Evaluation of Fixed Length Gene Genetic Programming (FLGGP) Method in Reservoir System Optimal Operation

Document Type : Research Paper

Authors

1 university of tehran

2 Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Abstract

This paper employs nonlinear programming (NLP), genetic algorithm (GA), and fixed length gene genetic programming (FLGGP) for the real-time operation of a three-reservoir system (Karoon4, Khersan1, and Karoon3 reservoirs) in which dependent and independent approaches are used to forecast the hydroelectric energy generated by the system. Unlike the forecast-independent approach, in the forecast-dependent approach, the value of release in each period depends on the reservoir in flow of the same period. Moreover, nonlinear decision rule (NLDR) curves are considered, and the total deficiency function as well as efficiency criteria are used to investigate the results of each procedure used. Finally, the performances of real-time operation of single- and three-reservoir systems are investigated and compared. Results indicate that the FLGGP gives the most efficient function for the extraction of reservoir operation rules in both the approaches examined. Comparison of the forecast-dependent and independent approaches revealed no significant differences. Therefore, the forecast-independent approach may be recommended for application in the extraction of reservoir operation rules due to its simplicity and ease of application.

Keywords

Main Subjects


1. Oliveira, R., and Loucks, D. P. (1997). “Operating rules for multi reservoir systems.” Water Resources Research, 33(4), 839-852.
2. Barros, M. T. L., Yang, S., Lopes, J. E. G., and Yeh, W. W-G. (2001). Large-scale hydropower system optimization, IAHR Publication No. 271: Integrated Water Resources Management, Wallingford, U.K.
3. Simonovic, S. P., and Marino, M. A. (1980). “Reliability programming in reservoir management. Single multipurpose reservoir.” Water Resource Research, 16(1), 844-888.
4. Tung, C., Hsu, S., Liu, C. M., and Li, Jr. Sh. (2003). “Application of the genetic algorithm for optimizing operation rules of the LiYuTan reservoir in Taiwan.” J. of American Water Resources Association, 39(3), 649-657.
5. Eberbach, E., and Burgin, M. (2009). “Evolutionary automata as foundation of evolutionary computation: Larry fogel was right.” IEEE Congress on Evolutionary Computation (CEC 2009), Trondheim, Norway, 2149-2156.
6. Hashimoto, T., Steninger, J. R., and Loucks, D. P. (1982). “Reliability, resiliency and vulnerability criteria for water resource system performance evaluation.” Water Resources Research, 18(1), 14-20.
7. Farhangi, M. (2010). “Effect of inflow uncertainty on the performance of multy-reservoir systems.” MSc. Thesis, Dept. of Eng. and Agricultural Tech., Tehran University, Tehran. (In Persian)
8. LINDO. (2004). LINGO user’s manual, LINDO System INC., <http://www.lindo.com/.>
9. Overman, E. (2011). A MATLAB tutorial, Department of Mathematics, The Ohio State University, Columbus, OH., USA.