IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지

Development of Operating Guidelines of a Multi-reservoir System Using an Artificial Neural Network Model

인공 신경망 모형을 활용한 저수지 군의 연계운영 기준 수립

  • Na, Mi-Suk (Department of Technology Strategy, Korea Institute for Advancement of Technology) ;
  • Kim, Jae-Hee (Division of Business Administration, Chonbuk National University) ;
  • Kim, Sheung-Kown (Graduate School of Information Management and Security, Korea University)
  • 나미숙 (한국산업기술진흥원 기술전략본부) ;
  • 김재희 (전북대학교 경영학부) ;
  • 김승권 (고려대학교 정보경영공학전문대학원)
  • Received : 2009.11.18
  • Accepted : 2010.09.19
  • Published : 2010.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.

Keywords

References

  1. Bhaskar, N. R. and Whitlatch, E. E. (1980), Derivation of Monthly Reservoir Release Policies, Water Resources Research, 16(6), 987-993. https://doi.org/10.1029/WR016i006p00987
  2. Chandramouli, V. and Raman, H. (2001), Multireservoir Modeling with Dynamic Programming and Neural Networks, Journal of Water Resources Planning and Management, 127(2), 89-98. https://doi.org/10.1061/(ASCE)0733-9496(2001)127:2(89)
  3. Clark, E. J. (1950), New York Control Curves, Journal of American Water Works Association, 42(9), 823-827.
  4. Govindaraju, R. S. (2000a), Artificial Neural Networks in Hydrology, I : Preliminary Concepts, Journal of Hydrologic Engineering, 5(2), 115-123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115)
  5. Govindaraju, R. S. (2000b), Artificial Neural Networks in Hydrology, II : Hydrologic Applications, Journal of Hydrologic Engineering, 5(2), 124-137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
  6. Haykin, S. (1994), Neural Networks : a Comprehensive Foundation, Macmillan College Publishing Company, New York.
  7. Karamouz, M. and Houck, H. (1982), Annual and Monthly Reservoir Operating Rules, Water Resources Research, 18(5), 1337-1344. https://doi.org/10.1029/WR018i005p01337
  8. Karamouz, M., Houck, M., and Delleur, J. (1992), Optimization and Simulation of Multiple Reservoir Systems, Journal of Water Resources Planning and Management, 118(1), 71-81. https://doi.org/10.1061/(ASCE)0733-9496(1992)118:1(71)
  9. Kim, J. H., Kim, S. K., and Park, Y. J. (2003), Utilization of a Mathematical Programming Data Structure for the Implementation of a Water Resources Planning System, IE Interfaces, 16(4), 485-495.
  10. Kim, S. K. (1988), Methodologies for Reservoir Operation (Bibliographical Review), Journal of Korea Association of Hydrological Sciences, 21(1), 16-24.
  11. Kim, S. K. (2006), Utilizations of Optimization Models in Water Resources Planning and Management, Magazine of Korea Water Resources Association: Water for Future, 39(9), 83-94.
  12. Kim, S. K., Lee, Y. D., Kim, J. H., and Ko, I. H. (2005), A Multiple Objective Mathematical Model for Daily Coordinated Multi-reservoir Operation, Water Science and Technology : water supply, 5(3), 81-88.
  13. Kim, S. K. and Park, Y. J. (1998), A Mathematical Model for Coordinated Multi-reservoir Operation, Journal of Korea Water Resources Association, 31(6), 777-791.
  14. K-water(Korea Water Resources Corporation), A Report for Development of Real-time Multi-reservoir Operating System (Mathematical Modeling Part), 2008.
  15. Lee, Y. D., Kim, S. K., and Ko, I. H. (2006), Two-Stage Stochastic Linear Programming Model for Coordinated Multi-Reservoir Operation, The 2006 Operations Management Conference about Operating Reservoirs in Changing Conditions sponsored by EWRI of ASCE, in Sacramento, California.
  16. Lee, Y. D., Kim, S. K., and Ko, I. H. (2008), Multistage Stochastic Linear Programming Model for Daily Coordinated Multi-reservoir Operation, Journal of Hydroinformatics, 10(1), 23-41. https://doi.org/10.2166/hydro.2008.007
  17. Maass, A., Hufschmidt, M. M., Dorfman, R., Thomas Jr., H. A., Marglin, S. A., and Fair, G. M. (1962), Design of Water Resources Systems, Harvard University Press, Cambridge, Mass., U.S.A.
  18. Moon, S. I. (1998), Comparison of Multiple Regression with Neural Network Model to Derive Optimal Operating Policies for Water Resources Management Systems, Master Thesis, Korea University
  19. Raman, H. and Chandramouli, V. (1996), Deriving a General Operating Policy for Reservoirs using Neural Network, Journal of Water Resources Planning and Management, 122(5), 342-347. https://doi.org/10.1061/(ASCE)0733-9496(1996)122:5(342)
  20. ReVelle, C. (2000), Research Challenges in Environmental Management, European Journal of Operational Research, 121(2), 218-231. https://doi.org/10.1016/S0377-2217(99)00213-1
  21. Rumelhart, D. E., and Mcclelland, J. L. (1987), Parallel Distributed Processing : Explorations in the Microstructure of Cognition, MIT Press, Cambridge, Mass., I and II.
  22. Shih, J. S. and ReVelle, C. (1995), Water Supply Operations during Drought : A Discrete Hedging Rule, European Journal of Operational Research, 82(1), 163-175. https://doi.org/10.1016/0377-2217(93)E0237-R
  23. Sveinsson, O. G. B., Salas, J. D., Lane, W. L., and Frevert, D. K. (2007), Stochastic Analysis, Modeling, and Simulation (SAMS), Technical Report, (11), Computing Hydrology Laboratory, Colorado State University, Colorado.