Brazilian Electricity Research Articles

Socioenvironmental impacts of dam in the Jari Valley, Amapa, Brazil: community perceptions

Abstract The expansion of the Brazilian electricity sector is based on the construction of new dams. However, dams generate impacts, especially on riverine communities. Thus, this paper examined the local communities perceptions about the socioeconomic and environmental impacts of the Santo Antônio do Jari Dam, in Amapá. Data were obtained through surveys using the snowball sampling method. To analyze the perception data the Bardin’s analysis of content was used. The research showed that, before the construction of the dam, there was a very intimate bond between the communities, the environment and the nature, which influenced the communities perceptions mainly regarding environmental impacts, relocation, changes in livelihood and access to natural resources.

Analytic Hierarchy Process Algorithm Applied to Battery Energy Storage System Selection for Grid Applications

HIGHLIGHTS The Brazilian electricity regulatory agency calls for energy storage projects Electric utilities evaluate battery energy storage for power quality control The Analytic Hierarchy Process (AHP) algorithm for battery technology: a strategic choice Environmental, Technological, Regulatory and Financial AHP criteria

Electricity price forecasting on electricity spot market: a case study based on the Brazilian Difference Settlement Price

Developing predictive models is a complex task since it deals with the uncertainty and the stochastic behavior of variables. Specifically concerning commodities, accurately predicting their future prices allows us to minimize risks and establish more reliable decision support mechanisms. Although the discussion on this question is extensive, there is academic attention being paid to the construction of nonparametric models applied to energy markets, as they have presented promising predictive results, what justifies the present study. This paper applies classical statistical models and Dynamic Time Scan Forecasting (DTSF) to the short-term electricity market prices, in Brazil, from 2006 to 2019. DTSF consists of scanning a time series and then identifying past patterns (so-called “matches”), similar to the last available observations. We predict Brazilian electricity spot prices, according the most similar matches, using aggregation functions, such as median. Recent research on the electricity spot market is increasing, indicating research significance. Our predictive approach exhibited greater accuracy than seminal statistical models. Our approach was designed for a high frequency series. Its predictive performance remained robust when other models presented both high predictive errors (spring), as well as when those models are highly accurate (winter). For future research, we recommend a more finely-tune study on DTSF parameters.

Cluster-Based Method to Determine Base Values for Short-Term Voltage Variation Indices

This paper proposes a methodology for establishing base values for short-term voltage variation indices. The work is focused on determining which variables best describe the disturbance and based on that, establish clusters that allow a more adequate definition of base values for the indices. To test the proposed methodology, real data from 19 distribution systems belonging to a Brazilian electricity utility were used and consequently the index presented in the country standard was considered. This study presents a general methodology that can be applied to all distribution systems in Brazil and could serve as a guide for the regulatory agencies in other countries, to establish base values for their indices. Furthermore, the objective is to show through the results that, with the database used is possible to establish clusters of distribution systems related to the voltage sag and with these establish a base impact factor, distinct for each distribution system.

Estudo comparativo da rentabilidade em empresas do setor elétrico brasileiro no período pós-privatizações

O setor elétrico brasileiro passou por transformações significativas ao longo da sua história. Desde 2003, intensificaram-se as privatizações, gerando um mercado no qual as empresas públicas e privadas operam conjuntamente em um ambiente de competição. Este trabalho teve como objetivo comparar a rentabilidade das empresas públicas e privadas do setor elétrico brasileiro no período pós-privatizações, compreendido entre os anos de 2003 a 2017. Para tanto, foram coletados dados anuais de empresas de capital aberto referentes ao período temporal coberto pela pesquisa. Com o fim de estabelecer a comparação do desempenho entre as empresas privadas e públicas, empregou-se o Teste U, de Mann-Whitney. Os resultados demonstraram que, no período analisado, as empresas privadas tiveram maiores rentabilidades do que as firmas públicas, apresentando ROA, ROIC e ROE sistematicamente superiores.

Wind energy investments facing uncertainties in the Brazilian electricity spot market: A real options approach

This study proposes a Real Options approach to investigate the economic feasibility of a wind power plant investment with the option of abandoning along the project life cycle. This novel approach considers uncertainties representation concerning electricity sales revenue in the spot market, and the uncertainty represented by the settlements of energy trading differences. Our results show that when considering these uncertainties, the abandonment option adds 30.3% to the value of the project, and the chance of not abandoning it until the end of the useful life is equal to 70.9%.

