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The subsector of pulp and paper presented in 2013 an electric power consumption of 9.3% of electricity consumption in the industrial sector in Brazil. It experienced a significant growth of production in the last 20 years, exceeding 160%, and their consumption of electricity increased by 130%, reaching the level of 19,594GWh. Due to its characteristics, with a known structure and production processes, and its importance in the country's industrial sector, this subsector was selected for study in this paper, to evaluate the behavior of its long-term annual electricity demand, at the horizon of 20 years ahead, in energy efficiency scenarios through bottom-up approach. This approach is data intensive and requires data with good quality; it consists of a hierarchical structure that enables to check how specific actions can affect the results at each level and its effect on higher levels. [ABSTRACT FROM AUTHOR]

This paper examines the inter-linkages between the water and energy sectors and their planning processes, by describing a comprehensive analytical tool developed to evaluate water energy nexus operational cost trends and planning to assist decision makers in exploring and evaluating alternative courses of action. Brazil has been chosen as a case study, because its electricity production is highly dependent on water to keep affordable tariffs, which in turn also serves as input to other important sectors, such as water services and sanitation, and raises disputes especially in basins marked by water scarcity, such as the São Francisco basin. In light of hydrological factors (e.g., droughts) and non-hydrological factors (e.g., chronic delays in delivery of new plants) there has been water availability constraints for electricity generation and energy prices have risen, while water quantity and quality have decreased for multiple users. Both of which impact negatively on water services and sanitation providers, because electricity figures as their fastest growing costs in times when they need more energy to source water from longer distances, or deeper levels because of water quantity and quality issues. Energy and water are characterized as common pool resources with planning processes along silos in Brazil that do not serve well the purpose of sustainable development. Better integrated water-energy plans at basin level is the alternative proposed under this paper to advance sustainability and mitigate the risks related to water scarcity that have resulted in negative impacts on both electricity and water sectors. [ABSTRACT FROM AUTHOR]

The presence of aerosols in atmosphere has the potential to decrease the efficiency of solar photovoltaic energy generation. The central region of Brazil presents a remarkable growth of these aerosols between March and October, when the region undergoes a period of intensive slash and burn agriculture. This paper aims to evaluate the impact of this anthropogenic aerosol on electric power generation by comparing data from geostationary satellites with photovoltaic power generation data. [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]

Study region: The Três Marias 396 MW power plant located on the São Francisco River in Brazil. Study focus: Hydropower generation is directly and indirectly affected by climate change. It is also a relevant source of energy for electricity generation in many countries. Thus, methodologies need to be developed to assess the impacts of future climate scenarios. This is essential for effective planning in the energy sector. Energy generation at the Três Marias power plant was estimated using the water balance of the reservoir and the future stream flow projections to the power plant, for three analysis periods: FUT1 (2011–2040); FUT2 (2041–2070); and FUT3 (2071–2100). The MGB-IPH hydrological model was used to assimilate precipitation and other climatic variables from the regional Eta climatic model, via global models HadGEM2-ES and MIROC5 for scenarios RCP4.5 and RCP8.5. New hydrological insights for the region: The results show considerable reductions in stream flows and consequently, energy generation simulations for the hydropower plant were also reduced. The average power variations for the Eta-MIROC5 model were the mildest, around 7% and 20%, while minimum variations for the Eta-HadGEM2-ES model were approximately 35%, and almost 65% in the worst-case scenario. These results reinforce the urgent need to consider climate change in strategic Brazilian energy planning. [ABSTRACT FROM AUTHOR]

