Abstract
Electric power from several sources in an electrical power system is to be accurately planned for economical and reliable operation. Power loss minimization, generation, and fuel cost minimization, voltage stability and carbon emission reduction are the prominent advantages of OFP. Thus, recently, the optimal solution of OPF become a valuable part of power system planning and optimization. This paper presents a mini-review on methods applied for the OPF solution. The applied methods include conventional and metaheuristic methodologies for solving the OPF problem. Moreover, the most recently applied metaheuristic methods for solving the OPF problem are covered and presented, including considered OFP type, validation test system, and numerous optimization objectives.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Elkadeem, M.R., Elaziz, M.A., Ullah, Z., Wang, S., Sharshir, S.W.: Optimal planning of renewable energy-integrated distribution system considering uncertainties. IEEE Access 7, 164887–164907 (2019)
Ullah, Z., Wang, S., Radosavljević, J.: A novel method based on PPSO for optimal placement and sizing of distributed generation. IEEJ Trans. Electr. Electron. Eng. 14, 1754–1763 (2019)
Ullah, Z., Elkadeem, M.R., Wang, S.: Artificial intelligence technique for optimal allocation of renewable energy based DGs in distribution networks. Advances on Broad-Band Wireless Computing, Communication and Applications BWCCA 2019. Lecture Notes in Networks and Systems, vol. 97, pp. 409–422. Springer, Cham (2020)
Elkadeem, M.R., Wang, S., Azmy, A.M., Atiya, E.G., Ullah, Z., Sharshir, S.W.: A systematic decision-making approach for planning and assessment of hybrid renewable energy-based microgrid with techno-economic optimization: a case study on an urban community in Egypt. Sustain. Cities Soc. 54, 102013 (2020)
Anuta, H., Ralon, P., Taylor, M.: Renewable power generation costs. In: 2018 International Renewable Energy Agency, IRENA (2019), Abu Dhabi, p. 2018 (2019)
Momoh, J.A.: IEEE Trans. Power Syst. 14(1), 96–104 (1999)
Mo, J.A., El-Hawary, M.E., Adapa, R.: Noh 11 Engineering: IEEE Transactions on A Review of Selected Optimal Power Flow Literature to 1993 Part II: Newton, Linear Programming and Interior Point Methods. Power Syst., vol. 14, no. 1, pp. 105–111 (1999)
Zhu, J.: Optimization of Power System Operation, 2nd edn. Wiley, New Jersey (2015)
Wells, D.W.: Method for economic secure loading of a power system. Proc. Inst. Electr. Eng. 115(8), 1190 (1968)
Stott, B.: Lip, Imx. Power, vol. 2(5) (1978)
Shen, C.M., Laughton, M.A.: Power-system load scheduling with security constraints using dual linear programming. Proc. Inst. Electr. Eng. 117(11), 2117–2127 (1970)
Shen, C.M., Laughton, M.A.: Determination of optimum power-system operating conditions under constraints. Proc. Inst. Electr. Eng. 116(2), 225 (1969)
Sasson, A.M.: Combined use of the Powell and Fletcher—Powell nonlinear programming methods for optimal load flows. IEEE Trans. Power Appar. Syst. PAS-88(10), 1530–1537 (1969)
El-Abiad, A.H., Jaimes, F.J., Fisher, G.J.: A method for optimum scheduling of power and voltage magnitude. IEEE Trans. Power Appar. Syst. PAS-88(4), 413–422 (1969)
Dommel, H.W., Tinney, W.F.: Optimal power flow solutions. IEEE Trans. Power Appar. Syst. PAS-87(10), 1866–1876 (1968)
Sasson, A.M.: Decomposition techniques applied to the nonlinear programming load-flow method. IEEE Trans. Power Appar. Syst. PAS-89(1), 78–82 (1970)
Contaxis, G.C., Delkis, C., Korres, G.: Decoupled optimal load flow using linear or quadratic programming. IEEE Trans. Power Syst. 1(2), 1–7 (1986)
Nabona, N., Freris, L.L.: Optimisation of economic dispatch through quadratic and linear programming. Proc. Inst. Electr. Eng. 120(5), 574–580 (1973)
Monticelli, A., Liu, W.H.E.: Adaptive movement penalty method for the Newton optimal power flow. IEEE Trans. Power Syst. 7(1), 334–342 (1992)
