Analyzing Wind Power Ramps for High Penetration of Variable Renewable Generation

Eltohamy, Mohammed Saber; Moteleb, Mohammed Said Abdel; Talaat, H.; Mekhemer, S.Fouad; Omran, Walid A.

doi:10.1109/mepcon47431.2019.9007951

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…The time interval (Δt) used to identify the ramp in minutes/hours is determined by the user. Given the differences in capacity and available flexibility with which these events are handled across various power systems, no standard definition for the ramp event has been agreed upon to date [65] [66]. Because the power signals for load/net-load are more stable than those for wind/PV, lower threshold values are given to load/net-load ramp events than to wind/PV ramp events.…”

Section: Characteristics Of Power Rampsmentioning

confidence: 99%

“…4, December 2023: 945 -966 946 ranges, whilst wind variations vary with seasons, and times of the day [14], therefore further data would contain additional information and could produce different ranges [15]. The investigation of increasing shares of variable RG on the characteristics of power ramping has been discussed in [16] [17] through statistical analysis of historical data. In [18] [19], new metrics have been introduced for understanding the characteristics of power ramping.…”

Section: Introductionmentioning

confidence: 99%

“…The standard deviation multiplier is used to cover almost every incidence of wind fluctuation with a given degree of certainty. After performing a long-term analysis of short-term variations in wind generation with increasing penetration over five years, using various metrics from [18] [19], the historical data available are used to create secure operating reserves since longer data sets with higher quality are essential to provide far more detail about the expected variations in wind power [61] [16]. In addition, the best-fitted probability distribution function (PDF) to short-term wind variations will be studied, as it is more important than ever to model this variability with increasing shares of wind generation.…”

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confidence: 99%

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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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Section: Characteristics Of Power Rampsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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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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“…In [24], the different definitions of ramp event in wind power, solar power, net load, and load were summarized; in which the threshold value of ramp event magnitude was determined by different percentages that ranged from 10%𝑃 𝑅 to 75%𝑃 𝑅 , whereas the threshold value of ramp duration extended from five minutes to six hours. In [14], the authors studied the variability of wind power at different time intervals, starting from 5 minutes to 1 hour with their corresponding thresholds from 1%𝑃 𝑅 to 15%𝑃 𝑅 respectively.…”

Section: Definitions Of Wind Power Ramp Eventmentioning

confidence: 99%

Wind Power Ramps Analysis for High Shares of Variable Renewable Generation in Power Systems

Eltohamy¹,

Moteleb²,

Talaat³

et al. 2020

IJEEI

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Power system operators should be provided with more information on the characteristics of variable generation power ramps because, although there has been an improvement in the forecasting of wind power, the percentage of error in forecasting is still high to some extent. As a result of the ongoing rise in the participation rate of variable generation, this error will have a significant impact on the balance of power generation and consumption. From the grid operators' viewpoint, in order to balance these ramp events, it is important to get the scale of ramp events in the system as well as the times during which collective events are most likely to arise in order to achieve flexibility and reliability in the power system. Digitization of power systems brings big data which opening opportunities for improving the efficiency of power system operation. This paper analyzes the historical data of powertime curve in two directions: vertical and horizontal, in order to gain details on the behavior of wind power ramps. The method of analysis will be demonstrated by an analysis of actual historical output power of aggregated Belgian wind farms every 15 minutes in 2017 and 2018. Comparing the results of the two years outlined that there are fixed percentages related to wind power ramping behavior and even if the wind capacity is increased, it is possible to determine the extent of these ramps.

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“…Isto faz com que a acurácia da previsão dessa fonte seja imprescindível para a operação, especialmente em tempo real, devidos aos efeitos das rampas de geração (Eltohamy et al, 2019).…”

Section: Introdu ç ãOunclassified

Desenvolvimento de um modelo de clusterização de usinas eólicas para atendimento dos modelos de previsão do ONS

Paulo Sérgio de Castro Nascimento,

Lucas De Souza Khenayfis,

Rogério José Menezes Alves

et al. 2022

Proceedings IX Simpósio Brasileiro De Sistemas Elétricos

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The present work proposes to obtain a clustering of wind farms using the Gaussian Mixture Models (GMM) method, based on the wind profile and geographic location of wind farms operating in the South and Northeast regions from Brazil. The purpose of the generated grouping is to provide inputs for the wind generation forecasting models, helping the daily schedule of the operation of the National Electric System Operator (ONS). The success of this application allows the creation of generation and wind series more assertively within each cluster, thus increasing the reliability of the system operation. Thus, the main contribution of this work is to propose an alternative form of clustering, in relation to what is currently used by the ONS. Resumo: O presente trabalho propõe a obtenção de uma clusterização de usinas eólicas com a utilização do método Gaussian Mixture Models (GMM), baseado no perfil de ventos e localização geográfica dos parques eólicos em operação nas regiões Sul e Nordeste do Brasil. O intuito do agrupamento gerado é fornecer insumos para os modelos de previsão de geração eólica, auxiliando a programação diária da operação do Operado Nacional do Sistema Elétrico (ONS). O sucesso dessa aplicação permite a criação de séries de geração e vento de forma mais assertiva dentro de cada cluster, assim aumentar a confiabilidade da operação do sistema. Dessa forma, a principal contribuição deste trabalho é propor uma forma alternativa de clusterização, em relação ao que atualmente é utilizado pelo ONS.

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Analyzing Wind Power Ramps for High Penetration of Variable Renewable Generation

Cited by 7 publications

References 21 publications

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

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

Wind Power Ramps Analysis for High Shares of Variable Renewable Generation in Power Systems

Desenvolvimento de um modelo de clusterização de usinas eólicas para atendimento dos modelos de previsão do ONS

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