A modified shuffle frog leaping algorithm for multi-objective optimal power flow

This paper presents an efficient and reliable evolutionary-based approach to solve the Optimal Power Flow (OPF) problem by considering the emission issue. The OPF problem has been widely used in power system operation and planning for determining electricity prices. Therefore, the conventional optimal power flow cannot meet the environmental protection requirements, because it only considers generation cost minimization. The multi-objective optimal power flow considers economical and emission issues. By adding the emission objective in the optimal power flow problem, this problem become more complicated than before and it needs to be solved with an accurate algorithm. This paper proposes an algorithm based on the Shuffle Frog Leaping Algorithm (SLFA) to solve the multi-objective OPF problem. Furthermore, this paper presents a modified SLFA called MSLFA algorithm which profits from a mutation in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The IEEE 30-bus test system is presented to illustrate the application of the proposed problem.

[1]  D. Sun,et al.  Optimal Power Flow Based Upon P-Q Decomposition , 1982, IEEE Transactions on Power Apparatus and Systems.

[2]  R. V. Mayorga,et al.  A Fuzzy Inference System Prototype for Indoor Air and Temperature Quality Monitoring and Hazard Detection , 2010 .

[3]  Carlos A. Coello Coello,et al.  An optimal power flow plus transmission costs solution , 2009 .

[4]  G. N. Tiwari,et al.  Simplified method of sizing and life cycle cost assessment of building integrated photovoltaic system , 2009 .

[5]  Carlos A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[6]  Ying Li,et al.  ENERGY AND ENVIRONMENTAL SYSTEMS PLANNING UNDER UNCERTAINTY—AN INEXACT FUZZY-STOCHASTIC PROGRAMMING APPROACH , 2010 .

[7]  Q. G. Lin,et al.  IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty , 2009 .

[8]  M. Abido.  Environmental/economic power dispatch using multiobjective evolutionary algorithms , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[9]  Kit Po Wong,et al.  Evolutionary programming based optimal power flow algorithm , 1999 .

[10]  G. Huang,et al.  An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level , 2010 .

[11]  Ali Maroosi,et al.  Application of shuffled frog-leaping algorithm on clustering , 2009 .

[12]  Guohe Huang,et al.  The Optimization of Energy Systems under Changing Policies of Greenhouse-gas Emission Control—A Study for the Province of Saskatchewan, Canada , 2010 .

[13]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[14]  Arthur Tay,et al.  A cooperative coevolutionary algorithm for multiobjective particle swarm optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  Taher Niknam,et al.  A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch , 2010 .

[16]  Isa S. Qamber,et al.  The established mega watt linear programming-based optimal power flow model applied to the real power 56-bus system in eastern province of Saudi Arabia , 2008 .

[17]  Alan D. Christiansen,et al.  Multiobjective optimization of trusses using genetic algorithms , 2000 .

[18]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[19]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[20]  Guohe Huang,et al.  An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems , 2010 .

[21]  K. Vaisakh,et al.  Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis , 2011, Expert Syst. Appl..

[22]  Gordon H. Huang,et al.  A dynamic inexact energy systems planning model for supporting greenhouse-gas emission management and sustainable renewable energy development under uncertainty—A case study for the City of Waterloo, Canada , 2009 .

[23]  C. Jeganathan,et al.  Multi-Objective Spatial Decision Model for Land Use Planning in a Tourism District of India , 2011 .

[24]  K. Fahd,et al.  Optimal Power Flow Using Tabu Search Algorithm , 2002 .

[25]  C. Fuerte-Esquivel,et al.  Incorporation of a UPFC model in an optimal power flow using Newton's method , 1998 .

[26]  J. S. Heslin,et al.  A multiobjective production costing model for analyzing emissions dispatching and fuel switching (of power stations) , 1989 .

[27]  C. Fuerte-Esquivel,et al.  Advanced SVC models for Newton-Raphson load flow and Newton optimal power flow studies , 2000 .

[28]  Samir Sayah,et al.  Modified differential evolution algorithm for optimal power flow with non-smooth cost functions , 2008 .

[29]  Weerakorn Ongsakul,et al.  Optimal Power Flow by Improved Evolutionary Programming , 2006 .

[30]  G. P. Granelli,et al.  Emission constrained dynamic dispatch , 1992 .

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

[32]  Andrew Kusiak,et al.  Optimization of wind turbine energy and power factor with an evolutionary computation algorithm , 2010 .

[33]  K. Fahd,et al.  Optimal Power Flow Using Tabu Search Algorithm , 2002 .

[34]  Guohe Huang,et al.  CCEM: A City-cluster Energy Systems Planning Model , 2009 .

[35]  Derya Birant.  Comparison of Decision Tree Algorithms for Predicting Potential Air Pollutant Emissions with Data Mining Models , 2011 .

[36]  Andrea Toffolo,et al.  Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design , 2002 .

[37]  Vahid Vahidinasab,et al.  Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach , 2010 .

[38]  Taher Niknam,et al.  A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration , 2010, Eng. Appl. Artif. Intell..

[39]  J.R. Cedeno-Maldonado,et al.  Differential evolution based economic environmental power dispatch , 2005, Proceedings of the 37th Annual North American Power Symposium, 2005..

[40]  Samir Sayah,et al.  Optimal power flow with environmental constraint using a fast successive linear programming algorithm: Application to the algerian power system , 2008 .

[41]  D. B. Das,et al.  New multi-objective stochastic search technique for economic load dispatch , 1998 .

[42]  A. C. Liew,et al.  Multiobjective generation scheduling using fuzzy optimal search technique , 1994 .

[43]  Gordon H. Huang,et al.  Planning of energy system management and GHG-emission control in the Municipality of Beijing--An inexact-dynamic stochastic programming model , 2009 .

[44]  Xing Zhang,et al.  An interval full-infinite mixed-integer programming method for planning municipal energy systems - A case study of Beijing , 2011 .

[45]  K. Vaisakh,et al.  A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis , 2010 .

[46]  M. A. Abido.  Environmental/economic power dispatch using multiobjective evolutionary algorithms , 2003 .

[47]  A. Sadegheih.  Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques , 2009 .

[48]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

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

[50]  R. Adapa,et al.  The quadratic interior point method solving power system optimization problems , 1994 .

[51]  A. A. El-Keib,et al.  Economic dispatch in view of the Clean Air Act of 1990 , 1994 .

[52]  Guohe Huang,et al.  Interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty—a case study for the Province of Ontario, Canada , 2011 .