article · Neural Computing and Applications
Providing reliable, low-carbon electricity to remote areas often requires hybrid energy systems combining multiple renewable sources and backup options. In southwest Egypt, a proposed stand-alone system incorporates solar power, wind energy, battery storage as an initial backup, and a diesel generator as a secondary backup. To determine the most cost-effective and reliable configuration, several recent meta-heuristic optimization algorithms were evaluated for system sizing, specifically the Chernobyl disaster optimizer, the dynamic control cuckoo search, and the gold rush optimizer. Comparative simulation analysis demonstrates that the dynamic control cuckoo search algorithm achieves superior performance over the alternative methods. This approach identifies optimal sizing parameters that minimise total system costs while maintaining high energy reliability standards for isolated rural communities.
Rural and off-grid communities face persistent challenges in securing dependable, affordable, and clean electricity. Correctly sizing hybrid renewable systems prevents expensive over-engineering and unexpected power shortages. Demonstrating which computational algorithms best balance affordability with continuous power supply gives energy planners practical tools to configure effective decentralised power networks in remote settings.
The findings are relevant to microgrid planners, energy engineering consultancies, and rural electrification agencies seeking to design off-grid infrastructure. Using the dynamic control cuckoo search method could improve decision-making tools for system sizing. Based on the simulation results reported in the abstract, the work is at an applied research stage and requires real-world piloting before commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
Abstract Hybrid energy system (HES) is considered a solution to the energy supply issue, particularly in rural areas to achieve their sustainable development goals. The rise in energy consumption has increased the appeal of renewable resources, because of their potential to supply consumers with competitive, carbon-free electricity. This paper suggests strategies for managing energy and the most recently published optimizers for designing a stand-alone HES positioned in a remote region of southwest Egypt. This HES includes two green energy sources (wind and solar) and a storage system for energy (battery) as the first backup in addition to a second backup (diesel). The most recent sizing techniques employing the Chernobyl disaster optimizer, dynamic control cuckoo search (DCCS), and gold rush optimizer have been suggested to obtain the optimal design of the utilized HES. Furthermore, an in-depth evaluation of the applied optimization approaches has been achieved based on a comparative study. A detailed analysis of the studied algorithms aims to identify the optimum algorithm that provides the lowest possible cost at the highest level of reliability for the proposed HES. The simulation results verified that, the DCCS algorithm outperformed other algorithms, indicating its potential for achieving promising solutions.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s00521-024-09902-9
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
