Solution of combined economic and emission dispatch problems using hybrid craziness-based PSO with differential evolution

In this paper, a hybrid craziness-based PSO with differential evolution (CRPSO-DE) algorithm is presented to solve complex, nonlinear, non-differentiable electric power system problems of combined economic and emission dispatch (CEED) with different cost functions and constraints. Traditionally, electric power systems are operated in such a way that the total fuel cost is minimized regardless of emissions produced. With increased requirements for environmental protection, alternative strategies are required. The proposed CRPSO-DE optimization algorithm attempts to reduce the production of atmospheric emissions (SOx and NOx oxides), caused by the operation of fossil-fueled thermal generation, along with the reduction of fuel cost. Such conflicting objectives are achieved by including equivalent cost of emissions as a part along with the fuel cost part in the multi-objective CEED optimization problem. The efficacy of the proposed optimization algorithm is tested on some test power systems taken from the literature and the results obtained are compared with those obtained by other methods reported in the state-of-the art literature. The results obtained demonstrate the effectiveness of the proposed algorithm for solving the CEED problems.

[1]  Ching-Tzong Su,et al.  New approach with a Hopfield modeling framework to economic dispatch , 2000 .

[2]  N. S. Babu,et al.  Analytical solution for combined economic and emissions dispatch , 2008 .

[3]  Waree Kongprawechnon,et al.  Ant colony optimisation for economic dispatch problem with non-smooth cost functions , 2010 .

[4]  L. L. Lai,et al.  A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer , 2000 .

[5]  Mohammad Ali Abido,et al.  Multiobjective evolutionary algorithms for electric power dispatch problem , 2006, IEEE Transactions on Evolutionary Computation.

[6]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[7]  K S Swarup,et al.  A Hybrid Interior Point Assisted Differential Evolution Algorithm for Economic Dispatch , 2011, IEEE Transactions on Power Systems.

[8]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[9]  Sakti Prasad Ghoshal,et al.  Combined economic and emission dispatch problems using biogeography-based optimization , 2010 .

[10]  Niladri Chakraborty,et al.  Effect of Control Parameters on Differential Evolution based Combined Economic Emission Dispatch with Valve-Point Loading and Transmission Loss , 2008 .

[11]  L. Coelho,et al.  Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect , 2006, IEEE Transactions on Power Systems.

[12]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[13]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[14]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[15]  H.C.S. Rughooputh,et al.  Environmental/economic dispatch of thermal units using an elitist multiobjective evolutionary algorithm , 2003, IEEE International Conference on Industrial Technology, 2003.

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

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

[18]  P. K. Chattopadhyay,et al.  Biogeography-Based Optimization for Different Economic Load Dispatch Problems , 2010, IEEE Transactions on Power Systems.

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

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

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

[22]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[23]  Xianzhong Duan,et al.  Study of differential evolution for optimal reactive power flow , 2007 .

[24]  Bin Li,et al.  Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems , 2010, Inf. Sci..

[25]  Manjaree Pandit,et al.  Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch , 2008, Appl. Soft Comput..

[26]  P. K. Chattopadhyay,et al.  Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch , 2010, IEEE Transactions on Power Systems.

[27]  A. T. Johns,et al.  Environmental/economic dispatch using fuzzy logic controlled genetic algorithms , 1997 .

[28]  Sakti Prasad Ghoshal,et al.  Seeker optimisation algorithm: application to the solution of economic load dispatch problems , 2011 .

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

[30]  Amit Konar,et al.  Improving particle swarm optimization with differentially perturbed velocity , 2005, GECCO '05.

[31]  Malabika Basu,et al.  Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II , 2008 .

[32]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..

[33]  Sakti Prasad Ghoshal,et al.  INTELLIGENT PARTICLE SWARM OPTIMIZED FUZZY PID CONTROLLER FOR AVR SYSTEM , 2007 .

[34]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .