Advertisement

Water Resources Management

, Volume 33, Issue 11, pp 4007–4026 | Cite as

A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System

  • Hu Hu
  • Kan YangEmail author
  • Lyuwen Su
  • Zhe Yang
Article
  • 99 Downloads

Abstract

Multi-objective long-term generation scheduling (MLGS) considering ecological flow demands is important for comprehensive utilization of water resources in cascade hydropower system (CHS). A novel adaptive multi-objective particle swarm optimization based on decomposition and dominance (D2AMOPSO) is developed in this paper to solve the MLGS problem. In D2AMOPSO, a constraint handling method based on repair strategy and individualconstraints and group constraints (ICGC) technique is embedded to address various constraints. An improved logistic map is adopted to initialize the population. During the evolutionary process, an improved Tchebycheff decomposition is introduced to select personal best and global best for each particle, and the non-dominated solutions found so far are stored in an external archive where crowding distance and elitist learning strategy are performed to improve its diversity. Meanwhile, an adaptive flight parameter adjustment mechanism based on Pareto entropy is adopted to balance the global exploration and local exploitation abilities of the population. A normal cloud mutation operator is used to keep the population diversity and escape local minima. In the case study of the Three Gorges Cascade hydropower system (TGC) under three typical years, the results of the proposed method and other four competitors show that D2AMOPSO can obtain better diversity and faster convergence solutions for the MLGS problem in less time.

Keywords

Multi-objective long-term generation scheduling Ecological flow Cascade hydropower system Particle swarm optimization Constraint handling method 

Notes

Acknowledgements

The achievements are funded by the National Key Basic Research Program of China (973 Program) (2012CB417006). The writers would like to thank the editors and anonymous reviewers for their thoughtful comments and suggestions.

Compliance with Ethical Standards

Conflict of Interest

None.