Copyright of Colloquium Exactarum is the property of Asociacao Prudentina de Educacao e Cultura (APEC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The global economy is advancing towards a low-carbon future. Alongside this energy transition, the sustainability concept has become a fundamental value of our society, encouraging a worldwide discussion on the consideration of socio-environmental issues by the energy sector. In Brazil, the electricity generation expansion planning has not been doing enough to incorporate these issues into its decision-making process, especially in regards to alternative and renewable energy sources. Considering that Life Cycle Sustainability Assessment (LCSA) is suitable for addressing this situation, we focus on one of its first steps, which consists of defining a set of indicators able to communicate sustainability issues. With this in mind, this paper aims at incorporating sustainability into the decision-making process concerning electricity generation in Brazil by formulating a set of indicators. We carry out a systematic review for collecting the leading environmental, social, and economic indicators used by scientific papers focused on recent and practical LCSA studies concerning electricity generation and that contemplate alternative and renewable energy sources. Thus, we analyze the frequency of indicators in a sample of 41 papers and screen them against selection criteria and their relevance for the Brazilian context. As a result, we establish a set of 28 indicators (18 environmental, four social, and six economic). It is clear from our findings that LCSA practitioners have been facing different levels of methodological and practical challenges depending on the dimension of sustainability, which reflects on the number of environmental, social, and economic indicators collected from the overall sample. • 28 indicators have been selected from a systematic review of practical LCSA studies. • Traditional communities and protected areas were included in this set of indicators. • The final set of indicators covers environmental, social, economic dimensions. • They can be used to incorporate sustainability into the Brazilian electricity sector. • Practical LCSA studies in the electricity sector mainly address environmental issues. [ABSTRACT FROM AUTHOR]

Stabilization at concentrations consistent with keeping global warming below 2 °C above the pre-industrial level will require drastic cuts in Greenhouse Gas (GHG) emissions during the first half of the century; net negative emissions approaching 2100 are required in the vast majority of current emission scenarios. For negative emissions, the focus has been on bioenergy with carbon capture and storage (BECCS), where carbon-neutral bioenergy would be combined with additional carbon capture thus yielding emissions lower than zero. Different BECCS technologies are considered around the world and one option that deserves special attention applies CCS to ethanol production. It is currently possible to eliminate 27.7 million tonnes (Mt) of CO 2 emissions per year through capture and storage of CO 2 released during fermentation, which is part of sugar cane-based ethanol production in Brazil. Thus, BECCS could reduce the country’s emissions from energy production by roughly 5%. Such emissions are additional to those due to the substitution of biomass-based electricity for fossil-fueled power plants. This paper assesses the potential and cost effectiveness of negative emissions in the joint production system of ethanol and electricity based on sugar cane, bagasse, and other residues in Brazil. An important benefit is that CO 2 can be captured twice along the proposed BECCS supply chain (once during fermentation and once during electricity generation). This study only considers BECCS from fermentation because capturing such CO 2 is straightforward, thus potentially representing a cost-effective mitigation option for Brazil compared to other alternatives. The assessment shows that fuel prices would increase by less than 3.5% due to the adoption of BECCS from fermentation, while increasing investors’ revenues are sufficient to compensate for the investment required. With appropriate government subsidies, or by sharing BECCS costs between all car fuels and all electricity supplied by hydro and bioelectricity, the increment in ethanol and electricity prices could be less than 1% for the final consumer. Meanwhile it would supply 77.3% of all cars’ fuel (private cars) and 17.9% of all electricity in Brazil. [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]

In Brazil, large players in Energy Market dedicate many efforts in valuation and optimal capital allocation decision for their projects implementation, due the large candidate projects number in their investment portfolios. These decisions aim to choose the projects subset to be implemented, because the monetary resources are generally smaller than necessary for all projects. There are presented many risks that increase as the risks and uncertainties deal with analysis and optimal decisions. The classical methods to investment portfolios valuation are based on maximizing the returns and minimizing the risks. These classical methods may not be able to handle properly the Real Options of projects, their risks and uncertainties. This paper presents a Fuzzy Real Option model portfolio analysis for valuation phase and a genetic algorithm for optimization phase. A real case is presented to illustrate the methodology. [ABSTRACT FROM AUTHOR]

