Search Results (33)

This study analyzes the economic impacts of integrating floating offshore wind farms with a Floating Production, Storage and Offloading (FPSO) unit to reduce carbon dioxide emissions. The idea is to replace the use of natural gas for power supply with an offshore wind farm, considering the effects of carbon pricing. Results show that wind integration reduces emissions by 23% to 76%, depending on the installed capacity. However, higher wind capacity increases total system costs, initial investment, electricity and operational expenses. The Brazilian carbon credit market adversely impacts existing FPSO units as a result of the compulsory carbon trading costs necessary to mitigate their emissions. In contrast, wind-integrated scenarios benefited from carbon pricing, improving financial indicators such as payback period and Return on Investment. Wind shares of 30% and 70% yielded the best financial results for carbon prices between 10 and 50 United States Dollars per ton, with higher penalties further improving viability. These findings elucidate the significance of carbon pricing in mitigating emissions and enhancing the economic feasibility of offshore wind farms within the context of the Brazilian national FPSO decarbonization strategy. Full article

Sugarcane products come from agro-industrial biomass that is increasingly used in the Brazilian energy matrix, which is important for the sustainability and diversification of renewable energy sources. This article examines the concentration and structure of the supply of sugarcane bioelectricity in Brazil from 1975 to 2023. It uses information on the quantity and cumulative licensed potential of sugarcane-based thermoelectric plants in operation, available from the National Electric Energy Agency (ANEEL) through its Generation Information System (SIGA). To measure regional concentration, the study considered geographical areas (large regions, states, intermediate regions and municipalities) using the following concentration indicators: the Concentration Ratio, Herfindahl–Hirschman Index, Theil Entropy, Comprehensive Concentration Index, and Hall–Tideman Index. The main results show a high concentration of sugarcane bioelectricity at regional and state levels, with a predominance in the Southeast-Central-West axis. During the period analyzed, the State of São Paulo remained the leader in terms of energy generated by sugarcane thermoelectric plants operating in Brazil. In the intermediate regions, the concentration was moderate, while at the municipal level, the concentration was low, indicating a highly competitive market. It can be concluded that the areas with the highest concentration are strategic for directing investments and guiding public policies for the sugarcane bioelectricity sector, which are priority locations for new opportunities. The identification of the most promising regions contributes to a more efficient development of the sector. Given that, a more equitable distribution of bioelectricity production across the country could enhance Brazil’s energy security, reduce regional vulnerabilities, and promote more resilient energy systems. Full article

In the electricity market, the issue of contract negotiation prices between generators/traders and buyers is of particular relevance, as an accurate contract modeling leads to increased financial returns and business sustainability for the various participating agents, encouraging investments in specialized sectors for price forecasting and risk analysis. This paper presents a methodology applied in experiments on energy forward curve scenarios using a set of techniques, including Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX), and Feature Engineering to generate a 10-year projection of the Conventional Long-Term Price. The model validation proved to be effective, with errors of only 4.5% by Root Mean Square Error (RMSE) and slightly less than 2% by Mean Absolute Error (MAE), for a time series spanning from 7 January 2012 to 31 August 2024, in the Brazilian energy market. Full article

This study evaluates whether pumped hydro storage (PHS) systems are economically competitive compared to natural gas thermal power plants in meeting peak load demand in Brazil and identifies the barriers and challenges that hinder their widespread adoption. It also examines the strategies, market mechanisms, and policy implications necessary to improve the economic and operational viability of PHS, enabling greater integration of variable renewable energy sources into the Brazilian power system. Using the levelized cost of electricity (LCOE) method, PHS is compared with natural gas thermoelectric plants for peak demand scenarios in Brazil. The results of simulations indicate that PHS is economically viable for operations exceeding seven hours per day, offering lower costs. In contrast, natural gas technologies are more cost-effective for shorter operations. The results provide two key contributions: they characterise the basic conditions under which PHS systems are more competitive than thermal power plants in meeting electricity demand, and they propose a methodology for calculating the LCOE of the analysed technological options, tailored to the Brazilian energy market. The conclusions highlight the potential of PHS to contribute to Brazil’s sustainable energy transition, provided that appropriate policies are implemented. These policies are especially crucial in scenarios where PHS is not economically competitive, to ensure compensation mechanisms for the flexibility services provided and the implementation of carbon pricing. Additionally, retrofitting existing hydropower plants to incorporate PHS components may reduce costs and mitigate environmental impacts compared to constructing new PHS facilities. Full article

This paper introduces an enhanced algorithm for computing generalized Nash equilibria for multiple player nonlinear games, which degenerates in a gradient algorithm for single player games (i.e., optimization problems) or potential games (i.e., equivalent to minimizing the respective potential function), based on the Rosen gradient algorithm. Analytical examples show that it has similar theoretical guarantees of finding a generalized Nash equilibrium when compared to the relaxation algorithm, while numerical examples show that it is faster. Furthermore, the proposed algorithm is as fast as, but more stable than, the Rosen gradient algorithm, especially when dealing with constraints and non-convex games. The algorithm is applied to an electricity market game representing the current electricity market model in Brazil. Full article

