Optimal integration of distributed generation resources in active distribution networks for techno-economic benefits

Energy Reports

ElSaeed A. Othman¹  Mohamed A. Ebrahim²  Fahmy M. Bendary³  Ahmed S. Hassan⁴ 

[1] Correspondence to: Ministry of Electricity and Renewable Energy (MOERE), Minister’s Technical Office, Office Room Number 128, 8 Ramsis Extension St., P.O. Box: 11517, P.Number 222, Cairo, Egypt.;Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt;Department of Electrical Engineering, Faculty of Engineering, Al Azhar University, Cairo, Egypt;Ministry of Electricity and Renewable Energy (MOERE), Cairo, Egypt;

关键词: Distributed generation;    Distribution networks;    Multi-objective;    Modified sine–cosine​ optimization technique;    Fast voltage stability index;    Power losses;   

DOI  :  

来源: DOAJ

【 摘 要 】

In recent years, modern electricity utilities face great challenges regarding the deregulated energy markets, transition toward sustainable smart grids and the increased load demand. These challenges are a part of the reasons that have paved the way toward the rapid progress of spreading the different Distributed Generation (DG) technologies into the modern Distribution Networks (DNs). DGs integration into DNs can be employed as a key solution for tackling the problems, facing the distribution systems, and verifying more technical and economic benefits while considering the systems’ uncertainties and the operational policies of the distribution utilities. This paper introduces the application of Modified Sine Cosine Algorithm (MSCA) for enhancing the DNs performance through the integration of multiple DG technologies in order to optimize the active power losses, the fast voltage stability index and the total costs, considering the DGs penetration level as well as the DG units’ operating power factor constraints. The proposed algorithm has been implemented using MATLAB software and applied on three-benchmark IEEE test systems (30-bus, 33-bus and 300-bus) as different models of electric power networks. The attained results show that the suggested optimization platform especially using MSCA, is more effective and successful in determining and finding better results than existing results.

【 授权许可】

Unknown