Copyright of Sustainability in Debate / Sustentabilidade em Debate is the property of University of Brasilia, Center for Sustainable Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingenius, Revista Ciencia y Tecnología is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Assessing the relative contribution of Land Use and Cover Changes (LUCC) and climate changes on runoff still represents a great challenge for water resources management. This issue is particularly critical for the Upper Paraná River Basin (UPRB), one of the most important basins in South America and responsible for most of the production of food, ethanol, and electricity generation in Brazil. In this paper, we used the Soil and Water Assessment Tool (SWAT) to quantitatively assess the relative contribution of both forcings. The simulation period included a time of great importance for climate studies, known as the 1970s global climate shift, and of great impact on river discharge within the UPRB. Three land use and cover scenarios were assigned to the 1961–1990 period of simulations, representing land use and cover during a pristine period (around the Year 1500), 1960, and 1985. Thirteen years of precipitation before and after the climate shift (considered to be the period 1974–1977) were analyzed and compared. Results showed a precipitation increase for the basin in general after the climate shift. The increase in rainfall reached up to 15% in many northern areas and more than 20% in the southern parts of the basin. By comparing all simulations, results indicate that both LUCC and precipitation increase due to the climate shift had a significant effect on the changes in annual discharge of the largest rivers of the UPRB. However, the results suggest that the impact of the precipitation increase on the discharge exceeded that of the LUCC. Between 1960 and 1985 the LUCC accounts for about 16% of the increase of the median annual discharge, whereas climate shift accounts for an increase of about 32%. These findings, suggesting a more relevant role for the climate, are consistent with two recent water crisis experienced by the country in the last decades, caused by prolonged below-normal rainfall throughout 2001/2002 and again in 2014/2015. [Display omitted] • We simulated land use and cover from a pristine period (Year 1500), 1960, and 1985. • We evaluated the effects of precipitation change on discharge from 1961 to 1990. • Natural vegetation fell by more than half of the original area up to 1985. • Precipitation increased by more than 20% after the 1970s global climate shift. • Results suggest that climate shift is the main driver in increasing annual discharge. [ABSTRACT FROM AUTHOR]

The insufficient supply of electrical energy, in addition to frequent disturbances and interruptions, has motivated the inclusion of solar, biogas, biomass or wind energy systems in many Brazilian farms. However, there are few studies that have addressed the technical and economic impacts of renewable sources for generating electricity in rural applications, leading farmers not to invest in these technologies for fear of financial losses. This study was carried out to evaluate the potential of grid-connected photovoltaic systems for supplying the electricity demand in dairy farms located at Minas Gerais State, Brazil. The electricity generated by grid-connected photovoltaic systems was estimated from global solar radiation measurements, considering six municipalities of Minas Gerais State, Brazil. Electricity consumption was monitored monthly during one year in 12 farms. The average percentages of electricity consumption in the main operations executed at farms were 4, 27, 12, 33 and 24% for lighting, milking, cleaning/disinfection (water heating and pumping), milk cooling/refrigeration and miscellaneous, respectively. The monthly differences between the electricity generation and consumption for the studied municipalities demonstrated the technical feasibility of grid-connected systems installed directly in the dairy farms, helping to achieve energy sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Políticas Públicas is the property of Revista Brasileira de Politicas Publicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Electric energy prices in Brazil are higher during 3h of peak consumption on working days; however, consumers could use a generator system for partially reducing the electricity costs from utility company during peak hours. The process of sizing a generator system begins developing the so called load capacity curve, using acquired building electricity consumption data. The local utility company supplies this data only for the last month of operation; consequently, data are unavailable for the rest of the year. In order to overcome the difficulty, this paper develops two methodologies to estimate the curve for every month of the year. The first one uses the frequency of occurrence of the measured values of the load power in each bin and the last one the monthly values of load power and energy consumption, as supplied by the local utility monthly bills. In order to validate the methodologies, monthly utility bills and consumption data of electric energy measured at Pontifical Catholic University of Rio de Janeiro, every 15min over a four year period, have been used. Finally a generator system was sized, minimizing the electrical energy cost supplied by it, when the generator meets partially the building load. [Copyright &y& Elsevier]

Abstract: Brazil is a country where the use of hydroelectric power for energy generation reaches the impressing amount of 70% in terms of electricity consumption, whereas the world average is around 16%. The current installed capacity in Brazil is approximately 114 GW, with an estimated total potential of 260 GW. The construction of reservoirs is associated with several environmental impacts, both of positive and negative nature. In the first group the most relevant are: energy production, employment generation, enhancement of water quality in the transformation from lotic to lentic ecosystems (turbidity reduction, higher water transparency, nutrients sedimentation and decreasing of eutrophication processes downstream), expansion of working posts offer during the construction of power plants; supply of reliable low cost energy to the region and substitution of diesel based thermoelectric generation by hydroelectric generation. The main impacts of negative nature are changes in water quality, people relocation, changes in the structure of the aquatic community, loss of genetic patrimony (flora and fauna), slopes destabilization and climatic alterations, including those related to the emission of greenhouse gases. In this sense several researches are currently being carried out in the country with the aim of quantifying the main emissions of CO2, CH4 and N2O. Eutrophication processes may also be developed in inundated areas, leading to the eventual presence of cyanobacteria. These organisms pose a special concern to Brazilian health authorities, since this was the first country to report human deaths caused by cyanotoxins (city of Caruaru, 1996). Another relevant aspect related with dams design is the possible creation of the so-called low flow reaches, downstream the reservoir. These river stretches should be kept with a minimum water flow (ecological flow), whose sound calculation is still subjected to some uncertainties. The paper presents a balance of the positive and negative environmental aspects of the construction and operation of hydroelectric reservoirs based on the large experience accumulated through research and consulting projects developed at Universidade Federal de Minas Gerais. [Copyright &y& Elsevier]

