Gencos wind–thermal scheduling problem using Artificial Immune System algorithm

Abstract This paper presents an Artificial Immune System approach for solving generation scheduling problem of a Genco comprised of thermal and wind energy systems. Wind–thermal scheduling problem determine the time of instant to start up and shut down the generating units over a scheduled time period, while satisfying the ‘system’ and ‘generator’ constraints including minimum up/down time; ramp rate limits of thermal units and wind power constraints. In this work, the impact of wind energy on short term generation scheduling problem is analyzed through the adaptive search which is inspired from the Artificial Immune System. The effectiveness of the proposed approach is demonstrated through a Genco consists of 10 thermal units with 2 wind farms and the results for the near optimal schedule are discussed.

[1]  M. Shahidehpour,et al.  Security-Constrained Unit Commitment With Volatile Wind Power Generation , 2008, IEEE Transactions on Power Systems.

[2]  Narayana Prasad Padhy,et al.  Unit commitment problem under deregulated environment-a review , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[3]  G. Strbac,et al.  Efficient Stochastic Scheduling for Simulation of Wind-Integrated Power Systems , 2012, IEEE Transactions on Power Systems.

[4]  Shyh-Jier Huang.  Enhancement of thermal unit commitment using immune algorithms based optimization approaches , 1999 .

[5]  Gwo-Ching Liao,et al.  Short-term thermal generation scheduling using improved immune algorithm , 2006 .

[6]  Loi Lei Lai,et al.  Power System Restructuring and Deregulation , 2001 .

[7]  Mehdi Vakilian,et al.  Stochastic unit commitment of wind farms integrated in power system , 2010 .

[8]  William D'haeseleer,et al.  Effect of the accuracy of price forecasting on profit in a Price Based Unit Commitment , 2010 .

[9]  Wei Li,et al.  A New Method Based on Immune Algorithm to Solve the Unit Commitment Problem , 2006, PROLAMAT.

[10]  Licheng Jiao,et al.  Immunodomaince based clonal selection clustering algorithm , 2010, IEEE Congress on Evolutionary Computation.

[11]  Bijay Ketan Panigrahi,et al.  Energy and spinning reserve scheduling for a wind-thermal power system using CMA-ES with mean learning technique , 2013 .

[12]  Sishaj P. Simon,et al.  Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs , 2012, Appl. Soft Comput..

[13]  Bagen.  Reliability and cost/worth evaluation of generating systems utilizing wind and solar energy , 2005 .

[14]  Ruey-Hsun Liang,et al.  A Fuzzy-Optimization Approach for Generation Scheduling With Wind and Solar Energy Systems , 2007, IEEE Transactions on Power Systems.

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

[16]  Wei Li,et al.  An immune-tabu hybrid algorithm for thermal unit commitment of electric power systems , 2009 .

[17]  Gerald B. Sheblé,et al.  A profit-based unit commitment GA for the competitive environment , 2000 .

[18]  S. M. Shahidehpour,et al.  A new approach for GenCos profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty , 2007 .

[19]  B. Vahidi,et al.  Bacterial Foraging-Based Solution to the Unit-Commitment Problem , 2009, IEEE Transactions on Power Systems.

[20]  J. Aghaei,et al.  Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty , 2012 .

[21]  P. Attaviriyanupap,et al.  A Hybrid LR-EP for Solving New Profit-Based UC Problem under Competitive Environment , 2002, IEEE Power Engineering Review.

[22]  Jamshid Aghaei,et al.  Stochastic techno-economic operation of power systems in the presence of distributed energy resources , 2013 .

[23]  Chun-Lung Chen,et al.  Optimal Wind–Thermal Generating Unit Commitment , 2008, IEEE Transactions on Energy Conversion.

[24]  D. P. Kothari,et al.  Solution to profit based unit commitment problem using particle swarm optimization , 2010, Appl. Soft Comput..

[25]  M. Bollen,et al.  Operation of restructured power systems , 2001 .

[26]  P. Sauer,et al.  Uncertainty Management in the Unit Commitment Problem , 2009, IEEE Transactions on Power Systems.

[27]  I. Erlich,et al.  A Stochastic Model for the Optimal Operation of a Wind-Thermal Power System , 2009, IEEE Transactions on Power Systems.

[28]  Mehdi Vakilian,et al.  Interval type-2 fuzzy modeling of wind power generation in Genco's generation scheduling , 2011 .

[29]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[30]  W.L. Kling,et al.  Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.

[31]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[32]  M. O'Malley,et al.  Unit Commitment for Systems With Significant Wind Penetration , 2009, IEEE Transactions on Power Systems.

[33]  Jorge Valenzuela,et al.  Making unit commitment decisions when electricity is traded at spot market prices , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[34]  Mehdi Vakilian,et al.  Electricity generation scheduling with large-scale wind farms using particle swarm optimization , 2009 .

[35]  Xiao Zhi Gao,et al.  Artificial immune optimization methods and applications - a survey , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[36]  Taher Niknam,et al.  Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties , 2013 .

[37]  M. Hosseini-Firouz.  Optimal offering strategy considering the risk management for wind power producers in electricity market , 2013 .

[38]  H. Monsef,et al.  A new approach for profit-based unit commitment using Lagrangian relaxation combined with ant colony search algorithm , 2008, 2008 43rd International Universities Power Engineering Conference.

[39]  Jamshid Aghaei,et al.  Mixed integer programming of multiobjective hydro-thermal self scheduling , 2012, Appl. Soft Comput..