A Multiobjective Bacterial Foraging Algorithm to Solve the Environmental Economic Dispatch Problem

In this article, multi-objective bacterial foraging with non-dominated sorting procedure is applied to solve the non-linear constrained environmental/economic dispatch problem. In the proposed work, we have considered the standard IEEE 30-bus 6-generator test system with fuel cost and emission as two conflicting objectives to be optimized simultaneously. The limits on generator real power and reactive power outputs, bus voltages and power flow of transmission lines, ramp rate limits and prohibited operating zones are considered as the constraints. The proposed work also includes the effect of having non-smooth cost characteristics to mimic the valve point loading effect. The simulation result reveals that the proposed approach is a competitive one to the existing methods for finding the best optimal Pareto front of two conflicting objectives and has the better robustness.

[1]  K. Passino,et al.  Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors , 2002 .

[2]  Li Xuebin RETRACTED: Study of multi-objective optimization and multi-attribute decision-making for economic and environmental power dispatch , 2009 .

[3]  Ivo F. Sbalzariniy,et al.  Multiobjective optimization using evolutionary algorithms , 2000 .

[4]  Lingfeng Wang,et al.  Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm , 2007 .

[5]  Kwang Y. Lee,et al.  Application of Multi Objective Evolutionary Programming to Combined Economic Emission Dispatch Problem , 2007, 2007 International Joint Conference on Neural Networks.

[6]  C. N. Bhende,et al.  Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation , 2007, IEEE Transactions on Power Delivery.

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

[8]  S. A. Al-Baiyat,et al.  Economic load dispatch multiobjective optimization procedures using linear programming techniques , 1995 .

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

[10]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[11]  H. Sasaki,et al.  Multiobjective optimal generation dispatch based on probability security criteria , 1988 .

[12]  Songfeng Lu,et al.  An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling , 2010 .

[13]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[14]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .

[15]  Kalyanmoy Deb,et al.  Stochastic Evolutionary Multiobjective Environmental/Economic Dispatch , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[16]  Y. W. Wong,et al.  Genetic and genetic/simulated-annealing approaches to economic dispatch , 1994 .

[17]  Sukumar Mishra,et al.  Transmission Loss Reduction Based on FACTS and Bacteria Foraging Algorithm , 2006, PPSN.

[18]  Dong Hwa Kim,et al.  A hybrid genetic algorithm and bacterial foraging approach for global optimization , 2007, Inf. Sci..

[19]  Manoj Kumar Tiwari,et al.  Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch , 2008, IEEE Transactions on Evolutionary Computation.

[20]  Cheng-Chien Kuo Generation dispatch under large penetration of wind energy considering emission and economy , 2010 .

[21]  Lawrence Eisenberg,et al.  An Application of the Economic-Environmental Power Dispatch , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

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

[23]  M. A. Abido,et al.  A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch , 2003 .

[24]  K. Deb,et al.  Evolutionary Multi-Objective Environmental / Economic Dispatch : Stochastic vs . Deterministic Approaches , 2004 .

[25]  Cao Yijia,et al.  Multiple objective particle swarm optimization technique for economic load dispatch , 2005 .

[26]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[27]  Kalyanmoy Deb,et al.  Evolutionary Multi-objective Environmental/Economic Dispatch: Stochastic Versus Deterministic Approaches , 2005, EMO.

[28]  Lixiang Li,et al.  A multi-objective chaotic particle swarm optimization for environmental/economic dispatch , 2009 .

[29]  J. Nanda,et al.  Economic emission load dispatch with line flow constraints using a classical technique , 1994 .

[30]  Sukumar Mishra,et al.  A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation , 2005, IEEE Transactions on Evolutionary Computation.

[31]  D. P. Kothari,et al.  Stochastic economic emission load dispatch , 1993 .

[32]  Kenneth A. De Jong,et al.  Measurement of Population Diversity , 2001, Artificial Evolution.

[33]  R. Hahn,et al.  Assessing the Influence of Power Pools on Emission Constrained Economic Dispatch , 1986, IEEE Power Engineering Review.

[34]  C. S. Chang,et al.  Security-constrained multiobjective generation dispatch using bicriterion global optimisation , 1995 .

[35]  J. T. Wood,et al.  Potential impacts of clean air regulations on system operations , 1995 .

[36]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[37]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .