Modeling and design study of energy generation through high-speed wind at the spillways of hydroelectric power station

Malik, Muhammad Suleman; Khan, Muhammad Omer; Aamir, Syed Muhammad

doi:10.1177/0309524x20912502

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…74,75 The government is seeking to increase the part of the energy, and plans to add approximately 3.5 GW of wind power capability. 76 Table 2. Hypothetical cumulative distribution function (CDF) for each distribution.…”

Section: Study Areamentioning

confidence: 99%

Retracted: “Economic regulation of energy costs when integrated into distribution networks of industrial enterprises”

2023

Energy & Environment

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The distribution models of wind speed data are essential to assess the potential wind speed energy because they decrease the uncertainty in estimating wind energy output. Therefore, before performing a detailed potential energy analysis, the precise distribution model for data relating to wind speed must be found. This research contains material from numerous goodness-of-fit tests, such as Kolmogorov-Simonov, Anderson-Darling, chi-square, root mean square error, Akaike information criterion, and Bayesian information criterion, which were combined finally to determine the wind speed of the best-fitted distribution. The suggested method collectively makes each criterion. This method was useful in statistically fitting 14 distribution models to wind speed data collected at four sites in Pakistan. The consequences show that this method provides the best source for selecting the most suitable wind speed statistical distribution. Also, the graphical representation is consistent with the analytical consequences. This research presents three estimation methods that can be used to calculate the different distributions used to estimate the wind. In the suggested maximum likelihood method, method of moments, and maximum likelihood estimation, the third-order moment used in the wind energy formula is a crucial function because it contributes to the precise estimate of wind energy. In order to prove the presence of the suggested method of moments, it was compared with well-known estimation methods, such as

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Section: Study Areamentioning

confidence: 99%

Retracted: “Economic regulation of energy costs when integrated into distribution networks of industrial enterprises”

2023

Energy & Environment

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“…74,75 The government is seeking to increase the part of the energy, and plans to add approximately 3.5 GW of wind power capability. 76…”

Section: Study Areamentioning

confidence: 99%

Comparison approach for wind resource assessment to determine the most precise approach

Khan

Ahmad

Wang

et al. 2022

Energy & Environment

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The distribution models of wind speed data are essential to assess the potential wind speed energy because they decrease the uncertainty in estimating wind energy output. Therefore, before performing a detailed potential energy analysis, the precise distribution model for data relating to wind speed must be found. This research contains material from numerous goodness-of-fit tests, such as Kolmogorov–Simonov, Anderson–Darling, chi-square, root mean square error, Akaike information criterion, and Bayesian information criterion, which were combined finally to determine the wind speed of the best-fitted distribution. The suggested method collectively makes each criterion. This method was useful in statistically fitting 14 distribution models to wind speed data collected at four sites in Pakistan. The consequences show that this method provides the best source for selecting the most suitable wind speed statistical distribution. Also, the graphical representation is consistent with the analytical consequences. This research presents three estimation methods that can be used to calculate the different distributions used to estimate the wind. In the suggested maximum likelihood method, method of moments, and maximum likelihood estimation, the third-order moment used in the wind energy formula is a crucial function because it contributes to the precise estimate of wind energy. In order to prove the presence of the suggested method of moments, it was compared with well-known estimation methods, such as the method of linear moments and maximum likelihood estimation. In the relative analysis, given several goodness-of-fit tests, the presentation of the considered techniques is estimated based on the actual wind speed evaluated in different periods. The results show that the method of moments provides a more precise estimation than other commonly used methods for estimating wind energy based on the 14 distributions. Therefore, the method of moments can be a better technique for assessing wind energy.

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“…Furthermore, as noted in references [4][5][6], variations in seasonal weather conditions, such as rainy and dry seasons, significantly impact the electricity generation capacity of hydroelectric power stations, making it challenging for individual stations to meet the power system's requirements during different seasons, especially over the course of a year. However, according to reference [7], it has been discovered that cascading hydroelectric power stations possess multi-level reservoir energy storage capacity, typically consisting of multiple reservoirs, which enables them to better cope with seasonal fluctuations and efficiently allocate water resources to meet various seasonal demands. Furthermore, as indicated in references [8][9][10], cascading hydroelectric power stations can achieve more flexible electricity generation scheduling by adjusting the water discharge and electricity generation levels of each reservoir, meeting the power system's demands across various time scales, including those spanning years.…”

Section: Introductionmentioning

confidence: 99%

Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations

Jia,

Zhang,

Zhou

et al. 2024

Energies

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There exists a temporal and spatial coupling effect among the hydropower units in cascade hydropower stations which constitutes a complex planning problem. Researching the multi-objective optimization scheduling of cascade hydropower stations under various spatiotemporal inflow impacts is of significant importance. Previous studies have typically only focused on the economic dispatch issues of cascade hydropower stations, with little attention given to their coupling mechanism models and the uncertainty impacts of inflows. Firstly, this paper establishes a coupled optimization scheduling model for cascade hydropower stations and elaborates on the operational mechanism of cascade hydropower stations. Secondly, according to the needs of actual scenarios, two types of optimization objectives are set, considering both the supply adequacy and peak-shaving capacity as indicators, with the total residual load and the peak-valley difference of the residual load as comprehensive optimization objectives. Subsequently, considering the uncertainty impact of the inflow side, a stochastic optimization model for inflow is established based on a normal distribution probability. Finally, case study analyses demonstrate that the proposed model not only effectively achieves supply stability but also reduces the peak-valley difference in load, and can achieve optimized scheduling under the uncertain environment of inflow.

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Modeling and design study of energy generation through high-speed wind at the spillways of hydroelectric power station

Cited by 5 publications

References 18 publications

Retracted: “Economic regulation of energy costs when integrated into distribution networks of industrial enterprises”

Retracted: “Economic regulation of energy costs when integrated into distribution networks of industrial enterprises”

Comparison approach for wind resource assessment to determine the most precise approach

Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations

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