A hybrid Mathematical Programming-Fuzzy Expert Systems Approach for Improving Combined Cycle Power Plants’ Operations

Document Type : Research Paper

Authors

Department of Industrial Engineering, University of Tehran, Iran

Abstract

By developing the level of life in societies, the need for new resources of energy becomes more important. In almost all cases, the aim is to convert one form of energy to the electrical form. With advances of technology and use of new electric and electronic devices and gadgets, the need for electrical energy is increasing and providing stable and continuous electric power becomes very important and urgent. The most common place to convert other forms of energy to electric energy is power plant. To maintain the stability and continuity of delivering the electric energy to costumers, the operation and control of the plant is great important. Advances in power plants’ technology have resulted in more reliance on human (i.e. expert-based) decision making. In this way, a mathematical programming-based decision support system could provide a suitable support for human decision making in optimizing power plant operations. In this study, a new approach is presented for optimizing the load distribution among units in a combined cycle (Gas and Steam Turbine) power plant. In the normal operations status, two parameters, i.e. the efficiency and risk are the most important factors for load distribution. Power is demanded by the control center (national electric grid control) and this needed power is often equally distributed by the control center, power market and operators between units. However, this distribution scheme is not economic in terms of efficiency and risk. In the suggested method, online data from units after fuzzification are fed into a fuzzy expert system and according to the unit conditions, two defuzzified scales, i.e. the efficiency scale and risk scale, are calculated for each unit. These scales are then used as coefficients in the proposed bi-objective mathematical programming model by which the best possible load distribution scheme is obtained according to the demanded power and the efficiency and risk of current units which is then displayed to the operator to set in the load control system. Finally, the efficiency and effectiveness of the proposed method is shown by a real case study.

Keywords

Main Subjects

References

1. Ghazanfary, M. (2011). “Expert Systems Fundamentals.” 3rd Edition.
2.  Negnevitsky, M. (2011). “Artificial Intelligence: A Guide to Intelligent Systems.” 3rd Edition.
3. Little, J.D.C. (1970). “Models and Managers: The Concept of a Decision Calculus.” ManagementScience, Vol. 16, No. 8.
4. Bonczek, R.H., Holsapple, C.W. and Whinston, A.B. (1980).“The Evolving Roles of Models in Decision Support Systems.” DecisionSciences, Vol. 11, No. 2.
5. Turban, Efraim, “Decision Support Systems And Intelligent Systems.” 7th Ed.
6. Pargar, F. (2011). “Developing Decision Support Systems in Excel.” 1st Ed.
7. Matthias Ehrgott, (2005). “Multicriteria Optimization.”  Springer, 2nd Ed.
8. George Mavrotas, (2009). “Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems.” AppliedMathematicsandComputation, 213.
9. Chankong, V. and  Haimes, Y.Y. (1983). “Multiobjective Decision Making: Theory and Methodology.” North-Holland, New York.
10. Cohon, J.L. (1978). “Multiobjective Programming and Planning.” Academic Press, New York.
11. Esmaili, M., Amjady, N. and  Shayanfar, H. A. (2011). “Multi-objective congestion management by modified augmented ε -constraint method.” AppliedEnergy, 88.
12.  Wood, AJ. and Wollenberg, BF. (1996). “Power generation, operation and control.” 2nd ed. Wiley, New York.
13. Lowery, P. G. (1966). “Generating unit commitment by dynamic programming.” IEEETransactionsonPowerApparatusandSystems, PAS-85(5):422-426.
14. Barry Rountree, (2011). “A Linear Programming Formulation of the Unit Commitment Problem.”
15. Sheble, G. B. and Fahd, G. N. (1994). “Unit Commitment Literature Synopsis.” IEEETrans. onPowerSystems, Vol. 9, No. 1, PP. 128-‌135.
16. Alice E. Smith, Jorge Valenzuela, (2000). “A Seeded Memetic Algorithm for Large Unit Commitment Problems.” JournalofHeuristics.
17. Lei Wu, Mohammad Shahidehpour and Tao Li, (2007). “Stochastic Security-Constrained Unit Commitment.”
18. Adrian Tică, Hervé Guéguen, Didier Dumur, Damien Faille, Frans Davelaar, (2012). “Design of a combined cycle power plant model for optimization.” AppliedEnergy, Vol. 98.
19. Tzolakis, G., Papanikolaou, P., Kolokotronis, D., Samaras, N., Tourlidakis, A. and Tomboulides, A. (2012). “ Simulation of a coal-fired power plant using mathematical programming algorithms in order to optimize its efficiency.” AppliedThermalEngineering, Vol. 48.
20. Joshua Clarke, Laura McLay, James T. McLeskey Jr., (2014). “Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant.” AdvancedEngineeringInformatics, Vol. 28, Issue 1.
21. Ganjehkaviri, A., Mohd Jaafar, M.N. and Hosseini, S.E. (2015). “Optimization and the effect of steam turbine outlet quality on the output power of a combined cycle power plant.” EnergyConversionandManagement, Vol. 89.
22. Hajabdollahi, F., Hajabdollahi, Z. and Hajabdollahi, H. (2012). “Soft computing based multi-objective optimization of steam cycle power plant using NSGA-II and ANN.” AppliedSoftComputing, Vol. 12, Issue 11.
Advances in Industrial Engineering
Volume 49, Issue 2 - Serial Number 2
پاییز و زمستان
October 2015
Pages 165-176

Files

Share

How to cite

Statistics