Wind power feasibility analysis under uncertainty in the Brazilian electricity market

Áquila, Giancarlo; Rotela, Paulo; Pamplona, Edson de Oliveira; Queiroz, Anderson Rodrigo de

doi:10.1016/j.eneco.2017.04.027

Cited by 46 publications

(31 citation statements)

References 42 publications

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“…Li and Park [165], proposed an advanced wind power bidding strategy in the short term electricity market incorporating required market information for a set of real data collected from a wind farm of the PJM market. Aquila et al [166], analyzed the feasibility of investment in wind power plants to identify their major uncertainty parameters and risks in the Brazilian electricity market employing value at risk technique. The proposed approach verified the higher probability of economic feasibility for such projects.…”

Section: K Electricity Market Challengesmentioning

confidence: 99%

Grid Integration Challenges of Wind Energy: A Review

et al. 2020

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The strengthening of electric energy security and the reduction of greenhouse gas emissions have gained enormous momentum in previous decades. The integration of large-scale intermittent renewable energy resources (RER) like wind energy into the existing electricity grids has increased significantly in the last decade. However, this integration poses many operational and control challenges that hamper the reliable and stable operation of the grids. This article aims to review the reported challenges caused by the integration of wind energy and the proposed solutions methodologies. Among the various challenges, the generation uncertainty, power quality issues, angular and voltage stability, reactive power support, and fault ride-through capability are reviewed and discussed. Besides, socioeconomic, environmental, and electricity market challenges due to the grid integration of wind power are also investigated. Many of the solutions used and proposed to mitigate the impact of these challenges, such as energy storage systems, wind energy policy, and grid codes, are also reviewed and discussed. This paper will assist the enthusiastic readers in seeing the full picture of wind energy integration challenges. It also puts in the hands of policymakers all aspects of the challenges so that they can adopt sustainable policies that support and overcome the difficulties facing the integration of wind energy into electricity grids. INDEX TERMS Angular stability, energy storage system, fault ride-through capability, frequency response, grid codes, reactive power support, voltage stability, wind intermittency. • Induction generator (squirrel cage (type 1) and wound rotor (type 2))

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Section: K Electricity Market Challengesmentioning

confidence: 99%

Grid Integration Challenges of Wind Energy: A Review

et al. 2020

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“…Aquila et al [44] present a framework for analysis of investments in wind power generation under uncertainty using Monte Carlo simulation. The authors use MCS for stochastic analysis, analyze the behavior of NPV for the targeted variables, and list those that had an impact on the NPV.…”

Section: B Analysis Of the Economic Feasibility Of Energy Generationmentioning

confidence: 99%

“…Depreciation for electrical equipment follows a specific schedule, and wind equipment has a 20-year useful life. As a result, they can be depreciated at an annual rate of 5% according to the COPEL manual [69], and also as adopted by Aquila et al [44].…”

Section: Depreciationmentioning

confidence: 99%

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

et al. 2020

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To reduce the risks of a new energy crisis and increase energy availability, the use of renewable energy sources (RES) is important and recommended. In Brazil, micro and small companies contribute about 25% of gross domestic product (GDP), and electric energy is employed intensively, so the importance of microgeneration is observable. This research aims to analyze the economic viability of the micro-generation wind energy project for micro and small businesses. Thus, three Brazilian states, Rio Grande do Norte, Rio Grande do Sul and Minas Gerais were considered, and different scenarios were proposed. A feasibility analysis is then performed, followed by a stochastic analysis using Monte Carlo simulation (MCS). Finally, models of artificial neural networks (ANN) are used to evaluate the relative importance (RI) of the variables. The results show that none of the states appears economically feasible under the conditions presented. In the stochastic analysis, the probability of viability is between 17% and 24% in all states, which shows the low probability of viability for microgeneration. Through ANN training, it was possible to calculate the RI, in which it is possible to identify the variables that have most impact on the net present value (NPV) in all states; it is considered the most important variable in the project's viability. In addition, the discussion explores the importance of public incentives for promoting investment in renewable energy, which can reduce investment costs and make it attractive to small and medium-sized businesses. INDEX TERMS Microgeneration, wind power, stochastic feasibility analysis, sensitivity analysis, artificial neural networks.

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“…Sensitivity analysis is, e.g., used in deterministic forecasts for investment calculation [10]. In [4], the authors use sensitivity analysis to proof the feasibility to integrate wind power generators into the Brazilian electricity market. Another application of sensitivity analysis shows the profitability and vulnerability of renewable energy resources [7].…”

Section: Related Workmentioning

confidence: 99%

“…However, even though many studies investigate influences and interactions of input features on the output of deterministic forecasts, e.g., [4]- [9], there has been little interest in conducting studies that are designed to evaluate influences in probabilistic forecasts. This article examines influences of features on the output of three different probabilistic wind power forecasting models with a focus on NWP data and technical features (e.g., maximum diameter and maximum power generation of a wind turbine) as input (which is typical for day-ahead forecasts).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Influences on Probabilistic Wind Power Forecasts

Schreiber

Sick

2018

E3S Web Conf.

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In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.

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Wind power feasibility analysis under uncertainty in the Brazilian electricity market

Cited by 46 publications

References 42 publications

Grid Integration Challenges of Wind Energy: A Review

Grid Integration Challenges of Wind Energy: A Review

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

Quantifying the Influences on Probabilistic Wind Power Forecasts

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