Assessing Competition in Brazil’s Electricity Market if Bid-Based Dispatch Were Adopted

Hochstetler, Richard L.; Cho, João D.

doi:10.1590/198055272322

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…In the past, these three rivers formed a single one, with springs located in Serra do Mar and mouths in Tietê River. The PR network is very important for the economy of the southeast region due to its high electrical potential producing more than 1 GW of electricity and mainly due to its high potential for the construction of urban waterways [2].…”

Section: Introductionmentioning

confidence: 99%

Magneto-optical Properties of Iron-bulk Metallic Glasses Sustainably Produced from Iron-rich Sand Sludge

Queiroz,

Cavallaro,

Queiroz

2024

J. Appl. Phys. Sci. Int.

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A large amount of sand sludge (SS) from rivers polluted with heavy metals is continuously dredged and often dumped into landfills, resulting in environmental pollution of soil and water. This work examines the feasibility of the utilization of iron-enriched SS from dredged polluted rivers to produce Fe-based bulk metallic glasses (Fe-BMG). Fe-BMG was prepared from SS by melt-quenching technique, and their magnetic and photochromic properties were studied. The chemical composition of the Fe-BMG was determined by X-ray fluorescence (XRF). The structural properties have been analyzed using the Raman spectroscopy (RS), vibrating sample magnetometer (VSM) and ultraviolet-visible (UV-VIS) spectroscopy. XRF results shows that Fe-BMG contains an iron proportion of 38.8% (wt.) as the hematite (\(\alpha\)-Fe2O3), and magnetite (Fe3O4) phases. The Raman “fingerprints” of \(\alpha\)-Fe2O3 and Fe3O4 were identified at 1330 cm−1, and 300-600 cm-1, respectively. The measured saturation magnetization of Fe-BMG samples is 3.0 emu/g at 60 kOe. A UV-VIS analysis of the Fe-BMG exposed to sunlight simulator reveals an enhanced transmission of light in the 400-700 cm-1 region. These findings confirmed the successful conversion of the hazardous SS into Fe-BMG with promising magneto-optical characteristics for a wide variety of technological applications.

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Section: Introductionmentioning

confidence: 99%

Magneto-optical Properties of Iron-bulk Metallic Glasses Sustainably Produced from Iron-rich Sand Sludge

Queiroz,

Cavallaro,

Queiroz

2024

J. Appl. Phys. Sci. Int.

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“…Nonetheless, game theory has been used to help policy makers to evaluate the impacts of changing the market design of Brazilian power market, Ref. [10] proposed a Cournot-Nash model to evaluate the implementation feasibility of bid-based pricing in their market, suggesting that this change is possible.…”

Section: Introductionmentioning

confidence: 99%

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

Santos

Lobato

Vieira³

et al. 2022

Energies

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This paper presents a Nash equilibrium approach to model a non-cooperative game that takes place among Brazilian hydro-generating companies in the annual process called “seasonalization”. We have “remade” the seasonalizations that occurred in the period of 2013–2020 by using our model and comparing the financial outcomes with the current ones that resulted from the seasonalizations the generators made each year, with financial improvements in almost every year. By using the Nash equilibrium approach, it is possible to achieve optimal decisions concerning the seasonalization process that were not evident by using traditional methods. This approach is useful for companies willing to enhance their income and to improve their risk management by making better choices in seasonalization.

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“…Moreover, as most system operational decisions occur as a response to data gathered and processed at the control center, the use of data-driven platforms are crucial to get useful information and make intelligent choices [2]. These data-guided frameworks are particularly important in the Brazilian context-as the goal of our work-since the national power grid is currently operated by a general grid operator that arbitrates when and how much each power plant will produce from official computer models [3]. As the Brazilian electricity matrix is mainly composed by renewable power sources, which vary in nature [2], the electricity market prices may reflect the stochastic behavior of these sources.…”

Section: Introductionmentioning

confidence: 99%

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

et al. 2020

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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.Techniques devoted to forecasting electricity demand aim at estimating the amount of energy needed, over a historical time series, for transmission and later consumption by others. Despite the adaptation of several machine learning models to properly address the problem [5-9], tracking the progressive use of the electricity is not a straightforward task in practice. In fact, the electricity dispatch is intrinsically related to the internal operations of the power systems such as the periodic scheduling of power generation in hydroelectric plants, the preventive maintenance of the generators, the reliability evaluation of power systems, etc. [10]. Moreover, the problem becomes even more challenging and especially interesting when one has to deal with the highly nonlinear tendency of power data, as it is mathematically modeled by highly-oscillating time series whose parameters can be affected by exogenous variables such as weather/ambient conditions [11] and economy-related factors [12].Formally, the problem of predicting the power demand on time series can be described as follows: given a time series of electric load X 1 , X 2 , . . . , X t , in which X i accounts for the historical energy load at the instant i, i = 1, 2, ..., t, the goal is to predict the quantity X (t+h) , where h establishes the forecast horizon [13,14]. Taxonomically speaking, this kind of prediction usually comprises three categories of planning horizons: (i) long-term (years/months); (ii) regular-term (days/weeks); and (iii) short-term (minutes/hours). Since estimating the electricity demand becomes harder as the planning horizon increases, the predictions can be strongly influenced by several nonuniform variables such as electric consumption, temperature, air humidity, and socioeconomic aspects. Moreover, long-and regular-term time series make the problem more difficult to be technically manag...

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Assessing Competition in Brazil’s Electricity Market if Bid-Based Dispatch Were Adopted

Cited by 4 publications

References 6 publications

Magneto-optical Properties of Iron-bulk Metallic Glasses Sustainably Produced from Iron-rich Sand Sludge

Magneto-optical Properties of Iron-bulk Metallic Glasses Sustainably Produced from Iron-rich Sand Sludge

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

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