Work

Optimal placement and sizing of distributed energy resources in active distribution networks under uncertainty: A multi-objective approach using electric eel foraging optimization

Year: 2025

Type: article

Abstract: The integration of Renewable Energy Sources (RESs) into active distribution networks presents a trade-off between loss reduction and operational uncertainties, as well as challenges to network stability. This paper proposes a novel techno-economic multi-objective function (MOF) to optimize the sizing and placement of RESs, fuel cells, and shunt capacitors. The proposed MOF maximizes the hosting capacity and voltage stability while minimizing power losses, voltage deviation, and costs. The proposed MOF utilizes the electric eel foraging optimization (EEFO) algorithm, which was benchmarked against seven metaheuristic algorithms (GA, PSO, GWO, SMA, MPA, AHA, and ARO) and tested on the modified IEEE 69-bus system under uncertainty. Simulation results demonstrate the superior performance of the EEFO-based MOF, achieving a 69.48% reduction in power losses. Under uncertain conditions, it attained the best overall performance: the lowest loss (9.46 kW), minimal cost ($226/h), least land use (5604.3 m²), and highest hosting capacity (55.1%).

Source: Computers & Electrical Engineering

DOI: 10.1016/j.compeleceng.2025.110715

Authors: A. Elsawy Khalil, Joseph S. Sedky, Ahmed M. Ibrahim, Tarek A. Boghdady

Institutions: Cairo University, Ahram Canadian University, Buraydah Colleges

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $2,880