A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

被引：10

作者：

Fettah K. ^{[1
]}

Guia T. ^{[1
]}

Salhi A. ^{[2
]}

Betka A. ^{[3
]}

Saidi A.S. ^{[4
,5
]}

Teguar M. ^{[6
]}

Ali E. ^{[7
]}

Bajaj M. ^{[8
,9
,10
]}

Mohammadi S.A.D. ^{[11
]}

Ghoneim S.S.M. ^{[12
]}

机构：

[1] Electrical Engineering Department, Laboratory (LNTDL), Hamma Lakhdar University of El Oued, El Oued

[2] Electrical Engineering Department, Laboratory (LGEB), Mohamed Khider University of Biskra, b7000, Biskra

[3] Electrical Engineering Department, Laboratory (LVCS), Hamma Lakhdar University of El Oued, El Oued

[4] Electrical Engineering Department, College of Engineering, King Khalid University, Abha

[5] Laboratoire Des Systèmes Électriques, École Nationale d’Ingénieurs de Tunis, Université de Tunis El Manar, Tunis

[6] Laboratoire de Recherche en Electrotechnique, Ecole Nationale Polytechnique, El-Harrach, Algiers

[7] Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura

[8] Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun

[9] Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman

[10] Graphic Era Hill University, Dehradun

[11] Department of Electrical and Electronics, Faculty of Engineering, Alberoni University, Kapisa

[12] Department of Electrical Engineering, College of Engineering, Taif University, Taif

来源：

Scientific Reports | / 14卷 / 1期

关键词：

Multi-objective flower pollination; Multi-objective jellyfish search; Multi-objective lichtenberg algorithm; Multi-objective multi-verse optimization; Pareto-optimal solutions; The best compromise solution;

D O I：

10.1038/s41598-024-61192-2

中图分类号：

学科分类号：

摘要：

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability. © The Author(s) 2024.

引用

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