Reservoir operation problems are challenging to efficiently optimize because of their high-dimensionality, stochasticity, and non-linearity. To alleviate the computational burden involved in large-scale and stringent constraint reservoir operation problems, we propose a novel search space reduction method (SSRM) that considers the available equality (e.g., water balance equation) and inequality (e.g., firm output) constraints. The SSRM can effectively narrow down the feasible search space of the decision variables prior to the main optimization process, thus improving the computational efficiency. Based on a hydropower reservoir operation model, we formulate the SSRM for a single reservoir and a multi-reservoir system, respectively. To validate the efficiency of the proposed SSRM, it is individually integrated into two representative optimization techniques: discrete dynamic programming (DDP) and the cuckoo search (CS) algorithm. We use these coupled methods to optimize two real-world operation problems of the Shuibuya reservoir and the Shuibuya-Geheyan-Gaobazhou cascade reservoirs in China. Our results show that: (1) the average computational time of SSRM-DDP is 1.81, 2.50, and 3.07 times less than that of DDP when decision variables are discretized into 50, 100, and 500 intervals, respectively; and (2) SSRM-CS outperforms CS in terms of its capability of finding near-optimal solutions, convergence speed, and stability of optimization results. The SSRM significantly improves the search efficiency of the optimization techniques and can be integrated into almost any optimization or simulation method. Therefore, the proposed method is useful when dealing with large-scale and complex reservoir operation problems in water resources planning and management.
Reservoir operation optimization Search space Reduction Constraints
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This study was supported by the Excellent Young Scientist Foundation of NSFC (51422907) and the National Natural Science Foundation of China (51579180). Sincere gratitude is extended to the editor and anonymous reviewers for their professional comments and corrections, which greatly improved the presentation of the paper.
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Conflict of Interest
The authors declare that they have no conflict of interest.
Bai T, Wu L, Chang J-X, Huang Q (2015) Multi-objective optimal operation model of Cascade reservoirs and its application on water and sediment regulation. Water Resour Manag 29(8):2751–2770. doi:10.1007/s11269-015-0968-0CrossRefGoogle Scholar
Cai X, Mckinney DC, Lasdon LS (2001) Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Adv Water Resour 24(6):667–676CrossRefGoogle Scholar
Chang FJ, Chen L, Chang LC (2005) Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol Process 19(11):2277–2289CrossRefGoogle Scholar
Civicioglu P, Besdok E (2011) A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee Colony algorithms. Artif Intell Rev 39(4):315–346. doi:10.1007/s10462-011-9276-0CrossRefGoogle Scholar
Guo S, Chen J, Li Y, Liu P, Li T (2011) Joint operation of the multi-reservoir system of the three gorges and the Qingjiang Cascade reservoirs. Energies 4(12):1036–1050. doi:10.3390/en4071036CrossRefGoogle Scholar
Peng Y et al (2016) Multi-Core parallel particle swarm optimization for the operation of Inter-Basin water transfer-supply systems. Water Resour Manag:1–15. doi:10.1007/s11269-016-1506-4
Piccardi C, Soncini-Sessa R (1991) Stochastic dynamic programming for reservoir optimal control: dense discretization and inflow correlation assumption made possible by parallel computing. Water Resour Res 27(5):729–741. doi:10.1029/90WR02766CrossRefGoogle Scholar
Tzabiras J, Vasiliades L, Sidiropoulos P, Loukas A, Mylopoulos N (2016) Evaluation of water resources management strategies to overturn climate change impacts on Lake Karla watershed. Water Resour Manag:1–26. doi:10.1007/s11269-016-1536-y
Yang X-S, Deb S (2010) Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 1(4):330–343CrossRefGoogle Scholar