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Preferred Economic Dispatch of Thermal Power Units

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DOI: 10.4236/jpee.2015.311005    3,951 Downloads   4,653 Views   Citations

ABSTRACT

Economic Dispatch (ED) problem is one of the main concerns of the power generation operations which are basically solved to generate optimal amount of power from the generating units in the system by minimizing the fuel cost and by satisfying the system constraints. The accuracy of ED solutions is highly influenced by the fuel cost parameters of the generating units. Generally, the parameters are subjected to transform due to aging process and other external issues. Further the parameters associated with the transmission line modelling also change due to aforementioned issues. The loss coefficients which are the functions of transmission line parameters get altered from the original value over a time. Hence, the periodical estimation of these coefficients is highly essential in power system problems for obtaining ideal solutions for ED problem. Estimating the ideal parameters of the ED problem may be the best solution for this issue. This paper presents the Teaching Learning Based Optimization (TLBO) algorithm to estimate the parameters associated with ED problem. The estimation problem is formulated as an error minimization problem. This work provides a frame work for the computation of coefficients for quadratic function, piecewise quadratic cost function, emission function, transmission line parameters and loss coefficients. The effectiveness of TLBO is tested with 2 standard test systems and an Indian utility system.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Durai, S. , Subramanian, S. and Ganesan, S. (2015) Preferred Economic Dispatch of Thermal Power Units. Journal of Power and Energy Engineering, 3, 47-69. doi: 10.4236/jpee.2015.311005.