Abstract: The increasing interest in the recuperation of the biogas coming from organic residues, associated with its energetic use is a subject that has been widely discussed. Biogas was merely seen as a sub-product obtained from anaerobic decomposition (without oxygen) of organic residue. In the paper is carried out an evaluation of the quantities of organic residues coming out from the sugar and alcohol industry (vinasse), urban solid and liquid wastes (garbage and sewage) and livestock residues (bovine and swine manure) in Brazil. Finally the electricity generation potential of biogas out of the evaluated sources of organic residues in Brazil is estimated. The results of this study indicate that the potential regarding the production of biogas out of the aforementioned organic residues of electricity production using could meet an energy demand of about 1.05 to 1.13%. Constraints for biogas energy utilization are identified and discussed. [Copyright &y& Elsevier]

The city of Aripuana is one of the largest wood producer in the state of Mato Grosso, Brazil. Wood residues are used in the electricity generation at three thermoelectric plants in this region. However, the plants have high costs in transporting the wood residues (due to poor road conditions). Hence, this paper compares the energy performance of wood residues in natura and compacted as briquettes by calculating the heating value and determining the influence of moisture content on the energy characteristics of wood residues. The goal is to demonstrate the viability of using briquettes in order to improve thermoelectric generation. The wood residues from this region are affected by the high humidity of the biome. An alternative to improve the use of energy contained in the wood residues is to produce briquettes with lower humidity. This allows one to maintain high levels of heat energy in a lower volume, facilitating handling and storage. The results show that the use of briquettes improved the performance of thermoelectric plants, generating 1 MW of electricity power with less than 1 ton of briquettes. This contributes to the preservation of the environment, reducing operating costs, transportation and storage of the raw materials. [ABSTRACT FROM AUTHOR]

Brazilian consumers have the possibility to produce their own energy using renewable sources or qualified cogeneration and even provide the surplus for the distribution network of their locality, as determined by Normative Resolution no. 482/2012 issued by the National Electric Energy Agency (ANEEL). In view of this, the use of photovoltaic systems for electricity generation has grown surprisingly reaching the mark of 8 gigawatts of installed power on Brazilian soil in the year 2021 [1]. In Brazil, public agencies represent 0.1% with 4.7MW installed [1], and 0.38% in the state of Mato Grosso [14]. Thus, aiming at the rise of electricity generation in public buildings due to the possibility of reducing the electric bill costs, the present work deals with the dimensioning, budget quotation and economic feasibility analysis regarding the installation of a grid-connected photovoltaic power generation system to meet the energy demands of Jorge de Abreu Regional Hospital in the city of Sinop, Mato Grosso state, Brazil. After carrying out the necessary research based on the techniques present in the economic sciences, as Minimum Attractiveness Rate (MRA), Net Present Value (NPV), Internal Rate of Return (IRR) and calculation of Payback, the present work considered a grid-connected solar energy generation system of 585 kW to supply the hospital's average monthly consumption of 93.829 kWh/month. The investment analysis showed that the project for the implementation of the photovoltaic system will have its NPV viable as of the 4th year of installation and the Payback (return on investment), observing the monetary corrections, of approximately four years and five months, thus configuring the system as economic financially. [ABSTRACT FROM AUTHOR]

