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Forest based power plants are alternatives in the diversification of domestic energy supply in Brazil, given the growing demand for electricity in recent decades. Evidences of space-time clusters contribute to the understanding of regional development associated with correlated activity. Therefore, this paper analyzed the space-time conglomerates for Brazilian forest-based power plants, from 2000 to 2019. The data used were from the Generation Information System of the National Electric Energy Agency (ANEEL)-SIGA. It detected the existence of clusters by means of scan statistics via space-time permutation, considering the high level of conglomeration. The results show the Center-South region of Brazil with the highest concentration of conglomerates, with black liquor and forest residues being the most used energy resources. The clusters with the highest installed power were in the early 2010s, with the black liquor plants. The regions with the formation of fast-growing forest plantations promoted the existence of conglomerates associated with the pulp and paper and steel industry complexes. It is concluded that there was a conglomeration of forest power plants in the central-south region of Brazil, in which they help in decision-making and guidance of public policies for forestry projects for energy. [ABSTRACT FROM AUTHOR]

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

The emerging energy transition is particularly described as a move towards a cleaner, lower-carbon system. In the context of the global shift towards sustainable energy sources, this paper reviews the potential and roadmap for hydrogen energy as a crucial component of the clean energy landscape. The primary objective is to present a comprehensive literature overview, illuminating key themes, trends, and research gaps in the scientific discourse concerning hydrogen production and energy policy. This review focuses particularly on specified geographic contexts, with an emphasis on understanding the unique energy policies related to renewable energy in Brazil, Austria, and Germany. Given their distinct social systems and developmental stages, this paper aims to delineate the nuanced approaches these countries adopt in their pursuit of renewable energy and the integration of hydrogen within their energy frameworks. Brazil exhibits vast renewable energy potential, particularly in wind and solar energy sectors, positioning itself for substantial growth in the coming years. Germany showcases a regulatory framework that promotes innovation and technological expansion, reflecting its highly developed social system and commitment to transitioning away from fossil fuels. Austria demonstrates dedication to decarbonization, particularly through the exploration of biomethane for residential heating and cooling. [ABSTRACT FROM AUTHOR]

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge's operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

The quality of electricity is one of the most discussed issues in the regulatory context, structured by guidelines that allow the assessment of efficiency in electricity supply. Regarding electricity distribution systems, it is essential to highlight the aspects associated with the definition of continuity indicators to measure the service provided by the utilities. The parameters used to calculate the indicators are set by specific agencies in each country based on studies and their characteristics. With the growth in demand for electricity, debates associated with reducing greenhouse gas emissions in the atmosphere, and the increase in Distributed Energy Resources (DERs) connected to the grid, a new trend related to the energy supply is verified. Given these new scenarios, this paper presents a literature review in the regulatory field of existing distribution systems in South American countries. The research proposes to analyze, compare, and expose the current models to evaluate their methods and indicators, including a complete case study in Brazil. This paper seeks to illustrate and point out perspectives, challenges, and future perspectives through the collected and analyzed data. [ABSTRACT FROM AUTHOR]

The world is moving towards a low-carbon economy through renewable energy sources. In this context, concentrating solar power (CSP) technologies can exploit the rich solar resource in Brazil, diversifying the national electricity mix. The aim of this paper is to support the insertion of CSP generation in the Brazilian bottom-up model MATRIZ by analyzing the environmental performance of three CSP technologies: (i) solar tower power (STP) with a thermal storage system (TSS) of 7.5 h; (ii) solar thermal parabolic trough (STPT) with a TSS of 6 h; and (iii) STPT without TSS. From a life cycle perspective, their freshwater consumption and greenhouse gas (GHG) emissions are addressed by employing the software SimaPro, the Ecoinvent database, and the ReCiPe 2016 method, as well as information from the system advisory model (SAM) and the literature. As a result, STP is the most environmentally sound CSP alternative, presenting lower GHG emissions and water consumption than the two STPT technologies. In general, CSP generation proves to be more environmentally attractive than some fossil-fired power plants and renewable energy sources. Moreover, moving the manufacturing of CSP technologies to Brazil has the potential to reduce GHG emissions from the upstream processes of their value chain. [ABSTRACT FROM AUTHOR]

On average, 70% of the world's freshwater is used in agriculture, with farmers transitioning to electrical irrigation systems to increase productivity, reduce climate uncertainties, and decrease water consumption. In Brazil, where agriculture is a significant part of the economy, this transition has reached record levels over the last decade, further increasing the impact of energy consumption. This paper presents a methodology that utilizes the U-Net model to detect flooded rice fields using Sentinel-2 satellite images and estimates the electrical energy consumption required to pump water for this irrigation. The proposed approach involves grouping the detected flooded areas using k-means clustering with the electricity customers' geographical coordinates, provided by the Power Distribution Company. The methodology was evaluated in a dataset of satellite images from southern Brazil, and the results demonstrate the potential of using U-Net models to identify rice fields. Furthermore, comparing the estimated electrical energy consumption required for irrigation in each cluster with the billed energy values provides valuable insights into the sustainable management of rice production systems and the electricity grid, helping to identify non-technical losses and improve irrigation efficiency. [ABSTRACT FROM AUTHOR]

This work discusses the use of a battery energy storage system applied to the smoothing of power generated at the output of wind turbines based on a fuzzy logic power control. The fuzzy control logic proposed can perform the aforementioned activity while the state of charge of the energy storage system is maintained within operational limits. In order to assess the fuzzy logic power control's effectiveness at maintaining the state of charge levels within the allowed range, two operating situations are explored: one in which the state of charge is above the upper limit allowed, and another in which the state of charge is in the minimum value allowed. The numerical results show that, when using the battery energy storage system in conjunction with the control logic proposed, the active power provided as the point of common coupling by the wind turbines can be smoothed, thus contributing to the Electric Power System reliability and stability. The main results of this paper are based on measurements of wind and active power associated with a wind generation plant installed in the northeast region of Brazil and equipped with 2.1 MW wind turbines. [ABSTRACT FROM AUTHOR]

