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

Ziad M. Ali, Ibrahim Mohamed Diaaeldin, Shady H. E. Abdel Aleem, Ahmed El-Rafei, Almoataz Y. Abdelaziz and Francisco Jurado
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Ziad M. Ali: Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
Ibrahim Mohamed Diaaeldin: Engineering Physics and Mathematics Department, Ain Shams University, Cairo 11517, Egypt
Shady H. E. Abdel Aleem: Technology and Maritime Transport, Electrical Energy Department, The College of Engineering and Technology, Arab Academy for Science, Giza 12577, Egypt
Ahmed El-Rafei: Engineering Physics and Mathematics Department, Ain Shams University, Cairo 11517, Egypt
Almoataz Y. Abdelaziz: Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt
Francisco Jurado: Department of Electrical Engineering, University of Jaén, EPS Linares, 23700 Jaén, Spain

Mathematics, 2020, vol. 9, issue 1, 1-31

Abstract: 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.

Keywords: distributed generation; graphically based network reconfiguration; hosting capacity maximization; power loss minimization; bilevel multi-objective nonlinear programming optimization; DG uncertainty; load uncertainty; TOPSIS; large distribution networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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