Abstract
Incorporating fluctuant and intermittent nature wind power and solar photovoltaic (PV) power in the power system lead to significant challenges for system planning and operation which are risen from uncertainties associated with renewable energy. This paper placed emphasis on OPF problem. Bird swarm algorithm (BSA) is employed to optimize power generation cost in power system network with handling the uncertainty of both wind power and solar PV power. To examine the effectiveness and accuracy of the BSA, the modified IEEE 30-bus system with two traditional thermal generators (TGs), two windfarms and two solar PV units is utilized.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Villanueva, D., Feijoo, A., Pazos, J.L.: Probabilistic load flow considering correlation between generation, loads and wind power. Smart Grid Renew. Energy 2(01), 12 (2011)
Qin, Z., Li, W., Xiong, X.: Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation. Appl. Energy 110, 285–294 (2013)
Biswas, P.P., Suganthan, P.N., Amaratunga, G.A.: Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers. Manag. 148, 1194–1207 (2017)
Abaci, K., Yamacli, V.: Differential search algorithm for solving multi-objective optimal power flow problem. Int. J. Electr. Power Energy Syst. 79, 1–10 (2016)
Daryani, N., Hagh, M.T., Teimourzadeh, S.: Adaptive group search optimization algorithm for multi-objective optimal power flow problem. Appl. Soft Comput. 38, 1012–1024 (2016)
Chaib, A.E., Bouchekara, H.R.E.H., Mehasni, R., Abido, M.A.: Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. Int. J. Electr. Power Energy Syst. 81, 64–77 (2016)
Bouchekara, H.R.E.H., Chaib, A.E., Abido, M.A., El-Sehiemy, R.A.: Optimal power flow using an improved colliding bodies optimization algorithm. Appl. Soft Comput. 42, 119–131 (2016)
Mohamed, A.A.A., Mohamed, Y.S., El-Gaafary, A.A., Hemeida, A.M.: Optimal power flow using moth swarm algorithm. Electr. Power Syst. Res. 142, 190–206 (2017)
Bouchekara, H.R.E.H., Chaib, A.E., Abido, M.A.: Optimal power flow using GA with a new multi-parent crossover considering: prohibited zones, valve-point effect, multi-fuels and emission. Electr. Eng. 100(1), 151–165 (2018)
Gacem, A., Benattous, D.: Hybrid genetic algorithm and particle swarm for optimal power flow with non-smooth fuel cost functions. Int. J. Syst. Assur. Eng. Manag. 8(1), 146–153 (2017)
Panda, A., Tripathy, M.: Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm. Int. J. Electr. Power Energy Syst. 54, 306–314 (2014)
Roy, R., Jadhav, H.T.: Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm. Int. J. Electr. Power Energy Syst. 64, 562–578 (2015)
Shi, L., Wang, C., Yao, L., Ni, Y., Bazargan, M.: Optimal power flow solution incorporating wind power. IEEE Syst. J. 6(2), 233–241 (2012)
Chang, T.P.: Investigation on frequency distribution of global radiation using different probability density functions. Int. J. Appl. Sci. Eng. 8(2), 99–107 (2010)
Meng, X.B., Gao, X.Z., Lu, L., Liu, Y., Zhang, H.: A new bio-inspired optimisation algorithm: bird swarm algorithm. J. Exp. Theor. Artif. Intell. 28(4), 673–687 (2016)
Shargh, S., Mohammadi-ivatloo, B., Seyedi, H., Abapour, M.: Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties. Renew. Energy 94, 10–21 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ahmad, M., Javaid, N., Niaz, I.A., Shafiq, S., Rehman, O.U., Hussain, H.M. (2019). Application of Bird Swarm Algorithm for Solution of Optimal Power Flow Problems. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-93659-8_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93658-1
Online ISBN: 978-3-319-93659-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)