Through the transformation that the electrical sector has been passing by, improvements in asset management and the guarantee of sustainable and quality services have become essential aspects for power companies. Thus, the digitalization of energy utilities presents itself as an important and crucial process. A concept that involves a variety of innovative trends is the digital twin. It consists of a 3D virtual replica of existing physical objects and real-time monitoring of certain measures. By developing a digital twin in the electrical power grid, a virtual replica of the network is obtained providing network virtual maps, 3D asset models, dynamic and real-time data of grid assets, and IoT sensing. All these data can feed a platform where AI-based models and advanced field operation technologies and solutions will be applied. With a Network Digital Twin©development, applications involving on-field activities can be improved through augmented reality (AR) and virtual reality (VR) to enhance workforce operations. This paper discusses the best practices for the development of a digital twin for the electrical power sector. These practices were found during the development of a project carried out by Enel Distribuição São Paulo, applying a living lab concept in the densest region of Brazil. The results of this paper present 3D images captured with specialized tools, and how they influence the workforce activities of human interface operation. Furthermore, financial and operational returns are presented through a cost–benefit analysis for each relevant aspect. [ABSTRACT FROM AUTHOR]

In this paper, based on the gneiss outcrop of Bozhong 196 gas field in China, uniaxial compression and Brazil splitting tests were conducted by using cores of different orientations. The following compression properties were studied: the elastic compression modulus, Poisson's ratio and compressive strength of the gneiss outcrop. The following tensile properties were studied: the tensile modulus, the tensile strength and peak energy rate of gneiss outcrop. The results demonstrate the following: (1) The elastic compression modulus, compressive strength, tensile strength and peak energy rate of gneiss specimens with horizontal core-taking are greater than those with vertical core-taking. (2) The elastic compression modulus, Poisson's ratio and compressive strength of horizontally cored gneiss specimens are 29.688–45.760 GPa, 0.186–0.386, and 174.94–147.80 MPa, respectively; the elastic compression modulus, Poisson's ratio and compressive strength of the vertical gneiss specimens are 26.541–32.602 GPa, 0.429–0.476 and 169.37–134.46 MPa. (3) The tensile modulus of the horizontal gneiss specimens is 4.93–5.98 GPa. The tensile modulus of the vertical gneiss specimens is 0.96–2.11 GPa. The tensile modulus of the horizontal gneiss specimens is five times that of the vertical gneiss specimens. The elastic compression modulus of gneiss is 5–20 times that of the tensile modulus. (4) The tensile strength and peak energy rate of horizontally cored gneiss specimens are 14.33–17.55 MPa and 2598.67–4049.53 J/m2, respectively. The tensile strength of the vertical gneiss specimens is 6.12–9.65 MPa, and the peak energy rate is 715.74–1515.30 J/m2. (5) There is a good linear relationship between the peak energy rate and tensile strength of gneiss. The research results can provide a scientific and reasonable reference for in situ fracturing of Bozhong 196 gas field. [ABSTRACT FROM AUTHOR]

In recent years, fleet electrification has become a viable alternative for reducing carbon emissions through laws and government incentives around the world. Not only that, such incentives have favored the development of new technologies for the sector that have resulted in the reduction of the manufacturing cost of electric vehicles, which in turn, enabled new business models favoring the evolution of the electromobility sector. However, Brazil has also shown itself to be a market on the rise. However, electromobility in Brazil is still at an early stage of development when compared to other countries. In this sense, this paper sought to present the current state of the Brazilian sector through existing laws, regulations and incentives. In addition, it discusses and proposes alternatives for the development and maturation of the sector. Finally, technical aspects related to the standardization of communication protocols and charger installations are also presented. [ABSTRACT FROM AUTHOR]

In the rural regions of southern Brazil, electricity is largely directed to irrigation activities on rice crops at restricted periods of the year. Typically, customers in these regions are called "irrigators", and have some characteristics different from loads in urban centers, such as high demand levels and sharp load variations. These characteristics can result in problems of excessive loading on distribution grids at certain times of the day, generating concerns for the power utilities in relation to the security of the electrical system, energy supply to customers, and the integrity of electrical equipment. An alternative to avoid or mitigate these possible problems may be the application of a demand management model to irrigator customers. In this context, a load shifting strategy can be inserted to reduce demand in more critical periods and move it to intervals with lower load on the power grid. In this context, this article presents a demand-side management methodology in distribution systems located in rural areas, employing the load shifting strategy for irrigator customers. The methodology proposed in this paper is not an entirely novel approach, but one specifically developed for the context of irrigator customers, a subject little studied in the literature. The load management model proposed by this study is segmented into three hierarchical levels. The first level is the identification of the electrical characteristics of the distribution systems, the second level is the power flow analysis of the distribution networks, and the third and last level consists in the application of load shifting to the irrigator customers of these electrical systems. The load shifting strategy is modeled by a linear programming algorithm and is only applied to irrigator customers in situations of excessive loading on power grid. The case studies were conducted on three distribution systems of a power utility, with more than 150 irrigator customers. The DSM model based on the load shifting strategy reduced the maximum demand and daily load variations on the three rural feeders evaluated. The proposed changes in load patterns can ensure the continuity of electric power supply service in future even with the high concentration of load on distribution networks, benefiting customers and power utilities. [ABSTRACT FROM AUTHOR]

