Optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants

The mitigation of the energy crisis necessitates the exploration of alternative sources, including hydrokinetic energy derived from downstream regions of hydroelectric facilities. In this context, harnessing the defluent flow from hydroelectric plants through hydrokinetic turbines has become increasingly vital. This study aims to develop a comprehensive model that furnishes essential parameters for the design of hydrokinetic turbines positioned downstream of dams. The model comprises two key modules: a module for predicting remaining energy and defluent flow, and a module for optimizing reservoir operation. The first module employs a Multi-Layer Perceptron (MLP) model with Backpropagation (MLP-BP) and integrates Autoregressive Integrated Moving Average (ARIMA) models. The second module leverages non-linear programming optimization techniques and advanced process modeling. This module ensures efficient reservoir operation by optimizing generation and defluent flow in hydroelectric plants. It enables sustainable operational simulations, capable of minimizing conflicts arising from periods of flood, drought, and high-energy demands. The results demonstrate the model’s fundamental significance in both the design and operation of hydrokinetic turbines installed downstream of hydroelectric plants. It enables the optimization of generation and defluent flow, even during challenging conditions, while facilitating sustainable operational simulations that mitigate conflicts of use. The developed model thus emerges as a crucial tool in enhancing the efficiency and sustainability of hydroelectric power generation.