Multi-objective reservoir optimization balancing energy generation and firm power

To maximize annual power generation and to improve firm power are important but competing goals for hydropower stations. The firm power output is decisive for the installed capacity in design, and represents the reliability of the power generation when the power plant is put into operation. To improve the firm power, the whole generation process needs to be as stable as possible, while the maximization of power generation requires a rapid rise of the water level at the beginning of the storage period. Taking the minimal power output as the firm power, both the total amount and the reliability of the hydropower generation are considered simultaneously in this study. A multi-objective model to improve the comprehensive benefits of hydropower stations are established, which is optimized by Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The Three Gorges Cascade Hydropower System (TGCHS) is taken as the study case, and the Pareto Fronts in different search spaces are obtained. The results not only prove the effectiveness of the proposed method, but also provide operational references for the TGCHS, indicating that there is room of improvement for both the annual power generation and the firm power.

[1]  William W.-G. Yeh,et al.  A diversified multiobjective GA for optimizing reservoir rule curves , 2007 .

[2]  Christine A. Shoemaker,et al.  Estimating Maximal Annual Energy Given Heterogeneous Hydropower Generating Units with Application to the Three Gorges System , 2013 .

[3]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[4]  Fang-Fang Li,et al.  An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System , 2012, Water Resources Management.

[5]  M. Janga Reddy,et al.  Ant Colony Optimization for Multi-Purpose Reservoir Operation , 2006 .

[6]  Francesco Gallerano,et al.  Multi-objective analysis of dam release flows in rivers downstream from hydropower reservoirs , 2012 .

[7]  Li Hui.  Maxmin Model for Determining the Guaranteed Output of a Hydropower Plant Based on Dynamic Programming , 2011 .

[8]  Mahdi Zarghami,et al.  Multi-Objective Reservoir Operation with Sediment Flushing; Case Study of Sefidrud Reservoir , 2014, Water Resources Management.

[9]  Youlin Lu,et al.  Multi-objective Cultured Differential Evolution for Generating Optimal Trade-offs in Reservoir Flood Control Operation , 2010 .

[10]  Li Liu,et al.  Novel Multiobjective Shuffled Frog Leaping Algorithm with Application to Reservoir Flood Control Operation , 2010 .

[11]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[12]  Taesoon Kim,et al.  Single-reservoir operating rules for a year using multiobjective genetic algorithm , 2008 .

[13]  Bin Xu,et al.  Decomposition–coordination model of reservoir group and flood storage basin for real-time flood control operation , 2015 .

[14]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[15]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[16]  Taesoon Kim,et al.  Application of multi-objective genetic algorithms to multireservoir system optimization in the Han River basin , 2006 .

[17]  David W. Watkins,et al.  LINEAR PROGRAMMING FOR FLOOD CONTROL IN THE IOWA AND DES MOINES RIVERS , 2000 .

[18]  Slobodan P. Simonovic,et al.  Multiobjective Evolutionary Approach to Optimal Reservoir Operation , 2013 .

[19]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[20]  J. Stedinger,et al.  Algorithms for Optimizing Hydropower System Operation , 1985 .

[21]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[22]  M. A. Mariño,et al.  Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules , 2013, Water Resources Management.

[23]  D. Nagesh Kumar,et al.  Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm , 2006 .

[24]  V. Jothiprakash,et al.  Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization , 2013, Water Resources Management.

[25]  Li Huaien,et al.  Application of multi-objective operation model to Nishan reservoir , 2006 .

[26]  Slobodan P. Simonovic,et al.  Optimal Operation of Reservoir Systems using Simulated Annealing , 2002 .

[27]  A. Turgeon.  Optimal short-term hydro scheduling from the principle of progressive optimality , 1981 .

[28]  Reza Kerachian,et al.  Ranking solutions of multi-objective reservoir operation optimization models using multi-criteria decision analysis , 2011, Expert Syst. Appl..

[29]  K. D. W. Nandalal,et al.  Dynamic Programming Based Operation of Reservoirs: Applicability and Limits , 2007 .