in the input significantly affects voltage sags severity indices or not. The effect of uncertainty on voltage sags indices SARFI90 and SARFI70 has been evaluated in realistic scenarios for IEEE test networks of 24 and 118 buses and in the power system of Ecuador with 357 nodes. General trends have been also established that help to understand the effect of the modeling of input parameters on the estimated number of sags.

Many control strategies to maximise the captured power by wave energy converters depend on the knowledge of the incoming wave in a short term future. However, the wave prediction may be unpractical in a wave farm, since many measurement systems would be required. As a part of a prediction system, this paper presents an estimator of the instantaneous sea surface elevation in an open-field sea area for potential wave farm deployment based on a single measurement point. The approach is based on a time delay artificial neural network and the paper explores the performance of the estimation for a given field and the sensitivity to different sea characteristics. The proposed realisation of the artificial neural network is found to be accurate and robust, resulting in a useful tool for a wave prediction system in a farm for control purposes.