Abstract
Integration of non-conventional renewables such as wind and solar to the power system may affect the system reliability, especially when the proportion of renewable power in the system is large. Therefore, with a significant level of renewable penetration, the intermittency and both diurnal and seasonal variations of renewable power generation should be deliberately modeled in order to accurately quantify the power system reliability. This paper presents a novel method based on Kernel Density Estimation (KDE) for modeling intermittency and both diurnal and seasonal variations of wind and solar power generation using historical renewable power generation data. The proposed KDE based renewable power models are used with non-sequential Monte Carlo simulation to evaluate the generation system adequacy. Several case studies are conducted on IEEE reliability test system to analyze the impact of increasing renewables on the generation system adequacy. The results show that the generation system adequacy tends to decay exponentially when the renewable integration is increased. It is shown that the reliability values obtained using the proposed approach are very close to those provided by the time-consuming sequential simulations. Importance of modeling seasonal variations of wind and solar is also investigated.
Highlights
Integration of renewable power, especially wind and solar PV is showing a rapid growth in modern power systems [1]
It is assumed that the Non-Sequential Monte Carlo Simulation (NSMCS) is converged if the Coefficient of Variation (COV) of Loss of Load Expectation (LOLE) is less than a defined margin ε
In this paper, a novel method based on Kernel Density Estimation (KDE) is proposed to model the intermittency and both diurnal and seasonal variations of renewable power generation
Summary
Integration of renewable power, especially wind and solar PV is showing a rapid growth in modern power systems [1]. The intermittency and both diurnal and seasonal variations of renewable power generation should be considered in the reliability evaluation of modern wind and solar integrated power systems. In the adequacy evaluation of wind and solar integrated power generation systems, SMCS struggles with the modeling of seasonal variations due to the computational cost of the ARIMA method [6], [11], [12]. KDE is used to find the probability densities of renewable power generation in different hours of the day and different seasons throughout the year Apart from modeling both diurnal and seasonal variations and intermittency, the proposed KDE based clustering approach has several advantages.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