Sun, D.I., Ashley, B., Brewer, B., Hughes, A., Tinney, W.F.: Optimal power flow by Newton approach. IEEE Trans. Power Appar. Syst. PAS-103(10), 2864–2880 (1984)
Der Chen, S., Chen, J.F.: A new algorithm based on the Newton-Raphson approach for real-time emission dispatch. Electr. Power Syst. Res. 40(2), 137–141 (1997)
Pagnetti, A., Ezzaki, M., Anqouda, I.: Impact of wind power production in a European optimal power flow. Electr. Power Syst. Res. 152(2017), 284–294 (2017)
Biswas, P.P., Suganthan, P.N., Amaratunga, G.A.J.: Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers. Manag. 148, 1194–1207 (2017)
Ponnambalam, K., Quintana, V.H., Vannelli, A.: A fast algorithm for power system optimization problems using an interior point method, pp. 393–400 (1992)
Momoh, J.A., Austin, R.F., Adapa, R., Ogbuobiri, E.C.: Application of interior point method to economic dispatch. In: Conference Proceedings of the - IEEE International Conference System Man, and Cybernetics, pp. 1096–1101 (1992)
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. CAD Comput. Aided Des. 43(3), 303–315 (2011)
Shilaja, C., Arunprasath, T.: Optimal power flow using moth swarm algorithm with gravitational search algorithm considering wind power. Futur. Gener. Comput. Syst. 98, 708–715 (2019)
Elattar, E.E., ElSayed, S.K.: Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy 178, 598–609 (2019)
Shilaja, C., Ravi, K.: optimal power flow using hybrid DA-APSO algorithm in renewable energy resources. Energy Procedia 117, 1085–1092 (2017)
Ullah, Z., Wang, S., Radosavljevic, J., Lai, J.: A solution to the optimal power flow problem considering WT and PV generation. IEEE Access 7, 46763–46772 (2019)
Bai, W., Eke, I., Lee, K.Y.: An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem. Control Eng. Pract. 61, 163–172 (2017)
Mohamed, A.A.A., Mohamed, Y.S., El-Gaafary, A.A.M., Hemeida, A.M.: Optimal power flow using moth swarm algorithm. Electr. Power Syst. Res. 142, 190–206 (2017)
Mahdad, B., Srairi, K.: Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Appl. Soft Comput. J. 46, 501–522 (2016)
Surender Reddy, S., Srinivasa Rathnam, C.: Optimal power flow using glowworm swarm optimization. Int. J. Electr. Power Energy Syst. 80, 128–139 (2016)
Jadhav, H.T., Bamane, P.D.: Temperature dependent optimal power flow using g-best guided artificial bee colony algorithm. Int. J. Electr. Power Energy Syst. 77, 77–90 (2016)
Mukherjee, A., Mukherjee, V.: Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm. Int. J. Electr. Power Energy Syst. 78, 700–714 (2016)
Mukherjee, A., Mukherjee, V.: Solution of optimal power flow using chaotic krill herd algorithm. Chaos, Solitons Fractals 78, 10–21 (2015)
Tan, Y., et al.: Improved group search optimization method for optimal power flow problem considering valve-point loading effects. Neurocomputing 148, 229–239 (2015)
Ayan, K., Kiliç, U., Barakli, B.: Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow. Int. J. Electr. Power Energy Syst. 64, 136–147 (2015)
Ebeed, M., Kamel, S., Youssef, H.: Optimal setting of STATCOM based on voltage stability improvement and power loss minimization using Moth-Flame algorithm. In: 2016 18th International Middle-East Power Systems Conference MEPCON 2016 - Proceedings, pp. 815–820 (2017)
Basu, M.: Group search optimization for solution of different optimal power flow problems. Electr. Power Compon. Syst. 44(6), 606–615 (2016)
El-Fergany, A.A., Hasanien, H.M.: Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms. Electr. Power Compon. Syst. 43(13), 1548–1559 (2015)
Trivedi, I.N., Jangir, P., Parmar, S.A., Jangir, N.: Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer. Neural Comput. Appl. 30(6), 1889–1904 (2018)
