Water Resources Management

, Volume 33, Issue 1, pp 207–228 | Cite as

The Cascade Reservoirs Multi-Objective Ecological Operation Optimization Considering Different Ecological Flow Demand

  • Zhe Yang
  • Kan YangEmail author
  • Hu Hu
  • Lyuwen Su


In order to coordinate the power generation and downstream ecological protection benefits,the optimal ecological scheduling of cascade reservoirs is pivotal. In current paper, an improved chaotic normal cloud shuffling frog leaping algorithm (CNSFLA) based on chaotic initialization, cloud model evolution strategy and heuristic frog activation mechanism is proposed to overcome defects of conventional SFLA. Moreover, the multi-objective ecological scheduling model for Qingjiang cascade reservoirs is established with consideration of basic, suitable and ideal ecological flow requirements in Geheyan and Gaobazhou control sections. Afterwards, the model established is applied to cascade reservoirs ecological scheduling in Qingjiang. The water level corridor and penalty function are used to handle constraints. The scheduling results for long series indicate that benefits of power generation and basic ecology flow requirement in downstream control sections are fulfilled completely. While for suitable and ideal ecology flow requirements, 98.33%, 99.17 and 86.33%, 88.17% guarantee rate corresponding to Geheyan and Gaobazhou control sections can be achieved during whole scheduling periods. In terms of typical dry year, the less inflow makes it hard to reach the ideal ecology flow requirement in control sections during several periods even though the scheduling by CNSFLA. The guarantee rates and mean monthly ecological flow shortage are 66.67%, 66.67% and 71, 36 m3/s, respectively. Finally, performance analysis of CNSFLA verifies its effective search ability with high quality & stability results. The cascade power generation obtained by CNSFLA in long series scheduling is 75.45(108 kW·h), corresponding guarantee rates of ideal ecological flow requirement are 86.33 and 88.17%.


Multi-objective ecological scheduling model Chaotic normal cloud shuffling frog leaping algorithm (CNSFLA) Chaotic initialization Cloud model evolution strategy Heuristic frog activation mechanism 



The research was funded by the National Key Basic Research Program of China (973 Program) (2012CB417006), the National Science Support Plan Project of China (2009BAC56B03).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that there is no conflict of interests.

