1. A support vector regression-based interval power flow prediction method for distribution networks with DGs integration.
- Author
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Liang, Xiaorui, Zhang, Huaying, Liu, Qian, Liu, Zijun, Liu, Huicong, Liang, Zipeng, Chen, Haoyong, Liu, Yun, Chen, Ge, and Khalid, Haris M.
- Subjects
ELECTRICAL load ,ARTIFICIAL neural networks ,ELECTRIC power ,RENEWABLE energy sources ,POWER distribution networks ,INTERVAL analysis ,DIGITAL communications - Abstract
This article discusses a support vector regression-based interval power flow prediction method for distribution networks with distributed generators (DGs) integration. The method aims to address the challenges of uncertain power flow analysis in distribution networks, particularly as the size of the system increases. The proposed method utilizes intervals to describe system uncertainty and employs support vector regression for model training. Simulation results demonstrate that the method exhibits high prediction accuracy, adaptability, and computation efficiency, meeting the requirements for rapid and real-time power flow analysis in distribution networks with DGs integration. The text also discusses the construction of an Interval Power Flow (IPF) prediction model for distribution networks using Support Vector Regression (SVR). The model is a multi-output model that predicts the upper and lower bounds of power flow results, taking into account uncertainties in the distribution system. The model has been shown to have high prediction accuracy and computational efficiency in studies conducted on IEEE 33bw and IEEE 69 cases. The text concludes by stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. The article titled "Distributionally robust multistage dispatch with discrete recourse of energy storage systems" by Zheng et al. explores the concept of distributionally robust multistage dispatch with discrete recourse of energy storage systems. The authors propose a mathematical model that considers uncertainties in renewable energy generation and electricity demand, and aims to optimize the dispatch of energy storage systems. The study highlights the importance of incorporating uncertainty [Extracted from the article]
- Published
- 2024
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