Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators. (October 2021)

• Record Type: Journal Article
• Authors: Fathy, Ahmed; Yousri, Dalia; Abdelaziz, Almoataz Y.; Ramadan, Haitham S.
• Is Part Of: Sustainable energy technologies and assessments. Volume 47(2021)
• Publication Date: 2021-10
• Subjects: Distributed generators -- Chimp optimization algorithm -- Distribution network -- Optimal allocation; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101359
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Integrating distributed generators (DGs) in radial distribution networks plays a vital role in improving the system performance via enhancing the bus voltage and minimizing the system losses. Nonetheless, uncoordinated DGs integration may cause technical issues if they are not efficiently planned, controlled, and operated. Therefore, this paper proposes a new methodology based on the recent metaheuristic chimp optimizer approach (CO) to identify DGs' optimal allocations and rated powers. This work's objective function is minimizing the total active power loss of the network; the considered constraints are load flow, buses' voltages, and transmission lines. The proposed CO is characterized by ease of implementation, high convergence rate, and avoiding stuck in local optima. CO is adapted such that the first design variables are integer numbers representing the locations of DGs while the others are assigned to be the DGs' powers. The proposed CO is applied on three radial networks, 33-bus, 69-bus, and 119-bus, moreover two modes of DGs, unity power factor (DGs generate only active power) and non-unity power factor (DGs generate active and reactive powers), are studied. The results obtained via the proposed CO are compared to other reported approaches of exhaustive load flow (ELF), genetic algorithm (GA), and different programmed approaches of particle swarm optimizer (PSO) and Archimedes optimization algorithm (AOA). The obtained results confirmed the superiority and reliability of the proposed CO methodology in achieving a minor power loss via installing the DGs in the correct sites.