Ramp Rate and Constriction Factor Based Dual Objective Economic Load Dispatch Using Particle Swarm Optimization
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Ramp Rate and Constriction Factor Based Dual Objective Economic Load Dispatch Using Particle Swarm Optimization

Authors: Himanshu Shekhar Maharana, S. K .Dash

Abstract:

Economic Load Dispatch (ELD) proves to be a vital optimization process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize ramp rate constriction factor based particle swarm optimization (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a dual objective function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight factor advanced ramp rate limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and constriction factor based PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with ramp rate and constriction factor based well defined ramp rate PSO to compute various objectives namely cost, emission and total objective etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution. 

Keywords: Economic load dispatch, constriction factor based particle swarm optimization, dispersed particle swarm optimization, weight improved particle swarm optimization, ramp rate and constriction factor based particle swarm optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130659

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[1] M. L Jose, L.T Alicia and S.R Jesus, “Short term hydrothermal coordination based on interior point nonlinear programming and genetic Algorithm”, EE Porto power Tech conference, 2001.
[2] M. G. CW, Aganagic JG, Tony M Jose B and Reeves S, “Experience with mixed integer linear programming based approach on short term hydrothermal scheduling, IEEE transaction on power system, Vol.16 (4), pp.743-749.
[3] Ng and G. Shelby, “Direct load control –a profit-based load management using linear programming, IEEE transaction on power system, Vol.13 (2), pp.688-694, 1998.
[4] Shi C C, Chun H.C, Fomg I. K and Lah PB , “Hydroelectric generation scheduling with an effective differential dynamic programming algorithm”, IEEE transaction on power system, Vol.5(3),pp.737-743, 1990.
[5] Erion Finardi C, Silva Edson LD and Laudiasagastizabal CV. “Solving the unit commitment problem of hydropower plants via Lagrangian relaxation and sequential quadratic programming”, Computational & Applied Mathematics, Vol. 24(3), pp. .317- 341, 2005.
[6] D I sun, B Ashley, B Brewer, A Hughes and W.F. Tinney, “Optimal power flow by Newton Approach”. IEEE transaction on power system, Vol 103(10), pp.2864-2880, 1984.
[7] Santos and G.R. da Costa, “Optimal power flow by Newton’s method applied to an augmented Lagrangian function” IEE proceedings generations, Transmission & distribution, Vol 142(1), pp.33-36, 1989.
[8] N Sinha, R. Chakrabarti and PK Chattopadhayay, “Evolutionary programming techniques for Economic load Dispatch. IEEE transactions on Evolutionary Computations,” Vol 7(1), pp.83-94, 2003.
[9] K. P. Wong and J Yuryevich, “Evolutionary based algorithm for environmentally constraints economic dispatch”, IEEE transaction on power system. ” Vol 13(2), pp. 301-306, 1998.
[10] L Lai & Mata Prasad. Application of ANN to economic load dispatch. Proceeding of 4th international conference on advance in power system control, operation and management, APSCOM-97, Hong-Kongpp.pp707-711, nov-1997.
[11] J. Kennedy and R. C. Ebert, “Particle Swarm Optimization,” proceeding of IEEE international conference on Neural networks, Vol.4, pp.1942-1948, 1995.
[12] C.H. Chen& S.N. Yeh, “PSO for Economic power dispatch with valve point effects,” IEEE PES transmission & Distribution conference and Exposition, pp.1-5Latin America, Venezuela, 2006.
[13] K. S. Swarup, “Swarm intelligence Approach to the solution of optimal power flow,” Indian Institute of science, pp.439-455, oct- 2006.
[14] K. T. Chaturvedi, M. panditand L .Srivastava, “Self organizing Hierarchical PSO for non-convex economic load dispatch,” IEEE transaction on power system,Vol.23(3), pp.1079-1087,Aug.2008.
[15] P. T. V. Dinhlunge and Joef, “A Novel weight-improved Particle swarm optimization algorithm for optimal power flow and economic load dispatch problem.” IEEE Transaction, pp.1-7, 2010.
[16] K. S. Kumar, V.Tamilselvan, N. Murali, R. Rajaram , N.S.Sundaram and T. Jayabharathi , “Economic Load Dispatch with Emission Constraints using Various PSO Algorithms”, WEAS Transactions on power systems,Vol3(9),pp.598 607,September 2008.
[17] Hardiansyah, “Solving Economic Dispatch Problem with Valve-Point Effect using a Modified ABC Algorithm”, International Journal of Electrical and Computer Engineering, Vol 3(3), pp.377-385, June 2013.