Optimal power flow by enhanced genetic algorithm

This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus voltage magnitude limits, and generator reactive capabilities, are included as penalties in the GA fitness function (FF). Advanced and problem-specific operators are introduced in order to enhance the algorithm's efficiency and accuracy. Numerical results on two test systems are presented and compared with results of other approaches.

[1]  G. L. Torres,et al.  An interior-point method for nonlinear optimal power flow using voltage rectangular coordinates , 1998 .

[2]  R. Bacher,et al.  Unlimited point algorithm for OPF problems , 1997, Proceedings of the 20th International Conference on Power Industry Computer Applications.

[3]  James A. Momoh,et al.  A generalized quadratic-based model for optimal power flow , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[4]  V. Quintana,et al.  A Penalty Function-Linear Programming Method for Solving Power System Constrained Economic Operation Problems , 1984, IEEE Transactions on Power Apparatus and Systems.

[5]  Luonan Chen,et al.  Surrogate constraint method for optimal power flow , 1997 .

[6]  James A. Momoh,et al.  Challenges to optimal power flow , 1997 .

[7]  Larry J. Eshelman,et al.  Biases in the Crossover Landscape , 1989, ICGA.

[8]  H. Happ,et al.  Large Scale Optimal Power Flow , 1982, IEEE Transactions on Power Apparatus and Systems.

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

[10]  I. Wangensteen,et al.  Transmission management in the deregulated environment , 2000, Proceedings of the IEEE.

[11]  Vassilios Petridis,et al.  Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[12]  B Stott,et al.  Linear Programming for Power-System Network Security Applications , 1979, IEEE Transactions on Power Apparatus and Systems.

[13]  James A. Momoh,et al.  Improved interior point method for OPF problems , 1999 .

[14]  Felix F. Wu,et al.  Large-scale optimal power flow , 1989 .

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

[16]  W. F. Tinney,et al.  Some deficiencies in optimal power flow , 1988 .

[17]  C. S. Chen,et al.  Application of load survey systems to proper tariff design , 1997 .

[18]  W. Tinney,et al.  Optimal Power Flow By Newton Approach , 1984, IEEE Transactions on Power Apparatus and Systems.

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

[20]  Luonan Chen,et al.  Mean field theory for optimal power flow , 1997 .

[21]  S.-K. Chang,et al.  Adjusted solutions in fast decoupled load flow , 1988 .

[22]  Lawrence Hasdorff,et al.  Economic Dispatch Using Quadratic Programming , 1973 .

[23]  W. Tinney,et al.  Discrete Shunt Controls in Newton Optimal Power Flow , 1992, IEEE Power Engineering Review.

[24]  R. Yokoyama,et al.  Improved genetic algorithms for optimal power flow under both normal and contingent operation states , 1997 .

[25]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[26]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[27]  Hua Wei,et al.  An interior point nonlinear programming for optimal power flow problems with a novel data structure , 1997 .

[28]  T. Numnonda,et al.  Optimal power dispatch in multinode electricity market using genetic algorithm , 1999 .

[29]  F. Galiana,et al.  Economic Dispatch Using the Reduced Hessian , 1982, IEEE Transactions on Power Apparatus and Systems.

[30]  Lawrence Davis,et al.  Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

[31]  Eric Hobson,et al.  Power System Security Control Calculations Using Linear Programming, Part II , 1978, IEEE Transactions on Power Apparatus and Systems.

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

[33]  O. Alsac,et al.  Fast Decoupled Load Flow , 1974 .

[34]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[35]  Felix F. Wu,et al.  Large-Scale Optimal Power Flow: Effects of Initialization, Decoupling & Discretization , 1989, IEEE Power Engineering Review.