Improved parameters for economic dispatch problems by teaching learning optimization

Abstract The solution of Economic Dispatch (ED) problems mainly depends on the modelling of thermal generators. The physical variations such as aging and ambient temperature affect the modelling parameters and are unavoidable. As these parameters are the backbone of ED solution, the periodical estimation of these characteristics coefficients is necessary for accurate dispatch. The process is formulated as an error minimization problem and a nature inspired algorithm namely Teaching Learning Based Optimization (TLBO) is proposed as an estimator. This work provides a frame work for the computation of coefficients for quadratic and cubic cost functions, valve point loading, piece-wise quadratic cost and emission functions. The effectiveness of TLBO is demonstrated on 5 standard test systems and a practical Indian utility system, involving varying degree of complexity. TLBO yields better results than benchmark Least Error Square (LES) method and other evolutionary algorithms. The economic deviation is also tested with existing systems.

[1]  J. D. Glover,et al.  Improved cost functions for economic dispatch compensations (on power systems) , 1991 .

[2]  A. K. Al-Othman,et al.  Estimating the input―output parameters of thermal power plants using PSO , 2009 .

[3]  J. Duncan Glover,et al.  IMPROVED COST FUNCTIONS FOR ECONOMIC DISPATCH COMPUTATIONS , 1991 .

[4]  Young Hoon Lee,et al.  A new economic dispatch algorithm considering any higher order generation cost functions , 2001 .

[5]  Vedat Toğan,et al.  Design of planar steel frames using Teaching–Learning Based Optimization , 2012 .

[6]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[7]  Provas Kumar Roy,et al.  Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint , 2013 .

[8]  A. K. Al-Othman,et al.  Particle Swarm Optimization Based Approach for Estimating the Fuel-cost Function Parameters of Thermal Power Plants with Valve Loading Effects , 2009 .

[9]  Shyh-Jier Huang,et al.  Application of sliding surface-enhanced fuzzy control for dynamic state estimation of a power system , 2003 .

[10]  S. C. Srivastava,et al.  Power system state forecasting using artificial neural networks , 1999 .

[11]  R. Shoults,et al.  Optimal Estimation of Piece-Wise Linear Incremental Cost Curves for EDC , 1984, IEEE Transactions on Power Apparatus and Systems.

[12]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[13]  A. M. AL-Kandari,et al.  A genetic-based algorithm for optimal estimation of input–output curve parameters of thermal power plants , 2007 .

[14]  S. O. Navarro,et al.  The Use of Cubic Cost Curves in the Economic Loading of Electric Power Systems , 1965 .

[15]  Srikrishna Subramanian,et al.  Design optimization of three‐phase energy efficient induction motor using adaptive bacterial foraging algorithm , 2010 .

[16]  Tomonobu Senjyu,et al.  Optimization of economic load dispatch of higher order general cost polynomials and its sensitivity using modified particle swarm optimization , 2009 .

[17]  Srikrishna Subramanian,et al.  Non-intrusive efficiency estimation method for energy auditing and management of in-service induction motor using bacterial foraging algorithm , 2010 .

[18]  Rahmat-Allah Hooshmand,et al.  Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder–Mead algorithm , 2012 .

[19]  Sydulu Maheswarapu,et al.  An effective non-iterative “λ-logic based” algorithm for economic dispatch of generators with cubic fuel cost function , 2010 .

[20]  R. Venkata Rao,et al.  Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm , 2013, Eng. Appl. Artif. Intell..

[21]  M. El-Shibini,et al.  A novel technique to estimate the fuel cost functions for economic operation of power systems , 1989 .

[22]  M. El-hawary,et al.  Performance Evaluation of Parameter Estimation Algorithms for Economic Operation of Power Systems , 1982, IEEE Transactions on Power Apparatus and Systems.

[23]  J. K. Mandal,et al.  Hierarchical dynamic state estimator using ANN-based dynamic load prediction , 1999 .

[24]  G. S. Christensen,et al.  Optimization of the optimal coefficients of non-monotonically increasing incremental cost curves , 1991 .

[25]  Fred J. Taylor,et al.  Recursive estimation of incremental cost curves , 1977 .

[26]  Chao-Lung Chiang,et al.  Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels , 2005 .

[27]  R. Venkata Rao,et al.  Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm , 2013 .

[28]  Srikrishna Subramanian,et al.  An accurate and economical approach for induction motor field efficiency estimation using bacterial foraging algorithm , 2011 .

[29]  Yusuf Sönmez,et al.  Estimation of fuel cost curve parameters for thermal power plants using the ABC algorithm , 2013 .

[30]  Provas Kumar Roy,et al.  Optimal capacitor placement in radial distribution systems using teaching learning based optimization , 2014 .