Version 1
: Received: 26 April 2024 / Approved: 26 April 2024 / Online: 26 April 2024 (10:01:59 CEST)
How to cite:
Tentu, P.; Kundala, P.K.Y.; Darimireddy, N.K. Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization. Preprints2024, 2024041738. https://doi.org/10.20944/preprints202404.1738.v1
Tentu, P.; Kundala, P.K.Y.; Darimireddy, N.K. Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization. Preprints 2024, 2024041738. https://doi.org/10.20944/preprints202404.1738.v1
Tentu, P.; Kundala, P.K.Y.; Darimireddy, N.K. Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization. Preprints2024, 2024041738. https://doi.org/10.20944/preprints202404.1738.v1
APA Style
Tentu, P., Kundala, P.K.Y., & Darimireddy, N.K. (2024). Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization. Preprints. https://doi.org/10.20944/preprints202404.1738.v1
Chicago/Turabian Style
Tentu, P., Praveen Kumar Yadav Kundala and Naresh Kumar Darimireddy. 2024 "Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization" Preprints. https://doi.org/10.20944/preprints202404.1738.v1
Abstract
The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.
Keywords
MOGA-GWO; Grey Wolf Optimizer; optimal power flow; RES
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
