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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]

This study undertakes a Life Cycle Assessment (LCA) of red and white wine production in Brazil, focusing on a leading winery in the Serra Gaúcha region. Given the notable lack of similar research in this area, our study employs LCA methodologies to evaluate the environmental impacts associated with the winery stage. Our results indicate that although white wine generally exhibits a lower environmental impact than red wine, the specific impacts differ substantially across various environmental categories, highlighting the necessity for context-specific assessments. We also find that electricity consumption, sugar usage, and liquid CO2 production are the primary drivers of environmental impacts, especially in toxicity-related categories. Moreover, our research critically examines the suitability of the Product Environmental Footprint Category Rules (PEFCR) within the Brazilian context and proposes a revised set of impact categories. This revision enhances the accounted impacts from 55% in the existing PEFCR 2.0 to 80% in our recommended framework, suggesting the need for more tailored environmental impact assessment tools in regional settings. Through these findings, our study not only addresses a significant gap in LCA research within the Brazilian wine industry but also sets the stage for improved environmental management practices across Brazilian wine industries. These enhancements are crucial for aligning local production processes with global sustainability standards and advancing the environmental stewardship of the wine sector in Brazil. [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]

In this paper, a methodology was developed to analyze the results of energy efficiency programs used in the electricity distribution sector in Brazil. Analyzing the gains obtained through the investments made, and classifying which actions resulted in the best performance, contributed to decision making on the best allocation of investments to obtain the greatest energy savings. The Brazilian Energy Efficiency program was analyzed with a developed non-parametric model, using the data envelopment analysis method, and the categories of projects with better performance were determined. A database of the results from 1704 projects, from 2008 to 2016 in the Energy Efficiency program in Brazil's electricity distribution sector, was used. The results obtained show that the best performance was achieved by projects in the industrial and cogeneration categories; however, in Brazil these constitute only 4.24% of the projects presented and 5.28% of the total investments in the last eight years, indicating a need to review the regulatory strategies for energy efficiency in this country. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of public policies for fare social tariffs of electricity in Brazil by using an economic model of the electricity market (TAROT-Optimized Tariff) that represents the regulated market of distribution of electrical energy. It was considered the scenario of an increasing number of prosumers (residential consumers who self-generate energy) in two of the five major regions of Brazil which have quite different socioeconomic characteristics. However, the current electricity regulation is the same for all concessionaires. In this work a new public policy is proposed, allowing the use of regulation in a different way aiming for a best result for Brazil and particularly for the poor population that today are not able to enjoy the benefits of electricity due to high tariff values. It is also discussed how this can contribute in a positive way to improve the income distribution in these regions, which is evaluated by using the GINI index. [ABSTRACT FROM AUTHOR]

Over the years, electricity consumption behavior in Brazil has been analyzed due to financial and social problems. In this context, it is important to simulate energy prices of the energy efficiency auctions in the Brazilian electricity market. The Markov Chain Monte Carlo (MCMC) method generated simulations; thus, several samples were generated with different sizes. It is possible to say that the larger the sample, the better the approximation to the original data. Then, the Kernel method and the Gaussian mixture model used to estimate the density distribution of energy price, and the MCMC method were crucial in providing approximations of the original data and clearly analyzing its impact. Next, the behavior of the data in each histogram was observed with 500, 1000, 5000 and 10,000 samples, considering only one scenario. The sample which best approximates the original data in accordance with the generated histograms is the 10,000th sample, which consistently follows the behavior of the data. Therefore, this paper presents an approach to generate samples of auction energy prices in the energy efficiency market, using the MCMC method through the Metropolis–Hastings algorithm. The results show that this approach can be used to generate energy price samples. [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]

The high consumption of electricity in Palmas, Brazil is a direct consequence of the high temperatures identified throughout the year. Therefore, it is necessary to search for sustainable technologies that contribute to comfort through passive cooling, thus reducing the energy consumption. This work presents the results of an experimental campaign of 11 months, in which the effects of shading devices, the use of a low-emissivity reflective film, the implementation of natural ventilation and the application of reflective painting are evaluated, when applied individually and combined. The results show that when applied individually, natural cross ventilation was the most effective at night, while reflective painting showed better performance during the day. Regarding the combinations of techniques, the best combination occurred when reflective painting was used together with natural ventilation, and the result was a relevant improvement in the performance, ensuring high temperature reductions when compared to the control. [ABSTRACT FROM AUTHOR]

Brazil is currently undergoing changes to regulations on distributed generation (DG), specifically for solar energy micro-generation. The changes proposed by the Brazilian Regulatory Agency suggest that only the cost of energy be compensated to investors. The service costs and other charges related to energy tariffs must be divided among consumers. Investors with existing installations and class entities have contested these proposals, calling them "sun-fees". To date, no scientific papers have been published discussing these changes. The new regulations propose an end to cross subsidies, where all consumers (even those who do not have DG) pay for the transmission and distribution systems. This study compares the economic feasibility of micro-generation before and after implementing the new standards proposed by the regulatory agency. We used data on average electrical energy demand, energy price, and solar radiation in different regions. The national averages were used as a base comparison with other scenarios. The results show that projects are viable for all analyzed scenarios, however, after implementing the proposed changes, the discounted payback time is extended. This, however, does not make projects unfeasible. [ABSTRACT FROM AUTHOR]

This study verified the environmental effectiveness of potentially less aggressive routes for the synthesis of poly(propylene/ethylene oxide) glycol (PPG). The analysis was developed in two stages. Firstly, the environmental performance of the conventional PPG processing route was compared to alternative variants—vegetal PPG and CO2-based PPG—applying the life cycle assessment technique to measure the primary energy demand, global warming potential, acidification, photochemical oxidation, and freshwater ecotoxicity impact categories. The synthesis of vegetable polyols from bio-based assets, such as vegetable oils, and the application of CO2 conversion routes as an alternative to technologies supported by petroleum and natural gas were studied. The use of CO2 recovered through carbon capture and usage practices resulted in environmental gains for PPG production. The processing routes within vegetal assets were not an environmentally attractive option as the performance was worse than the conventional arrangement by 144% for the global warming impact category, an increase related to the deforestation carried out to expand soybean cultivation in Brazil. Secondly, improvement scenarios to mitigate the environmental impacts of alternative routes were performed. The hypothesis of using cleaner inputs to obtain a more ecofriendly route was tested. The analysis concluded that the use of high-purity CO2 brings fewer benefits compared to other capturing sources that need a purification process before feeding the PPG synthesis. [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]