João Paulo Abreu Vieira

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners.

The identification of vegetation contact with the power grid is a critical issue for utilities, posing significant risks to the population. Undetected events can lead to system failures, such as short circuits, outages, cable degradation, and environmental hazards, including large-scale wildfires. Vegetation pruning near distribution networks is common but costly due to the difficulty of covering extensive areas. The low current magnitudes from these events, known as vegetation high-impedance faults, often require high-frequency components for detection. However, high-frequency signal acquisition technologies are expensive and impractical for widespread deployment, prompting alternative methods for identification and vegetation management. This study proposes a practical and reliable framework for detecting vegetation contact with the distribution grid. It employs IEC 61850 standard and extended time-window analysis, capturing up to one minute of neutral current signals. These signals are processed by an AI algorithm that classifies events and delivers results. The proposed framework was tested in one feeder on real-time simulation but it is scalable to all substation feeders using technology of centralized protection. The approach achieved 95% accuracy and a false positive rate below 1% .

High‐impedance faults (HIFs) location is an increasingly relevant reliability issue in the power distribution industry. The development of practical and accurate one‐terminal HIF‐locating methods is vital for reducing long‐duration outage restoration time and cost. However, the dependency on the estimation of both fault model parameters and fault current signal can jeopardize the accuracy and practicality of existing one‐terminal HIF‐locating methods. This paper proposes a one‐terminal fault‐model‐free iterative method based on zero‐crossings for locating HIFs in overhead distriFbution networks. Two insights into voltage signal relationships are provided to eliminate the need for estimating fault model parameters and the fault current signal in the HIF‐locating process. The first one is based on zero‐crossings of the calculated voltage drop signal for estimating two parameters of the voltage signal on the fault point. The other insight is based on the zero‐crossing of the voltage signal on the fault point, in which the two parameters were previously estimated, for calculating the fault distance from the k th node. Simulation results on a modified IEEE 34‐node test feeder validate the high accuracy and robustness of the proposed method, considering the effect of several factors on fault distance estimation. In addition, the method convergence performance is assessed.

The detection and localisation of high impedance faults (HIFs) in overhead distribution networks, particularly those caused by tree contact, remain challenging due to their low fault current magnitude, nonlinear behaviour, and high waveform variability. This study addresses these challenges by experimentally validating and parameterising a two‐variable‐resistor series model for HIFs. Controlled tests were conducted at the 13.8 kV Extra High Voltage Laboratory (LEAT) of the Federal University of Pará (UFPA), Brazil, using three tree types to collect waveform data. The faults were modelled in ATPDraw software and validated through waveform comparisons, achieving an R ² coefficient above 0.89, demonstrating excellent agreement between simulated and measured signals. Additionally, comprehensive parameterised data tables detailing resistance variations over time and voltage are provided. These findings not only reduce the need for costly experimental setups in early‐stage studies but also provide a solid foundation for developing and testing advanced HIF detection and localisation algorithms, thus contributing to improved power distribution system reliability.

Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies.

Electric vehicles (EVs) are transforming the transportation sector, driven by the rapid expansion of charging infrastructure, including fast-charging stations (FCSs), significantly reducing charging time compared to standard charging stations. Despite the advantages of faster charging, the substantial power demand of EVs poses significant technical challenges for distribution networks. In particular, the existing literature has a research gap regarding how FCSs may impact or interact with step-voltage regulators’ (SVRs) tap operations. In this study, we characterize and evaluate the effects of fast recharging at varying penetration levels (PLs) on SVRs’ tap operations using probabilistic simulations and sensitivity analysis. To address these challenges, we propose a local and innovative application of the Volt/Var control on EV fast charging. The proposed application aims to inject reactive power into the network, depending on the FCS’s nominal active power, when the bus voltage connected to the FCS exceeds a minimum value. Our research on an actual feeder in northern Brazil reveals that reducing the active power supplied to the vehicle or oversizing the charging station power converters is unnecessary. Furthermore, our strategy reduces the probability of undervoltage violations and minimizes SVR tap changes, mitigating EVs’ impact on voltage quality.

This paper presents industrial insights into on-line robust local tap changer runaway inhibition issues in renewable distributed energy resources (DERs)-rich reconfigurable distribution networks with step-voltage regulators (SVRs). Based on the industrial insights, an algorithm is proposed for on-line robust local bi-directional inhibition of runaway events from only one tap change with robustness guarantees and without the need for coordinated test tap changes in cascade SVRs. The main contributions of the paper are the innovative application of the algorithm on on-line robust local bi-directional inhibition of tap changer runaway and the introduction to the industrial insights. The accuracy and robustness of the proposed algorithm are verified by time-series flow power simulation tests conducted on two test networks, with noise and gross errors in measurements, using extensive Monte Carlo simulations. Uncoordinated operation of test tap changes in cascade SVRs is examined through case studies on an actual long rural distribution network. Finally, the effect of photovoltaic (PV) power output variability on the proposed algorithm performance is assessed.

Detecting and locating high impedance faults (HIF) in overhead distribution networks (ODN) remains one of the biggest challenges for manufacturers and researchers due to the complexity of this phenomenon, where the electrical current magnitude is similar to that of the loads. To simulate HIF, the selection of the HIF model is important, because it has to correctly reproduce the characteristics of this phenomenon, so that it does not negatively influence the simulations results. Therefore, HIF models play a fundamental role in proposing solutions and validating the effectiveness of the proposed methods to detect and localize HIF in ODN. This paper presents a systematic review of HIF models. It is intended to facilitate the selection of the HIF model to be considered. The models are validated based on experimental data from medium voltage (MV) laboratories, specifically, recorded waveforms from two HIF tests conducted in an MV lab were analyzed and compared with three established HIF models. The efficacy of these models was assessed against MV lab test data to ensure a precise representation of both transient and steady-state conditions for fault conductance and current waveforms. The findings show that the two nonlinear resistor models better approximate the waveforms obtained in the experimental tests performed in this study.

This paper presents a comparative analysis between grid-side solutions and distributed generation (DG) side solutions for overvoltage mitigation in low-voltage distribution networks with penetration of photovoltaic (PV) generation. The analysis is based on calculating the percentage of consumer units with voltage transgression over a simulation day for each increment in the degree of DG penetration and each mitigation technique studied. Grid-side solutions consist of network reinforcement and permanently changing the tap of MV/LV transformers, as well as both techniques together. The solutions on the DG side correspond to the voltwatt control and volt-var control functions in PV inverters and battery energy storage systems (BESS). The results presents that for the simulated case, with the exception of the permanent tap switch, all solutions promote a decrease in the percentage of overvoltage caused in the consumer units, with the volt-watt control and the BESS, solutions by DG side, increasing the hosting capacity of the network by up to 400% for heavy load demand.