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series. [ABSTRACT FROM AUTHOR]

Substation (SS) auxiliary systems (SAux) are facilities responsible for hosting the alternating (AC) and direct current (DC) busbar to serve the equipment and systems that perform the substation's protection, control, and supervision. External and internal power supplies typically ensure the continuity of such a facility. The electricity support will be restricted to diesel emergency generators (DG) if the external power supply is unavailable due to a contingency. The DG present a slower response time and are susceptible to starting failures. Microgrids with Battery Energy Storage Systems (BESS) paired with photovoltaic systems (PV) are presented as an innovative and reliable solution for powering the SAux. In this article, tests were carried out on the microgrid of the Edson Mororó Moura Institute of Technology (ITEMM) in Brazil to support the use of microgrids BESS/PV in the SAux of a transmission SS of the São Francisco Hydroelectric Company (Chesf). Without an external power supply, BESS commands the action of islanded operation, maintaining both voltage and frequency requirements of the microgrid without load shedding. It was possible to observe all operations of the microgrid. The experimental results showed that the solution proposed in the paper implements a dependable self-dispatchable autonomous power supply. [ABSTRACT FROM AUTHOR]

The energy balance constitutes a powerful management instrument for government agencies, as it offers an overview of the energy situation of the country (or region) and serves as a guide for energy policies and monitoring of these policies. Although Brazil has published the national energy balance for more than half a century, the national publication does not adequately address energy statistics at the level of the states. This occurs either due to the lack of specific data or the absence of total disaggregation. Accordingly, the elaboration and implementation of public policies for the energy sector in the Brazilian states lack consistent energy statistics. Therefore, this paper aims to present a model for the Brazilian states to elaborate the energy balance. The proposed model consists of applying internationally referenced methodologies to develop a user-friendly software, which includes automatic energy unit conversions, different chart styles, high-level data organization, and Sankey diagrams. As a result, the software can be adopted by local governments as a tool to maintain the state energy balance publication periodically, and hence obtain the detailed information necessary to manage and formulate energy policies. The advantage of the software is that it can be operated by non-experts and the energy flow as well as the entire report can be generated automatically. The proposed software was successfully used to generate the energy balance of the Mato Grosso do Sul state. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are dependent on solar irradiation and environmental temperature to achieve their best performance. One of the challenges in the photovoltaic industry is performing maintenance as soon as a system is not working at its full generation capacity. The lack of a proper maintenance schedule affects power generation performance and can also decrease the lifetime of photovoltaic modules. Regarding the impact of environmental variables on the performance of PV systems, research has shown that soiling is the third most common reason for power loss in photovoltaic power plants, after solar irradiance and environmental temperature. This paper proposes a new statistical predictor for forecasting PV power generation by measuring environmental variables and the estimated mass particles (soiling) on the PV system. Our proposal was based on the fit of a nonlinear mixed-effects model, according to a log-logistic function. Two advantages of this approach are that it assumes a nonlinear relationship between the generated power and the environmental conditions (solar irradiance and the presence of suspended particulates) and that random errors may be correlated since the power generation measurements are recorded longitudinally. We evaluated the model using a dataset comprising environmental variables and power samples that were collected from October 2019 to April 2020 in a PV power plant in mid-west Brazil. The fitted model presented a maximum mean squared error (MSE) of 0.0032 and a linear coefficient correlation between the predicted and observed values of 0.9997 . The estimated average daily loss due to soiling was 1.4 % . [ABSTRACT FROM AUTHOR]

The EOSOLAR project was designed to investigate the structure of the atmospheric boundary layer in an equatorial coastal zone, where the discontinuity of surface conditions induces non-stationarity gradients of wind speeds and the development of internal boundary layers. The proposed methodology considers several aspects of the sea–land transition meteorology that are essential for precisely estimating wind–solar energy potential and assessment of structural loads on wind turbines. Infrared (LIDAR) and acoustic (SODAR) ground-based remote sensing instruments and micrometeorological towers were installed in a near-shore equatorial area of northeast Brazil, in order to provide a comprehensive view of meteorological processes. This paper reports a description of the project study area, methodology, and instrumentation used. Details of instruments configurations, a validation of micrometeorology towers, and a comparison between the LIDAR and SODAR are presented. Results of the first field campaign measuring the coastal flow, integrating the micrometeorological tower and LIDAR observations are described. [ABSTRACT FROM AUTHOR]

Policies for reducing the environmental impact of light-duty vehicles are restarting in Brazil. While electrification attracts more engagement than other forms of carbon footprint reduction, the use of ethanol could achieve faster gains with the existing infrastructure. The prominence of entry-level cars has reduced, which may be due to several factors, making these vehicles uneconomical for manufacturers to produce. This situation is different from those in other countries. It is our premise that other modes, such as walking, cycling, and public transportation, are the main methods of decarbonization. However, most need improvements in terms of security and attractiveness for consumers. Hence, the internal combustion engine will dominate the Brazilian fleet for quite some time. This article discusses the policies used to incentivize the implementation of entry-level vehicles in contrast with electric vehicles based on regression models of the Brazilian fleet. By providing a history of the 1.0 L engine in Brazil, this article debates replacing the indirect displacement taxation with direct CO2 taxation, which can make the Brazilian entry-level car practical again, with the possibility of adopting some incentives. This logic will cause a fleet renovation and more rational energy use. The distinguishing feature of this article is the proposition of possible public policies based on a regression model of the national fleet. The key findings show that the fleet is getting older and heavier, consuming more energy, and emitting greater amounts of CO2 per unit of displacement, for example, 1.0 L. Furthermore, the carbon footprint of the electricity mix is similar to that of the produced ethanol (from well to wheel, the carbon footprint is the same). This highlights the necessity for national policy adjustments and effective public policies to reduce carbon emissions from the energy sector for small vehicles. [ABSTRACT FROM AUTHOR]

