Multiobjective Operation Optimization of a Cascaded Hydropower System
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 10
Abstract
In order to satisfy the practical requirement of the power grid in China, this paper presents a multiobjective operation model for a cascaded hydropower system simultaneously considering the maximization of gross generation and firm output, as well as various complex constraints. Then, a multiobjective particle swarm optimization (MOPSO) is presented here to solve this problem. This algorithm combines the merits of chaos theory, genetic operators, adaptive adjustment strategy, and the heuristic constraint-handling method to help the population explore the search space efficiently: the logistic map is introduced to generate the initial population distributed uniformly in problem space; both the inertia weight and learning coefficients are dynamically changed as iteration goes on; an archive set is used to conserve the nondominated solutions found during evolution; the personal and global best position for each particle are determined by crowding distance and a feasibility-based dominance relationship; the classical mutation and crossover operators are introduced to enhance the population diversity; a novel constraint-handling method is proposed to address the complicated constraints. To testify to its effectiveness, the MOPSO is applied to the Wu River cascade hydropower system in southwest China. The results in different cases indicate that MOPSO is able to provide better solutions than the NSGA-II method, providing an effective technique for the operation of a hydropower system.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This paper is supported by the Fundamental Research Funds for the Central Universities (HUST: 2017KFYXJJ193) and the Natural Science Foundation of China (91547208 and 91547201). The authors would like to thank the reviewers and editors for their valuable comments.
References
Agrawal, S., Panigrahi, B. K., and Tiwari, M. K. (2008). “Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch.” IEEE Trans. Evol. Comput., 12(5), 529–541.
Bai, T., Chang, J., Chang, F., Huang, Q., Wang, Y., and Chen, G. (2015). “Synergistic gains from the multi-objective optimal operation of cascade reservoirs in the Upper Yellow River basin.” J. Hydrol., 523, 758–767.
Bi, W., Dandy, G. C., and Maier, H. R. (2016). “Use of domain knowledge to increase the convergence rate of evolutionary algorithms for optimizing the cost and resilience of water distribution systems.” J. Water Res. Plann. Manage., 04016027.
Cai, D., Wang, Y., and Miao, Y. (2015). “A new multi-objective particle swarm optimization algorithm based on decomposition.” Inform. Sci., 325(C), 541–557.
Cai, X., McKinney, D. C., and Lasdon, L. S. (2001). “Solving nonlinear water management models using a combined genetic algorithm and linear programming approach.” Adv. Water Resour., 24(6), 667–676.
Cheng, C., Shen, J., Wu, X., and Chau, K. (2012). “Operation challenges for fast-growing China’s hydropower systems and respondence to energy saving and emission reduction.” Renewable Sustainable Energy Rev., 16(5), 2386–2393.
Cheng, C., Yan, L., Mirchi, A., and Madani, K. (2016). “China’s booming hydropower: Systems modeling challenges and opportunities.” J. Water Res. Plann. Manage., 02516002.
Chiu, S. Y., Sun, T. Y., Hsieh, S. T., and Lin, C. W. (2007). “Cross-searching strategy for multi-objective particle swarm optimization.” IEEE Congress on Evolutionary Computation, IEEE, New York, 3135–3141.
Clerc, M., and Kennedy, J. (2002). “The particle swarm—Explosion, stability, and convergence in a multidimensional complex space.” IEEE Trans. Evol. Comput., 6(1), 58–73.
Duan, C., Wang, X., Shu, S., Jing, C., and Chang, H. (2014). “Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm.” Energy Convers. Manage., 84, 88–96.
Feng, Z. K., Niu, W. J., and Cheng, C. T. (2017a). “Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling.” Energy, 131, 165–178.
Feng, Z. K., Niu, W. J., Cheng, C. T., and Liao, S. L. (2017b). “Hydropower system operation optimization by discrete differential dynamic programming based on orthogonal experiment design.” Energy, 126, 720–732.
Feng, Z. K., Niu, W. J., Zhou, J. Z., Cheng, C. T., Qin, H., and Jiang, Z. Q. (2017c). “Parallel multi-objective genetic algorithm for short-term economic environmental hydrothermal scheduling.” Energies, 10(2), 163.
Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., and Reed, P. M. (2016). “Curses, tradeoffs, and scalable management: Advancing evolutionary multiobjective direct policy search to improve water reservoir operations.” J. Water Res. Plann. Manage., 04015050.
Goh, C. K., Tan, K. C., Liu, D. S., and Chiam, S. C. (2010). “A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design.” Eur. J. Oper. Res., 202(1), 42–54.
He, L., Huang, G., Zeng, G., and Lu, H. (2008). “Fuzzy inexact mixed-integer semiinfinite programming for municipal solid waste management planning.” J. Environ. Eng., 572–581.
Hu, Q., Huang, G., Cai, Y., and Xu, Y. (2013). “Energy and environmental systems planning with recourse: Inexact stochastic programming model containing fuzzy boundary intervals in objectives and constraints.” J. Energy Eng., 169–189.
Ji, C., Jiang, Z., Sun, P., Zhang, Y., and Wang, L. (2014). “Research and application of multidimensional dynamic programming in cascade reservoirs based on multilayer nested structure.” J. Water Res. Plann. Manage., 04014090.
Kamodkar, R. U., and Regulwar, D. G. (2014). “Optimal multiobjective reservoir operation with fuzzy decision variables and resources: A compromise approach.” J. Hydro-Environ. Res., 8(4), 428–440.
Labadie, J. (2004). “Optimal operation of multireservoir systems: State-of-the-art review.” J. Water Res. Plann. Manage., 93–111.
Laumanns, M., Thiele, L., Deb, K., and Zitzler, E. (2002). “Combining convergence and diversity in evolutionary multi-objective optimization.” Evol. Comput., 10(3), 263–282.
Li, C., Zhou, J., Lu, P., and Wang, C. (2015). “Short-term economic environmental hydrothermal scheduling using improved multi-objective gravitational search algorithm.” Energy Convers. Manage., 89, 127–136.
Li, F., Shoemaker, C. A., Wei, J., and Fu, X. (2013). “Estimating maximal annual energy given heterogeneous hydropower generating units with application to the Three Gorges system.” J. Water Res. Plann. Manage., 265–276.
Li, F. F., and Qiu, J. (2015). “Multi-objective reservoir optimization balancing energy generation and firm power.” Energies, 8(7), 6962–6976.
Li, X. (2003). “A non-dominated sorting particle swarm optimizer for multiobjective optimization.” Lecture Notes Comput. Sci., 2723, 37–48.
Li, X., Wei, J., Li, T., Wang, G., and Yeh, W. W. G. (2014). “A parallel dynamic programming algorithm for multi-reservoir system optimization.” Adv. Water Resour., 67, 1–15.
Lian, S., and Chen, X. (2011). “Traceable content protection based on chaos and neural networks.” Appl. Soft Comput., 11(7), 4293–4301.
Lior, N. (2012). “Sustainable energy development: The present (2011) situation and possible paths to the future.” Energy, 43(1), 174–191.
Madani, K. (2011). “Hydropower licensing and climate change: Insights from cooperative game theory.” Adv. Water Resour., 34(2), 174–183.
Madani, K., Guégan, M., and Uvo, C. B. (2014). “Climate change impacts on high-elevation hydroelectricity in California.” J. Hydrol., 510, 153–163.
Madani, K., and Hooshyar, M. (2014). “A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems.” J. Hydrol., 519, 732–742.
Matrosov, E. S., Huskova, I., Kasprzyk, J. R., Harou, J. J., Lambert, C., and Reed, P. M. (2015). “Many-objective optimization and visual analytics reveal key trade-offs for London’s water supply.” J. Hydrol., 531, 1040–1053.
Mendes, L. A., de Barros, M. T. L., Zambon, R. C., and Yeh, W. W. (2015). “Trade-off analysis among multiple water uses in a hydropower system: Case of São Francisco River Basin, Brazil.” J. Water Res. Plann. Manage., 04015014.
Moosavian, N., and Lence, B. J. (2016). “Nondominated sorting differential evolution algorithms for multiobjective optimization of water distribution systems.” J. Water Res. Plann. Manage., 04016082.
