Abstract
Power flow optimization is a growing issue today. The system does not grow or develop, in contrast to the electricity power demand from consumers. Therefore, smart efforts have to be done to overcome this increasing electricity power demand. Power flow optimization is one of the efforts that can be done to optimize the current system. The use of Artificial Bee Colony algorithm can give an optimal result without being disturbed by mathematical problems that need much computation time. From the simulation result on Java-Bali 500 kV System, an optimal result has been achieved, in which this method can reduce system power losses from active power losses of 297.607 MVA and reactive power losses of 2926.825 MVAR to become 71.292 MVA and 530.241 MVAR, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cai, H.R., Chung, D.Y., Wong, K.P: Application of differential evolution algorithm transient stability constrained optimal power flow. IEEE Trans. Power Sys. 23, 719–728 (2008)
Vaisakh, K., Srinivas, L.R., Meah, K.: Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost function and statictical analysis. Appl. Soft Comput. 13(12), 4579–4593 (2013)
Xia, S.W., Zhou, B., Chan, K.W., Guo, Z.Z.: An improved GSO method for discontinuous non-convex transient stability contrained optimal power flow with complex system model. Electr. Power Energy Syst. 64, 483–492 (2015)
Momoh, J.A., El-Hawary, M.E., Adapa, R.: A review of selected optimal powerflow literature to 1993. I. Nonlinear and quadratic programming approaches, IEEE Trans. Power Syst. 14(1), 96–104 (1999)
Momoh, J.A., El-Hawary, M.E., Adapa, R.: A review of selected optimal powerflow literature to 1993. II. Newton, linear programming and interior point methods. IEEE Trans. Power Syst. 14(1), 105–111 (1999)
Nguyen, T., Pai, M.A.: Dynamic security-constrained rescheduling of power systems using trajectory sensitivities. IEEE Trans. Power Syst. 18(2), 848–854 (2003)
Shubhanga, K.N., Kulkarni, A.M.: Stability-constrained generation rescheduling using energy margin sensitivities. IEEE Trans. Power Syst. 19(3), 1402–1413 2004)
Gan, D., Thomas, R.J., Zimmerman, R.D.: Stability-constrained optimal power-flow. IEEE Trans. Power Syst. 15 (2), 535–540 (2000)
Yuan, Y., Kubkawa, J., Sasaki, H.: A solution of optimal power flow with multicontingency transient stability constraints. IEEE Trans. Power Syst. 18 (3), 1094–1102 (2003)
Layden, D., Jeyasurya, B.: Integrating security constraints in optimal power flow studies. In: Proceedings of IEEE Power Engineering Society. General Meeting, vol. 1 (2004)
Chen, L., Tada, Y., Okamoto, H., Tanabe, R., Ono, A.: Optimal operation solutions of power systems with transient stability constraints. IEEE Trans. Circuits Syst. I 48(3), 327–339 (2001)
Sayah, S., Zehar, K.: Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers. Manage. 49, 3036–3042 (2008)
Acknowledgements
The authors are very grateful to the Department of Electrical Engineering, National Institute of Technology (ITN) Malang Indonesia, to all facilities provided during this research. The authors would like to thank Indonesian Directorate of Higher Education for the research Grant No. DIPA-023.04.1.672453/2015, revision 01, dated March 03, 2015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Sulistiawati, I.B., Ashari, M.I. (2016). Artificial Bee Colony Algorithm for Optimal Power Flow on Transient Stability of Java-Bali 500 KV. In: Pasila, F., Tanoto, Y., Lim, R., Santoso, M., Pah, N. (eds) Proceedings of Second International Conference on Electrical Systems, Technology and Information 2015 (ICESTI 2015). Lecture Notes in Electrical Engineering, vol 365. Springer, Singapore. https://doi.org/10.1007/978-981-287-988-2_26
Download citation
DOI: https://doi.org/10.1007/978-981-287-988-2_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-287-986-8
Online ISBN: 978-981-287-988-2
eBook Packages: EngineeringEngineering (R0)