Nature-inspired swarm intelligence algorithms for optimal distributed generation allocation: A comprehensive review for minimizing power losses in distribution networks

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in...

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Bibliographic Details
Published in:Alexandria engineering journal 2024-10, Vol.105, p.692-723
Main Authors: Nizamani, Qirat, Hashmani, Ashfaque Ahmed, Leghari, Zohaib Hussain, Memon, Zeeshan Anjum, Munir, Hafiz Mudassir, Novak, Tomas, Jasinski, Michal
Format: Article
Language:English
Subjects:
Distributed generation
Distribution networks
Nature-inspired
Optimization algorithms
Power loss minimization
Swarm-intelligence
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Summary:The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field.
ISSN:1110-0168
DOI:10.1016/j.aej.2024.08.033