Performance analysis of numerical methods for determining Weibull distribution parameters applied to wind speed in Mato Grosso do Sul, Brazil

Brazil is one of the countries that invests the most in renewable energy generation. The share of wind power sources in the Brazilian Electricity Matrix rose from 0.9% in 2012 to 7.6% in 2018. The wind resource assessment is a crucial step in planning a wind energy project. Weibull distribution function is used to determine wind power potential for the investigated site and the distribution parameters are estimated by numerical methods. This study focus on the similarity and analyses of hourly wind speed data series. In this study, six numerical methods (Graphical Method, Maximum Likelihood Method, Modified Maximum Likelihood Method, Moment Method, Empirical Method and Power Density Method) are applied for estimating Weibull distribution parameters in 27 stations in the state of Mato Grosso do Sul, Brazil. The stations were grouped according to the similarity of their data series, and for each group the data series were separated into data series for calibration and data series for the accuracy analysis of the methods. The accuracy of the methods was evaluated using three different statistical analysis techniques. The Maximum Likelihood Method and the Modified Maximum Likelihood Method were the best methods for analyzing most of the data series in the stations.

Performance evaluation of controllers applied to power generator set operating with wastewater biogas

The improvement of renewable energy resource-based power plants is a real and modern global challenge, and therefore, biogas applied in reciprocating Internal Combustion Engines is an excellent alternative to this challenging task. Biogas can be obtained from various sources and its composition varies according to the biomass type used in its production, but the majority of this fuel is composed of methane ( $$\text {CH}_4$$ ) and carbon dioxide ( $$\text {CO}_2$$ ). However, there is no reliable control to hold the power quality following Brazilian Electricity Regulatory Agency (ANEEL) requirements. In such a context, this paper presents four control schemes to assure the power quality by a generator set operating with biogas from wastewater treatment. In order to accomplish it, a mathematical model was developed to represent the mechanical and electrical phenomena involved in the generation process. These control systems were evaluated in load and biogas composition variation scenario at same time in an environment developed in MATLAB/Simulink. The performed tests showed that the scheme composed by two Proportional Integral Derivative in parallel operation switched by fuzzy logic controller with particle swarm optimization tuning is better than all schemes proposed in this work and this layout enables the power plant to remain connected to the grid in all simulation scenario, because power frequency and voltage remained within limits imposed by national standards.

INSTITUTIONAL ENVIRONMENT AND THE STRATEGIES OF THE FIRMS OF THE BRAZILIAN ELECTRICITY INDUSTRY

The main goal of this paper is to analyze how the evolution of the institutional environment of the Brazilian Electricity Industry (BEI) has influenced the strategies of the firms. The main characteristics of the BEI are: almost 90% the electricity is generated from hydroelectric plants; central coordination is required to minimize costs, optimize the operation of the system and mitigate risks; hybrid governance structure, where private firms compete with public owned firms; specific assets; and a complex and uncertain business environment. Since 1996, the BEI has faced two distinct reforms. The first, more market oriented, was characterized by the privatization of 80% of distribution firms and 20% of generation assets. But in 2001 Brazil faced its worse electricity crisis in history, so the new government in 2003 proposed a new model for the BEI. This model re-established the role of the government as a planner and tried to create a more competitive market through auctions. We analyze the long term contracts to buy energy and new investments in generation in the BEI. We show that in Brazil there were risks of gaming and it is difficult to allocate the residual rights perfectly and this has an influence in the fact that firms are buying electricity from firms of the same economic group. We conclude that the industry characteristics, the evolution of its governance structure and the transition between institutional models lead to an elevated potential for vertical integration.

The trade-off between demand growth and renewables: A multiperiod electricity planning model under [formula omitted] emission constraints

Under the Paris Agreement, each participant country established its Nationally Determined Contribution aiming at reducing its CO2 emissions. This makes the trade-off between the electricity capacity expansion planning to meet the increase of demand and the reduction of greenhouse gas emissions a challenge, specially for developing countries, which require a higher rate of economic growth. To consider this trade-off, and identify the feasibility of the targets of the electricity expansion planning, a multiperiod optimization model is proposed considering the seasonality of supply and demand and the peak period demand. The goal is to minimize the total cost, satisfying demand constraints, the maximum CO2 emission constraints and the power expansion supply restrictions for each source. An analysis of the Brazilian electricity matrix for the years 2020–2033 is performed considering two scenarios for the growth of the demand and two scenarios for the CO2 target emissions The numerical simulations indicate that the present Brazilian electricity expansion planning seems adequate to meet the Nationally Determined Contribution only under a mild economic growth rate scenario. A higher economic growth rate would require a stronger economic policy related to the power expansions of the renewable sources.

