Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos

Abstract

Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence, slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm (CGA) based on the chaos optimization algorithm (COA) and genetic algorithm (GA), which makes use of the ergodicity and internal randomness of chaos iterations, is presented to overcome premature local optimum and increase the convergence speed of genetic algorithm. CGA integrates powerful global searching capability of the GA with that of powerful local searching capability of the COA. Two measures are adopted in order to improve the performance of the GA. The first one is the adoption of chaos optimization of the initialization to improve species quality and to maintain the population diversity. The second is the utilization of annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. The Rosenbrock function and Schaffer function, which are complex and global optimum functions and often used as benchmarks for contemporary optimization algorithms for GAs and Evolutionary computation, are first employed to examine the performance of the GA and CGA. The test results indicate that CGA can improve convergence speed and solution accuracy. Furthermore, the developed model is applied for the monthly operation of a hydropower reservoir with a series of monthly inflow of 38 years. The results show that the long term average annual energy based CGA is the best and its convergent speed not only is faster than dynamic programming largely, but also overpasses the standard GA. Thus, the proposed approach is feasible and effective in optimal operations of complex reservoir systems.

Extended Muskingum method for flood routing

“…Chau et al (2005) employed the Genetic Algorithm based Artificial Neural Network (ANN-GA) and the Adaptive Network based Fuzzy Inference System (ANFIS), for flood forecasting in a reach of the Yangtze River in China. Similar studies are reported by Cheng et al ( , 2008a.…”

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

“…Therefore, the improved chaos logistic map is applied to frog population initialization to strengthen population diversity and distribution uniformity during the initialization process. It is beneficial to promote overall quality of initial population and locate feasible search zone effectively [53,54] Similarly, the chaotic sequence creation process is according to (17), and chaotic variables are mapped to feasible solution space of variable on the basis of (18). In IR-SFLA, variable is real coded, which denotes the power load in ELD subproblem and the frog position updating formula is in line with (14).…”

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

“…We do however acknowledge that other algorithms also exist as reviewed by Ahmad et al (2014). For instance, some papers consider a genetic algorithm (Hincal et al 2011;Cheng et al 2008) or a simulated annealing one (Teegavarapu and Simonovic 2002). Some papers also investigate multi-objective optimization rather than one objective function (Liao et al 2014;Kougias et al 2012).…”