New Evolutionary Algorithm for Optimizing Hydropower Generation Considering Multireservoir Systems

Abstract

In recent decades, solving complex real-life optimization problems has attracted the full attention of researchers. Dam and reservoir operation rules are considered one of the most complicated optimization engineering problems. In fact, the operation rules of dams and reservoirs are multisystematic and highly stochastic and have highly nonlinear system constraints due to the direct influence of environmental conditions: Therefore, these rules are considered highly complex optimization problems. Recently, metaheuristic methods inferred from nature have been broadly utilized to elucidate the way optimal solutions are provided for several complex optimization engineering applications, and these methods have achieved interesting results. The major advantage of these metaheuristic methods over conventional methods is the unnecessity to identify a particular initial condition, convexity, continuity, or differentiability. The present study investigated the potential of using a new metaheuristic method (i.e., the crow algorithm (CA)) to provide optimal operations for multireservoir systems, with the aim of optimally improving hydropower generation. A multireservoir system in China was considered to examine the performance of the proposed optimization algorithm for several operation scenarios. The results obtained the average hydropower generation by considering all examined operation scenarios based on the operation rule achieved using the CA, which outperformed the other metaheuristic methods. In addition, compared to other metaheuristic methods, the proposed CA lessened the time required to search for the optimal solution. In conclusion, the proposed CA has high potential for achieving optimal solutions to complex optimization problems associated with dam and reservoir operations.

Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

“…where C is the penalty factor, ξ − i and ξ + i are penalties of training data of which the error is outside of the ε range, w i is the weight, x i is the input variable, and y i is the output variable. The values of w and b are calculated by Equation (2) and then substituted in Equation (1). There are multiple kernel functions for the support vector machine, and according to the previous studies, the radial kernel function (Equation (3) is an effective and widely used function.…”

“…Finally, monthly precipitation maps were generated based on the MLP-FRA model and Scenario 3 using the weighted interpolation method, which showed significant precipitation in spring and winter and a low level of precipitation in summer. The results of the present study showed that MLP-FRA has high capability to predict hydrological variables and can be used in future research.Consecutive droughts and increasing water demands mean that prediction and planning for the usage of precipitation are necessary for decision makers [1,2]. In addition, precipitation forecasting is necessary to prevent floods and construct flood-controlling structures.…”

“…Consecutive droughts and increasing water demands mean that prediction and planning for the usage of precipitation are necessary for decision makers [1,2]. In addition, precipitation forecasting is necessary to prevent floods and construct flood-controlling structures.…”

Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm

“…The probable inflows are random than the dependable inflows. JA has been developed with the help of MATLAB for population size of 30 after performing sensitivity analysis for population sizes of 5,10,15,20,25,30,35,40,45, and 50. The optimal value is achieved for 150,000 FEs; the square root of the objective function achieved is 218.88 MCM.…”

“…The results proved superiority and robustness in comparison to other approaches. The Crow Algorithm (CA) proved its potential for reservoir operation optimization studies in [10]. It was implemented in the case of a multi-reservoir system in China for hydropower generation and was found to outperform other metaheuristic approaches by taking less computational time.…”