A Mini-review: Conventional and Metaheuristic Optimization Methods for the Solution of Optimal Power Flow (OPF) Problem

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.

[1]  M. A. Abido,et al.  Optimal power flow using the league championship algorithm: A case study of the Algerian power system , 2014 .

[2]  Shin-Der Chen,et al.  A new algorithm based on the Newton -Raphson approach for real-time emission dispatch , 1997 .

[3]  Fernando J. Jaimes,et al.  A Method for Optimum Scheduling of Power and Voltage Magnitude , 1969 .

[4]  A. Ramesh Kumar,et al.  Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization , 2015 .

[5]  Salkuti Surender Reddy,et al.  Optimal Power Flow using Glowworm Swarm Optimization , 2016 .

[6]  Kumaraswamy Ponnambalam,et al.  A fast algorithm for power system optimization problems using an interior point method , 1991 .

[7]  Harish Pulluri,et al.  A solution network based on stud krill herd algorithm for optimal power flow problems , 2018, Soft Comput..

[8]  Mojtaba Ghasemi,et al.  An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow , 2015 .

[9]  Indrajit N. Trivedi,et al.  Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer , 2016, Neural Computing and Applications.

[10]  R. Adapa,et al.  A review of selected optimal power flow literature to 1993. I. Nonlinear and quadratic programming approaches , 1999 .

[11]  Vivekananda Mukherjee,et al.  Solution of optimal power flow using chaotic krill herd algorithm , 2015 .

[12]  Vivekananda Mukherjee,et al.  Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm , 2016 .

[13]  Provas Kumar Roy,et al.  Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization , 2014, Appl. Soft Comput..

[14]  C. Shilaja,et al.  Optimal Power Flow Using Hybrid DA-APSO Algorithm in Renewable Energy Resources , 2017 .

[15]  M. A. Abido,et al.  Optimal power flow using Teaching-Learning-Based Optimization technique , 2014 .

[16]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[17]  Ehab E. Elattar,et al.  Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement , 2019, Energy.

[18]  Jose A. Regalado,et al.  Modified bio-inspired optimisation algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem , 2017 .

[19]  Serhat Duman,et al.  Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones , 2017, Neural Computing and Applications.

[20]  C. M. Shen,et al.  Power-system load scheduling with security constraints using dual linear programming , 1970 .

[21]  Swellam W. Sharshir,et al.  Optimal Planning of Renewable Energy-Integrated Distribution System Considering Uncertainties , 2019, IEEE Access.

[22]  Ahmed M. Azmy,et al.  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 , 2020, Sustainable Cities and Society.

[23]  Mousumi Basu,et al.  Group Search Optimization for Solution of Different Optimal Power Flow Problems , 2016 .

[24]  Albert M. Sasson.  DecompositionTechniques Applied totheNonlinear Programming Load-Flow Method , 1970 .

[25]  C. M. Shen,et al.  Determination of optimum power-system operating conditions under constraints , 1969 .

[26]  Ponnuthurai Nagaratnam Suganthan,et al.  Optimal power flow solutions incorporating stochastic wind and solar power , 2017 .

[27]  Hany M. Hasanien,et al.  Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms , 2015 .

[28]  R. Adapa,et al.  Application of interior point method to economic dispatch , 1992, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics.

[29]  Salah Kamel,et al.  Optimal setting of STATCOM based on voltage stability improvement and power loss minimization using Moth-Flame algorithm , 2016, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON).

[30]  Kwang Y. Lee,et al.  An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem , 2017 .

[31]  Albert M. Sasson,et al.  Combined Use of the Powell and Fletcher - Powell Nonlinear Programming Methods for Optimal Load Flows , 1969 .

[32]  P. R. Bijwe,et al.  Differential evolution-based efficient multi-objective optimal power flow , 2017, Neural Computing and Applications.

[33]  A. Monticelli,et al.  Adaptive movement penalty method for the Newton optimal power , 1991, IEEE Power Engineering Review.

[34]  C. K. Babulal,et al.  Fuzzy harmony search algorithm based optimal power flow for power system security enhancement , 2016 .

[35]  D. W. Wells,et al.  Method for economic secure loading of a power system , 1968 .

[36]  Al-Attar Ali Mohamed,et al.  Optimal power flow using moth swarm algorithm , 2017 .

[37]  C Shilaja,et al.  Optimal power flow using Moth Swarm Algorithm with Gravitational Search Algorithm considering wind power , 2019, Future Gener. Comput. Syst..

[38]  Ajoy Kumar Chakraborty,et al.  Solution of optimal power flow using non dominated sorting multi objective opposition based gravitational search algorithm , 2015 .

[39]  H. R. E. H. Bouchekara,et al.  Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm , 2016 .

[40]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[41]  Ragab A. El-Sehiemy,et al.  Optimal power flow using an Improved Colliding Bodies Optimization algorithm , 2016, Appl. Soft Comput..

[42]  Sahand Ghavidel,et al.  Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: A comparative study , 2014, Inf. Sci..

[43]  Yuehua Huang,et al.  A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem , 2015 .

[44]  Yalin Chen,et al.  A modified MOEA/D approach to the solution of multi-objective optimal power flow problem , 2016, Appl. Soft Comput..

[45]  Ulaş Kılıç,et al.  Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones , 2014, Electrical Engineering.

[46]  R. Adapa,et al.  A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods , 1999 .

[47]  Kadir Abaci,et al.  Differential search algorithm for solving multi-objective optimal power flow problem , 2016 .

[48]  Shaorong Wang,et al.  Artificial Intelligence Technique for Optimal Allocation of Renewable Energy Based DGs in Distribution Networks , 2019, BWCCA.

[49]  Belkacem Mahdad,et al.  Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm , 2016, Appl. Soft Comput..

[50]  Djilani Benattous,et al.  Hybrid genetic algorithm and particle swarm for optimal power flow with non-smooth fuel cost functions , 2017, Int. J. Syst. Assur. Eng. Manag..

[51]  Alberto Pagnetti,et al.  Impact of wind power production in a European Optimal Power Flow , 2017 .

[52]  Canbing Li,et al.  Improved group search optimization method for optimal power flow problem considering valve-point loading effects , 2015, Neurocomputing.

[53]  G. C. Contaxis,et al.  Decoupled Optimal Load Flow Using Linear or Quadratic Programming , 1986, IEEE Transactions on Power Systems.

[54]  Yanbin Yuan,et al.  Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm , 2017 .

[55]  Harish Pulluri,et al.  An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow , 2017, Appl. Soft Comput..

[56]  H. T. Jadhav,et al.  Temperature dependent optimal power flow using g-best guided artificial bee colony algorithm , 2016 .

[57]  Amin Mahmoudi,et al.  A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm , 2015 .

[58]  L. Freris,et al.  Optimisation of economic dispatch through quadratic and linear programming , 1973 .

[59]  V. Mukherjee,et al.  Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem , 2016, Appl. Soft Comput..

[60]  Jordan Radosavljević,et al.  A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation , 2019 .

[61]  Ulaş Kılıç,et al.  Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow , 2015 .

[62]  Shaorong Wang,et al.  A Solution to the Optimal Power Flow Problem Considering WT and PV Generation , 2019, IEEE Access.

[63]  Ragab A. El-Sehiemy,et al.  Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm , 2016 .