Enhancing Cellular Automata via Tabu Search for Optimal Operation of Hydropower Systems

Document Type : Research Paper

Authors

1 Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.

Abstract

One of the environmentally-friendly solutions to meeting energy consumption is multi-reservoir hydropower systems. The operation of a multi-reservoir hydropower system is an entirely complex problem due to a wide range of decision variables. Classic algorithms often get stuck in the local optimum and cannot successfully address these problems. Modern algorithms are more effective than classic ones, although their computational time is very high. In this study, an innovative hybrid model is proposed, called cellular automata-tabu search (CA-TS) to optimally operate multi-reservoir systems. For simplification, CA divides the problem into several sub-problems, which the number of them is the same as the length of operation period. Each sub-problem is solved by TS so that the net benefit of the power generation is maximized. For comparison purpose, a non-linear four-reservoir benchmark problem is considered to evaluate the proposed method. Finally, the results are compared with the existing results obtained by GA, PSO, and CA-NLP, showing the efficiency of CA-TS.

Keywords

References

  1. Ellis, J. H., & ReVelle, C. S. (1988). A SEPARABLE LINEAR ALGORITHM FOR HYDROPOWER OPTIMIZATION1.
  2. Yoo, J. H. (2009). Maximization of hydropower generation through the application of a linear programming model. Journal of Hydrology, 376(1), 182-187.
  3. Zambelli, M. S., Luna, I., & Soares, S. (2009). Long-Term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models. In PowerTech, 2009 IEEE Bucharest (pp. 1-8). IEEE.
  4. Arunkumar, R., & Jothiprakash, V. (2012). Optimal reservoir operation for hydropower generation using non-linear programming model. Journal of the Institution of Engineers (India): Series A, 93(2), 111-120.
  5. SHEN, J., CHENG, C., CHENG, X., & WU, X. (2014). A hybrid nonlinear optimization method for operation of large-scale cascaded hydropower plants. Scientia Sinica (Technologica), 3, 010.
  6. Allen, R. B., & Bridgeman, S. G. (1986). Dynamic programming in hydropower scheduling. Journal of Water Resources Planning and Management, 112(3), 339-353.
  7. Labadie, J. W. (2004). Optimal operation of multireservoir systems: state-of-the-art review. Journal of water resources planning and management, 130(2), 93-111.
  8. Yurtal, R., Seckin, G., & Ardiclioglu, G. (2005). Hydropower optimization for the lower Seyhan system in Turkey using dynamic programming. Water international, 30(4), 522-529.
  9. Zhao, T., Zhao, J., & Yang, D. (2012). Improved dynamic programming for hydropower reservoir operation. Journal of Water Resources Planning and Management, 140(3), 365-374.
  10. Li, Y., & Zuo, J. (2012). Optimal scheduling of cascade hydropower system using grouping differential evolution algorithm. In Computer Science and Electronics Engineering (ICCSEE), 2012 International
  11. Zhang, Z., Zhang, S., Wang, Y., Jiang, Y., & Wang, H. (2013). Use of parallel deterministic dynamic programming and hierarchical adaptive genetic algorithm for reservoir operation optimization. Computers & Industrial Engineering, 65(2), 310-321.
  12. Oliveira, R., & Loucks, D. P. (1997). Operating rules for multireservoir systems. Water resources research, 33(4), 839-852.
  13. Wardlaw, R., & Sharif, M. (1999). Evaluation of genetic algorithms for optimal reservoir system operation. Journal of water resources planning and management, 125(1), 25-33.
  14. Chen Li (2003). Real coded genetic algorithm optimization of long term reservoir operation1. Journal of the American Water Resource Association, 39(5), 1157-1165.
  15. Cinar, D., Kayakutlu, G., & Daim, T. (2010). Development of future energy scenarios with intelligent algorithms: case of hydro in Turkey. Energy, 35(4), 1724-1729.
  16. Tayebiyan, A., Ali, T. A. M., Ghazali, A. H., & Malek, M. A. (2016). Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm. Water Resources Management, 1-14.
  17. Kumar, D. N., & Reddy, M. J. (2006). Ant colony optimization for multi-purpose reservoir operation. Water Resources Management, 20(6), 879-898.
