Complementarity modeling of monthly streamflow and wind speed regimes based on a copula-entropy approach: A Brazilian case study

Ávila, Raúl; Mine, Miriam Rita Moro; Kaviski, Eloy; Detzel, Daniel Henrique Marco; Fill, Heinz; Bessa, Marcelo Rodrigues; Pereira, Guilherme Armando de Almeida

doi:10.1016/j.apenergy.2019.114127

Cited by 20 publications

(8 citation statements)

References 44 publications

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“…Three Archimedes joint functions are used to construct the joint distribution of forecast errors. The Clayton‐copula, Gumbel‐copula, and Frank‐copula (Ávila R et al., 2020; Naseri & Hummel, 2022), and their joint distribution functions are represented in Table 2, as well as their conditional distribution functions. The AIC and OLS criteria are used to select the optimal copula joint function and can be found in the Supplements.…”

Section: Case Studymentioning

confidence: 99%

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

Guo,

Xu,

et al. 2024

Water Resources Research

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Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

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Section: Case Studymentioning

confidence: 99%

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

Guo,

Xu,

et al. 2024

Water Resources Research

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“…Because they can fit the marginal distribution functions of different distribution characteristics, Archimedean copula is more commonly used. For example, Frank copula can measure the negative correlation between different variables, so it is very suitable to analyze the complementary characteristics of wind and solar resources [27] . Ref.…”

Section: Complementary Characteristics Analysismentioning

confidence: 99%

Research on Complementarity of Multi-Energy Power Systems: A Review

Hu,

Dong,

Zhang

et al. 2023

iEnergy

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In the background of the large-scale development and utilization of renewable energy, the joint operation of a variety of heterogeneous energy sources has become an inevitable development trend. However, the physical characteristics of different power sources and the inherent uncertainties of renewable energy power generation have brought difficulties to the planning, operation and control of power systems. For now, the utilization of multi-energy complementarity to promote energy transformation and improve the consumption of renewable energy has become a common understanding among researchers and the engineering community. This paper makes a review of the research on complementarity of new energy high proportion multi-energy systems from uncertainty modeling, complementary characteristics, planning and operation. We summarize the characteristics of the existing research and provide a reference for the further work.

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“…1. First, in the light of time-varying elements, complementarities reside in wind-solar [16,17], wind-hydro [18,19], solar-hydro [20,21] and other combinations, even between power generation and load demand [3] (under certain situations). Among them, wind and solar complementarity is most commonly mentioned in literatures.…”

Section: Complementarity Analysismentioning

confidence: 99%

Complementary operation of multi-renewable energy system with pumped storage

2021

CSEE JPES

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Complementarity is an important attribute between different renewable energy sources (RESs). Multi-renewable energy system (MRES) offers an alternative to aggregate diverse RESs and make the best of their mutual characteristics. However, few papers conduct convincing research into the complementary operation of MRES. This paper investigates the complementarities in MRES that contains wind, photovoltaic, run -off hydro, pumped storage power plants and incorporates complementarity into optimal operation of MRES. The complementarity is theoretically summarized and firstly classified into natural and synthetical one. To effectively capture the uncertainty and complementarity of RESs, the time-dependent clustering simulation technique is employed to generate the joint-scenarios. An equivalent run-off hydropower plant index is proposed, based on which a novel complementary operation model that considers volatility and self-sufficient adequacy is established. Results are presented and compared in the case study, which demonstrate the validity and scalability of the proposed methodology, reveal the complementarities under multiple time-scales, and illustrate the significance of complementary operation.

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Complementarity modeling of monthly streamflow and wind speed regimes based on a copula-entropy approach: A Brazilian case study

Cited by 20 publications

References 44 publications

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

Research on Complementarity of Multi-Energy Power Systems: A Review

Complementary operation of multi-renewable energy system with pumped storage

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