International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
www.irjet.net
OPTIMIZED GENERATOR PLACEMENT IN A DISTRIBUTION NETWORK USING A HYBRID BUTTERFLY OPTIMIZATION ALGORITHM WITH GRADIENT DESCENT Abhay Koundal1, Dr. Geena sharma2, Er.Vinod Kumar3 1,2,3Electrical engineering
1,2,3Baddi University of Emerging Science and Technology , Baddi HP
--------------------------------------------------------------------------***-------------------------------------------------------------------------interest to the regulators of the industry and its utility. This Abstract
concept covers the additional benefits related to the distributed or dispersed types of compatible resources in the distinct locations of the network. These resources include small storage or modular based generation. Depending upon the changes done in the electrical industry, the use of such small portable or modular generation types provides a great interest. The rising issues of siting the big station plants, an increase in demand have made such modular resources as an additional benefit attracting the consumers based on the methods or the ability to change the projecting conditions. This basically provides dispersed forms of small modular installations very close to point-of-end use. Hence, the dispersed or distributed network has become a major electrical energy driven source in the present as well as the future-based generation. So, the main reason to use such dispersed systems relies on the following fact: The deregulation of power has encouraged the public investment in order to continue the power demand. This has resulted in breaking investments for the power development.
The increasing demand for reliable and efficient energy supply, coupled with the challenges posed by deregulated environments, has driven the need for advanced optimization strategies in distributed generation (DG) planning. Traditional approaches, such as Genetic Algorithms (GA), have been widely applied but often face limitations in handling complex, nonlinear optimization problems. To address these challenges, this study proposes a hybrid Butterfly Optimization Algorithm with Gradient Descent (BOA–GD) for optimal generator placement and system performance enhancement. The method is tested on benchmark IEEE 14-Bus and IEEE 69-Bus systems, with performance compared against GA and Bacterial Foraging Optimization with Gradient Descent (BFO-GD). The results demonstrate that the hybrid approach effectively minimizes total power and reactive losses, improves voltage deviation, and significantly reduces total operational costs. For instance, in the IEEE 69-Bus system, BFO-GD reduced total loss from ~50 MW to ~40 MW and lowered the operational cost from ~$40M to ~$35M, indicating superior performance over GA. Voltage deviation also improved consistently, ensuring greater system stability and reliability. These results validate the efficiency of hybrid optimization in solving multi-objective power system problems, making it a promising tool for future power system planning, distributed generation integration, and cost-effective grid management.
Keywords: distributed generation, optimization, GA, BFO-GD, IEEE bus systems, power loss reduction
I Introduction
The process of distributed generation in a deregulated environment plays a significant role in fulfilling the energy demands of future providing the free environment and the flexibility to its users in developing and planning the type of installation required as per the load critical conditions. It has the capability to serve as an alternate possible
The use of renewable technologies is usually restricted to areas with low load and population densities. The distribution networks in such areas are constructed or designed to provide the increasing demands of the consumers that tend to decrease with transmission system distance. So, the use of such a network provides a great
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The emergence of new technologies with large profitability, benefits, and smaller ratings. The rising demands and the saturation of the networks that already exist. The distributed resources should be located optimally to minimize the line loadings, reactive power need, and the losses of the network. The whole process of optimization should actively work on land costs, availability of the site, maintenance costs, plant operations conditions, etc.
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