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios. [ABSTRACT FROM AUTHOR]

The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility. [ABSTRACT FROM AUTHOR]

This paper empirically examined relevant data on BRICS CO2 emissions, financial development, and economic growth in the past 40 years, and analyzed the correlation between them. Using the cointegration test, it found that there is a clear correlation between the variables in China and South Africa, which show that there is a two-way relationship between CO2 emissions, financial development, and economic growth in both countries. Using the quantile regression method in the analysis, the results demonstrated that at the 0.6th quartile, South Africa's financial development had a negative impact on CO2 emissions, while Brazil's CO2 emissions had a negative impact on financial development. Economic growth was subsequently added as a control variable, and the quantile-on-quantile regression method was used to test the correlation between the financial development of the BRICS countries and their CO2 emissions. Finally, based on empirical conclusions, this paper proposed that BRICS countries should focus on sustainable economic development; when government departments formulate emission-reduction policies, they must reasonably consider the relationship between financial development and emission-reduction policies. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic pricing scheme for demand response with individualized tariffs by consumption profile, aiming to benefit both customers and utility. The proposed method is based on the genetic algorithm, and a novel operator called mutagenic agent is proposed to improve algorithm performance. The demand response model is set by using price elasticity theory, and simulations are conducted based on elasticity, demand, and photovoltaic generation data from Brazil. Results are evaluated considering the integration effects of renewable energy sources and compared with other two pricing strategies currently adopted by Brazilian utilities: flat tariff and time-of-use tariff. Simulation results show the proposed dynamic tariff brings benefits to both utilities and consumers. It reduces the peak load and average cost of electricity and increases utility profit and load factor without the undesirable rebound effect. [ABSTRACT FROM AUTHOR]

The maritime transportation sector (MTS) is undertaking a major global effort to reduce emissions of greenhouse gases (GHG), e.g., sulfur oxides, nitrogen oxides, and the concentration of particulates in suspension. Substantial investment is necessary to develop alternative sustainable fuels, engines, and fuel modifications. The alternative fuels considered in this study include liquified natural gas, nuclear energy, hydrogen, electricity, and biofuels. This paper focuses on biofuels, in particular fast pyrolysis bio-oil (FPBO), a serious partial alternative in MTS. There are some drawbacks, e.g., biofuels usually require land necessary to produce the feedstock and the chemical compatibility of the resulting biofuel with current engines in MTS. The demand for sustainable feedstock production for MTS can be overcome by using cellulose-based and agroforestry residues, which do not compete with food production and can be obtained in large quantities and at a reasonably low cost. The compatibility of biofuels with either bunker fuel or diesel cycle engines can also be solved by upgrading biofuels, adjusting the refining process, or modifying the engine itself. The paper examines the possibilities presented by biofuels, focusing on FPBO in Brazil, for MTS. The key issues investigated include FPBO, production, and end use of feedstocks and the most promising alternatives; thermal conversion technologies; potential applications of FPBO in Brazil; sustainability; biofuels properties; fuels under consideration in MTS, challenges, and opportunities in a rapidly changing maritime fuel sector. Although the focus is on Brazil, the findings of this paper can be replicated in many other parts of the world. [ABSTRACT FROM AUTHOR]

Acidification, a widely employed technique for well stimulation, utilizes a variety of acid systems. However, the literature provides limited insights into the economic and oil recovery implications of these treatments. Therefore, this work aims to analyze the economic impact on the oil recovery of different acid treatment designs in formations with varying degrees of heterogeneity and flow capacity. For this purpose, matrix acidization simulations were conducted using Matrix software to obtain the skin profile for each type of treatment. Subsequently, numerical simulations were performed using IMEX software from the CMG Group to analyze oil production and compare the economic results of different acid treatments. The main findings indicate that reservoirs with low flow capacities benefit more from stimulation operations, yielding a higher financial return when stimulated compared to untreated wells. In fact, there seems to be a range of flow capacity values, from around 20,000 to 40,000 mD.m, above which the net present value (NPV) of stimulated wells is equal to or even lower than that of damaged untreated wells. [ABSTRACT FROM AUTHOR]

Global environmental impacts such as climate change require behavior from society that aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels with other energy sources. An important aspect of efficient and sustainable management of the electricity supply in Brazil is the prediction of some variables of the national electric system (NES), such as the price of differences settlement (PLD) and wind speed for wind energy. In this context, the present study investigated two distinct forecasting approaches. The first involved the combination of deep artificial neural network techniques, long short-term memory (LSTM), and multilayer perceptron (MLP), optimized through the canonical genetic algorithm (GA). The second approach focused on machine committees including MLP, decision tree, linear regression, and support vector machine (SVM) in one committee, and MLP, LSTM, SVM, and autoregressive integrated moving average (ARIMA) in another. The results indicate that the hybrid AG + LSTM algorithm demonstrated the best performance for PLD, with a mean squared error (MSE) of 4.68. For wind speed, there is a MSE of 1.26. These solutions aim to contribute to the Brazilian electricity market's decision making. [ABSTRACT FROM AUTHOR]

