Thiago José da Luz

Although Brazil is in the last mile of achieving universal electricity access, the Amazon region represents the country’s most critical electrification frontier, where remote energy expansion has largely failed to integrate clean alternatives. This study presents the design and techno-economic assessment of a hybrid off-grid microgrid tailored for the Amazon, incorporating a multi-source architecture that combines micro-hydro (MH), wind power (WT), solar photovoltaics (PV), diesel generation (DG), and battery energy storage systems (BESS). Three locations in Amazonas, Roraima, and Tocantins were evaluated under three scenarios: a hybrid system with DG, a fully renewable system without DG, and a green scenario that accounts for demand-side flexibility and selected technical and economic conditions. Optimization was performed using the HOMER tool for a typical isolated community. The hybrid configuration achieved a DG generation share of 0.7 (RSS, DG–WT–PV–MH–BESS), 0.9% (Jalapão, DG–WT–PV–MH–BESS), and 6.8% (Labrea, DG–PV–BESS). The fully renewable scenario resulted in Levelized Cost of Energy (COE) values ranging from 0.146 USD/kWh (RSS) to 0.388 USD/kWh (Lábrea), while the green scenario yielded COEs between 0.089 USD/kWh (RSS) and 0.190 USD/kWh (Lábrea). This study reveals the role of MH in reducing storage requirements, installed capacity, and system cost. The results are promising for presenting a competitive cost of energy.

The demand for engineering programmes has declined by 30% over the last decade, creating a critical need for retention strategies. In 2021, Brazil mandated integrating extension activities into at least 10% of the undergraduate curriculum. This exploratory case study investigates the impact of these activities on the holistic development and retention of nontraditional engineering students (working adults) at a private institution. It analyses extension's potential to mitigate the "engineering blackout" and labour market skills gap. The study evaluates service-learning implementation across two Electrical Engineering courses (2024–2025) involving 38 students. Interventions included nine projects ranging from technical consultancy to community co-creation. Individual self-assessments and technical reports were analysed using the Queiruga-Dios framework and Freire's theoretical perspective. Findings show 92% of students developed technical skills and 89% improved soft skills (e.g., teamwork, adaptability). Additionally, 76% demonstrated shifted social responsibility attitudes, and four projects achieved a transformative approach via direct co-creation and dialogue. Crucially, extension courses achieved a 97.4% success rate, contrasting sharply with a 36.1% failure/dropout rate in standard technical courses. The study concludes that mandatory extension democratises professional skill acquisition and fosters organic community engagement, serving as a vital retention strategy for private institutions despite resource constraints.

Variable renewable energies are leading the energy transition and power system flexibility has become a global priority. Incorporating short-term assessment into long-term planning is essential to capture the operational characteristics of generation and address flexibility issues. This paper presents a novel model for generation and transmission expansion planning with economic dispatch. In the generation expansion planning, the capacity expansion is optimized. In the economic dispatch problem, power plant operation, hydropower generation, and line transmission power flow are optimized. The model is a mixed-integer linear programming problem solved in MATLAB using Gurobi toolbox. A model is proposed to optimize the reservoir of hydropower systems and run-of-river. A case study for supplying Brazilian demand in 2050 is presented. A scenario with greenhouse gas emission costs and no deficit is proposed. This study has shown that the current penalty cost for loss of load is not sufficient to avoid a deficit. It is possible to supply 2050 demand, without large reservoirs or deficits, with a 5.5% curtailment, using 13% of total storage capacity, however; peaking power plants are required.

Power distribution utilities effort to ensure the quality of energy to their consumers and the reliability of their power distribution system. It is necessary that maintenance activities are planned with the aim of maintaining or improving reliability indicators in supply to consumers. In this paper, a computational model based on integer nonlinear multiobjective programming is presented to improve the maintenance of equipment in the power distribution system. Since it is a reliability-centered approach, a probabilistic failure model is first used to obtain equipment reliability values at each time point through fuzzy inference. Three objective functions are optimized: i) minimizing maintenance cost, ii) minimizing failure frequency, and iii) maximizing equipment reliability. The optimization problem is also formed by three sets of constraints: i) individual and collective continuity indicators; ii) task execution time; and iii) maintenance limit for each type of equipment. Lichtenberg's algorithm is used to solve the model. A case study is performed for a feeder section consisting of twenty-eight distribution equipment. The results obtained using the Pareto constraints show scenarios that can help maintenance teams to make decisions and develop the preventive maintenance planning. Adding constraints on the duration and frequency of collective interruptions indicators improves the power quality of the distribution system; however, it requires an increase in investment by 36%.