This study provides a global warming impact analysis of environmentally friendly refrigerants used as replacements for R134a and R245fa. R290, R1234yf, R1234ze(E), R513A and R450A are considered as refrigerants to replace R134a in medium temperature applications. For R245fa, there are five alternative refrigerants, R1224yd(Z), R600, R1336mzz(Z), R1233zd(E) and R1234ze(Z), which are selected for high-temperature applications. The analysis is done considering the emission factors in Brazil, Sweden, Canada and Poland. In Sweden and Brazil, the total equivalent warming impact per heating capacity of R134a is higher than its alternative refrigerants in medium temperature application, although R134a exhibits a higher coefficient of performance than its alternatives. In high-temperature applications, R1336mzz(Z) has the lowest total equivalent warming impact per heating capacity due to its higher coefficient of performance than other tested refrigerants. The highest total equivalent warming impact per heating capacity belongs to R245fa in all countries except in Poland, where R600 exhibits a higher value due to its lower coefficient of performance and the relatively higher emission factor in Poland compared to other selected countries. These results revealed that in addition to the global warming potential, the emission factor associated with the sources of electricity generation has a crucial impact on indirect emissions. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Gestão Ambiental e Sustentabilidade (GeAS) is the property of Revista de Gestao Ambiental e Sustentabilidade / UNINOVE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The conversion efficiency of a crystalline silicon photovoltaic (PV) module increases as its temperature decreases. Considering this characteristic of the PV technology, in this study the authors analyse the use of cooled floating PV modules in a fraction of the area occupied by three reservoirs - Castanhão, Orós and Banabuiú - in the Brazilian semi-arid region. Comparing with conventional ground-mounted PV modules, an average conversion efficiency increase of 12.5% was measured for floating PV modules under the local weather conditions. In a second phase, measurements are used as input data in the PVSYST software in conjunction with a spreadsheet for the construction of electricity production scenarios. According to these scenarios, the potential PV production in the mentioned reservoirs would supply 7.4 and 18.8% of the electricity demand of Brazil's Ceará state (8.84 million inhabitants) and its largest city Fortaleza (2.6 million inhabitants), respectively. [ABSTRACT FROM AUTHOR]

The ocean wave energy resource is a real alternative to supply part of the energy demand in various countries, since some locations have a remarkable capacity to generate electricity. The objective of this study is to evaluate the energy resource of ocean waves in the coast of Rio Grande do Sul, the southern state of Brazil. This note presents the first results. The wave data used were collected in the sea area near the Port of Rio Grande during the years 1996 to 1999, amounting to sixteen months of monitoring. The data set was treated and grouped resulting information monthly, seasonal and annual basis. The annual average was found to be 8.6 kW per meter of wave front, reaching 14.0 kW per meter for the month of May and 4.0 kW per meter for the month of January. The results indicate good perspectives in obtaining power supplies. [ABSTRACT FROM AUTHOR]

The ocean can be a strategic alternative for obtaining energy supplies, both from ocean waves as from sea currents and tides. Among these features, the power generation projects based on ocean currents are still under development. Generating energy from ocean can have great impact on the Brazilian energy grid, since Brazil has a vast coastline, with more than 9,000 km long, with potential for generating energy from ocean currents not fully estimated. This article presents a preliminary determination of the energy potential for power generation from ocean currents along the coast of Rio Grande do Sul, the southernmost state of Brazil, and also presents notes that contribute to the characterization of the system of ocean currents in the region. The data used were obtained in two areas near Tramandaí, allowing the determination of velocities and directions of the currents on a seasonal basis. The maximum speeds obtained rarely exceed 0.750 m/s, while the average speeds do not exceed 0.200 m/s. A relationship with the prevailing winds in the region was identified. Unfortunately, the results do not allow optimism about the power generation from ocean currents on the southern coast of Brazil, at least over the continental shelf. [ABSTRACT FROM AUTHOR]