This article examines the potential for wind and solar energy generation in the state of Amapá, Brazil, using ERA5 data from between 1991 and 2020. Key metrics considered include wind power density, capacity factor, photovoltaic potential, and concentrated solar power output. Analyses revealed pronounced wind speeds offshore during summer and in continental regions during spring. Solar irradiance was notably higher in the spring. Differences in wind potential were observed between northern and southern offshore areas. Concentrated solar power efficiency and photovoltaic potential were influenced by location and cloud cover, respectively. Overall, summer presents the best offshore wind energy potential, while spring is optimal for onshore solar energy in Amapá. This study underscores the importance of understanding local climatic patterns when planning energy installations in the region. [ABSTRACT FROM AUTHOR]

The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience. [ABSTRACT FROM AUTHOR]

Definitions of methodologies to regulate the quality of electricity supply services are a topic under active discussion in Brazil and worldwide. There are various ways to define limits and quality service goals. In Brazil, the regulation of limit indicators for consumer unit sets is carried out by the National Electric Energy Agency. Its latest revision took place in 2014, under the framework of Public Announcement No. 29/2014. The primary contribution of this research is the proposition of an artificial intelligence-assisted methodology, specifically utilizing machine-learning techniques capable of organizing and selecting the most relevant attributes for representing similar consumer sets. Tests were conducted with real data from the 2020 system. The results demonstrated that this methodology can select attributes from different categories, achieving data representativeness and clustering scores superior to those attained with attributes selected by the current ANEEL methodology. Furthermore, the proposed methodology exhibits greater replicability compared to the current approach. These outcomes contribute to the modernization of quality regulation in the electricity distribution sector, benefiting all stakeholders in the industry. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources has become strategic in the production of electricity worldwide due to global efforts to increase energy efficiency and achieve a net zero carbon footprint. Hybrid systems can maximize stability and reduce costs by combining multiple energy sources. A conventional metric, such as the levelized cost of energy (LCOE), that is appropriate for assessing the cost-effectiveness of an option may not be appropriate when evaluating the economic feasibility of hybrid systems. This study proposes a stochastic discounted cash flow model (DCF) to assess the economic viability of a hybrid renewable energy system (HRES) in Brazil. The objective is to determine the combinations that will provide the highest 50th percentile internal rate of return (IRR) and the lowest coefficient of variation (CV). Model variables include capital expenditures (CAPEX), operation and maintenance (O&M) costs, sectoral charges, taxes, and long-term energy production metrics. The results demonstrate that the synergies modeled contributed to the higher economic outcomes for the HRES obtained by combining both energy sources rather than opting for a stand-alone configuration. A wind-dominant combination of 60% wind was able to increase the 50th percentile of the IRR, while a solar-dominant combination of 65% solar minimized the CV. [ABSTRACT FROM AUTHOR]

Exergy is a thermodynamic concept that ponders the quality of energy. It evaluates the irreversibilities of a machine, demonstrating its capacity to perform work associated with energy conversion. This article focuses on directing public policies and vehicle development toward their most proper usage worldwide. In the urban mobility scenario, there is an obvious demand to decrease greenhouse gas (GHG) emissions. In addition, the internal combustion engine (ICE) experiences considerable energy losses through heat exchange through the radiator and exhaust flow gases, which are not considerable in battery electric vehicles (BEVs) since there are no exhaust gases subsequent to combustion, nor combustion itself. This work presents longitudinal dynamics simulations of passenger vehicles to understand the magnitude of exergy destruction in ICEVs and BEVs, considering the Brazilian and European Union electric energy mix. Overall, the method can be applied to any other country. The simulation and model parameters were configured to match production road vehicles commercialized in the Brazilian market based on different versions of the same model. Two vehicle dynamic duty cycles were used, one relating to urban usage and another to highway usage, resulting in an overall exergy efficiency of around 50–51% for BEVs considering the exergy destruction in power plants. In contrast, ICE has an average efficiency of 20% in the urban cycle and around 30% in the highway cycle. By comparing the overall equivalent CO2 emissions, it is possible to conclude that EVs in the European energy matrix produce more GHG than ICE vehicles running on ethanol in Brazil. Nevertheless, there are increasing uses of coal, natural gas, and oil thermal electric power plants, raising the question of how the transition may occur with a general increase in electrification since there is an increasing electric expenditure in all sectors of society, and the renewable energy plants may not meet all of the demand. [ABSTRACT FROM AUTHOR]

The micro- and mini-distributed generation (MMDG) has significantly increased after the normative resolution No. 482/2012 in Brazil; the installed capacity surpassed 7 GW in 2021. In the international context, a similar event was observed, whose process generated a cross-subsidy for other consumers, in addition to other problems that affect the economic balance of concessionaires. To mitigate this issue, the National Electric Energy Agency (ANEEL) is in the process of revising current rules. Thus, this study estimates the weight of this decision, through a methodology adapted from international assessment models, based on information from the Brazilian regulatory system. In order to achieve it, this paper presents metrics to define the potential market MMDG, based on the consumption patterns of consumers. Then, through time series analysis, the MMDG demand curve is estimated under two scenarios up to 2030. Finally, the economic impact on tariff adjustments and revisions, and their effect on the electric power concessionaires are evaluated. In the distribution companies of the Enel Group alone, economic losses are estimated at USD 1.2 billion by 2030; 53% of this will be passed on to consumers' tariffs. Thus, based on international experiences, it can be concluded that the best model is the adequate grid remuneration. [ABSTRACT FROM AUTHOR]

This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil. [ABSTRACT FROM AUTHOR]