References

  1. Al-Aqeeli YH, Lee TS, Aziz SA (2016) Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq. SpringerPlus 5(1):797Google Scholar
  2. Al Moubayed N, Petrovski A, McCall J (2014) D2MOPSO: MOPSO based on decomposition and dominance with archiving using crowding distance in objective and solution spaces. Evol Comput 22(1):47–77Google Scholar
  3. Bai T, Chang JX, Chang FJ, Huang Q, Wang YM, Chen GS (2015) Synergistic gains from the multi-objective optimal operation of cascade reservoirs in the upper Yellow River basin. J Hydrol 523:758–767Google Scholar
  4. Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In Proc IEEE world Cong Comput Intell (CEC’02): 1051–1056Google Scholar
  5. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601Google Scholar
  6. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In Proc 6th Int Symp micro machine and human Sci: 39–43Google Scholar
  7. 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
  8. Feng ZK, Niu WJ, Zhou JZ, Cheng CT (2017) Multiobjective operation optimization of a cascaded hydropower system. J Water Res Plann Manage 143(10):05017010Google Scholar
  9. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation discussion and generalization. In Proc 5th Int Conf genetic algorithms: 416–423Google Scholar
  10. Hakimi-Asiabar M, Ghodsypour SH, Kerachian R (2010) Deriving operating policies for multi-objective reservoir systems: application of self-learning genetic algorithm. Appl Soft Comput 10(4):1151–1163Google Scholar
  11. Han H, Lu W, Qiao J (2017) An adaptive multiobjective particle swarm optimization based on multiple adaptive methods. IEEE Trans Cybern 47(9):2754–2767Google Scholar
  12. He Y, Yang S, Xu Q (2013) Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map. Commun Nonlinear Sci Numer Simulat 18(7):1746–1756Google Scholar
  13. Hu W, Yen GG (2015) Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system. IEEE Trans Evol Comput 19(1):1–18Google Scholar
  14. Kamodkar RU, Regulwar DG (2014) Optimal multiobjective reservoir operation with fuzzy decision variables and resources: a compromise approach. J Hydro-Environ Res 8(4):428–440Google Scholar
  15. Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multi-objective optimization. Evol Comput 10(3):263–282Google Scholar
  16. Liao SL, Liu BX, Cheng CT, Li ZF, Wu XY (2017) Long-term generation scheduling of hydropower system using multi-Core parallelization of particle swarm optimization. Water Resour Manag 31(9):2791–2807Google Scholar
  17. Li C, Zhou J, Lu P, Wang C (2015a) Short-term economic environmental hydrothermal scheduling using improved multi-objective gravitational search algorithm. Energy Convers Manag 89:127–136Google Scholar
  18. Li FF, Shoemaker CA, Qiu J, Wei JH (2015b) Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the three gorges system. Environ Model Softw 69:319–329Google Scholar
  19. Li F, Liu J, Tan S, Yu X (2015c) R2-MOPSO: a multi-objective particle swarm optimizer based on R2-indicator and decomposition. In Proc IEEE Cong Evol Comput: 3148–3155Google Scholar
  20. Lior N (2010) Sustainable energy development: the present (2009) situation and possible paths to the future. Energy 35(10):3976–3994Google Scholar
  21. Li YH, Zhou JZ, Zhang YC, Hui Q, Li L (2010) Novel multiobjective shuffled frog leaping algorithm with application to reservoir flood control operation. J Water Res Plann Manage 136(2):217–226Google Scholar
  22. Luo J, Chen C, Xie J (2015) Multi-objective immune algorithm with preference-based selection for reservoir flood control operation. Water Resour Manag 29(5):1447–1466Google Scholar
  23. Martínez SZ, Coello CAC (2011) A multiobjective particle swarm optimizer based on decomposition. In Proc 13th genetic Evol Comput: 69–76Google Scholar
  24. Ma X, Zhang Q, Tian G, Yang J, Zhu Z (2018) On Tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Trans Evol Comput 22(2):226–244Google Scholar
  25. 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
  26. Peng W, Zhang Q (2008) A decomposition-based multi-objective particle swarm optimization algorithm for continuous optimization problems. In Proc Conf Granular Comput: 534–537Google Scholar
  27. Raquel CR, Naval Jr PC (2005) An effective use of crowding distance in multiobjective particle swarm optimization. In Proc 7th Conf genetic Evol Comput: 257–264Google Scholar
  28. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255Google Scholar
  29. Reddy MJ, Kumar DN (2007) Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrol Process 21(21):2897–2909Google Scholar
  30. Schardong A, Simonovic SP, Vasan A (2012) Multiobjective evolutionary approach to optimal reservoir operation. J Comput Civ Eng 27(2):139–147Google Scholar
  31. Schott JR (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization. M.S. thesis, Dept of aeronaut and astronaut, Mass Inst of Technol, CambridgeGoogle Scholar
  32. Sierra MR, Coello CAC (2005) Improving PSO-based multi-objective optimization using crowding, mutation and ∈−dominance. In Int Conf Evol Multi-Criterion Optim: 505–519Google Scholar
  33. Tripathi PK, Bandyopadhyay S, Pal SK (2007) Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf Sci 177(22):5033–5049Google Scholar
  34. Wang C, Zhou J, Lu P, Yuan L (2015) Long-term scheduling of large cascade hydropower stations in Jinsha River, China. Energy Convers Manag 90:476–487Google Scholar
  35. Wu X, Cheng B, Cao J, Cao B (2008) Particle swarm optimization with normal cloud mutation. In Proc 7th world Congr Intell Cont auto: 2828–2832Google Scholar
  36. Yoo JH (2009) Maximization of hydropower generation through the application of a linear programming model. J Hydrol 376(1–2):182–187Google Scholar
  37. Zhang H, Zhou J, Fang N, Zhang R, Zhang Y (2013) An efficient multi-objective adaptive differential evolution with chaotic neuron network and its application on long-term hydropower operation with considering ecological environment problem. Int J Electr Power Energy Syst 45(1):60–70Google Scholar
  38. Zhang H, Chang J, Gao C, Wu H, Wang Y, Lei K, Long R, Zhang L (2019) Cascade hydropower plants operation considering comprehensive ecological water demands. Energy Convers Manag 180:119–133Google Scholar
  39. Zhang J, Tang Q, Li P, Deng D, Chen Y (2016) A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. Appl Soft Comput 47(C):494–514Google Scholar
  40. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731Google Scholar
  41. Zhang R, Zhou J, Zhang H, Liao X, Wang X (2014) Optimal operation of large-scale cascaded hydropower Systems in the Upper Reaches of the Yangtze River, China. J Water Res Plann Manage 140(4):480–495Google Scholar
  42. Zhang R, Zhou J, Wang Y (2012) Multi-objective optimization of hydrothermal energy system considering economic and environmental aspects. Int J Electr Power Energy Syst 42(1):384–395Google Scholar
  43. Zhan ZH, Zhang J, Li Y, Chung SH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern 39(6):1362–1381Google Scholar
  44. Zheng F, Zecchin AC, Maier HR, Simpson AR (2016) Comparison of the searching behavior of NSGA-II, SAMODE, and Borg MOEAs applied to water distribution system design problems. J Water Res Plann Manage 142(7):04016017Google Scholar
  45. Zhou Y, Guo S, Chang FJ, Liu P, Chen AB (2018) Methodology that improves water utilization and hydropower generation without increasing flood risk in mega cascade reservoirs. Energy 143:785–796Google Scholar
  46. Zhou Y, Guo S, Xu CY, Liu P, Qin H (2015) Deriving joint optimal refill rules for cascade reservoirs with multi-objective evaluation. J Hydrol 524:166–181Google Scholar
  47. Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Hydrology and Water ResourcesHohai UniversityNanjingChina

Personalised recommendations