Abstract
Economic issues of power systems are formulated as optimization problems to enhance reliable operation and safe security of the real-time and hierarchical systems including complex control structures. The optimization problems have been formulated as combination of objective functions and constraints which Optimal Power Flow (OPF) must be increased to combine security constraints . The OPF problem is basically a network analysis challenge and the main objective of this challenge is to plan and to predict the undesirable situations that may arise by adding various assumptions to the account. This challenge can be solved using well-known numerical approaches, however these include derivatives and the solution of them is relatively difficult. However, the Evolutionary Computation (EC) based optimization algorithms provide more easy solutions for the OPF. In this chapter, the algorithms that contain the heuristic methods used on EC based algorithms and their applications on OPF are described.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M. Zima, M. Bockarjova, Operation, Monitoring and Control Technology of Power Systems, Lecture Notes 227-0528-00 (ITET ETH, Zurich, 2007)
B. Stott, O. Alsac, A.J. Monticelli, Security analysis and optimization. IEEE Invited Pap. 75(12), 1623–1644 (1986)
P. Schavemaker, L. Van der Sluis, Electrical Power System Essentials (Wiley, 2017)
http://www.yildiz.edu.tr/~inan/LU_Hesap/Yuk_Akisi_Genel_Bilgi.doc
J. Carpentier, Contribution to the study of the economic dispatching. Bull La Societe Fr Des Electr 3, 431–447 (1962)
B. Ghaddar, J. Marecek, M. Mevissen, Optimal power flow as a polynomial optimization problem. IEEE Trans. Power Syst. 31, 539–546 (2016)
J. Lin, V.O.K. Li, K.C. Leung, A.Y.S. Lam, Optimal power flow with power flow routers. IEEE Trans. Power Syst., 1–13 (2016)
S. Frank, I. Steponavice, S. Rebennack, Optimal power flow: a bibliographic survey-I: formulations and deterministic methods. Energy Syst. 3, 221–258 (2012)
K.C. Almeida, A. Kocholik, Solving III-posed optimal power flow problems via Fritz-John optimality conditions. IEEE Trans. Power Syst. 1–10 (2016)
A. Vaccaro, C.A. Canizares, A knowledge-based framework for power flow and optimal power flow analyses, IEEE Trans. Smart Grid 1–11 (2016)
V. Radziukynas, I. Radziukyniene, Optimization Methods Application to Optimal Power Flow in Electric Power Systems (Springer, Berlin Heidelberg, 2009)
A.M. Shaheen, R.A. El-Sehiemy, S.M. Farrag, Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm, 10, 1634–1647 (2016)
S. Gill, I. Kockar, G.W. Ault, Dynamic optimal power flow for active distribution networks. IEEE Trans. Power Syst. 29, 121–131 (2014)
T. Niknam, M.R. Narimani, M. Jabbari, Dynamic optimal power flow using hybrid particle swarm optimization and simulated annealing. Int. Trans. Electr. Energy Syst. 23, 975–1001 (2013)
Y. Xu, J. Ma, Z.Y. Dong, D.J. Hill, Robust transient stability-constrained optimal power flow with uncertain dynamic loads. IEEE Trans. Smart Grid, 1–11 (2016)
Y. Xu, Z.Y. Dong, Z. Xu, R. Zhang, K.P. Wong, in Power System Transient Stability Constrained Optimal Power Flow: A Comprehensive Review, IEEE Power Energy Society General Meeting, San Diego, CA (2012), pp. 1–7
M. Perninge, C. Hamon, A stochastic optimal power flow problem with stability constraints—part II: the optimization problem. IEEE Trans. Power Syst. 28, 1849–1857 (2013)
A. Vaccaro, C. Canizares, An Affine arithmetic-based framework for uncertain power flow and optimal power flow studies. IEEE Trans. Power Syst. 1–15 (2016)
M. Bazrafshan, N. Gatsis, Decentralized stochastic optimal power flow in radial networks with distributed generation. IEEE Trans. Smart Grid, 1–15 (2016)
J. Gong, D. Xie, C. Jiang, Y. Zhang, A new solution for stochastic optimal power flow: combining limit relaxation with iterative learning control. J. Electr. Eng. Technol. 9, 80–89 (2014)
H. Zhang, P. Li, Probabilistic analysis for optimal power flow under uncertainty. IET Gener. Transm. Distrib. 4, 553–561 (2010)
A. Schellenberg, W. Rosehart, J. Aguado, Cumulant-based probabilistic optimal power flow (P-OPF) with gaussian and gamma distributions. IEEE Trans. Power Syst. 20, 773–781 (2005)
