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Large Scale Reservoirs System Operation Optimization: the Interior Search Algorithm (ISA) Approach

Abstract

Reservoirs are built to provide a powerful tool to control and manage surface water resources in order to cover inconsistency between water resources and demands. Due to finite available water and the increasing demands for water especially in arid and semi-arid regions like Iran, reservoirs must be optimally operated in order to use water in the most efficient way. This study applies the Interior Search Algorithm (ISA) to solve large scale reservoirs system operation optimization problems. The ISA is a meta-heuristic algorithm inspired from a systematic methodology of architecture process and mirror work utilized by Persian designers for decoration. Unlike other meta-heuristic algorithms, the ISA just have one parameter to tune which is a great advantage. In this study the parameter of the ISA tuned automatically using a linear equation. A real-world one-reservoir operation problem (i.e. Karun-4) and two large scale benchmark problems (i.e. four-reservoir and ten-reservoir operation problem) were employed to show the effectiveness of the ISA. The results shows the high ability of the ISA to solve reservoirs system operation problems as it achieved solutions 99.97, 99.99 and 99.95 % of global optimum for Karun-4 reservoir, four-reservoir and ten-reservoir system operation problems, respectively. These results are the best results reported so far in the studied problems. Comparing results of the ISA with those of non-linear programming (NLP), linear programming (LP), genetic algorithm (GA) and other meta-heuristic algorithms indicates fast convergence to global optimum. Considering the results, it can be stated that the ISA is a powerful tool to optimize complex large scale reservoir system operation problems.

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References

  1. Afshar A, Massoumi F, Afshar A, Mariño MA (2015) State of the art review of ant colony optimization applications in water resource management. Water Resour Manag 1–14. doi:10.1007/s11269-015-1016-9

  2. Ahmad A, El-Shafie A, Razali SFM, Mohamad ZS (2014) Reservoir optimization in water resources: a review. Water Resour Manag 28(11):3391–3405

  3. Arunkumar R, Jothiprakash V (2012) Optimal reservoir operation for hydropower generation using non-linear programming model. J Inst Eng India Ser 93(2):111–120

  4. Asgari HR, Bozorg Haddad O, Pazoki M, Loáiciga HA (2015) Weed optimization algorithm for optimal reservoir operation. J Irrig Drain Eng. doi:10.1061/(ASCE)IR.1943-4774.000096

  5. Bashiri-Atrabi H, Qaderi K, Rheinheimer DE, Sharifi E (2015) Application of harmony search algorithm to reservoir operation optimization. Water Resour Manag 29(15):5729–5748

  6. Blanchin F, Ukovich W (1993) Linear programming approach to the control of discrete-time periodic systems with uncertain inputs. J Optim Theory Appl 78(3):523–539

  7. Bozorg-Haddad O, Afshar A, Mariño MA (2010) Multireservoir optimisation in discrete and continuous domains. Proc ICE Water Manag 164(2):57–72

  8. Bozorg-Haddad O, Karimirad I, Seifollahi-Aghmiuni S, Loáiciga HA (2014) Development and application of the bat algorithm for optimizing the operation of reservoir systems. J Water Resour Plan Manag. doi:10.1061/(ASCE)WR.1943-5452.0000498

  9. Bozorg-Haddad O, Hosseini-Moghari SM, Loáiciga HA (2015) Biogeography-based optimization algorithm for optimal operation of reservoir systems. J Water Resour Plan Manag. doi:10.1061/(ASCE)WR.1943-5452.0000558

  10. Celeste AB, Billib M (2009) Evaluation of stochastic reservoir operation optimization models. Adv Water Resour 32(9):1429–1443

  11. Chetty S, Adewumi AO (2014) Comparison study of swarm intelligence techniques for the annual crop planning problem. IEEE Trans Evol Comput 18(2):258–268

  12. Cho H, Olivera F (2012) Application of multimodal optimization for uncertainty estimation of computationally expensive hydrologic models. J Water Resour Plan Manag 140(3):313–321

  13. Chow VT, Cortes-Rivera G (1974) Application of DDDP in water resources planning. University of Illinois at Urbana-Champaign, Water Resources Center

  14. Dariane AB, Sarani S (2013) Application of intelligent water drops algorithm in reservoir operation. Water Resour Manag 27(14):4827–4843

  15. Davidsen C, Pereira-Cardenal SJ, Liu S, Mo X, Rosbjerg D, Bauer-Gottwein P (2014). Using stochastic dynamic programming to support water resources management in the Ziya River Basin, China. J Water Resour Plan Manag 141(7). doi:10.1061/(ASCE)WR.1943-5452.0000482

  16. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183

  17. Gandomi AH, Roke D (2014) Engineering optimization using interior search algorithm. In: Swarm Intelligence (SIS), 2014 I.E. Symposium on (pp 1–7). IEEE.

  18. Gaur S, Srinivasa Raju K, Kumar DN, Graillot D (2015) Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method. Hydrol Process. doi:10.1002/hyp.10441

  19. Gil C, Baños R, Ortega J, Márquez AL, Fernández A, Montoya MG (2011) Ant colony optimization for water distribution network design: a comparative study. In: Advances in computational intelligence. Springer, Berlin, pp 300–307

  20. Haddad OB, Moravej M, Loáiciga HA (2014) Application of the water cycle algorithm to the optimal operation of reservoir systems. J Irrig Drain Eng 141(5):04014064. doi:10.1061/(ASCE)IR.1943-4774.0000832

  21. Hashemi SS, Tabesh M, Ataeekia B (2014) Ant-colony optimization of pumping schedule to minimize the energy cost using variable-speed pumps in water distribution networks. Urban Water J 11(5):335–347

