Artigos em Revistas

Large electricity consumers under Brazilian Group A tariffs face a cost–risk trade-off when defining contracted demand, since inadequate sizing leads either to payments for unused capacity or to penalties for exceeding regulatory limits. Despite this relevance, practical decision-support tools that convert tariff rules into reproducible contract optimization remain limited. This paper presents DSManager (version 0.0), a tool based on Python (version 3.14.4) developed to optimize tariff modality and contracted demand for Group A consumer units. The framework incorporates the mathematical formulation of Brazilian tariff structures, including green and blue time-of-use modalities, taxes, and excess-demand penalties, and evaluates two optimization strategies: the Maximum Recorded Demand method and a Grid Search procedure for direct minimization of the billing cost function. The tool was implemented with Pandas (version 3.0.2) and Streamlit (version 1.57.0) and validated using real billing data from two consumer units in the Equatorial Pará concession area. In the retrospective case, the green tariff combined with Grid Search produced projected savings of US$9554.20 relative to the unchanged contract. In the real implementation case, reducing contracted demand from 450 kW to 240 kW yielded an observed average saving of US$1944.31 per month. The results demonstrate the practical value of the proposed tool for tariff management and electricity cost reduction in large consumers.

CLEYDSON MATOS LIMA, JONATHAN MUÑOZ TABORA, CEZAR AUGUSTO ROCHA 1, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, UBIRATAN H. BEZERRA, MARIAEMÍLIA DE LIMA TOSTES

This paper presents an integrated validation framework for EV taxi charging-hub plans that combines spatial accessibility, grid deployability, and operational performance. Candidate hub configurations are first generated through a demand-weighted p-median model based on 175 taxi stands and 2825 cooperative members in Belém, Brazil. The assigned demand is then translated into charger requirements through stochastic sizing, and the resulting infrastructure is screened against the available headroom of 12,905 medium-voltage transformers. Finally, the selected solution is evaluated through an Erlang-C queueing model under peak-demand concentration. The final plan, obtained with 14 hubs, achieved a weighted mean distance of 0.724 km and a weighted P95 distance of 1.488 km, while requiring 46 chargers and 2610 kW of installed capacity. Of these, 45 chargers were successfully allocated in the grid-screening stage, corresponding to a placement rate of 97.83%.

JOSIVAN RODRIGUES DOS REIS, RAFAEL MAXIMINO BASTOS, BRUNO SANTANA DE ALBUQUERQUE, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, UBIRATAN HOLANDA BEZERRA, JONATHAN MUÑOZ TABORA, MARIA EMÍLIA DE LIMA TOSTES, AYRTON LUCAS LISBOA DO NASCIMENTO

Este estudo investiga o impacto de técnicas de feature engineering na previsão de preços de energia elétrica no curto prazo. Utilizando dados históricos semanais do mercado brasileiro, diferentes grupos de atributos, incluindo variáveis defasadas, estatísticas móveis e representações sazonais, são avaliados por meio de uma abordagem incremental. O objetivo é analisar a contribuição de cada grupo de atributos no desempenho do modelo. Os resultados demonstram que a inclusão de estatísticas móveis e variáveis sazonais categóricas melhora significativamente a precisão das previsões em comparação com modelos baseados apenas em variáveis defasadas. Os achados evidenciam a importância da construção de atributos em problemas de previsão de séries temporais, especialmente em mercados altamente voláteis como o de energia elétrica.

CARLOS SÁVIO SARUBI DE SOUZA, FLÁVIA PESSOA MONTEIRO, JOSIVAN RODRIGUES DOS REIS, UBIRATAN HOLANDA BEZERRA, MARIA EMÍLIA DE LIMA TOSTES

