Mixed Copula-Based Uncertainty Modeling of Hourly Wind Farm Production for Power System Operational Planning Studies

Philippe, Wolf Peter Jean; Zhang, Shan; Eftekharnejad, Sara; Ghosh, Prasanta Kumar; Varshney, Pramod K.

doi:10.1109/access.2020.3012437

Cited by 23 publications

(8 citation statements)

References 68 publications

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“…Copulas can be used to model the correlation structure between different random variables, allowing for separate modeling of the correlation and the marginal distributions. Copulas have been successfully applied to modeling loads in power system studies [19][20][21][22][23], most often focusing on modeling wind power generation.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models

Bernards¹,

Westering

Morren

et al. 2020

Energies

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The energy transition poses a challenge for the electricity distribution network design as new energy technologies cause increasing and uncertain network loads. Traditional static load models cannot cope with the stochastic nature of this new technology adoption. Furthermore, traditional nonlinear power methods have difficulty evaluating very large networks with millions of cables, because they are computationally expensive. This paper proposes a method which uses copulas for modeling the uncertainty of technology adoption and load profiles, and combines it with a fast linear load flow model. The copulas are able to accurately model the stochastic behavior of solar irradiance, load measurements, and mobility data, converting them into electricity load profiles. The linear load flow model has better scalability and stability compared to traditional load flow models. The models are applied to a case study which uses a real-world dataset consisting of a realistic technology adoption scenario and a low-voltage network with millions of cables, which considers both voltage and current problems. Results show that risk profiles can be generated for all cables in the network, resulting in a valuable map for the district network operator as to where to focus their efforts.

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Section: Related Workmentioning

confidence: 99%

Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models

Bernards¹,

Westering

Morren

et al. 2020

Energies

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“…Another remarkable fact worth considering is the response of wind energy in the face of exceptional times such as COVID-19 pandemic and the current war between Russia and Ukraine, as the fossil fuels sector during these events experienced market volatility, whereas the wind energy sector exhibited a more stable response [4] [5]. However, RG increases energy variability and uncertainty [6], since its production changes based on climatic conditions. Thus, power system planners are looking for flexible generating sources that support renewable instead of just looking for sources of generation to meet demand [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Short-Term Wind Power Variations and Estimation of Reserve Requirements for High Wind Generation Shares

Eltohamy,

Abdel Moteleb,

Talaat

et al. 2023

IJEEI

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The need to deal with variability in wind power output is one of the greatest challenges connected with adopting a considerable amount of wind power into power grids. Power system operators need to acquire more information on this variability, which can be utilized in the mitigation of high ramping events, especially when these events synchronize with a large error in the prediction, ensuring flexibility and reliability in the power system besides the economic considerations. The paper analyses short-term variability in output power using actual data obtained from aggregated wind farms from 2015 to 2020, where power ramping characteristics are described using a variety of measurements. The use of the standard deviation of short-term wind power variation as a reserve measure will be investigated in detail since there is no consensus about the ideal confidence level value as a multiplier of σ, which ranges from 3 to 6 times σ. The paper addresses how large this confidence level should be, as well as developing a data-driven approach for estimating this reserve with increasing wind shares and evaluating the proper distribution of short-term wind variation. The results illustrate that the stochastic variations in wind power can retain many of their characteristics from year to year, even when the share of wind capacity is raised.

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“…For this reason, refs. [7] and [8] build Gumbel copula and mixed-copula models based on the edge probability distribution of variables directly to model the probability of long-time scale spatial correlation of renewable energy output, and capture the tail correlation between variables with strong randomness. However, the copula models above are static, which lack consideration of the changes of correlation structure between variables, and have a great impact on the accuracy of correlation model.…”

Section: Introductionmentioning

confidence: 99%

Variable‐structure copula model for multi‐scenario bi‐level transmission and generation expansion planning

Wang

Yang

et al. 2022

IET Generation Trans & Dist

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With the large-scale integration of renewable energy into power systems, the strong correlation of wind power output as an important feature will inevitably bring huge impact on power grid planning and operation. However, due to the proliferation of planning and operation scenarios, the traditional power grid planning method can no longer guarantee the operation flexibility and stability under the fluctuation of new energy. In this work, a bi-level transmission and generation expansion planning (TGEP) model considering the correlation of wind power based on mixed integer linear programming (MILP) is proposed. Firstly, a variable-structure point diagnosis method is proposed to improve the fitting accuracy of the correlation. Secondly, a scenario reduction method considering the correlation, joint probability distribution and geometric characteristics is proposed, which effectively provides a more matching operating scenarios basis for planning. Thirdly, a multi-scenario bi-level TGEP model is proposed. The upper-level investment decision model aims at the economical goal, while the lower-level multi-scenario coordinated optimization operation model aims to minimize the weighted operation index. The impact of the correlation on the results of TGEP is examined and verified in case study.

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Mixed Copula-Based Uncertainty Modeling of Hourly Wind Farm Production for Power System Operational Planning Studies

Cited by 23 publications

References 68 publications

Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models

Analysis of Energy Transition Impact on the Low-Voltage Network Using Stochastic Load and Generation Models

Characterization of Short-Term Wind Power Variations and Estimation of Reserve Requirements for High Wind Generation Shares

Variable‐structure copula model for multi‐scenario bi‐level transmission and generation expansion planning

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