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

IF 6.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal Pub Date : 2024-08-21 DOI:10.1016/j.aej.2024.08.033

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Abstract

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.

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用于优化分布式发电分配的自然启发群智能算法：减少配电网电能损耗的综合评述

能源需求的持续增长给配电网络带来压力，导致电力损耗增加，整体性能下降。这对配电公司的利润产生了负面影响，并增加了消费者的用电成本。配电网络中分布式发电（DG）的优化配置可以通过增强供电能力和改善网络性能来缓解这些问题。然而，实现最佳分布式发电分配是一个复杂的优化问题，需要先进的数学技术。基于自然启发（NI）的蜂群智能（SI）优化技术通过模拟动物的自然集体行为，提供了潜在的解决方案。本文回顾了 NI-SI 算法在优化风电机组分配中的应用，特别是将减少功率损耗作为关键目标函数。综述分析了大量文献，这些文献证明了 NI-SI 技术在解决配电网络电能损耗难题方面的有效性。此外，还提供了未来的研究方向，以指导该领域的进一步探索。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

alexandria engineering journal Engineering-General Engineering

CiteScore

11.20

自引率

4.40%

发文量

1015

审稿时长

43 days

期刊介绍： Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification: • Mechanical, Production, Marine and Textile Engineering • Electrical Engineering, Computer Science and Nuclear Engineering • Civil and Architecture Engineering • Chemical Engineering and Applied Sciences • Environmental Engineering

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