Advertisement

Multi-objective optimized scheduling model for hydropower reservoir based on improved particle swarm optimization algorithm

  • Ruiming FangEmail author
  • Zouthi Popole
Sustainable development of energy, water and environment systems
  • 13 Downloads

Abstract

In order to make hydropower station’s development and operation harmonious with ecological protection, the optimal operation of hydropower stations to meet the needs of ecological protection is studied. Firstly, the ecological protection function of river course is defined according to the minimum ecological runoff and suitable ecological runoff. Then, a multi-objective optimal running model of reservoir which can maximize the capacity of ecological protection and generation is proposed. Finally, an improved multi-objective particle swarm optimization algorithm (MOPSO), which can construct a neighborhood for each particle and choose the neighborhood optimal solution by adopting self-organizing mapping (SOM) method, is proposed to solve the model. The model is applied to the Shui-Kou Hydropower Station in Minjiang, China. The results show that the model can get the optimal schedule with balanced consideration of ecological benefits and power generation benefits, which has not a great impact on the economic benefits of reservoirs while achieving the goal of ecological environment. The research results can provide theoretical basis and concrete scheme reference for reservoir operation.

Keywords

Hydropower reservoir Multi-objective optimized scheduling Improved particle swarm optimization algorithm Ecological protection 

Notes

References

  1. Castelletti A, Pianosi F, Soncini-Sessa R (2008) Water reservoir control under economic, social and environmental constraints. Automatica 44(6):1595–1607Google Scholar
  2. Chen L, Chang FJ (2010) Applying a real-coded multi-population genetic algorithm to multi-reservoir operation. Hydrol Process 21(5):688–698Google Scholar
  3. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279Google Scholar
  4. Davidsen C, Liu S, Mo X, Holm PE, Trapp S, Dan R et al (2015) Hydro-economic optimization of reservoir management under downstream water quality constraints. J Hydrol 529(529):1679–1689Google Scholar
  5. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197Google Scholar
  6. Feng ZK, Niu WJ, Cheng CT (2018) Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm. ENERGY 153:706–718Google Scholar
  7. Grygier JC, Stedinger JR (1985) Algorithms for optimizing hydropower system operation. Water Resour Res 21(1):1–10Google Scholar
  8. Han M, Zhang L (2017) Bi-group multi-objective particle swarm optimization algorithm based on diversity metric. Control Decis 32(12):2268–2272Google Scholar
  9. Giagkiozis I, Purshouse RC, Fleming PJ (2015) An overview of population-based algorithms for multi-objective optimisation. Int J Syst Sci 46(9):1572–1599Google Scholar
  10. Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Resour Plan Manag 2(93):93–111Google Scholar
  11. Madani K (2011) Hydropower licensing and climate change: insights from cooperative game theory. Adv Water Resour 34(2):174–183Google Scholar
  12. Naresh R, Sharma J (2000) Hydro system scheduling using ANN approach. IEEE Trans Power Syst 15(1):388–395Google Scholar
  13. Niu WJ, Feng ZK, Cheng CT, Wu XY (2018) A parallel multi-objective particle swarm optimization for cascade hydropower reservoir operation in Southwest China. Appl Soft Comput 70:562–575Google Scholar
  14. Pan A, Wang L, Guo W, Wu Q (2018) A diversity enhanced multi-objective particle swarm optimization. Inform Sci 436:441–465Google Scholar
  15. Pan ZR, Ruan XH, Xu J (2013) A new calculation method of instream basic ecological water demand. J Hydraul Eng 44(1):119–126Google Scholar
  16. Pires EJS, Machado JAT, Oliveira PBDM (2013) Entropy diversity in multi-objective particle swarm optimization. Entropy-Switz 15(12):5475–5491Google Scholar
  17. Reddy JM, Kumar ND (2010) Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrol Process 21(21):2897–2909Google Scholar
  18. Tan KC, Lee TH, Khor EF (2002) Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons. Artif Intell Rev 17(4):251–290Google Scholar
  19. Wang H, Hui L, Changhe, Rahnamayan et al (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inform Sci 223(2):119–135Google Scholar
  20. Yin XA, Yang ZF (2011) Development of a coupled reservoir operation and water diversion model: balancing human and environmental flow requirements. Ecol Model 222(2):224–231Google Scholar
  21. Zhang H, Zhou A, Song S, Zhang Q, Gao XZ, Zhang J (2016) A self-organizing multi-objective evolutionary algorithm. IEEE Trans Evol Comput 20(5):792–806Google Scholar
  22. Zitzler E, Thiele L, Laumanns M, Fonseca CM, Fonseca VGD (2003) Performance assessment of multi-objective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringHuaqiao UniversityXiamenChina

Personalised recommendations