Morankar, D. V., Srinivasa Raju, K., Vasan, A., and Ashoka Vardhan, L. (2016). “Fuzzy multiobjective irrigation planning using particle swarm optimization.” J. Water Res. Plann. Manage., 05016004.
Mostaghim, S., and Teich, J. (2003). “Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO).” Proc., Swarm Intelligence Symp., IEEE, Piscataway, NJ, 297–302.
Rezaei, F., Safavi, H. R., Mirchi, A., and Madani, K. (2017). “f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management.” J. Hydro-Environ. Res., 14, 1–18.
Smith, R., Kasprzyk, J., and Zagona, E. (2016). “Many-objective analysis to optimize pumping and releases in multireservoir water supply network.” J. Water Res. Plann. Manage., 04015049.
Tripathi, P. K., Bandyopadhyay, S., and Pal, S. K. (2007). “Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients.” Inform. Sci., 177(22), 5033–5049.
Tsai, W., Chang, F., Chang, L., and Herricks, E. E. (2015). “AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands.” J. Hydrol., 530, 634–644.
Tu, M., Hsu, N., Tsai, F. T. C., and Yeh, W. W. G. (2008). “Optimization of hedging rules for reservoir operations.” J. Water Res. Plann. Manage., 3–13.
Wang, Y., Wong, K. W., Liao, X., and Chen, G. (2011). “A new chaos-based fast image encryption algorithm.” Appl. Soft Comput., 11(1), 514–522.
Wu, X., Cheng, C., Zeng, Y., and Lund, J. (2016). “Centralized versus distributed cooperative operating rules for multiple cascaded hydropower reservoirs.” J. Water Res. Plann. Manage., 05016008.
Yang, L., and Lin, B. (2016). “Carbon dioxide-emission in China’s power industry: Evidence and policy implications.” Renewable Sustainable Energy Rev., 60, 258–267.
Yang, Y. E., Zhao, J., and Cai, X. (2012). “Decentralized optimization method for water allocation management in the Yellow River Basin.” J. Water Res. Plann. Manage., 313–325.
Zaman, F., Elsayed, S. M., Ray, T., and Sarker, R. A. (2016). “Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems.” Eng. Appl. Artif. Intell., 53, 105–125.
Zhang, R., Zhou, J., Zhang, H., Liao, X., and 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., 480–495.
Zhang, Y., Gong, D. W., and Ding, Z. (2012). “A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch.” Inform. Sci., 192(6), 213–227.
Zhang, Y., Gong, D. W., and Ding, Z. H. (2011). “Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer.” Expert Syst. Appl., 38(11), 13933–13941.
Zhang, Y., Jiang, Z., Ji, C., and Sun, P. (2015). “Contrastive analysis of three parallel modes in multi-dimensional dynamic programming and its application in cascade reservoirs operation.” J. Hydrol., 529, 22–34.
Zhao, T., Zhao, J., Lund, J., and Yang, D. (2014). “Optimal hedging rules for reservoir flood operation from forecast uncertainties.” J. Water Res. Plann. Manage., 04014041.
Zheng, F., Simpson, A. R., and Zecchin, A. C. (2014). “An efficient hybrid approach for multiobjective optimization of water distribution systems.” Water Resour. Res., 50(5), 3650–3671.
Zheng, F., and Zecchin, A. (2014). “An efficient decomposition and dual-stage multi-objective optimization method for water distribution systems with multiple supply sources.” Environ. Modell. Software, 55(C), 143–155.
Zheng, F., Zecchin, A. C., Maier, H. R., and Simpson, A. R. (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., 04016017.
Zhou, J., Lu, P., Li, Y., Wang, C., Yuan, L., and Mo, L. (2016). “Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm.” Energy Convers. Manage., 123, 116–129.
Zhou, Y., Guo, S., Xu, C., Liu, P., and Qin, H. (2015). “Deriving joint optimal refill rules for cascade reservoirs with multi-objective evaluation.” J. Hydrol., 524, 166–181.
Information & Authors
Information
Published In
Copyright
©2017 American Society of Civil Engineers.
History
Received: Oct 7, 2016
Accepted: Apr 17, 2017
Published online: Jul 17, 2017
Published in print: Oct 1, 2017
Discussion open until: Dec 17, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.