In recent years electricity sectors worldwide have undergone major transformations, referred to as the "energy transition". This has required energy planning to quickly adapt to provide useful inputs to the regulation activity so that a cost-effective electricity market emerges to facilitate the integration of renewables. This paper analyzes the role of system planning and regulations on two specific elements in the energy market design: the concept of firm capacity and the presence of distributed energy resources, both of which can be influenced by regulation. We assess the total cost of different regulatory mechanisms in the Brazilian and Mexican systems using optimization tools to determine optimal long-term expansion for a given regulatory framework. In particular, we quantitatively analyze the role of the current regulation in the total cost of these two electricity systems when compared to a reference "efficient" energy planning scenario that adopts standard cost-minimization principles and that is well suited to the most relevant features of the new energy transformation scenario. We show that two very common features of regulatory designs that can lead to distortions are: (i) renewables commonly having a lower "perceived cost" under the current regulations, either due to direct incentives such as tax breaks or due to indirect access to more attractive contracts or financing conditions; and (ii) requirements for reliability are often defined more conservatively than they should be, overstating the hardships imposed by renewable generation on the existing system and underestimating their potential to form portfolios. [ABSTRACT FROM AUTHOR]

Forecasting photovoltaic electricity generation is one of the key components to reducing the impacts of solar power natural variability, nurturing the penetration of renewable energy sources. Machine learning is a well-known method that relies on the principle that systems can learn from previously measured data, detecting patterns which are then used to predict future values of a target variable. These algorithms have been used successfully to predict incident solar irradiation, but the results depend on the specificities of the studied location due to the natural variability of the meteorological parameters. This paper presents an extensive comparison of the three ML algorithms most used worldwide for forecasting solar radiation, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), aiming at the best prediction of daily solar irradiance in a São Paulo context. The largest dataset in Brazil for meteorological parameters, containing measurements from 1933 to 2014, was used to train and compare the results of the algorithms. The results showed good approximation between measured and predicted global solar radiation for the three algorithms; however, for São Paulo, the SVM produced a lower Root-Mean-Square Error (RMSE), and ELM, a faster training rate. Using all 10 meteorological parameters available for the site was the best approach for the three algorithms at this location. [ABSTRACT FROM AUTHOR]

In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio's revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework. [ABSTRACT FROM AUTHOR]

Renewable sources stand out in energy planning due to their contribution to greenhouse gas emission reduction when displacing fossil fuels and the enhancement of energy security through the diversification of the energy matrix. Understanding and optimizing the complementary operative synergy between different energy sources over time and space leads to efficient policies. This article uses an hourly Pearson's correlation coefficient to explore the complementarity between offshore wind and other power generation sources in the Brazilian matrix. An analysis of offshore wind power feasibility in the Brazilian power system will be conducted, considering environmental implications, synergies with the oil industry, costs, and complementarities with other energy sources. The methodology uses an optimization model to minimize costs and optimize the production mix while considering the time series of renewable energy, subject to demand constraints, renewable resource availability, reservoir storage, capacity limitations, and thermal generation. The study concludes that the northeast and southeast electrical subsystems must start offshore wind installation in Brazil due to their complementarity with hydropower production, synergy with the oil and gas industry, and proximity to the largest consumption spots. [ABSTRACT FROM AUTHOR]

The evaluation of the quality of electric power distribution services in Brazil is regulated and monitored by the National Electric Energy Agency ANEEL, which uses metrics related to the duration frequency of power interruptions that occur in the power utilities' networks. The methodology applied by the agency to establish financial compensation due to violations of the quality standards does not take into consideration the consumers' production industry when establishing compensation for the transgression of service quality indicators. This study will analyze a case study of a group of industrial consumers linked to agribusiness in the southern region of Brazil, which have strongly seasonal use of distribution networks and are scattered in large, dispersed areas. Based on the evaluation of the impact of service quality indicators on financial compensation, a regulatory mechanism is proposed in the form of an interruptible tariff duly quantified in the form of a non-linear programming problem to find a discount range for uninterruptible tariffs. The results obtained with the real data for the group of irrigating consumers demonstrate the feasibility regarding the application of the proposed approach, whether due to the discounts offered or even the repercussions of the calculated financial amounts. [ABSTRACT FROM AUTHOR]

Energy auctions are commonly used to contract energy projects and are extensively studied from the regulator's perspective. However, analyzing auctions from the stakeholders' perspective is critical to determine the impact of regulatory details on the bidder's revenues. In this study, we analyze a public energy auction in Roraima, a Brazilian state with a significant energy deficit and no grid connection, where many projects were successful in the non-intermittent Power Product category, typically unfavorable for biomass. Using Linear Programming to maximize bidders' revenues, we examine the regulatory formulas that contributed to the success of these projects and compare the optimization results to actual revenues. Our analysis shows that certain regulatory elements can benefit stakeholders by allowing them to make unconventional project design decisions. In addition, we identify a possible loophole in the formula that can have the opposite effect of the regulator's intent in the renewable Power Product category. Our findings can help bidders increase profits through optimization and regulators to change formulas if objectives are not met. This study brings the often-overlooked perspective of stakeholders to energy auctions, adding to the literature on this topic. [ABSTRACT FROM AUTHOR]

This research aims at developing methodological tools for decision makers (DMs) to determine the locations for generating sites for hybrid renewable energy systems, considering complementarity characteristics of the corresponding sources and their seasonal availability. The decision process on new sites starts using Geographic Information Systems for modeling relevant spatial criteria. The problem solution is associated with using the Slide-OWA operator, which permits one to control the inter-criteria compensation levels by adjusting pessimism and optimism parameters. After constructing multicriteria estimates for generating sites, the general scheme of multi-objective decision-making is applied in conditions of uncertainty since these estimates are subject to significant levels of uncertainty. Such a scheme is based on the possibilistic approach, which involves the construction of payoff matrices. At this point, it is possible that some alternatives cannot be distinguished based only on the criteria considered so far, or the DM may want to evaluate the alternatives from the point of view of additional considerations. For this purpose, the framework of multi-attribute decision-making models is applied by employing methods for preference modeling in a fuzzy environment. The present work results are applied to a case study of the Minas Gerais State in Brazil. These results play a strategic role for governments and investors in their decisions while considering the ability to meet a wide range of criteria. [ABSTRACT FROM AUTHOR]

