Development of Firefly Algorithm Based Method for Distributed Generation Planning in an Unbalanced Three-phase Distribution Network using Voltage Stability Index

Filed in Articles by on October 31, 2022

Development of Firefly Algorithm Based Method for Distributed Generation Planning in an Unbalanced Three-phase Distribution Network using Voltage Stability Index.


This research work presents the development of the Firefly algorithm (FA) and the application of voltage stability index (VSI) for optimal planning of Distribution Generation (DG) in an unbalanced three-phase distribution network.

The VSI was used to find the DG location while the FA was used for the DG sizing. The developed method was implemented on a standard IEEE 37-bus Radial distribution network test system and a local 19-bus Mahuta feeder.

The results obtained from the IEEE 37-bus were validated by comparing with similar work. For the standard IEEE 37-bus unbalanced radial distribution network (URDN), the total power loss obtained is 31.3543 kW and 15.2829 kVAr for active and reactive power respectively without DG in the network.

When the developed method is applied, a DG optimal location was found at bus 34 and a DG size of 356 kW and 170 kVAr for active and reactive respectively are obtained.

The active and reactive power loss was found to be 19.8329 kW and 10.0014 kVAr respectively. The developed method recorded a loss reduction of 36.75% and 34.56% for both active and reactive power respectively over the base case.

Also, the maximum liability of the network was found to be 18% and 8% of the initial loading with and without DG respectively. For the 19-bus Mahuta feeder, the location for the DG is bus 17 and the DG size of 201.58 kW and 115 kVAr are obtained for active and reactive power respectively.

A power loss reduction of 4.48% and 5.62% for active and reactive power were recorded over the base case respectively.

The maximum liability of the network for both the developed method and base case was found to be 119% of the initial loading. When compared with research on similar work, the developed method achieved a loss reduction of 8.14% and 30.42% for active and reactive power respectively over the method applied in the work. 


The electric power system majorly includes a generating plant, a transmission system, and a distribution network (Subramanyam et al., 2015).

Modern power systems are evolving from centralized bulk systems, with generation plants connected to the transmission network, to more decentralized systems, with smaller generating units connected directly to distribution networks close to the demand site.

The distribution network is mainly a passive network where the flow of both real and reactive power is unidirectional (Satish & Navuri, 2012).

However, with significant penetration of distributed generation, the power flow may become reversed, hence the distribution network is no longer a passive system but an active system. In this active system, the power flows and voltages are determined by the topology of the network generation sources as well as the loads (Mahmud et al., 2011).

Distributed Generation (DG) also termed embedded generation, dispersed generation, or decentralized generation is defined as a small electric power source that can be connected to a distribution network by a distribution company (DISCO) at any node or by the customer at the customer side of the meter (Payasi et al., 2012).

DG, unlike conventional generation, aims to generate part of the required electrical energy on small scale, closer to the area of consumption, and also to augment the electrical power from the grid within the network.

It represents a change in the conceptual framework of electrical energy generation. DG can be an alternative for residential, commercial, and industrial applications (Murthy & Kumar, 2013).

Electrical Distribution Systems (EDS) is expected to experience considerable growth in the near future, with respect to the penetration of DG. This will be mainly due to several factors, ranging 2 from environmental concerns to new technologies such as fuel cells and other alternative energy sources. 


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CSN Team.

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