References

[1] El-Hawary, M.E. and Mansour, S.Y. (1982) Performance Evaluation of Parameter Estimation Algorithms for Economic Operation of Power Systems. IEEE Transaction on Power Apparatus Systems, 101, 574-582.
http://dx.doi.org/10.1109/TPAS.1982.317270
[2] Soliman, S.A., Emam, S.E.A. and Christensen, G.S. (1991) Optimization of the Optimal Coefficients of Non-Monotonically Increasing Incremental Cost Curves. Electric Power System Research, 21, 99-106.
http://dx.doi.org/10.1016/0378-7796(91)90023-G
[3] Liang, Z.X. and Glover, J.D. (1991) Improved Cost Functions for Economic Dispatch Computations. IEEE Transactions of Power System, 6, 821-829.
http://dx.doi.org/10.1109/59.76731
[4] Chen, H.Y.K. and Postel, C.E. (1986) On-Line Parameters Identification of Input Output Curves for Thermal Units. IEEE Transaction Power System, 1, 221-224.
http://dx.doi.org/10.1109/TPWRS.1986.4334933
[5] Grainger, J. and Stevenson, W. (1994) Power System Analysis. McGraw-Hill., New York.
[6] Chan, S.M. (1993) Computing Overhead Line Parameters. Computer Applications and Power, 6, 43-45.
http://dx.doi.org/10.1109/67.180436
[7] Dommel, H.W. (1985) Overhead Line Parameters from Handbook Formulas and Computer Programs. IEEE Transactions on Power Apparatus and Systems, 4, 366-372.
http://dx.doi.org/10.1109/TPAS.1985.319051
[8] Thorp, J.S., Phadke, A.G., Horowitz, S.H. and Begovic, M.M. (1988) Some Applications of Phasor Measurements To Adaptive Protection. IEEE Transaction Power System, 3, 791-798.
http://dx.doi.org/10.1109/59.192936
[9] Chen, C.S., Liu, C.W. and Jiang, J.A. (2002) A New Adaptive PMU Based Protection Scheme for Transposed/ Untransposed Parallel Transmission Lines. IEEE Transactions on Power Delivery, 17, 395-404.
http://dx.doi.org/10.1109/61.997906
[10] Kim, I.-D., and Aggarwal, R.K. (2006) A Study on the On-Line Measurement of Transmission Line Impedances for Improved Relaying Protection. Electric Power and Energy System, 28, 359-366.
http://dx.doi.org/10.1016/j.ijepes.2006.01.002
[11] Liao, Y. and Kezunovic, M. (2009) Online Optimal Transmission Line Parameter Estimation for Relaying Applications. IEEE Transactions on Power Delivery, 24, 96-102.
http://dx.doi.org/10.1109/TPWRD.2008.2002875
[12] Sivanagaraju, G., Chakrabarti, S. and Srivastava, S.C. (2014) Uncertainty in Transmission Line Parameters Estimation and Impact on Line Current Differential Protection. IEEE Transactions on Instrumentation and Measurement, 63, 1496-1504.
http://dx.doi.org/10.1109/TIM.2013.2292276
[13] Wagenaars, P., Wouters, P.A.A.F., van der Wielen, P.C.J.M. and Steennis, E.F. (2010) Measurement of Transmission Line Parameters of Three-Core Power Cables with Common Earth Screen. IET Science Measurement and Technology, 4, 146-155.
http://dx.doi.org/10.1049/iet-smt.2009.0062
[14] Indulkar, C.S. and Ramalingam, K. (2008) Estimation of Transmission Line Parameters from Measurements. Electric Power and Energy Systems, 30, 337-342.
http://dx.doi.org/10.1016/j.ijepes.2007.08.003
[15] Al-Kandari, A.M. and El-Naggar, K.M. (2006) A Genetic-Based Algorithm for Optimal Estimation of Input-Output Curve Parameters of Thermal Power Plants. Electrical Engineering, 89, 585-590.
http://dx.doi.org/10.1007/s00202-006-0047-x
[16] El-Naggar, K.M. and Alrashidi, M.R. and Al-Othman, A.K. (2009) Estimating the Input-Output Parameters of Thermal Power Plants Using PSO. Energy Conversion and Management, 50, 1767-1772.
http://dx.doi.org/10.1016/j.enconman.2009.03.019
[17] Alrashidi, M.R., El-Naggar, K.M. and Al-Othman, A.K. (2009) Particle Swarm Optimization Based Approach for Estimating the Fuel-Cost Function Parameters of Thermal Power Plants with Valve Loading Effects. Electric Power Components and Systems, 37, 1219-1230. http://dx.doi.org/10.1080/15325000902993589.
[18] Sonmez, Y. (2013) Estimation of Fuel Cost Curve Parameters for Thermal Power Plants Using the ABC Algorithm. Turkish Journal of Electrical Engineering and Computer Science, 21, 1827-1841.
http://dx.doi.org/10.3906/elk-1203-10
[19] Sinha, A.K. and Mandal, J.K. (1999) Hierarchical Dynamic State Estimator Using ANN-Based Dynamic Load Prediction. IET Generation Transmission and Distribution, 146, 541-549. http://dx.doi.org/10.1049/ip-gtd:19990462
[20] Kumar, D.M.V. and Srivastava, S.C. (1999) Power System State Forecasting Using Artificial Neural Networks. Electric Machine and Power System, 27, 653-664. http://dx.doi.org/10.1080/073135699269091
[21] Lin, J.M., Huang, S.J. and Shih, K.R. (2003) Application of Sliding Surface Enhanced Fuzzy Control for Dynamic State Estimation of a Power System. IEEE Transaction Power System, 18, 570-577.
http://dx.doi.org/10.1109/TPWRS.2003.810894
[22] Bhuvaneswari, R., Subramanian, S. and Madhu, A. (2008) A Novel State Estimation Based on Minimum Errors between Measurements Using Ant Colony Optimization Technique. International Journal of Electric Engineering, 15, 457-568.
[23] Sakthivel, V.P., Bhuvaneswari, R. and Subramanian, S. (2010) Design Optimization of Three-Phase Energy efficient Induction Motor Using Adaptive Bacterial Foraging Algorithm. International Journal of Computation and Mathematics in Electrical and Electronics Engineering, 29, 699-726.
[24] Sakthivel, V.P., Bhuvaneswari, R. and Subramanian, S. (2010) Non-Intrusive Efficiency Estimation Method for Energy Auditing and Management of In-Service Induction Motor Using Bacterial Foraging Algorithm. IET Electric Power Applications, 4, 579-590.
http://dx.doi.org/10.1049/iet-epa.2009.0313
[25] Sakthivel, V.P., Bhuvaneswari, R. and Subramanian, S. (2011) An Accurate and Economical Approach for induction motor Field Efficiency Estimation Using Bacterial Foraging Algorithm. Measurement, 44, 674-684.
http://dx.doi.org/10.1016/j.measurement.2010.12.008
[26] Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011) Teaching-Learning-Based Optimization: A Novel Method for Mechanical Design Optimization Problems. Computer Aided Design, 43, 303-315.
http://dx.doi.org/10.1016/j.cad.2010.12.015
[27] Rao, R.V. and Waghmare, G.G. (2014) Complex Constrained Design Optimisation Using an Elitist Teaching-Learning-Based Optimisation. International Journal of Metaheuristics, 3, 81-102.
http://dx.doi.org/10.1504/IJMHEUR.2014.058863
[28] Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2012) Teaching-Learning-Based Optimization: An Optimization Method for Continuous Non-Linear Large Scale Problems. Information Science, 183, 1-15.
http://dx.doi.org/10.1016/j.ins.2011.08.006
[29] Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011) Teaching-Learning-Based Optimization: A Novel Method for Mechanical Design Optimization Problems. Computer-Aided Design, 43, 303-315.
http://dx.doi.org/10.1016/j.cad.2010.12.015
[30] Durai, S., Subramanian, S. and Ganesan, S. (2015) Improved Parameters for Economic Dispatch Problems by Teaching Learning Optimization. Electric Power and Energy System, 67, 11-24.
http://dx.doi.org/10.1016/j.ijepes.2014.11.010
[31] Chiang, C.-L. (2005) Improved Genetic Algorithm for Power Economic Dispatch of Units with Valve-Point Effects and Multiple Fuels. IEEE Transaction Power System, 20, 1690-1699.
http://dx.doi.org/10.1109/TPWRS.2005.857924
[32] Tamilnadu Electricity Board Statistics at a Glance (1999-2000), Compiled by Planning Wing of Tamilnadu Electricity Board, Chennai, India.

  
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