Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model

Abstract

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high‐fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high‐fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high‐fidelity hydrodynamic and water quality model CE‐QUAL‐W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high‐fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. The reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high‐fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

Realizing ecosystem-safe hydropower from dams

“…Optimization of reservoir operations has been extensively studied for various operating objectives at short-and long-term operation scales (Labadie 2004;Yeh and Becker 1982;Barros et al 2003;Ahmad et al 2014). Multi-objective optimization for hydropower has been performed to satisfy other stakeholder benefits of flood control, water supply, irrigation and water quality (Le Ngo et al 2007;Yazicigil et al 1983;Shaw et al 2017;Asadieh and Afshar 2019). Ahmad and Hossain (2020) optimized daily operations of two dams in US to maximize hydropower without compromising flood control.…”

Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm

“…Artificial Neural Network (ANN) is a research hotspot in the field of artificial intelligence since the 1980s. For the water conservancy project, ANN is a new and very important frontier research topic, and they have mature applications in many aspects including hydrological simulating, flood predicting, safety monitoring, and comprehensive evaluating [10][11][12][13][14][15][16][17][18][19]. Dawson et al [10] used the Radial Basis Function Neural Network (RBFNN) to simulate the local rainfall-runoff evolution of the Yangtze River in China, which promoted its development in the field of hydrological simulation.…”

“…Li et al [17] applied the Back Propagation Neural Network (BPNN) to analyze the relationship between the sediment-flushing efficiency of the ree Gorges Reservoir and its influencing factors. Shaw et al [18] successfully simulated the prediction ability of a high-fidelity hydrodynamic and water quality model using the ANN. Bui et al [19] proposed a novel hybrid artificial intelligent approach for modeling and predicting of the horizontal displacement of hydropower dams.…”

A novel framework of deriving joint impoundment rules for large-scale reservoir system based on a classification-aggregation-decomposition approach

“…Regarding another well-known 'high dimensionality' in the PSO framework, the original simulation model is usually replaced by a surrogate model for simplification. The surrogate should preserve and describe the main features of the original model (Chu et al, 2015;Shaw et al, 2017;Zhang et al, 2017). The subtle combination of the PSO framework and a surrogate model has indeed made some achievements in addressing inflow stochasticity and dimensional curse of multireservoir hydropower (Glotic and Zamuda, 2015;Valdes et al, 1992) and flood control operations (Zhang et al, 2019), but is seldom utilized in large-scale impoundment operation.…”