Pulluri, H., Naresh, R., Sharma, V.: A solution network based on stud krill herd algorithm for optimal power flow problems. Soft. Comput. 22(1), 159–176 (2018)
Barocio, E., Regalado, J., Cuevas, E., Uribe, F., Zúñiga, P., Torres, P.J.R.: Modified bio-inspired optimisation algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem. IET Gener. Transm. Distrib. 11(4), 1012–1022 (2017)
Kahourzade, S., Mahmoudi, A., Bin Mokhlis, H.: A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm. Electr. Eng. 97(1), 1–12 (2014)
Ramesh Kumar, A., Premalatha, L.: Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. Int. J. Electr. Power Energy Syst. 73, 393–399 (2015)
Bouchekara, H.R.E.H., Abido, M.A., Chaib, A.E., Mehasni, R.: Optimal power flow using the league championship algorithm: a case study of the Algerian power system. Energy Convers. Manag. 87, 58–70 (2014)
Ghasemi, M., Ghavidel, S., Ghanbarian, M.M., Massrur, H.R., Gharibzadeh, M.: Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: a comparative study. Inf. Sci. (Ny) 281, 225–247 (2014)
Bouchekara, H.R.E.H., Abido, M.A., Boucherma, M.: Optimal power flow using Teaching-Learning-Based Optimization technique. Electr. Power Syst. Res. 114, 49–59 (2014)
Mandal, B., Kumar Roy, P.: Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization. Appl. Soft Comput. J. 21, 590–606 (2014)
Duman, S.: Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Comput. Appl. 28(11), 3571–3585 (2017)
Ghasemi, M., Ghavidel, S., Gitizadeh, M., Akbari, E.: An improved teaching-learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. Int. J. Electr. Power Energy Syst. 65, 375–384 (2015)
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. J. 42, 119–131 (2016)
Bhowmik, A.R., Chakraborty, A.K.: Solution of optimal power flow using non dominated sorting multi objective opposition based gravitational search algorithm. Int. J. Electr. Power Energy Syst. 64, 1237–1250 (2015)
Pulluri, H., Naresh, R., Sharma, V.: An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow. Appl. Soft Comput. J. 54, 229–245 (2017)
Yuan, X., et al.: Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm. Energy 122, 70–82 (2017)
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)
Zhang, J., Tang, Q., Li, P., Deng, D., Chen, Y.: A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. Appl. Soft Comput. J. 47, 494–514 (2016)
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)
Kılıç, U.: Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones. Electr. Eng. 97(2), 101–110 (2015)
Surender Reddy, S., Bijwe, P.R.: Differential evolution-based efficient multi-objective optimal power flow. Neural Comput. Appl. 31, 509–522 (2019)
Shaheen, A.M., El-Sehiemy, R.A., Farrag, S.M.: Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm. IET Gener. Transm. Distrib. 10(7), 1634–1647 (2016)
Pandiarajan, K., Babulal, C.K.: Fuzzy harmony search algorithm based optimal power flow for power system security enhancement. Int. J. Electr. Power Energy Syst. 78, 72–79 (2016)
Singh, R.P., Mukherjee, V., Ghoshal, S.P.: Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem. Appl. Soft Comput. J. 40, 161–177 (2016)
Yuan, X., Wang, P., Yuan, Y., Huang, Y., Zhang, X.: A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem. Energy Convers. Manag. 100, 1–9 (2015)
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(January), 146–153 (2017)
Acknowledgments
The authors would like to thank the State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology for providing the essential facilities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ullah, Z. et al. (2020). A Mini-review: Conventional and Metaheuristic Optimization Methods for the Solution of Optimal Power Flow (OPF) Problem. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-44041-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44040-4
Online ISBN: 978-3-030-44041-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)