G. Verbic, C.A. Canizares, Probabilistic optimal power flow in electricity markets based on a two-point estimate method. IEEE Trans. Power Syst. 21, 1883–1893 (2006)
D. Ke, C.Y. Chung, Y. Sun, A novel probabilistic optimal power flow model with uncertain wind power generation described by customized gaussian mixture model. IEEE Trans. Sustain Energy 7, 200–212 (2016)
M. Oua, Y. Xue, X.P. Zhang, Iterative DC optimal power flow considering transmission network loss. Electr. Power Compon. Syst., 1–11 (2016). http://dx.doi.org/10.1080/15325008.2016.1147104
V. Sarkar, S.A. Khaparde, Optimal LMP decomposition for the ACOPF calculation. IEEE Trans. Power Syst. 26, 1714–1723 (2011)
T. Akbari, M. Tavakoli Bina, Linear approximated formulation of AC optimal power flow using binary discretisation. IET Gener. Transm. Distrib. 10, 1117–1123 (2016)
W. Feng, L.A. Tuan, L.B. Tjernberg, A. Mannikoff, A. Bergman, A new approach for benefit evaluation of multiterminal VSC-HVDC using a proposed mixed AC/DC optimal power flow. IEEE Trans. Power Deliv. 29, 432–443 (2014)
S. Bahrami, F. Therrien, V.W.S. Wong, J. Jatskevich, Semidefinite relaxation of optimal power flow for AC-DC grids. IEEE Trans. Power Syst., 1–16 (2016)
A.K. Khamees, N.M. Badra, A.Y. Abdelaziz, Optimal power flow methods: a comprehensive survey. Int. Electr. Eng. J. (IEEJ) 7(4), 2228–2239 (2016)
T. Lewis, A Brief History of Artificial Intelligence, Live Science (22 June 2015)
S.S. Rao, Engineering Optimization: Theory & Practice, 4th edn. (Wiley, 2009)
E.K.P. Chong, S.H. Zak, An Introduction to Optimization, 2nd edn. (Wiley, 2001)
W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
L. Zade, Fuzzy set as a basis for theory of possibility. Fuzzy Sets Syst. 1(2), 3–28 (1976)
R.B. Devi, E. Barlaskar, O.B. Devi, S.P. Medhi, R.R. Shimray, Survey on evolutionary computation techniques and its application in different fields. Int. J. Inf. Theory 3(3) (2014)
Y. Liu, K.M. Passino, Swarm Intelligence: Literature Overview (The Ohio State University Dept. of Electrical Engineering, 2000)
J.H. Holland, Adaptation in Natural and Artificial Systems, 1st edn. (University of Michigan Press, Cambridge, MA 1975)
D. Whitley, A.M. Sutton, in Genetic Algorithms—A Survey of Models and Methods. Handbook of Natural Computing, (Springer, 2012), pp. 637–671. ISBN: 978-3-540-92909-3
Y. Liu, K.M. Passino, Swarm Intelligence: Literature Overview (Ohio State University Dept. of Electrical Engineering, 2000)
G. Beni, J. Wang, in Swarm Intelligence in Cellular Robotic Systems, NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, 1989
A. Chakraborty, A.K. Kar, Swarm intelligence: a review of algorithms, in Nature-Inspired Computing and Optimization, Modeling and Optimization in Science and Technologies, vol. 10 (Springer, 2017), pp. 475–494
S. Baluja, Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning (Technical Report CMU-CS-94-163), Carnegie Mellon University, USA, 1994
S. Yang, X. Yao, Population-based incremental learning with associative memory for dynamic environments. IEEE Trans. Evol. Comput. 12(5), 542–561 (2008). https://doi.org/10.1109/TEVC.2007.913070
F. Glover, Tabu search: part I. ORSA J. Comput. 1(3), 190–206 (1989)
F. Glover, Tabu search: part II. ORSA J. Comput. 2(1), 4–32 (1990)
A. Ibrahim, Sh. Rahnamayan, M. Vargas Martin, Miguel, Simulated raindrop algorithm for global optimization. Can. Conf. Electr. Comput. Eng., 1–8 (2014). https://doi.org/10.1109/ccece.2014.6901103
http://www.cleveralgorithms.com/nature-inspired/probabilistic/boa.html
M. Mitchell, J.H. Holland, S. Forrest, When will a genetic algorithm outperform hill climbing, in Advances in Neural Information Processing Systems, (1994), pp. 51–58
J.R. Sampson, Adaptive Information Processing (Springer, 1976), pp. 131–135
T. Kohonen, The self-organizing map. Neurocomputing 21(1), 1–6 (1998)
J. Kregting, R.C. White, Adaptive Random Search (Technical Report TH-Report 71-E-24), Eindhoven University of Technology, Eindhoven, Netherlands, 1971
http://www.cleveralgorithms.com/natureinspired/stochastic/hill_climbing_search.html
M. Sayadi, R. Ramezanian, N. Ghaffari-Nasab, A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. Int. J. Ind. Eng. Comput. 1(1), 1–10 (2010)
J. Duch, A. Arenas, Community detection in complex networks using extremal optimization. Phys. Rev. E 72(2), 027104 (2005)
