Scenario-Based Network Reconfiguration and Renewable Energy Resources Integration in Large-Scale Distribution Systems Considering Parameters Uncertainty

Mathematics

Francisco Jurado¹  ZiadM. Ali²  IbrahimMohamed Diaaeldin³  Ahmed El-Rafei³  AlmoatazY. Abdelaziz⁴  Shady H. E. Abdel Aleem⁵ 

[1] Department of Electrical Engineering, University of Jaén, EPS Linares, 23700 Jaén, Spain;Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia;Engineering Physics and Mathematics Department, Ain Shams University, Cairo 11517, Egypt;Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt;Technology and Maritime Transport, Electrical Energy Department, The college of Engineering and Technology, Arab Academy for Science, Giza 12577, Egypt;

关键词: distributed generation;    graphically based network reconfiguration;    hosting capacity maximization;    power loss minimization;    bilevel multi-objective nonlinear programming optimization;    DG uncertainty;   

DOI  :  10.3390/math9010026

来源: DOAJ

【 摘 要 】

Renewable energy integration has been recently promoted by many countries as a cleaner alternative to fossil fuels. In many research works, the optimal allocation of distributed generations (DGs) has been modeled mathematically as a DG injecting power without considering its intermittent nature. In this work, a novel probabilistic bilevel multi-objective nonlinear programming optimization problem is formulated to maximize the penetration of renewable distributed generations via distribution network reconfiguration while ensuring the thermal line and voltage limits. Moreover, solar, wind, and load uncertainties are considered in this paper to provide a more realistic mathematical programming model for the optimization problem under study. Case studies are conducted on the 16-, 59-, 69-, 83-, 415-, and 880-node distribution networks, where the 59- and 83-node distribution networks are real distribution networks in Cairo and Taiwan, respectively. The obtained results validate the effectiveness of the proposed optimization approach in maximizing the hosting capacity of DGs and power loss reduction by greater than 17% and 74%, respectively, for the studied distribution networks.

【 授权许可】

Unknown