  22. Hossain MS, El-shafie A (2013) Intelligent systems in optimizing reservoir operation policy: a review. Water Resour Manag 27(9):3387–3407

  23. Hosseini-Moghari SM, Banihabib ME (2014) Optimizing operation of reservoir for agricultural water supply using firefly algorithm. J Soil Water Resour Conserv 3(4):17–31

  24. Hosseini-Moghari SM, Morovati R, Moghadas M, Araghinejad S (2015) Optimum operation of reservoir using two evolutionary algorithms: Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA). Water Resour Manag 29(10):3749–3769. doi:10.1007/s11269-015-1027-6

  25. Jalali MR, Afshar A, Marino MA (2007) Multi-colony ant algorithm for continuous multi-reservoir operation optimization problem. Water Resour Manag 21(9):1429–1447

  26. Jha MK, Sahoo S (2015) Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater. Hydrol Process 29(5):671–691

  27. Jothiprakash V, Shanthi G (2006) Single reservoir operating policies using genetic algorithm. Water Resour Manag 20(6):917–929

  28. Kaini P, Artita K, Nicklow JW (2012) Optimizing structural best management practices using SWAT and genetic algorithm to improve water quality goals. Water Resour Manag 26(7):1827–1845

  29. Karamouz M, Houck MH (1987) Comparison of stochastic and deterministic dynamic programming for reservoir operating rule generation. Water Resour Bull 23(1):1–9

  30. Kumar DN, Reddy MJ (2006) Ant colony optimization for multipurpose reservoir operation. Water Resour Manag 20(6):879–898

  31. Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Resour Plan Manag 130(2):93–111

  32. Loucks DP, Van Beek E, Stedinger JR, Dijkman JP, Villars MT (2005) Water resources systems planning and management: an introduction to methods, models and applications. UNESCO, Paris

  33. McPhee J, Yeh WWG (2004) Multiobjective optimization for sustainable groundwater management in semiarid regions. J Water Resour Plan Manag 130(6):490–497

  34. Mendes L, de Barros M, Zambon R, Yeh W (2015) Trade-off analysis among multiple water uses in a hydropower system: case of São Francisco River basin, Brazil. J Water Resour Plan Manag. doi:10.1061/(ASCE)WR.1943-5452.0000527, 04015014

  35. Ming B, Chang JX, Huang Q, Wang YM, Huang SZ (2015) Optimal operation of multi-reservoir system based-on cuckoo search algorithm. Water Resour Manag 29(15):5671–5687

  36. Mora-Melia D, Iglesias-Rey PL, Martinez-Solano FJ, Ballesteros-Perez P (2015) Efficiency of evolutionary algorithms in water network pipe sizing. Water Resour Manag 29(13):4817–4831

  37. Mousavi SJ, Ponnambalam K, Karray F (2005) Reservoir operation using a dynamic programming fuzzy rule–based approach. Water Resour Manag 19(5):655–672

  38. Murray DM, Yakowitz SJ (1979) Constrained differential dynamic programming and its application to multireservoir control. Water Resour Res 15(5):1017–1027

  39. Odan F, Ribeiro Reis L, Kapelan Z (2015). Real-time multiobjective optimization of operation of water supply systems. J Water Resour Plan Manag. doi:10.1061/(ASCE)WR.1943-5452.0000515, 04015011

  40. Ponnambalam K, Vannelli A, Unny TE (1989) An application of Karmarkar’s interior-point linear programming algorithm for multi-reservoir operations optimization. Stoch Hydrol Hydraul 3(1):17–29

  41. Rani D, Moreira MM (2010) Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24(6):1107–1138

  42. Reshma T, Reddy KV, Pratap D, Ahmedi M, Agilan V (2015) Optimization of calibration parameters for an event based watershed model using genetic algorithm. Water Resour Manag 29(13):4589–4606

  43. Revelle C, Joeres E, Kirby W (1969) The linear decision rule in reservoir management and design: 1. Development of the stochastic model. Water Resour Res 5(4):767–777

  44. Schardong A, Simonovic S (2015) Coupled self-adaptive multiobjective differential evolution and network flow algorithm approach for optimal reservoir operation. J Water Resour Plan Manag. doi:10.1061/(ASCE)WR.1943-5452.0000525, 04015015

  45. Sharif M, Swamy VSV (2014) Development of LINGO-based optimisation model for multi-reservoir systems operation. Int J Hydrol Sci Technol 4(2):126–138

  46. Skardi MJE, Afshar A, Saadatpour M, Solis SS (2015) Hybrid ACO–ANN-based multi-objective simulation–optimization model for pollutant load control at basin scale. Environ Model Assess 20(1):29–39

  47. Szemis JM, Maier HR, Dandy GC (2014) An adaptive ant colony optimization framework for scheduling environmental flow management alternatives under varied environmental water availability conditions. Water Resour Res 50(10):7606–7625

  48. Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resour Plan Manag 125(1):25–33

  49. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

  50. Yeh WWG (1985) Reservoir management and operations models: a state-of-the-art review. Water Resour Res 21(12):1797–1818

  51. Zahraie B, Hosseini SM (2009) Development of reservoir operation policies considering variable agricultural water demands. Expert Syst Appl 36(3):4980–4987

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Correspondence to Mojtaba Moravej.

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Moravej, M., Hosseini-Moghari, S. Large Scale Reservoirs System Operation Optimization: the Interior Search Algorithm (ISA) Approach. Water Resour Manage 30, 3389–3407 (2016). https://doi.org/10.1007/s11269-016-1358-y

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Keywords

  • Interior Search Algorithm
  • Reservoir operation
  • Karun-4 reservoir
  • Optimization
  • Four-reservoir system
  • Ten-reservoir system