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Summary: Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium‐sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4‐months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID‐19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Summary: An adaptation of the portfolio theory (PT) is proposed in this article, denoted as PrevPT, "Previsão" (in Portuguese) by PT, to integrate the three artificial neural networks, namely multilayer perceptron (MLP) backpropagation, radial basis function (RBF), and self‐organizing map (SOM), based forecasting techniques, aiming to analyze the impact of wind speed forecasting errors and achieve more accurate results. In its first use, the PT goal was to maximize a financial return, at any risk, through the diversification of securities or investments that are not positively correlated. Based on the development of PrevPT, which was used until this work only for solar forecasting, the proposed technique is applied in this paper to integrate and improve the results of individual wind forecasts. Four‐year wind speed data (January 2007 to December 2010) from two different locations (Algeciras, Spain and Petrolina, Brazil) were used. Our methodology develops a topology that integrates the forecasts obtained by MLP, RBF, and SOM aiming to obtain smaller forecast errors. By diversifying the forecasted asset, when one of the assets has negative prediction errors, another compensates for them and, thus, the total or partial cancellation of the errors occurs. PrevPT obtains a mean absolute percentage error of 1.13% for Spain and 2.35% for Brazil. PrevPT surpassed the results obtained by the three techniques applied individually in the two locations. The main innovations of the methodology are the significant reduction of errors and optimization of resource planning, and the beneficial features compared to other predictor integration techniques. [ABSTRACT FROM AUTHOR]

Summary: Brazil has one of the richest river basins in the world and an energy matrix based mostly on hydroelectric power. Current national policy is increasingly promoting renewable sources like wind power and solar photovoltaic power. Technologies used in generation energy via hydro, wind, and solar sources can be employed in decentralized generation. This study describes a turbine for hydro energetic use in flow sites with heads up to 2.5 (m). A hydrodynamic study was performed using computational fluid dynamics (CFD) analysis to evaluate the performance of this turbine. For this, a three‐dimensional model of the turbine was considered, consisting of three computational domains, using periodicity to decrease the computational cost. The variation of the angular velocity and the volumetric flow were the boundary conditions. The turbulence model used was the shear stress transport. The draft tube geometry was modified according to the results of the CFD analysis. The efficiency and performance of the turbine improved from 79% to 80% as a result of this modification. A case study was conducted for a comparative financial analysis of the turbine proposed in this paper, with wind power and solar photovoltaic decentralized generation in Brazil. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to investigate the influence of photovoltaic (PV) generation on reliability evaluation of distribution systems. Two PV generation models are used to predict the output power injected into the grid, taking into account the main relevant environmental variables, the irradiance and ambient temperature. Issues that directly affect the output power, such as the spatial smoothing effect due to the plant size and the influence of the irradiance and temperature measurement interval are taken into consideration. Using measurement time series of irradiance and local temperature, the models are used to generate power series in 4‐minute and hourly resolutions. The generated power series are used in a reliability assessment model, with the objective of evaluating the impact of solar resource variability on the reliability indices of the system. Case studies on the IEEE RBTS‐Bus 2 and on the real distribution system of Fernando de Noronha in Brazil are presented and discussed, for power plants of different capacities, considering the effect of the PV generation models, the temporal resolution of the time series and the spatial smoothing of the power output fluctuations. The results show that the power time series in hourly resolution significantly underestimates the frequency of interruptions. For the real system, this index is underestimated at the system level (up to 43%) and at the load points (up to 72%). On the other hand, for the interruption duration index, the temporal aggregation results in a small underestimation (just 4%). The results also indicates that the smoothing effect is irrelevant for typical PV system sizes of distribution systems with discretization equal to or above 4 minutes. [ABSTRACT FROM AUTHOR]

Summary: Alternative sources of electricity have been growing recently despite their intermittence, which makes it impossible for these sources to guarantee constant and uninterrupted supply of electricity. Energy storage systems, such as pumped‐storage (PS) power plants, can help to mitigate the intermittence of these sources. In Brazil, intense growth of intermittent sources has led the generation sector to high exposure to the spot market. Thus, Brazilian electricity sector has recently turned its attention to energy storage methods as a way of diminishing such exposure. However, Brazilian electricity‐sector regulatory framework is not prepared for energy storage systems remuneration. This paper proposes a regulatory framework to insert hybrid electric generation units composed of PS and intermittent sources in Brazilian energy market. A linear optimization model exemplifies the operation of such system by minimizing costs for 1 day. The model was tested in three different scenarios, allowing comparison of operational costs and other aspects of the operation. The results demonstrate how a PS can help covering peak demand by storing energy to be used at moments when generation from intermittent sources is not sufficient. In the proposed regulatory framework for hybrid plants, expenses with spot market purchases can be diminished either by completely avoiding it or by purchasing only during lower demand level periods. [ABSTRACT FROM AUTHOR]

