Enhanced protection scheme design of power distribution networks through optimal DG integration using MALO and ETAP

Ensuring the optimal functioning of Distribution Networks (DNs) has become a critical priority in modern power systems due to the rapid integration of Renewable Energy Sources (RESs) and the complex operational challenges they introduce, such as intermittent generation, dynamic load fluctuations, and the need for reliable protection coordination. While the incorporation of Distributed Generators (DGs) significantly enhances system efficiency and voltage stability, it simultaneously complicates loss minimization and protection design. This paper presents a novel integrated optimization–protection framework that simultaneously addresses optimal DG placement and sizing as well as coordinated protection design under variable operating conditions. This paper presents a novel, integrated optimization–protection framework that simultaneously addresses optimal optimizes DG placement and sizing as well as and designs coordinated protection design under variable operating conditions. Unlike existing works that focus solely on loss minimization or voltage profile (VP) improvement, this study uniquely combines optimization, load flow, short-circuit (SC) analysis, and protection coordination within a single framework. The methodology is validated on three DNs (IEEE 33-bus, IEEE 69-bus, and a newly developed 19-bus system) that closely represent real operational conditions. The results confirm the superiority of MALO, achieving remarkable reductions in Active Power Loss (APL) by 92.25 %, 94.05 %, and 73.5 %, and in Reactive Power Loss (RPL) by 92.43 %, 91.94 %, and 50.65 %, respectively, along with improved VPs. Optimization and load flow simulations were performed using MATLAB, while Electrical Transient Analyzer Program (ETAP) was utilized for SC current analysis and protection coordination. A customized protection plan was designed for the 19-bus system, ensuring high reliability, time selectivity, and effective coordination before and after DG integration. The key novelty of this work lies in its comprehensive integration of optimization and protection planning, representing a practical and original contribution toward the development of self-adaptive, intelligent protection systems in smart distribution grids. Future research will extend this framework using hybrid intelligent algorithms and deep learning techniques in collaboration with SONELGAZ, Algeria’s national utility, to further enhance the reliability of real-world DNs.