This study explores the application of Failure Modes and Effects Analysis (FMEA) to ground-mounted and Floating Photovoltaic (FPV) systems, with and without tracking systems, considering the particularities of each configuration. The FMEA methodology was applied to identify and prioritize failure modes, analyzing severity, occurrence and detection, as well as calculating the Risk Priority Number (RPN) and the Action Priority (AP). The FMEA methodology was adopted due to its preventive capability in identifying early-stage component failures and prioritizing actions. To this end, detailed analyses of the main failure modes and effects were carried out, covering critical components such as photovoltaic modules, inverters and support systems. In addition, preventive and corrective actions will be proposed. The study includes the case study of the Research Network for Technological Development and Innovative Extension of Advanced Materials in Energy and Mobility applied to the Amazon context (EMOBAMAZON). This study represents a novel application of FMEA to Floating Photovoltaic (FPV) systems in tropical environments, demonstrating its potential to improve predictive maintenance and operational reliability in renewable energy facilities. The integration of risk classification approaches has enabled a comprehensive and well-founded view of failures, contributing to the reliability, safety and operational efficiency of photovoltaic systems in the Amazon context.

ELEN PRISCILA LOBATO, EDUARDO MONTEIRO, YASMIM LISBOA, JOÃO ARAÚJO, WELLINGTON FONSECA

The global transition toward sustainable mobility and renewable energy integration demands intelligent energy management frameworks capable of coupling electric mobility, distributed generation, and energy storage. This study presents a comprehensive evaluation of the SIMA Project (Sistema Inteligente Multimodal da Amazônia), an innovative mobility pilot implemented at the Federal University of Pará, Brazil. The SIMA consists of the monitoring building, photovoltaic systems, lithium-based energy storage systems, and electric transportation modes (including urban and intercity buses, as well as a solar-powered catamaran), all interconnected within a microgrid. Field monitoring, data processing, and simulation analyses were conducted to assess energy performance, consumption patterns, and the operational feasibility of these electric systems under Amazonian conditions. The results indicate that the PV systems supply most of the SIMA’s demand, with the laboratory building accounting for 70% of total consumption and electric vehicles for 30%. Simulated full operation scenarios reveal the potential for near net-zero energy balance when energy management strategies are applied to generation, storage and charging. The findings demonstrate the technical viability of integrated mobility–energy systems in tropical contexts and provide practical insights for future low-carbon transport infrastructures in isolated or city-scale networks.

BRUNO SANTANA DE ALBUQUERQUE, AYRTON LUCAS LISBOA DO NASCIMENTO, MARIA EMÍLIA DE LIMA TOSTES, UBIRATAN HOLANDA BEZERRA, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, JONATHAN MUÑOZ TABORA

This study focuses on the mathematical modeling, control design, and analysis of an interleaved bidirectional high-voltage-gain DC-DC converter for energy management in supercapacitors. The state of the art is reviewed, with an emphasis on research related to DC-DC converters and energy storage systems. The characteristics and modeling of the supercapacitors are thoroughly analyzed. The converter’s operation in both buck and boost modes is described, detailing its operating stages, design parameters, and component sizing. The modeling accounts for the dynamics of the converter in both operational modes. PI controllers and compensation techniques were implemented to ensure the desired performance and meet the design criteria. Simulations were conducted using PSIM software, version 2023.1, with a power flow of 1 kW, a 48 V DC bus (buck mode), and a 162 V supercapacitor module (boost mode), operating at 500 kHz. The performance of the controllers was evaluated during both the charging and discharging processes of the supercapacitor, analyzing the dynamic response and behavior in the continuous mode, even in the presence of system disturbances.

JESSICA C. A. SOUSA, THIAGO M. SOARES, JONATHAN M. TABORA, HUGO G. LOTT

This study proposes a simplified mathematical formulation for optimizing isolated microgrids, enhancing computational efficiency while preserving solution quality. The research focuses on the influence of Operation and Maintenance (O&M) costs for Non-Dispatchable Generators (NDGs) and the relationship between costs and pollutant emissions. The proposed simplification reduces computational requirements, improves result interpretability, and increases the scalability of optimization techniques. The O&M costs of photovoltaic and wind systems were excluded from the initial optimization and calculated afterward. A Student’s t-test yielded a p-value of 87.3%, confirming no significant difference between the tested scenarios, ensuring that the simplification does not impact solution quality while reducing computational complexity. For emission-related costs, scenarios with single and multiple pollutant generators were analyzed. When only one generator type is present, modifications are needed to enable effective multi-objective optimization. To address this, two alternative mathematical formulations were tested, offering more suitable approaches for the problem. However, when multiple pollutant sources exist, cost and emission differences naturally define the problem as multi-objective without requiring adjustments. Future work will explore grid-connected microgrids and additional optimization objectives, such as loss minimization, voltage control, and device lifespan extension.