The transport sector, and especially the increase in individual vehicle ownership, contribute significantly to air pollution. The transition to electric vehicles (EVs) is seen as a sustainable alternative to reduce emissions of polluting gases. However, in Brazil, the EV market has not yet reached a significant size. Given this scenario, this study aims to analyze the factors that influence the decision to buy EVs in Brazil, highlighting personal, psychological, economic, performance, and environmental variables and barriers. The aim is also to develop a model with guidelines that can help stakeholders. The quantitative stage of the study involved a survey of 514 respondents. The data were analyzed using statistical methods, including structural equation modeling (SEM), which allowed for a deeper investigation of the proposed hypotheses. The survey findings reveal that, in the Brazilian context, performance factors—such as autonomy, availability of recharging infrastructure, and maintenance—are the main drivers influencing EV purchase decisions. Environmental factors, including energy reuse, pollution reduction, and minimizing environmental impacts, have also gained significant importance. Economic factors are crucial, particularly concerning cost–benefit perceptions. The differences between Brazil and other regions highlight the importance of accounting for cultural and economic variations when analyzing consumer behavior towards EVs. Full article

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period. Full article

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model’s performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape. Full article

Despite the growth of renewable energy, fossil fuels dominate the global energy matrix. Due to expanding proved reserves and energy demand, an increase in natural gas power generation is predicted for future decades. Oil reserves from the Brazilian offshore Pre-Salt basin have a high gas-to-oil ratio of CO₂-rich associated gas. To deliver this gas to market, high-depth long-distance subsea pipelines are required, making Gas-to-Pipe costly. Since it is easier to transport electricity through long subsea distances, Gas-to-Wire instead of Gas-to-Pipe is a more convenient alternative. Aiming at making offshore Gas-to-Wire thermodynamically efficient without impacting CO₂ emissions, this work explores a new concept of an environmentally friendly and thermodynamically efficient Gas-to-Wire process firing CO₂-rich natural gas (CO₂ > 40%mol) from high-depth offshore oil and gas fields. The proposed process prescribes a natural gas combined cycle, exhaust gas recycling (lowering flue gas flowrate and increasing flue gas CO₂ content), CO₂ post-combustion capture with aqueous monoethanolamine, and CO₂ dehydration with triethylene glycol for enhanced oil recovery. The two main separation processes (post-combustion carbon capture and CO₂ dehydration) have peculiarities that were addressed at the light shed by thermodynamic analysis. The overall process provides 534.4 MW of low-emission net power. Second law analysis shows that the thermodynamic efficiency of Gas-to-Wire with carbon capture attains 33.35%. Lost-Work analysis reveals that the natural gas combined cycle sub-system is the main power destruction sink (80.7% Lost-Work), followed by the post-combustion capture sub-system (14% Lost-Work). These units are identified as the ones that deserve to be upgraded to rapidly raise the thermodynamic efficiency of the low-emission Gas-to-Wire process. Full article

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. Full article

Microgrids have emerged as a popular solution for electric energy distribution due to their reliability, sustainability, and growing accessibility. However, their implementation can be challenging, particularly due to regulatory and market issues. Building smaller-scale microgrids, also known as nanogrids, can present additional challenges, such as high investment costs that need to be justified by local demands. To address these challenges, this work proposes an economic feasibility assessment model that is applied to a real nanogrid under construction in the Brazilian electrical system, with electric vehicle charging stations as its main load. The model, which takes into account uncertainties, evaluates the economic viability of constructing a nanogrid using economic indicators estimated by the Monte Carlo simulation method, with the system operation represented by the OpenDSS software. The model also considers aspects of energy transactions within the net-metering paradigm, with energy compensation between the nanogrid and the main distribution network, and investigates how incentives can impact the viability of these microgrids. Full article

Electric vehicles and photovoltaic power stations can play an important role in replacing fossil fuels. This article presents a case study on the placement of charging stations powered by photovoltaic energy along an important highway in Brazil. A demand model was adopted to elaborate three scenarios for 2030 with different participation levels of electric vehicles in the Brazilian market. An optimized allocation model was used to derive the location and number of charging stations required to meet the charging demand. The results provided a list of adequate locations for installing the charging stations and offered insights into the consumed electricity and greenhouse gas emissions that could be mitigated by these actions. A financial analysis was conducted, and it was determined that the charging costs, based on the Internal Rate of Return calculation, were 10%. These costs were compared to the fueling costs of other traditional vehicles. The results showed that the costs can be 72% lower than the cost of refueling current conventional automobiles. The results of this study can serve as a reference in the public policy debate, as well as for investors in fast charging stations. Full article

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. Full article

This paper conducts an empirical analysis of risk premiums in the Brazilian electricity market, a critical but understudied field. Employing two distinct methodologies—Average Forward Prices and Last Observed Forward Prices—the study calculates risk premiums between spot and forward electricity prices. Our analysis consistently identifies negative risk premiums, which serve as indicators that the market may be underestimating certain types of risk. These underestimations are potentially influenced by inherent market uncertainties, including volatile demand, unpredictable supply, and frequent regulatory shifts. Additionally, we observe a high volatility in risk premiums, signifying a dynamic and ever-changing market where expectations are continuously recalibrated. Such conditions present possible arbitrage opportunities for market actors and underline the need for policymakers to introduce measures mitigating market unpredictability. By focusing on these nuances, this paper enriches the broader discourse on risk premiums in electricity markets and underscores the necessity for further research aimed at devising effective risk management strategies. Full article

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. Full article