A smart hybrid optimization model for DSSE in renewable energy-powered distribution networks

Reem R. Elbanna, Mohamed H. ElMessmary, Hatem Diab, Mahmoud Abdelsalam

Abstract


Accurate Distribution System State Estimation (DSSE) is essential for the reliable and efficient operation of modern power distribution networks, especially with the increasing penetration of renewable energy sources (RES) such as solar photovoltaics (PV) and wind energy. However, the nonlinearities, unbalanced loads, bidirectional power flows, and incomplete measurements in these networks present significant challenges. The integration of distribut-ed generation (DG) units further complicates traditional DSSE methods, requiring advanced optimization tech-niques to enhance estimation accuracy. This paper introduces a novel hybrid optimization algorithm that combines Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Egyptian Stray Dog Optimization (ESDO) to ad-dress these challenges in renewable-rich DSSE environments. The hybrid PSO-GA-ESDO algorithm leverages the global search capabilities of PSO, the evolutionary principles of GA, and the adaptive social behavior of ESDO, en-suring robust optimization with faster convergence and higher accuracy. The proposed methodology is imple-mented on the IEEE 13-bus system using MATLAB simulations, focusing on minimizing discrepancies between measured and estimated state variables while accounting for the variability of distributed renewable generation. Simulation results demonstrate that the hybrid PSO-GA-ESDO algorithm outperforms conventional optimization methods in terms of estimation accuracy, convergence speed, and robustness to noisy and incomplete measure-ments, even in scenarios with high renewable energy penetration. These findings highlight the proposed approach as an effective and scalable solution for DSSE in unbalanced, DG-integrated distribution networks, enhancing grid reliability, stability, and efficient real-time monitoring in modern smart and sustainable energy systems.

 

Received: 23 March 2025

Accepted: 25 June 2025

Published: 16 September 2025


Keywords


DSSE; Metaheuristic; ESDO; PSO; GA

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References


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DOI: https://dx.doi.org/10.21622/resd.2025.11.2.1271

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Copyright (c) 2025 Reem R. Elbanna, Mohamed H. ElMessmary, Hatem Diab, Mahmoud Abdelsalam

Renewable Energy and Sustainable Development

E-ISSN: 2356-8569

P-ISSN: 2356-8518

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

resd@aast.edu