VITOR DOS SANTOS BATISTA, THIAGO MOTA SOARES, MARIA EMÍLIA DE LIMA TOSTES, UBIRATAN HOLANDA BEZERRA, HUGO GONÇALVES LOTT

Photovoltaic systems are among the renewable energy sources with the greatest global impact, driven by technologies that enable real-time monitoring, predictive maintenance, and intelligent integration with the electricity grid. In this context, this paper presents the design and implementation of an embedded Internet of Things (IoT) system to monitor temperature and humidity in photovoltaic systems in the Amazon region. The system was implemented in a photovoltaic solar plant located at the Federal University of Pará and used to monitor parameters such as local humidity and temperature, with the latter being considered at three strategic points: the surface of the photovoltaic module exposed to direct solar radiation, the shaded area of the module, and the ambient temperature. The results obtained showed good performance from the embedded system, with emphasis on the ease of remotely updating the embedded system’s code and centralized visualization of the monitored data in an IoT middleware. The device proved to be resistant to the adverse climatic conditions of the Amazon, allowing the operators and managers of the photovoltaic plant to monitor and visualize the measured variables and to draw up preventive and corrective maintenance strategies. In this way, the embedded system designed and implemented is a valuable tool for the photovoltaic plant’s operators and managers, promoting greater energy efficiency, reducing operating costs and increasing the useful life of the modules. It also contributes to the Sustainable Development Goals (SDGs), such as SDG 7 (Clean and affordable energy) and SDG 13 (Climate action).

YASMIM LISBOA, LUCAS SANTOS, ELEN LOBATO, WELLINGTON FONSECA, KAYLANE SILVA, IRIS RODRIGUES, MARCELO SILVA

Estimating utility demand remains a significant challenge worldwide, being accuracy often compromised by numerous variables involved and limited relevant data available; this compromises models and impacts resource planning, infrastructure, and energy purchases. Energy systems must prioritize efficient resource utilization to address these challenges in the context of technological advances, economic changes, and environmental concerns. This paper conducts a bibliometric and systematic review of energy forecasting methods; the literature review covers the main studies conducted, including the most commonly used variables in forecasting studies, the techniques used, and the forecasting time horizon. As a result, the review presented here will facilitate the selection of the best models, variables, and time horizons for different forecasting applications.

JOSIVAN RODRIGUES DOS REIS, JONATHAN MUÑOZ TABORA, MATHEUS CARVALHO DE LIMA, FLÁVIA PESSOA MONTEIRO, SUZANE CRUZ DE AQUINO MONTEIRO, UBIRATAN HOLANDA BEZERRA

Electric mobility is a global trend and necessity, with electric and solar boats offering a promising alternative for transportation electrification and carbon emission reduction, especially in the Amazon region. This study analyzes the system of a solar boat from an electric mobility project—to be implemented at Universidade Federal do Pará (UFPA)—using MATLAB software for modeling. The Simulink tool was utilized to model the system, focusing on operational parameters such as module voltage, converter voltage, and speed. The results indicate that the solar boat’s operational cost is significantly lower compared to a similar internal combustion model, considering diesel’s high consumption and cost. The environmental impact is also reduced, with nearly 72 tons of CO2 emissions avoided annually, thanks to Brazil’s renewable energy matrix. Simulations confirmed the project’s parameters, demonstrating the efficiency of digital-twin technology in monitoring and predicting system performance. The study underscores the importance of digital twins and renewable energy in promoting sustainable transportation solutions, advocating for the replication of such projects globally. Future research should focus on further advancing digital-twin applications in electric mobility to enhance predictive maintenance and operational efficiency.

BRUNO SANTANA DE ALBUQUERQUE, MARIA EMÍLIA DE LIMA TOSTES, UBIRATAN HOLANDA BEZERRA, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, AYRTON LUCAS LISBOA DO NASCIMENTO