Low or zero carbon fuels are crucial for maritime transportation decarbonization goals. This paper assesses potential localities for maritime biofuels (biobunkers) production in Brazil, Europe, South Africa, and United States considering geographical, logistic, and economic aspects. This assessment combines georeferenced and techno-economic analyses to identify suitable fuel production hotspots based on not only plant performance and costs but also on logistic integration and biomass seasonality. Five technology pathways were considered: Straight vegetable Oils (SVO), Hydrotreated Vegetable Oils (HVO), Fischer–Tropsch Biomass-to-liquids (FT-BTL), Alcohol oligomerization to middle distillates (ATD), and Hydrotreated Pyrolysis Oil (HDPO). Findings reveal that biomass concentration in Brazil makes it the region with highest biobunker potential, which are mostly close to coastal areas and surpasses regional demand. Although other regions registered more limited potentials, hotspots proximity to ports would enable fossil fuel replacements in these areas. For all cases, biobunker costs (USD 21–104/GJ) are higher than conventional marine fuels prices (USD 11–17/GJ). Only 15% of the hotspots' carbon prices that would allow its competitiveness are lower than USD 100/tCO2. Alternatives to incentivize biobunker production would be, first, to establish mandatory fuel blends and second, to join forces with other sectors that would be benefited from the co-production of advanced biofuels. [ABSTRACT FROM AUTHOR]

For international civil aviation to be able to significantly reduce its greenhouse gas (GHG) emissions, the use of Sustainable Aviation Fuels (SAF) needs to be made feasible. This paper presents the results of an assessment of the feasibility of production of SAF in Brazil, considering three certified routes, based on the dedicated production of eucalyptus, soy, sugarcane and corn. The results presented here refer to the production of biomass in selected locations, aiming to reduce GHG emissions and minimise production costs. Considering that the opportunity costs of feedstocks were not observed, the minimum selling price (MSP) of SAF in the reference case was estimated at 13.4 EUR·GJ−1 for the production based on soybean oil (HEFA-SPK route), 21.0 EUR·GJ−1 for the production based on ethanol from sugarcane and corn (ATJ-SPK) and 32.0 EUR·GJ−1 from eucalyptus (FT-SPK). These values refer to SAF's nth industrial plant and biomass costs that are compatible with the current agricultural yields in Brazil but which are also the highest. The MSP results are relatively low compared to the estimates available in the literature, but they do not show the strict economic viability of SAFs in the short- to medium-term, mainly because of the low prices of fossil fuels. [ABSTRACT FROM AUTHOR]

This review investigates the effects of the Brazilian agriculture production and forestry sector on carbon dioxide (CO2) emissions. Residual biomasses produced mainly in the agro-industrial and forestry sector as well as fast-growing plants were studied. Possibilities to minimize source-related emissions by sequestering part of carbon in soil and by producing biomass as a substitute for fossil fuel were extensively investigated. The lack of consistency among literature reports on residual biomass makes it difficult to compare CO2 emission reductions between studies and sectors. Data on chemical composition, heating value, proximate and ultimate analysis of the biomasses were collected. Then, the carbon sequestration potential of the biomasses as well as their usability in renewable energy practices were studied. Over 779.6 million tons of agricultural residues were generated in Brazil between 2021 and 2022. This implies a 12.1 million PJ energy potential, while 4.95 million tons of forestry residues was generated in 2019. An estimated carbon content of 276 Tg from these residues could lead to the production of approximately 1014.2 Tg of CO2. Brazilian biomasses, with a particular focus on agro-forest waste, can contribute to the development of sustainable alternative energy sources. Moreover, agro-waste can provide carbon credits for sustainable Brazilian agricultural development. [ABSTRACT FROM AUTHOR]

Distributed generation, which is mainly deployed with PV systems that benefit economically prosumers, has soared in use in Brazil. Despite this, PV capacity in excess may cause technical issues which concern planning engineers who have adopted rules of thumb to screen interconnection requests without any detailed study. Recently, the hosting capacity concept has been employed to assess how much PV capacity a distribution grid can host without deteriorating grid parameters, reliability, or power quality. A steady-state and worst-case-based scenario was used to run deterministic power flow simulations to estimate the hosting capacity of a specific radial circuit at a campus of the University of São Paulo, referred to as "USP-105". Although the result may be not completely accurate, it was found that USP-105 can accommodate 103% of its peak load or 4970.6 kW of PV power, which reduced the circuit's annual peak load by 9%. Another finding was that hosting capacity increased when PV-DG deployment was dispersed along the circuit rather than concentrated on a single location (e.g., closest, or furthest to the substation). Utilities may therefore benefit from a simple and quick assessment to obtain an overview of how specific circuits behave on PV deployment and indicate which locations are technically more beneficial. [ABSTRACT FROM AUTHOR]