Evaluating Public Policies for Fair Social Tariffs of Electricity in Brazil by Using an Economic Market Model

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.

Effects of the COVID‐19 pandemic on the Brazilian electricity consumption patterns

The implementation of mobility restrictions and home office schemes due to the COVID-19 pandemic have influenced electricity consumption patterns and levels This study analyzes the effect of physical distancing measures regarding mobility on the energy consumption trends for the Brazilian energy system and its subsystems (Northeast, North, South, and Southeast-Midwest) Trends were evaluated by the Joinpoint software, and the analysis comprehended the period between January 1 and May 27, 2020 Daily load data was grouped into weeks, with the calculation of weekly percentage changes considering a 95% confidence interval andp < 0 05 The weekly electricity loads were compared in the periods before and after the isolation decrees were enforced in Brazil (March 15, 2020) Statistically significant decreases were observed in the levels of electricity consumption, with trends represented by two joinpoints Due to the different profiles of consumption across the geographic regions, the resulting electricity dynamics were also different This is the first study to employ joinpoint analysis for the calculation of energy consumption trends focusing on the COVID-29 pandemic Data presented herein is unique, in its focus on Brazil, which enables more accurate implications to be drawn for Brazilian policy makers

Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method

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.

Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil

Spatiotemporal models to estimate electricity demand are scarce in the existing literature. In this paper, we compare three models to estimate elasticities and forecast demand for residential electricity in Brazil. The Dynamic Spatial Durbin Model presented the best goodness of fit, with results that confirm the need to consider spatial dependence in the Brazilian regions. The results showed temporal inertia, inelasticity of demand concerning price and income, and a significant impact of the temperature and the number of households connected to the grid. We conclude that omitting the spatiotemporal dynamic could lead to bias in the models used by Brazilian utilities.

Integration of renewable energies using the surplus capacity of wind farms to generate H2 and electricity in Brazil and in the Rio Grande do Sul state: energy planning and avoided emissions within a circular economy

Hydrogen is a clean fuel capable of promoting sustainable energy development. The effective use of surplus energy from wind plants appears as a promising method to produce hydrogen. Accumulating surplus energy through hydrogen production and storage can solve the problem of energy excess, making energy available on-demand. This article explored the potential of hydrogen production from wind energy in three distinct scenarios of surplus energy, and the amount of electricity generation for Brazil and its regions. To a scenario of 6-h of energy excess, the potential for hydrogen production from wind energy was equal to 1.48E + 07 Nm3.d−1. The state of Rio Grande do Sul reached a potential of 1.10E+06 MWh.month-1 of electricity generation using H2. Taking into account a payback-time of 3.5 years, the cost of hydrogen production was 0.402 US$.kWh−1. Hydrogen ensures greater energy security in times of energy shortages through biofuel storage. The main goal was to show the possibilities of diversifying the national electrical matrix and the wind resources contribution to the Circular and H2 Economy in the country.

Benchmarking the operational expenditures of Brazilian transmission utilities by using DEA models

In Brazil, the transmission assets are remunerated for their availability regardless of the full utilization of their capacities. The annual allowed revenue for each transmission utility is determined by the Brazilian Electricity Regulatory Agency (ANEEL). ANEEL has been using Data Envelopment Analysis (DEA) to define the regulatory operational expenditure (OPEX) for each utility, a key element for the annual allowed revenue assessment. Based on the ANEEL's DEA model this work proposes improvements in representing the quality of service, transmission capacity and transmission network length in the DEA model. Furthermore, this paper presents a more robust methodology to evaluate the regulatory operational expenditure of transmission utilities by applying several distinct DEA models; then a global efficiency score is defined by the geometric mean of the efficiency scores resulting from the set of DEA models analyzed. The performance of the proposed approach is illustrated with real data of the Brazilian transmission utilities.