Summary: Considering Brazil an important player in the global market for the electricity production from solar sources and the recent investments in large power plants through public policies, this work evaluated a very large‐scale commercial photovoltaic power plant (VLS‐PVPP) performing an attributional life cycle assessment (LCA) in accordance with ISO 14040/44 standards. The comparative results obtained, for 18 impact categories, show that the use of global data tends to overestimate the impacts of energy production in the VLS‐PVPP implemented in Brazil, by at least double. When establishing scenarios for different regions of the Brazilian territory, the energy performance and emissions indicators used varied around 17% due to the high sensitivity of the solarimetric radiation index in the modeling. The carbon footprint ranged between 38.3 and 44.8 kgCO2eq/kWh, and the energy payback time (EPBT) ranged between 4.5 and 5.3 years. The fossil energy replacement ratio (FER) indicator was calculated to quantify the power plant sustainability, which, in the best scenario, showed that the system replenishes, with clean energy, 7.7 times the fossil energy used in its construction. The sensitivity study associated with the uncertainty analysis for the manufacturing location of photovoltaic modules showed little influence (about 1%) on these indicators, showing that the decision to import or manufacture PV modules does not involve an environmental bias, overturning this hypothesis. Furthermore, the sensitivity study of the photovoltaic modules showed that the multicrystalline silicon technology is still the best choice compared to the monocrystalline one, since the latter increased the environmental cost of the evaluated indicators by 20%. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicles are becoming more popular in the automobile sector since questions about the environment and quality of life have become increasingly important. New ways of commercializing energy should be regulated, like the sale prices applied to service stations for electric vehicles. This study adopted a model for calculating tariffs at a charging station using the optimized tariff methodology—Tarot, a new application for this method. The charging station proposed uses an alternative energy generation system comprised of photovoltaic panels and wind turbines. We evaluated four regions in Brazil with different solar and wind potentials. This is done to see how the tariff can be impacted by the use of natural resources. As a result, we indicate that the application of the Tarot methodology adapted to the concept of charging electric vehicles from the use of renewable sources becomes an opportunity for the sector to apply a tariff adjusted to national regulatory guidelines and to provide a better cost/benefit for the consumer. Of the four regions analyzed, Region 2 proved to be the best region for the implementation of this type of enterprise with the use of a wind and solar distributed generator. The difference in billing value with the average tariff applied with the aid of the distributed system and purely energy network reaches 6% in Region 2. If the photovoltaic generation is added in 30% in this location, it can cause a reduction of up to 7% in the tariff for the sale of energy to vehicles. [ABSTRACT FROM AUTHOR]

Summary: The implementation of mobility restrictions and home office schemes due to the COVID‐19 pandemic have influenced electricity consumption patterns and levels. This study analyzes the effect of physical distancing measures regarding mobility on the energy consumption trends for the Brazilian energy system and its subsystems (Northeast, North, South, and Southeast‐Midwest). Trends were evaluated by the Joinpoint software, and the analysis comprehended the period between January 1 and May 27, 2020. Daily load data was grouped into weeks, with the calculation of weekly percentage changes considering a 95% confidence interval and p < 0.05. The weekly electricity loads were compared in the periods before and after the isolation decrees were enforced in Brazil (March 15, 2020). Statistically significant decreases were observed in the levels of electricity consumption, with trends represented by two joinpoints. Due to the different profiles of consumption across the geographic regions, the resulting electricity dynamics were also different. This is the first study to employ joinpoint analysis for the calculation of energy consumption trends focusing on the COVID‐29 pandemic. Data presented herein is unique, in its focus on Brazil, which enables more accurate implications to be drawn for Brazilian policy makers. [ABSTRACT FROM AUTHOR]

Summary: This study proposed an environmental sustainability index (ESI) to aid the identification of suitable areas for the installation of tidal power plants (TPPs) in coastal areas. Geographic Information System (GIS) and multicriteria analysis (MCA) were integrated to construct the proposed method. ESI consisted of a subindex of environmental fragility (EFI) with six components (geology, geomorphology, vegetation, slope, pedology, and land use), selected from the scientific literature. Therefore, the method serves as a guiding tool for coastal regions without existing information on the environmental impacts generated by this type of energy system. A consultation process with experts, the Delphi method, was conducted to assign weights to the components and subcomponents of the proposed ESI method. The study tested the method on a case study at the sector III of the coastal zone of the state of Pará (CZP), Brazil. The simulations considered scenarios with the installation of TPP of different scales. Scores close to 3 were considered less fragile sites in the ESI. The municipalities with the best ESI performance were São Sebastião da Boa Vista for scenarios I (2.7721) and II (2.126), and Soure for scenario III (1.9908). Moreover, ESI analysis revealed that small tidal dams for sector III are more feasible than medium and large dams. The use of GIS integrated with MCA provided a useful tool to analyze different parameters and visualize areas with the greatest environmental potential. There are challenges associated with complexity of MCA, dependency on expert's opinion, and subjectivity of the weighting system. In addition, limited data is available for the region, which hindered us to explore more fragility parameters. [ABSTRACT FROM AUTHOR]

Summary: Intermittent energy sources such as wind and solar have recently been growing a lot faster than dispatchable energy sources in Brazil, which made investments in energy storage systems become an attractive possibility in the country. Current operational policies for energy dispatch do not consider storage systems and need adjustments to fit this technology. With this motivation, we use reinforcement learning techniques to develop policies for managing storage systems in a grid that can handle time‐varying inputs and loads, with rolling forecasts. We use a deterministic lookahead (DLA) policy which has been parametrically modified to perform well in the presence of uncertain forecasts. For realistic simulations, the base model considers important characteristics in a grid that influence the interaction between scheduling and real‐time operation such as power and ramping capacities, notification times, and stochastic forecasts. The parametric modification with tunable parameters allows an optimal balance between two conflicting services provided by the storage system: time‐shifting and spinning reserves. Optimal reserves ranged from 35% to 100%, depending on the tested dataset, which shows the importance of tuning. Differently from stochastic lookahead policies, which are computationally expensive, parameterized DLA policies can be applied to real‐time operation after being optimized in a stochastic base model. [ABSTRACT FROM AUTHOR]