Projections of climate change in streamflow and affluent natural energy in the Brazilian hydroelectric sector of CORDEX models

Silva, Marx Vinícius Maciel da; Silveira, Cleiton da Silva; Silva, Greicy Kelly da; Pedrosa, Wanderson Hugues de Vasconcelos; Júnior, Antônio Duarte Marcos; Filho, Francisco de Assis de Souza

doi:10.1590/2318-0331.252020200020

Cited by 9 publications

(10 citation statements)

References 32 publications

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“…As a result, the average energy generated by the set of hydroelectric plants in the National Interconnected System-SIN-can reduce by up to 30% [5]. Similar results were obtained with simulations of climate projections from CMIP5 [48], also for the SIN, with most models indicating trends towards reductions in flows and a consequent drop in energy generated. When individual plants are analyzed, reduction trends exceed 50% in many cases [5,48].…”

Section: Impacts On Power Generationsupporting

confidence: 73%

“…The average inflow rate decreased from 630 m 3 /s to 395m 3 /s, approximately 40%, preliminarily confirming the results of several studies related to the impacts of climate change on water resources, indicating that historical hydrological series data may not be representative of real future scenarios. In other words, policy makers in the Brazilian electricity sector need to be very careful when considering stationary stream flow time series data in planning and operating water infrastructures [47,48]. The results presented here, as well as in other studies, show that it is important to develop methodologies to consider the possible effects of climate change on planning and operational policy within the Brazilian electrical sector.…”

Section: Projection Of Stream Flowsmentioning

confidence: 74%

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Assessment of Climate Change Impact on Hydropower Generation: A Case Study for Três Marias Power Plant in Brazil

da Silva,

Virgílio,

Nogueira

et al. 2023

Climate

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Study region: The Três Marias 396 MW power plant located on the São Francisco River in Brazil. Study focus: Hydropower generation is directly and indirectly affected by climate change. It is also a relevant source of energy for electricity generation in many countries. Thus, methodologies need to be developed to assess the impacts of future climate scenarios. This is essential for effective planning in the energy sector. Energy generation at the Três Marias power plant was estimated using the water balance of the reservoir and the future stream flow projections to the power plant, for three analysis periods: FUT1 (2011–2040); FUT2 (2041–2070); and FUT3 (2071–2100). The MGB-IPH hydrological model was used to assimilate precipitation and other climatic variables from the regional Eta climatic model, via global models HadGEM2-ES and MIROC5 for scenarios RCP4.5 and RCP8.5. New hydrological insights for the region: The results show considerable reductions in stream flows and consequently, energy generation simulations for the hydropower plant were also reduced. The average power variations for the Eta-MIROC5 model were the mildest, around 7% and 20%, while minimum variations for the Eta-HadGEM2-ES model were approximately 35%, and almost 65% in the worst-case scenario. These results reinforce the urgent need to consider climate change in strategic Brazilian energy planning.

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Section: Impacts On Power Generationsupporting

confidence: 73%

Section: Projection Of Stream Flowsmentioning

confidence: 74%

Assessment of Climate Change Impact on Hydropower Generation: A Case Study for Três Marias Power Plant in Brazil

da Silva,

Virgílio,

Nogueira

et al. 2023

Climate

View full text Add to dashboard Cite

show abstract

“…Hydroclimatic projections indicate that future flows are likely to become drier in the study area (Brêda et al., 2020; Ribeiro Neto et al., 2016; Silva et al., 2020), which will require adaptation of the system operation to mitigate likely energy losses. Dalcin et al.…”

Section: Applicationmentioning

confidence: 99%

Dynamic Adaptive Environmental Flows (DAE‐Flows) to Reconcile Long‐Term Ecosystem Demands With Hydropower Objectives

Dalcin

Marques

Tilmant

et al. 2023

Water Resources Research

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Decisions on how we operate and allocate water resources, combined with hydroclimatic conditions, can significantly limit the capacity of freshwater ecosystems to cope with and recover from stress periods (e.g., drought events). In reservoir systems, allocating water to environmental demands, characterized by flow regimes (e-flows), is necessary to mitigate impacts on ecosystem functioning (e.g., supporting fish reproduction cycles), but it also presents tradeoffs as it bounds the water storage availability and therefore water allocation to present and future uses in the watershed (Grantham et al., 2014;Wineland et al., 2022).Among the several academic contributions to calculate reoperation tradeoffs between environmental needs and other competing demands with optimization frameworks, a common approach represents e-flows constraining water allocation to multiple economic uses (Chen et al., 2013;Xu et al., 2020). This may include explicit

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“…The transition to renewable and sustainable energy sources is a crucial response to today's environmental challenges and energy crises (Khelil et al, 2021;Zhang et al, 2022). As an abundant and pollution-free resource, wind emerges as one of the leading candidates in the field of renewable energy (Silva et al, 2020). The effectiveness of wind energy, characterized by its affordability, cost-effectiveness, and eco-friendly nature, is strongly influenced by the predictability of wind speed (Shi, 2019;Fanel et al, 2021;Akhlagi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Wind speed prediction model based on DWT and Randon Forest

Santos,

Luiz Souza Monteiro,

Santos

et al. 2023

CLIUM

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Wind energy is one of the fastest power generation technologies in the power generation industry and one of the most cost-effective methods of generating electrical power. For system reliability, improving highly appropriate wind speed forecasting methods is desirable. The wavelet transform is a powerful mathematical technique that converts an analyzed signal into a time-frequency representation. This technique helps forecast non-stationary time series. The objective is to evaluate the performance of the "DWT-Random Forest" model in predicting wind speed through a comparative analysis of performance metrics (MSE, RMSE, and MAPE) with similar studies. Our motivation is rooted in the pressing need to improve wind forecasting methods to optimize renewable energy generation. The method involved implementing the model, which presented performance metrics: MSE of 0.0099, RMSE of 0.0996, and MAPE of 0.0779. However, comparative analysis with previous studies reveals that our model demonstrates competitive performance. The main result of this study is the finding that the "DWT-Random Forest" model exhibits a respectable performance in predicting wind speed, although there is room for improvement.

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Projections of climate change in streamflow and affluent natural energy in the Brazilian hydroelectric sector of CORDEX models

Cited by 9 publications

References 32 publications

Assessment of Climate Change Impact on Hydropower Generation: A Case Study for Três Marias Power Plant in Brazil

Assessment of Climate Change Impact on Hydropower Generation: A Case Study for Três Marias Power Plant in Brazil

Dynamic Adaptive Environmental Flows (DAE‐Flows) to Reconcile Long‐Term Ecosystem Demands With Hydropower Objectives

Wind speed prediction model based on DWT and Randon Forest

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