In Northeast Brazil, the use of biomass for energy generation is settled on traditional productive arrangements, such as a sugarcane production system in the humid Atlantic coastal area and firewood extraction from native tropical dry forests in the west. In parallel, substantial amounts of other biomass sources, such as residues from agricultural or urban processes, are still little used or wholly wasted, fudging the opportunity to generate new value chains based on these biomass sources. We hypothesize that using these non-traditional biomass sources to produce biofuels would significantly increase the regional bioenergy supply. In this context, this article discusses the potential for the production and use of biofuels and bioenergy in Northeast Brazil and its effects on regional development, which may be useful for both private actors and policymakers in the energy sector. The use of biomass sources for energy in the region is significant, reaching approximately 8.8 million tons of oil equivalent (toe) per year, emphasizing the already consolidated production of sugarcane and its derivatives. The use of all biomass resources in the Northeast region could supply around 4% of the Brazilian national electrical energy demand, with an environmental footprint of 0.055 tCO2eq per toe, which would contribute to reducing emissions from the Brazilian energy matrix generation. Regarding the spatial distribution of biomass sources, sugarcane prevails on the coast, firewood and livestock manure in the dryland area towards the west, and municipal solid waste is distributed throughout the region within urban areas. Different from what we expected, the potential energy recovery from municipal waste and animal manure would increase by only 17% the current bioenergy supply. In the future, since the majority of the region presents a semi-arid climate with limited rainfall, to increase the use of biomass as an energy source, there is a need to increase the supply of biomass sources with high efficiency in water use and good yields in drylands. For this, the cultivation and use of cacti and agave, for example, could contribute to making biorefineries viable in the region. Above all, public policies for harnessing bioenergy in NE Brazil must seek opportunities associated with the carbon/decarbonization economy, with studies being needed to assess the technical, economic, social, and environmental viability of future productive arrangements. [ABSTRACT FROM AUTHOR]

In this article, we seek to present the synthesis of the global ethanol market in the United States (US) and Brazil by using caloric equivalence to empirically characterize the elasticity of supply and demand. We also seek to evaluate the relationship between world ethanol production and prices of agricultural commodities (wheat, rice, corn, soybeans and sugar) from 1981 to 2016 using climate-induced yield shocks as instrumental variables. The main results for the world market indicate that the production and demand for ethanol respond elastically to changes in commodity prices. The world ethanol production has no significant relationship with food prices. However, evaluation of the ethanol market and its interaction with the agricultural commodities market confirms the hypothesis that Brazilian ethanol is weakly related to the price of food. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTLs) are one of the main problems that electricity distribution utilities face in developing regions such as Latin America, the Caribbean, sub-Saharan Africa, and South Asia. Particularly in Brazil, based on the socioeconomic and market variables concerning all the distribution utilities, the National Electric Energy Agency (ANEEL) has formulated several specifications of econometric models for panel data with random effects, all aimed at determining an index that reflects the difficulty of combating NTLs according to the intrinsic characteristics of each distribution area. Nevertheless, given the exhaustive search for combinations of explanatory variables and the complexity inherent to defining regulatory NTL targets, this process still requires the evaluation of many models through hypothesis and goodness-of-fit tests. In this regard, this article proposes an automatic model-selection technique for panel data regressions to better assist the Agency in establishing NTL regulatory targets for the distribution of utilities in this country. The proposed technique was applied to panel data containing annual observations from 62 Brazilian electricity distribution utilities from 2007 to 2017, thus generating 1,097,789 models associated with the regression types in the panel data. The main results are three selected models that showed more adherence to the actual capacity of Brazilian distribution utilities to reduce their NTLs. [ABSTRACT FROM AUTHOR]

The search for charging electric vehicles using renewable energy sources and ensuring the stability of the electrical system has been growing. This has led to the development of charging stations that integrate photovoltaic systems, energy storage systems in batteries, and charging stations. The storage system allows for applications that increase the reliability of the electrical network. The objective of this work is to study, based on real operational scenarios, the impacts, and criteria of energy quality of a charging solution containing a photovoltaic system, a storage system, and a charging station. In the solution, the batteries are lead-carbon type and photovoltaics have priority in energy supply, using excess generation to charge the batteries. In this aspect, the work presents relevant contributions to the study of energy quality. An improvement in energy quality is pointed out with the use of batteries, proving that even in the most diverse operating situations, such as partial shading of the photovoltaic modules, with 20% shading relative to the nominal power, charging is not impacted and reductions in distortions are achieved, with a 50% reduction in voltage imbalance, when the storage system is a voltage reference and reference to the microgrid. [ABSTRACT FROM AUTHOR]

This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer's risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse consumers. Under this condition, the PV/battery system is viable for a reduction of 87% in the battery investment cost. An important conclusion is that the risk analysis tool is essential to assist consumers in the decision-making process of investing in HEES. [ABSTRACT FROM AUTHOR]

This article addresses the development of the energy compensation method used for the design of hybrid energy storage systems—HBESS. The combination of two battery technologies offers better cost and performance when considering microgrid systems to provide uninterrupted power to sensitive loads (substation auxiliary system) and also provides greater energy security. In the event of a failure, the load needs to continue operating, and batteries such as lithium ions have a fast response, but are expensive for large-scale systems. However, some technologies offer low-cost and good availability of energy for long hours of discharge, such as lead–acid batteries. Consequently, different battery technologies can be used to meet all the needs of the sensitive loads. A specific method for sizing a HBESS was developed for islanded microgrids to support sensitive loads. This method was developed to meet the demand for substations outside the Brazilian standard of power systems that lack an uninterrupted and reliable energy source. The method is validated by designing a microgrid to support the auxiliary systems of a transmission substation in northeastern Brazil. The results showed a system with a capacity of 1215 kWh of lead-carbon and 242 kWh of lithium ions is necessary to maintain an islanded microgrid for at least 10 h. Furthermore, the microgrid comprises a PV plant with an AC output power of 700 kW in connected operation and 100 kW when islanded from the grid. [ABSTRACT FROM AUTHOR]