Meireles, A.J. A, Gorayeb, A, Silva, D.R.F, Lima, G.S, 2013. Socio-environmental impacts of wind farms on the traditional communities of the western coast of Ceará, in the Brazilian Northeast The global expansion of wind power has resulted from the development of scientific research that aims to establish an energy network which minimizes the emission of pollutants derived from the combustion of fossil fuels. Ceará, in the Brazilian Northeast, has a considerable potential for the generation of environment-friendly power, due to the occurrence of strong winds on many parts of the coast and at higher elevations. Given this, the present study aimed to evaluate the socio-environmental damage caused by the installation of a wind farm on the coastal plain of the municipality of Camocim, on the western coast of the state, and to identify the consequences for the coastal environment and the traditional community of Xavier, which survives primarily on fishing, agriculture, and the extraction of natural resources. The study was based on the recognition and definition of the environmental components of the local landscapes, and the mapping of the community's territory, based on the social cartography approach. The study area is characterized by mangrove ecosystems, dune fields, interdunal lakes, fluvial-lacustrine coastal plains, and a sandy beach. The integration of these different environments underpins the traditional lifestyle of the local community, which is based primarily on the exploitation of natural resources. Given this, the community of Xavier, which is made up of 20 families, was affected directly by the installation and subsequent operation of the local wind farm (one of the largest in the Brazilian Northeast, with 50 wind turbines generating a maximum output of 104.4 MW, and approximately 135 km of transmission lines) through (1) the gratuitous occupation of a significant portion of the community's territory, (2) deforestation of fixed dunes, (3) the suppression of dunes by bulldozing, (4) burying of interdunal lakes, (5) disturbance of the margins of fluvial-lacustrine systems, (6) compaction and impermeabilization of the soil, and (7) artificial retention of mobile dunes. These findings indicate that the licensing of wind farms in the state ignores a set of socio-environmental principles. The results of this analysis indicate the need for a regional blueprint for the development of effective socio-environmental policies for the sustainable exploitation of the coastline of the Brazilian Northeast, and in particular for the definition of the sectors most appropriate for the installation of wind farms, such as the coastal plains, which have been classified as the alternative sites with the best potential, as long as effective licensing measures are implemented. [ABSTRACT FROM AUTHOR]

The objective of this work is to present a methodology capable of estimating the incremental payoff of the carbon market for grid-connected renewable power generation projects in Brazil. The proposed methodology consists of using the NEWAVE model to determine the optimal operating policy for the Brazilian Interconnected System over the medium-term, and to establish hydrological dispatch scenarios for the power plants connected to this system. Based on these results, the criteria defined in the methodology ACM0002 were used to estimate the baseline of the projects developed in response to the Clean Development Mechanism of the Kyoto Protocol. Having estimated the baseline for the project, the incremental payoff obtained from the sale of Certified Emission Reductions is estimated using the real options theory. Subsequently, the value of the option is added to the conventional cash flow of the project, determining the impact of the carbon market on its internal rate of return. [ABSTRACT FROM PUBLISHER]

Hydroelectric dams represent major investments and major sources of environmental and social impacts. Powerful forces surround the decision-making process on public investments in the various options for the generation and conservation of electricity. Brazil’s proposed Belo Monte Dam (formerly Kararaô) and its upstream counterpart, the Altamira Dam (better known by its former name of Babaquara) are at the center of controversies on the decision-making process for major infrastructure projects in Amazonia. The Belo Monte Dam by itself would have a small reservoir area (440 km2) and large installed capacity (11, 181.3 MW), but the Altamira/Babaquara Dam that would regulate the flow of the Xingu River (thereby increasing power generation at Belo Monte) would flood a vast area (6140 km2). The great impact of dams provides a powerful reason for Brazil to reassess its current policies that allocate large amounts of energy in the country’s national grid to subsidized aluminum smelting for export. The case of Belo Monte and the five additional dams planned upstream (including the Altamira/Babaquara Dam) indicate the need for Brazil to reform its environmental assessment and licensing system to include the impacts of multiple interdependent projects. [ABSTRACT FROM AUTHOR]

Brazil’s Samuel Dam, which formed a 540-km2 reservoir in the state of Rondônia in 1988, provides lessons for development decisions throughout Amazonia and in other tropical areas. The decision to build the dam was heavily influenced by its role in the political strategies of key decision makers. Samuel illustrates both impacts and benefits of electricity supply and the dilemmas facing decision makers regarding the various options for planned electricity generation. Environmental costs included flooding forest and stimulating illegal logging activity throughout western Amazonia because of an exception opened for Samuel in Brazil’s prohibition of export of raw logs. Samuel emitted substantially more greenhouse gases than would have been emitted by generating the same amount of electricity from oil. Contamination of fish in the reservoir resulted from methylation of mercury present in the soil. Social costs of the dam included resettlement of 238 families of farmers; impacts on indigenous people were indirect. Mitigating measures included faunal rescue and creation of a forest reserve. The lessons of Samuel include the need to consider a full range of alternatives prior to making decisions in practice and the importance of adhering to the logical sequence of decision making, where information is gathered and compared prior to the decision. It also shows the need to maintain flexibility when the costs and benefits of different alternatives change significantly over the course of the project’s planning and execution, as occurred at Samuel. [ABSTRACT FROM AUTHOR]

