Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization

Utilization of renewable energy resources such as wind energy for electric power generation has assumed great significance in recent years. Wind power is a source of clean energy and is able to spur the reductions of both consumption of depleting fuel reserves and emissions of pollutants. However, since the availability of wind power is highly dependent on the weather conditions, the penetration of wind power into traditional utility grids may incur certain security implications. Therefore, in economic power dispatch including wind power penetration, a reasonable tradeoff between system risk and operational cost is desired. In this chapter, a bi-objective economic dispatch problem considering wind penetration is first formulated, which treats operational costs and security impacts as conflicting objectives. Different fuzzy membership functions are used to reflect the dispatcher’s attitude toward the wind power penetration. A multi-objective memetic particle swarm optimization (MOMPSO) algorithm is adopted to develop a power dispatch scheme which is able to achieve compromise between economic and security requirements. Numerical simulations including comparative studies are reported based on a typical IEEE test power system to show the validity and applicability of the proposed approach.

[1]  R. Piwko,et al.  Wind energy delivery issues [transmission planning and competitive electricity market operation] , 2005, IEEE Power and Energy Magazine.

[2]  E. Muljadi,et al.  Making connections [wind generation facilities] , 2005, IEEE Power and Energy Magazine.

[3]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[4]  Prakash Kumar Hota,et al.  Multiobjective Generation Dispatch Through a Neuro-Fuzzy Technique , 2004 .

[5]  ZitzlerE.,et al.  Multiobjective evolutionary algorithms , 1999 .

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[8]  T. Jayabarathi,et al.  Evolutionary programming techniques for different kinds of economic dispatch problems , 2005 .

[9]  Jürgen Teich,et al.  Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[10]  Russell C. Eberhart,et al.  Particle swarm with extended memory for multiobjective optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[11]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[12]  Kay Chen Tan,et al.  A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  Jason R. Schott Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[15]  June Ho Park,et al.  Adaptive Hopfield neural networks for economic load dispatch , 1998 .

[16]  E.A. DeMeo,et al.  Wind plant integration [wind power plants] , 2005, IEEE Power and Energy Magazine.

[17]  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).

[18]  V. Miranda,et al.  Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers , 2005, IEEE Transactions on Power Systems.

[19]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[20]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[21]  J. C. Smith Winds of change issues in utility wind integration - Guest editorial , 2005 .

[22]  P. B. Eriksen,et al.  System operation with high wind penetration , 2005, IEEE Power and Energy Magazine.