DESENVOLVIMENTO DE UM SISTEMA DE CARACTERIZAÇÃO DAS CARGAS COMO AUXÍLIO DE DECISÃO NA ADESÃO DA MELHOR MODALIDADE TARIFÁRIA

The Brazilian electricity sector currently needs a technological and sustainable reform to boost the country development. At the same time, restructuring has been taking place around the world through the insertion of alternative energy sources, new tariffs systems, the adaptation of new technologies and habits changes that have an impact on energy consumption. The consumer attitude towards the electric power consumption is a fundamental factor against the economic, social and environmental perspectives, praising the importance of having a population engaged and aware about their consumption acts impacts. Starting from the point that access to information and knowledge is the way to achieve significant changes, this study presents the development of a system for load characterization in white, conventional or horo-seasonal tariff modalities - according to consumer classification - in order to obtain comparison parameters that aid in the decision making as to the adhesion to the most appropriate tariff modality. The research techniques used were the bibliography and case study based on energy loads from a supermarket in the city of Vitoria, ES. In this way, after receiving the input parameters, load configuration and consumption schedules, the system generates the values associated to the tariffs rates and tariff calculations. Furthermore, the system presents the monthly load curve and the electric consumption profile of an installation from the survey of equipment possessions and use habits. The system developed based on the loads in a supermarket and proved successful in processing the demand curves. Keywords: Demand Curve. Tariff Modalities. Consumption Management

The complementary nature between wind and photovoltaic generation in Brazil and the role of energy storage in utility-scale hybrid power plants

Solar and wind sources together provided more than half of the Brazilian Northeast electricity generation in 2019. This growing share of renewable energies in the Brazilian energy matrix increases the importance of portfolio optimization and the energy storage management. This paper assesses the complementary nature between wind and photovoltaic generation in the Brazilian Northeast, and how this complementarity, together with energy storage, can reduce the shortcomings that the corresponding natural resource intermittency imposes on these sources. The work consists of two main analyses: (i) analysis of the capability in supplying the Brazillian Northeast region power demand with a hybrid wind + solar + storage power plant; and (ii) contingency optimization analysis for hybrid wind + solar + storage power plants. The results show that wind and solar resources are consistently complementary in the region, with a daily Pearson’s Correlation Coefficient of −0.51. Also, the load supply analysis shows that a renewable energy mix based on a 40% wind and 60% solar share would require the equivalent of only 6% of its annual generation in storage capacity. An energy curtailment analysis showed that the complementary nature of the wind and solar resources, together with energy storage, can lead to a reduction of up to 11% in transmission capacity demand.

Probabilistic evaluation of distribution power transformers reliability indices considering load transfers and mobile unit substations

This paper proposes a new and practical probabilistic method for calculating reliability indices and costs associated with distribution substations that share spare transformers of a same group. Based on Monte Carlo simulation, the proposed algorithm considers the chronological representation of failure occurrences, and enhances previous methods by modeling emergency actions, such as transferring load to neighboring feeders and connecting a mobile unit substation to ensure electricity supply to customers during the replacement of failed transformers. Thus, spare transformer requirements with new acquisitions to replenish the stock or even to expand the system are probabilistically assessed. All waiting times associated to these actions can be duly represented by any probability distribution, including the usual exponential model. A more accurate calculation of reliability indices and costs associated with the interruption of consumers is achieved. The corresponding computational algorithm is used in several numerical applications, considering a class of transformers of a Brazilian electricity utility. The results of these applications are deeply discussed to demonstrate the practical relevance of the proposed method.

Empirical continuous metaheuristic for multiple distributed generation scheduling considering energy loss minimisation, voltage and unbalance regulatory limits

Distributed generation (DG) and other electric resources such as batteries and electric vehicles are transforming the planning and operation of power distribution system all over the world. Although the operation gets more complex in the presence of DG, it also brings some potential benefits to the grid. In this study, the authors propose an optimisation approach for multiple DG units scheduling, considering a daily load profile. The main objective is to minimise the total energy loss in a period of time, dealing with a specific voltage and unbalance constraints, required by the Brazilian Electricity Regulatory Agency. The problem formulation results in a discontinuous non-convex objective function. An empirical continuous metaheuristic (ECM) is proposed to solve this challenging optimisation problem. As metaheuristic methods are suitable for this kind of problems, they present some limitations regarding final results variability, relative dependence on initial conditions and usually a large set of parameters to tune. ECM confronts directly these limitations, presenting good quality results in comparison to other well-known algorithms. By using the Open Distribution System Simulator – OpenDSS, and the well-known IEEE-123 distribution system, the proposed approach shows its effectiveness and efficiency to optimise the grid operation, with special attention to the Brazilian requirements for unbalance.