Abstract
The ongoing study aims to establish a direct probabilistic load flow (PLF) for the analysis of wind integrated radial distribution systems. Because of the stochastic output power of wind farms, it is very important to find a method which can reduce the calculation burden significantly, without having compromising the accuracy of results. In the proposed approach, a K-means based data clustering algorithm is employed, in which all data points are bunched into desired clusters. In this regard, probable agents are selected to run the PLF algorithm. The clustered data are used to employ the Monte Carlo simulation (MCS) method. In this paper, the analysis is performed in terms of simulation run-time. Also, this research follows a two-fold aim. In the first stage, the superiority of data clustering-based MCS over the unsorted data MCS is demonstrated properly. Moreover, the impact of data clustering-based MCS and unsorted data-based MCS is investigated using an indirect probabilistic forward/backward sweep (PFBS) method. Thus, in the second stage, the simulation run-time comparison is carried out rigorously between the proposed direct PLF and the indirect PFBS method to examine the computational burden effects. Simulation results are exhibited on the IEEE 33-bus and 69-bus radial distribution systems.
Highlights
Integration of distributed generators (DGs) such as wind farms, photovoltaic generation, diesel generators, and fuel cells gives some salient advantages to the distribution network, such as active power loss reduction, improvement in voltage profile, and enhancement of the reliability of the network [1]
This study intended to develop a fast and accurate probabilistic load flow (PLF) method for radial distribution systems interconnected with wind farms
The K-means data clustering-based Monte Carlo Simulation is proposed in this study, to mitigate the calculation run-time, without compromising the calculation accuracy
Summary
Integration of distributed generators (DGs) such as wind farms, photovoltaic generation, diesel generators, and fuel cells gives some salient advantages to the distribution network, such as active power loss reduction, improvement in voltage profile, and enhancement of the reliability of the network [1]. Reference [9] employed a probabilistic approach to evaluate the power flow in radial distribution systems. This approach involves two different methods: (i) the compensation-based PLF method and,. The correlation between the wind speed, the output power of WF and system load should be considered when evaluating the PLF [21] Such uncertainty imposed by the WFs should be considered by researchers to gain more efficient PLF methods. To compare the direct PLF approach with an indirect PLF, the probabilistic forward/backward sweep (PFBS) method is run for the radial distribution system in the presence of the WFs by the two above mentioned approaches: K-means data clustering-based and simple data-based.
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.
