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Impact Assessment and mitigation Techniques for high Penetration Levels of Renewable energy Sources in Distribution Networks:Voltage-control Perspective

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Journal of modern Power Systems and Clean energy 2022年第2期10卷 450-458页

作者： Ahmed S.A.Awad Dave Turcotte tare. h.m.e.-fouly Natural Resources Canada Can-metENERGYVarennesCanadaon leave from the Electrical Power and Machines DepartmentFaculty of EngineeringAin Shams UniversityCairo 11566Egyp Natural Resources Canada CanmetENERGYVarennesCanada Advanced Power and energy Center Khalifa UniversityAbu DhabiUnited Arab Emirates

The integration of renewable distributed generation(RDG)into distribution networks is promising and increasing ***,high penetration levels of distributed generation(DG)are often limited as they may have an adverse effect on the operation of distribution *** of the operation challenges is the interaction between DG and voltage-control equipment,e.g.,an under-load tap changer(ULTC),which is basically designed to compensate for voltage changes caused by slow load *** integration of variable DGs leads to rapid voltage fluctuations,which can negatively affect the tap operation of *** paper investigates the impact of high penetration levels of RDG on the tap operation of ULTC in distribution networks through *** mitigation techniques that can alleviate this impact are also *** these techniques,constant power-factor mode is regarded as the best trade-off between the simplicity and effectiveness of minimizing the number of tap *** are performed on a Canadian benchmark rural distribution feeder using OpenDSS software.

关键词： Renewable energy distributed generation(DG) voltage regulator load tap changer distribution network.

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A hybrid machine-learning model for solar irradiance forecasting

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Clean energy 2024年第1期8卷 100-110页

作者： Ameera m.Almarzooqi maher maalouf tare. h.m.e.-fouly Vasileios e.Katzourakis mohamed S.el moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems engineering Khalifa UniversityAbu DhabiPO Box 127788UAE electrical engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and environmental engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and environmental engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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