  18. Jalali, M. R., Afshar, A., & Marino, M. A. (2007). Reservoir operation by ant colony optimization algorithms. Iranian Journal of Science and Technology, Transaction B: Engineering, 30(B1), 107-117.
  19. Moeini, R., & Afshar, M. H. (2013). Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems. J Hydroinformatics, 15(1), 155-173.
  20. Cheng, C. T., Liao, S. L., Tang, Z. T., & Zhao, M. Y. (2009). Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch. Energy Conversion and Management, 50(12), 3007-3014.
  21. Afshar, M. H. (2013). A cellular automata approach for the hydro-power operation of multi-reservoir systems. Proceedings of the Institution of Civil Engineers–Water Management (Vol. 166, No. 9, pp. 465-478).
  22. Kiruthiga, D., & Amudha, T. (2016). Optimal Reservoir Release for Hydropower Generation Maximization Using Particle Swarm Optimization. Innovations in Bio-Inspired Computing and Applications (pp. 577-585). Springer International Publishing.
  23. Zhang, X., Yu, X., & Qin, H. (2016). Optimal operation of multi-reservoir hydropower systems using enhanced comprehensive learning particle swarm optimization. Journal of Hydro-environment Research, 10, 50-63.
  24. Teegavarapu, R. S., & Simonovic, S. P. (2002). Optimal operation of reservoir systems using simulated annealing. Water Resources Management, 16(5), 401-428.
  25. Tospornsampan, J., Kita, I., Ishii, M., & Kitamura, Y. (2005). Optimization of a multiple reservoir system using a simulated annealing--A case study in the Mae Klong system, Thailand. Paddy and Water Environment, 3(3), 137-147.
  26. Kangrang, A., Compliew, S., & Hormwichian, R. (2010). Optimal reservoir rule curves using simulated annealing. Proceedings of the ICE-Water Management, 164(1), 27-34.
  27. Afshar, A., Haddad, O. B., Mariño, M. A., & Adams, B. J. (2007). Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. Journal of the Franklin Institute, 344(5), 452-462.
  28. Niknam, T., Taheri, S. I., Aghaei, J., Tabatabaei, S., & Nayeripour, M. (2011). A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Applied Energy, 88(12), 4817-4830.
  29. Lu, Y., Zhou, J., Qin, H., Wang, Y., & Zhang, Y. (2010). An adaptive chaotic differential evolution for the short-term hydrothermal generation scheduling problem. Energy Conversion and Management, 51(7), 1481-1490.
  30. Li, Y., & Zuo, J. (2012). Optimal scheduling of cascade hydropower system using grouping differential evolution algorithm. In Computer Science and Electronics Engineering (ICCSEE), 2012 International
  31. Asgari, H. R., Bozorg Haddad, O., Pazoki, M., & Loáiciga, H. A. (2015). Weed Optimization Algorithm for Optimal Reservoir Operation. Journal of Irrigation and Drainage Engineering, 04015055.
  32. Azizipour, M., Ghalenoei, V., Afshar, M. H., & Solis, S. S. (2016). Optimal operation of hydropower reservoir systems using weed optimization algorithm. Water Resources Management, 30(11), 3995-4009.
  33. Afshar, M. H., & Shahidi, M. (2009). Optimal solution of large-scale reservoir-operation problems: Cellular-automata versus heuristic-search methods. Engineering Optimization, 41(3), 275-293.
  34. Afshar, M. H., & Azizipour, M. (2016, September). Chance-constrained water supply operation of reservoirs using cellular automata. In International Conference on Cellular Automata (pp. 201-209). Springer, Cham.
  35. Azizipour, M., & Afshar, M. H. (2017). Adaptive Hybrid Genetic Algorithm and Cellular Automata Method for Reliability-Based Reservoir Operation. Journal of Water Resources Planning and Management, 143(8), 04017046.
  36. Azizipour, M., & Afshar, M. H. (2018). Reliability-based operation of reservoirs: a hybrid genetic algorithm and cellular automata method. Soft Computing, 22(19), 6461-6471.
  37. Glover, F (1989). "Tabu Search – Part 1". ORSA Journal on Computing. 1 (2): 190–206
  38. Laguna, M., A guide to implementing tabu search. Investigación Operativa, 4(1), 5-25 (1994).
  39. Al-Sultan, K.S. and C.A. Fedjki, A tabu searchbasedalgorithm for the fuzzy clustering problem. Pattern Recogn., 30(12), 2023~2030 (1997).
  40. Chow, V., Cortes-Rivera, G., & No, A. (1974). Application of DDDP in water resources planning. University of Illinois at Urbana-Champaign, Water Resources Center.
Journal of Hydraulic Structures
Volume 6, Issue 3 - Serial Number 13
September 2020
Pages 83-91

Files

Share

How to cite

Statistics