Ethical Standard

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Cai WJ, Zhang LL, Zhu XP et al (2013) Optimal reservoir operation to balance human and environmental requirements: a case study for the three gorges and Gezhouba dams, Yangtze River basin, China. Ecol Inform 18:40–48Google Scholar
  2. Dong ZR, Sun DY, Zhao JY (2007) Multigoal ecological operation of reservoirs. Water Resources & Hydropower Engineering 38(1):28–32Google Scholar
  3. Eusuff MM, Lansey KE (2003) Optimization of water sistribution network design using the shuffled frog leaping algorithm[J]. J Water Resour Plan Manag 129(3):210–225CrossRefGoogle Scholar
  4. Feng ZK, Liao SL, Niu WJ, Shen JJ, Cheng CT, Li ZH (2015) Improved quantum-behaved particle swarm optimization and its application in optimal operation of hydropower stations[J]. Adv Water Sci 26(3):413–422Google Scholar
  5. Feng ZK, Niu WJ, Cheng CT, Wu XY (2016) Multi-reservoir operation using elite-gather social spider optimization[J]. J Hydraul Eng 47(6):826–833Google Scholar
  6. Field RC (2007) Multi-objective optimization of Folsom reservoir operation. University of California, SacramentoGoogle Scholar
  7. Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA, Mariño MA (2016) Application of the firefly algorithm to optimal operation of reservoirs with the purpose of Irrigation supply and hydropower production[J]. J Irrig Drain Eng 142(10) Article Number: 04016041 Published: OCT 2016.
  8. Harman C, Stewardson M (2005) Optimizing dam release rules to meet environment flow targets. River Res Appl 21(2–3):113–129CrossRefGoogle Scholar
  9. 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)[J]. Water Resour Manag 29(10):3749–3769CrossRefGoogle Scholar
  10. Jager HI, Smith BT (2008) Sustainable reservoir operation: can we generate hydropower and preserve ecosystem values. River Res Appl 24(3):340–352CrossRefGoogle Scholar
  11. Li DY, Meng HJ, Shi XM (1995) Membership clouds and membership cloud generators. J Comput Res Dev 32(6):15–20Google Scholar
  12. Li YH, Zhou JZ, Zhang YC, Qin H, Liu L (2010) Novel multiobjective shuffled frog leaping algorithm with application to reservoir flood control operation.[J]. J Water Resour Plan Manag 136(2):217–226CrossRefGoogle Scholar
  13. Lu YL, Zhou JZ, Wang H, Zhang YC (2011) Multi-objective optimization model for ecological operation in three gorges cascade hydropower stations and its algorithms. Adv Water Sci 22(06):780–788Google Scholar
  14. Luo JP, Li X, Chen MR, Liu HW (2015) A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows. Inf Sci 316:266–292CrossRefGoogle Scholar
  15. Lyu W, Wang H, Yin JX, Zhu XY (2016) On ecological operation of cascade hydropower stations along Wujiang River in Guizhou province. Adv Water Sci 27(6):918–927Google Scholar
  16. Ma Y, Tian WJ, Fan YY (2013) Quantum adaptive immune clone algorithm based on cloud model. Chin J Comput Phys 30(4):627–632Google Scholar
  17. Mei C, Yin MW, Li M (2017) Optimal operation research on Central Guizhou reservoir group considering different ecological flow constraint. China Rural Water and Hydropower 05:174–180Google Scholar
  18. Ming B, Huang Q, Wang YM, Liu DF, Bai T (2015) Cascade reservoir operation optimization based-on improved cuckoo search. J Hydraul Eng 46(03):341–349Google Scholar
  19. Moravej M, Hosseini-Moghari SM (2016) Large scale reservoirs system operation optimization: the interior search algorithm (ISA) approach[J]. Water Resour Manag 30(10):3389–3407CrossRefGoogle Scholar
  20. Richter BD, Thomas GA (2007) Restoring environmental flows by modifying dam operations. Ecol Soc 12(1):12CrossRefGoogle Scholar
  21. Saberchenari K, Abghari H, Tabari H (2016) Application of PSO algorithm in short-term optimization of reservoir operation[J]. Environ Monit Assess 188(12):667CrossRefGoogle Scholar
  22. Shiau JT, Wu FC (2007) A dynamic corridor-searching algorithm to seek time-varying instream flow releases for optimal weir operation: comparing three indices of overall hydrologic alteration. River Res Appl 23(1):35–53CrossRefGoogle Scholar
  23. Steinschneider S, Bernstein A, Palmer R (2014) Reservoir management optimization for basin-wide ecological restoration in the Connecticut River. J Water Resour Plan Manag 140(9):1–10CrossRefGoogle Scholar
  24. Suen JP, Eheart JW (2006) Reservoir management to balance ecosystem and human needs: incorporating the paraigm of the ecological flow regime. Water Resour Res 42(3):1–9CrossRefGoogle Scholar
  25. Sun DY, Dong ZR, Zhao JY (2007) Adaptive management methodologies in river restoration. Water Resources & Hydropower Engineering 38(2):57–59Google Scholar
  26. Sun P, Jiang ZQ, Wang TT, Zhang YK (2016) Research and application of parallel normal cloud mutation shuffled frog leaping algorithm in cascade reservoirs optimal operation. Water Resour Manag 30:1019–1035CrossRefGoogle Scholar
  27. Wang SH, Li YZ, Yang HY (2017) Self-adaptive differential evolution algorithm with improved mutation mode. Appl Intell 47(3):644–658CrossRefGoogle Scholar
  28. Xu G, Ma GW, Liang WH, Chen JC, Wu SY (2005) Application of ant colony algorithm to reservoir optimal operation. Adv Water Sci 16(03):397–400Google Scholar
  29. Zhang Q, Li PC (2015) Adaptive grouping chaotic cloud model shuffled frog leaping algorithm for continuous space optimization problems. Kongzhi Yu Juece/control & Decision 30(5):923–928Google Scholar
  30. Zhong PA, Zhang WG, Zhang YL, Zhao YF (2014) Comprehensive modified differential evolution algorithm for optimal operation of the hydropower station. J Hydraul Eng 46(10):1147–1155Google Scholar
  31. Zhou YQ (2005) Study on conjunctive operation of sanxia cascade reservoirs and qingjiang cascade reservoirs [D]. Wuhan University, WuhanGoogle Scholar
  32. Zou CR, Zhang XD, Zhao L (2012) Review of shuffled frog leaping algorithm. Electron Eng 38(5):1–5Google Scholar
  33. Zou Q, Wang XM, Li AQ, He XC, Luo B (2016) Optimal operation of flood control for cascade reservoirs based on parallel chaotic quantum particle swarm optimization. J Hydraul Eng 47(8):967–976Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.College of Hydrology and Water ResourcesHohai UniversityNanjingChina

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