Z.I. Botev, A. Ridder, L. Rojas Nandayapa, Semiparametric cross entropy for rare-event simulation. J. Appl. Probab. 53(3), 633–649 (2016)
Ch. Ramdane, S. Chikhi, Negative selection algorithm: recent improvements and its application in intrusion detection system. Int. J. Comput. Acad. Res. (IJCAR) 6(2), 20–30 (2017)
J. Kytojoki, T. Nuortio, O. Braysy, M. Gendreau, An efficient variable neighborhood search heuristic for very large scale vehicle routing problems. Comput. Oper. Res. 34(9), 2743–2757 (2007)
B. Khan, P. Singh, Optimal power flow techniques under characterization of conventional and renewable energy sources: a comprehensive analysis. Hindawi J. Eng. (2017)
http://futurearchitectureplatform.org/news/28/ai-architecture-intelligence/
B. Baydar, H. Gozde, M.C. Taplamacioglu, A research on evolutionary computation techniques in optimal power flow solution. Int. J. Tech. Phys. Probl. Eng. (IJTPE) 9(33), no. 4, 26–33 (2017)
L.L. Lai, J.T. Ma, R. Yokoyama, M. Zhao, Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Electr. Power Energy Syst. 19(5), 287–292 (1997). (Elsevier)
M.A. Abido, Optimal power flow using particle swarm optimization. Electr. Power Energy Syst. 24, 563–571 (2002). (Elsevier)
M.A. Abido, Optimal power flow using Tabu search algorithm. Electr. Power Compon. Syst. 30(5), 469–483 (2002)
T. Bouktir, L. Slimani, Optimal power flow of the algerian electrical network using an ant colony optimization method. Leonardo J. Sci. 6, 43–57 (2005)
S. Sayah, K. Zehar, Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers. Manage. 49, 3036–3042 (2008). (Elsevier)
A.A. Abou El Elaa, M. Abido, S.R. Spea, Optimal power flow using differential evolution algorithm. Electr. Power Syst. Res. 80, 878–885 (2010). (Elsevier)
M. Li, W. Liu, X. Wang, An improved particle swarm optimization algorithm for optimal power flow. IPEMC 25, 2448–2450 (2009)
S.R. Tsai, R.H. Liang, Y.T. Chen, W.T. Tseng, Optimal power flow by a fuzzy based hybrid particle swarm optimization approach. Electr. Power Syst. Res. 81, 1466–1474 (2011). (Elsevier)
I. Oumarou, D. Jiang, C. Yijia, Particle swarm optimization applied to optimal power flow solution. Int. Conf. Nat. Comput. (ICNC) 8(9), 284–288 (2009)
C. Sumpavakup, I. Srikun, S. Chusanapiputt, A solution to the optimal power flow using artificial bee colony algorithm. Int. Conf. Power Syst. Technol. (ICPST) 7(10) (2010)
A. Bhattacharya, P.K. Chattopadhyay, Application of biogeography-based optimization to solve different optimal power flow problems. Inst. Eng. Technol. (IETDL) 5(1), 70–80 (2011)
A.A. Esmin, G.L. Torres, Application of particle swarm optimization to optimal power systems. Int. J. Innovative Comput. Inf. Control (ICIC) 8(3(A)) (2011)
U. Guvenc, S. Duman, Y. Sonmez, N. Yorukeren, Optimal power flow using gravitational search algorithm. Energy Convers. Manage. 59, 86–95 (2012). (Elsevier)
C. Sumpavakup, I. Srikun, S. Chusanapiputt, A solution to multi-objective optimal power flow using hybrid cultural-based bees algorithm. IEEE 2(12) (2012)
O. Herbadji, K. Nadhir, L. Slimani, T. Bouktir, Optimal power flow with emission controlled using firefly algorithm. IEEE 9(13), 4673–5814 (2013)
P.D. Bamane, A.N. Kshirsagar, S. Raj, H.T. Jadhav, Temperature Dependent Optimal Power Flow Using GBest - Guided Artificial Bee, International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), vol. 1, issue 14, pp. 321–327, 2014
S.S. Reddy, S. Rathnam, Optimal power flow using glowworm swarm optimization. Electr. Power Energy Syst. 80, 128–139 (2016). (Elsevier)
A.A. Mohamed, Y.S. Mohamed, A.A. El-Gaafary, Optimal power flow using moth swarm algorithm. Electr. Power Syst. Res. 142, 190–206 (2017). (Elsevier)
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 chapter
Cite this chapter
Baydar, B., Gozde, H., Taplamacioglu, M.C., Kucuk, A.O. (2019). Resilient Optimal Power Flow with Evolutionary Computation Methods: Short Survey. In: Mahdavi Tabatabaei, N., Najafi Ravadanegh, S., Bizon, N. (eds) Power Systems Resilience. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-94442-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-94442-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94441-8
Online ISBN: 978-3-319-94442-5
eBook Packages: EnergyEnergy (R0)