Development of Firefly Algorithm Based Method for Distributed Generation Planning in an Unbalanced Three-phase Distribution Network using Voltage Stability Index
Development of Firefly Algorithm Based Method for Distributed Generation Planning in an Unbalanced Three-phase Distribution Network using Voltage Stability Index.
ABSTRACT
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.
INTRODUCTION
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.
REFERENCES
Abdelaziz, A., Hegazy, Y., El-Khattam, W., & Othman, M. (2015). Optimal allocation of
stochastically dependent renewable energy based distributed generators in unbalanced
distribution networks. Electric power systems research, 119, 34-44.
Acharya, N., Mahat, P., & Mithulananthan, N. (2006). An analytical approach for DG allocation
in primary distribution network. International Journal of Electrical Power & Energy
Systems, 28(10), 669-678.
Afolabi, O. A., Ali, W. H., Cofie, P., Fuller, J., Obiomon, P., & Kolawole, E. S. (2015). Analysis
of the Load Flow Problem in Power System Planning Studies. Energy and Power
Engineering, 7(10), 509.
Al-Sabounchi, A., Gow, J., Al-Akaidi, M., & Al-Thani, H. (2011). Optimal sizing and location of
a PV system on three-phase unbalanced radial distribution feeder avoiding reverse power
flow. Paper presented at the IEEE Electrical Power and Energy Conference (EPEC), 2011.
Anwar, A., & Pota, H. (2012). Optimum capacity allocation of DG units based on unbalanced
three-phase optimal power flow. Paper presented at the IEEE Power and Energy Society
General Meeting 2012.
Balamurugan, K., & Srinivasan, D. (2011). Review of power flow studies on distribution network
with distributed generation. Paper presented at the Power Electronics and Drive Systems
(PEDS), 2011 IEEE Ninth International Conference on, Singapore.
Bhimarasetti, R. T., & Kumar, A. (2014). Distributed generation in Unbalanced Mesh Distribution
System with different unbalances. Paper presented at the 6th IEEE Power India
International Conference (PIICON), 2014.
Blume, S. W. (2008). Electric power system basics for the nonelectrical professional (Vol. 32):
John Wiley & Sons.
Brajevic, I., Tuba, M., & Bacanin, N. (2012). Firefly Algorithm with a Feasibility-Based Rules for
Constrained Optimization. Paper presented at the Proceedings of the 6th WSEAS European
Computing Conference (ECC’12), ISBN.
Cespedes, R. (1990). New method for the analysis of distribution networks. IEEE Transactions on
Power Delivery, 5(1), 391-396.
Chou, H.-M., & Butler-Purry, K. L. (2014). Investigation of voltage stability in three-phase
unbalanced distribution systems with DG using modal analysis technique. Paper presented
at the North American Power Symposium (NAPS), 2014.
Dahal, S., & Salehfar, H. (2016). Impact of distributed generators in the power loss and voltage
profile of three phase unbalanced distribution network. International Journal of Electrical
Power & Energy Systems, 77, 256-262.
CSN Team.
