A hybrid optimization approach based on human memory optimization algorithm and differential evolution for optimal DG placement and sizing under diverse load models and loading conditions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259312" target="_blank" >RIV/61989100:27240/25:10259312 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/25:10259312

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2772671125002554" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2772671125002554</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.prime.2025.101139" target="_blank" >10.1016/j.prime.2025.101139</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A hybrid optimization approach based on human memory optimization algorithm and differential evolution for optimal DG placement and sizing under diverse load models and loading conditions

Popis výsledku v původním jazyce

The transformation of passive distribution networks into microgrid-oriented active systems enables the integration of diverse distributed generation (DG) units. However, the benefits of DGs, such as improved voltage profile, reduced losses, and enhanced reliability, are not fully realized without optimal placement and sizing, especially under varying loading conditions. With this motivation, the present study proposes a novel hybrid optimization technique, termed HMOADE, which synergistically combines the Human Memory Optimization Algorithm (HMOA) and Differential Evolution (DE) to address the Optimal Distributed Generation Placement and Sizing Problem (ODGPSP). The algorithm leverages human memory-inspired learning, retention, and recall mechanisms alongside DE&apos;s robust global search capabilities. Furthermore, the approach introduces adaptive parameter tuning and dual-phase search strategies to enhance both exploration and exploitation during the optimization process. The ODGPSP is formulated as a single-objective problem, focusing on minimizing active power loss, subject to operational constraints such as voltage limits, line capacities, DG generation bounds, and power balance equations. The study incorporates multiple static load models, constant power (CP), constant current (CI), constant impedance (CZ), and ZIP (composite), to realistically reflect real-world demand characteristics under light, full, and heavy loading scenarios. A cost-benefit analysis is also performed, assessing the reduction in energy loss and the costs of DG generation. The proposed HMOADE algorithm is validated on IEEE 69-bus and IEEE 85-bus radial distribution systems, demonstrating its robustness, accuracy, and computational efficiency. Simulation results reveal significant improvements in voltage profile, reductions in active power losses (up to ∼90 %), and substantial cost savings, with Type-III DGs showing the most favourable performance. A comparative analysis with higher accuracy, reduced computational time, simplified formulation, and consistency confirms the superiority of HMOADE, particularly in handling complex, nonlinear, and load-sensitive scenarios, over existing techniques. © 2025 The Author(s)

Název v anglickém jazyce

A hybrid optimization approach based on human memory optimization algorithm and differential evolution for optimal DG placement and sizing under diverse load models and loading conditions

Popis výsledku anglicky

The transformation of passive distribution networks into microgrid-oriented active systems enables the integration of diverse distributed generation (DG) units. However, the benefits of DGs, such as improved voltage profile, reduced losses, and enhanced reliability, are not fully realized without optimal placement and sizing, especially under varying loading conditions. With this motivation, the present study proposes a novel hybrid optimization technique, termed HMOADE, which synergistically combines the Human Memory Optimization Algorithm (HMOA) and Differential Evolution (DE) to address the Optimal Distributed Generation Placement and Sizing Problem (ODGPSP). The algorithm leverages human memory-inspired learning, retention, and recall mechanisms alongside DE&apos;s robust global search capabilities. Furthermore, the approach introduces adaptive parameter tuning and dual-phase search strategies to enhance both exploration and exploitation during the optimization process. The ODGPSP is formulated as a single-objective problem, focusing on minimizing active power loss, subject to operational constraints such as voltage limits, line capacities, DG generation bounds, and power balance equations. The study incorporates multiple static load models, constant power (CP), constant current (CI), constant impedance (CZ), and ZIP (composite), to realistically reflect real-world demand characteristics under light, full, and heavy loading scenarios. A cost-benefit analysis is also performed, assessing the reduction in energy loss and the costs of DG generation. The proposed HMOADE algorithm is validated on IEEE 69-bus and IEEE 85-bus radial distribution systems, demonstrating its robustness, accuracy, and computational efficiency. Simulation results reveal significant improvements in voltage profile, reductions in active power losses (up to ∼90 %), and substantial cost savings, with Type-III DGs showing the most favourable performance. A comparative analysis with higher accuracy, reduced computational time, simplified formulation, and consistency confirms the superiority of HMOADE, particularly in handling complex, nonlinear, and load-sensitive scenarios, over existing techniques. © 2025 The Author(s)

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

e-Prime - Advances in Electrical Engineering, Electronics and Energy

ISSN

2772-6711

e-ISSN

2772-6711

Svazek periodika

101139

Číslo periodika v rámci svazku

12/2025

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

54

Strana od-do

1-54

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105025546958