Kriging Assisted Surrogate Evolutionary Computation to Solve Optimal Power Flow Problems

This paper proposes a Kriging assisted strategy to expedite evolutionary computation for solving Optimal Power Flow (OPF) problems. First, two algorithms were developed–a Kriging Assisted Genetic Algorithm (KAGA) and a Kriging Assisted Particle Swarm Optimization (KAPSO) - and tested using unconstrained benchmark functions; it was found that both algorithms provided reliable and robust solutions. Accordingly, KAGA and KAPSO were selected and tested on the IEEE 30 and 118 bus systems for minimizing generation costs and active power losses. It is shown that the proposed KAPSO outperforms other algorithms, especially in terms of the computation time. In reference to the solution quality yielded by KAGA and KAPSO, the proposed Kriging assisted strategy offers a promising method to improve the performance of evolutionary based computation when solving OPF problems.

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

[2]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[3]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  S. Surender Reddy,et al.  Faster evolutionary algorithm based optimal power flow using incremental variables , 2014 .

[5]  Katta G. Murty,et al.  Nonlinear Programming Theory and Algorithms , 2007, Technometrics.

[6]  Steffen Rebennack,et al.  Optimal power flow: a bibliographic survey II , 2012, Energy Systems.

[7]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[8]  Jan K. Sykulski,et al.  A Kriging-Based Optimization Approach for Large Data Sets Exploiting Points Aggregation Techniques , 2017, IEEE Transactions on Magnetics.

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

[10]  William J. Welch,et al.  Computer experiments and global optimization , 1997 .

[11]  Christos Douligeris,et al.  Optimal power flow by linear programming based optimization , 1992, Proceedings IEEE Southeastcon '92.

[12]  P. Ajay D. Vimal Raj,et al.  Adaptive biogeography based predator–prey optimization technique for optimal power flow , 2014 .

[13]  H. Glavitsch,et al.  Quadratic Loss Formula for Reactive Dispatch , 1983, IEEE Power Engineering Review.

[14]  Vincent Roberge,et al.  Optimal power flow based on parallel metaheuristics for graphics processing units , 2016 .

[15]  P. K. Chattopadhyay,et al.  Application of biogeography-based optimisation to solve different optimal power flow problems , 2011 .

[16]  Bernhard Sendhoff,et al.  Fitness Approximation In Evolutionary Computation - a Survey , 2002, GECCO.

[17]  Yonghua Song,et al.  Modern Power Systems Analysis , 2008 .

[18]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..

[19]  Yaochu Jin,et al.  Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..

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

[21]  M. El-Hawary,et al.  Hybrid Particle Swarm Optimization Approach for Solving the Discrete OPF Problem Considering the Valve Loading Effects , 2007, IEEE Transactions on Power Systems.

[22]  M. Rotaru,et al.  A study of evolutionary based optimal power flow techniques , 2016, 2016 51st International Universities Power Engineering Conference (UPEC).

[23]  Jan K. Sykulski,et al.  Adaptive Weighted Expected Improvement With Rewards Approach in Kriging Assisted Electromagnetic Design , 2013, IEEE Transactions on Magnetics.

[24]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[25]  D. Uthitsunthorn,et al.  Power loss minimization using optimal power flow based on particle swarm optimization , 2010, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[26]  D. Birla,et al.  A New Attempt to Optimize Optimal Power Flow Based Transmission Losses Using Genetic Algorithm , 2012, 2012 Fourth International Conference on Computational Intelligence and Communication Networks.

[27]  L. Wehenkel,et al.  Sensitivity-Based Approaches for Handling Discrete Variables in Optimal Power Flow Computations , 2010, IEEE Transactions on Power Systems.

[28]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[29]  Jan K. Sykulski,et al.  A Dual Kriging Approach With Improved Points Selection Algorithm for Memory Efficient Surrogate Optimization in Electromagnetics , 2016, IEEE Transactions on Magnetics.

[30]  W. Marsden.  I and J , 2012 .