We consider the inclusion of commitment of thermal generation units in the optimal management of the Brazilian power system. By means of Lagrangian relaxation we decompose the problem and obtain a nondifferentiable dual function that is separable. We solve the dual problem with a bundle method. Our purpose is twofold: first, bundle methods are the methods of choice in nonsmooth optimization when it comes to solve large-scale problems with high precision. Second, they give good starting points for recovering primal solutions. We use an inexact augmented Lagrangian technique to find a near-optimal primal feasible solution. We assess our approach with numerical results. [ABSTRACT FROM AUTHOR]

Coordination of the operation of the large Brazilian multi- owned hydrothermal generating system, at the institutional level, was achieved through a pool structure in which all member utilities share the cost of operating the thermoelectric system. At the technical level, a stochastic dynamic programming model, SDP, determines the optimal allocation of hydro and thermal resources in the system. The SDP model was extensively validated by 18 electrical utilities and officially adopted by the pool in 1979. Comparisons with a previously adopted model projected expected savings of $87 million in five years, a 28 percent reduction in operating costs. Actual savings from 1979 to 1984 are about $260 million. The SDP model is also the focal point of a chain of generation expansion models, insuring that the probabilistic criteria used for planning and the operation of the Brazilian system are compatible. [ABSTRACT FROM AUTHOR]

The energy sector and electricity generation in particular, is responsible for a great share of the global greenhouse gas (GHG) emissions. World electricity generation is still largely based on the burning of fossil fuels. However, Brazil has already a very low electricity carbon intensity due to the country's large hydropower capacity. In countries with low grid carbon intensities such as Brazil, the investment in photovoltaic solar systems (PVSS) even if it is cost-effective, might become challenging as any new generation competes essentially against other renewable generation and the carbon offset is not a key driver for investment anymore. This study builds further upon that case to examine if national renewable energy incentives could actually lead to an increase of global net carbon emissions from the installation of PVSS in countries with a low grid carbon intensity. The study presents a life cycle analysis (LCA) of ten photovoltaic systems representative of the different operational conditions in regions across Brazil. It was found that the average energy payback time of the studied PV plants is between 3 and 5 years of operation. This result shows the feasibility and viability of such investments in the Brazilian context. When the LCA was integrated into the analysis though, the results showed that the "local" direct emissions avoidance from two out of ten studied PV plants would not manage to offset their "global" life cycle emissions due to the 2020 projected Brazilian grid emission factor which is already low. It is important to recognize that public policies of unrestricted, unconditional stimulus to photovoltaic systems investment might not help towards reducing global net emissions when the PV systems are installed at countries with a low carbon emission electric matrix. That is also something to consider for other countries as the carbon intensity of their grids will start reducing at levels similar to Brazil's. It is likely that in the near future, the real net carbon offset achieved by PV systems at the global level will be largely defined by the manufacture procedures and the production's carbon intensity at the country of origin of the PV panels. [ABSTRACT FROM AUTHOR]

This study analyzes the road freight sector of São Paulo state to identify the best options to reduce greenhouse gases emissions and local pollutants, such as particulate matter, nitrogen oxides, carbon monoxide, and hydrocarbons. Additionally, the investment cost of each vehicle is also analyzed. Results show that electric options, including hybrid, battery, and hydrogen fuel-cell electric vehicles represent the best options to reduce pollutants and greenhouse gases emissions concomitantly, but considerable barriers for their deployment are still in place. With little long-term planning on the state level, electrification of the transport system, in combination with increased renewable electricity generation, would require considerable financial support to achieve the desired emissions reductions without increasing energy insecurity. [ABSTRACT FROM AUTHOR]

The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios. [ABSTRACT FROM AUTHOR]

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option. [ABSTRACT FROM AUTHOR]