Boualem Hadjerioua

Stream-Reach Identification for New Run-of-River Hydropower Development through a Merit Matrix–Based Geospatial Algorithm

AbstractEven after a century of development, the total hydropower potential from undeveloped rivers is still considered to be abundant in the United States. However, unlike evaluating hydropower potential at existing hydropower plants or nonpowered dams, locating a feasible new hydropower plant involves many unknowns; hence, the total undeveloped potential is harder to quantify. In light of the rapid development of multiple national geospatial data sets for topography, hydrology, and environmental characteristics, a merit matrix–based geospatial algorithm is proposed to identify possible hydropower stream reaches for future development. These hydropower stream reaches—sections of natural streams with suitable head, flow, and slope for possible future development—are identified and compared by using three different scenarios. A case study was conducted in the Alabama-Coosa-Tallapoosa and Apalachicola-Chattahoochee-Flint hydrologic subregions. It was found that a merit matrix–based algorithm, which is based...

Development and Implementation of an Optimization Model for Hydropower and Total Dissolved Gas in the Mid-Columbia River System

AbstractManaging energy, water, and environmental priorities and constraints within a cascade hydropower system is a challenging multiobjective optimization effort that requires advanced modeling a...

Modeling Total Dissolved Gas for Optimal Operation of Multireservoir Systems

AbstractOne important environmental issue of hydropower in the Columbia and Snake River Basins (Pacific Northwest region of United States) is elevated total dissolved gas (TDG) downstream of a dam,...

The Economic Benefits of Multipurpose Reservoirs in the United States-Federal Hydropower Fleet

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Evaluation of the feasibility and viability of modular pumped storage hydro (m-PSH) in the United States

The viability of modular pumped storage hydro (m-PSH) is examined in detail through the conceptual design, cost scoping, and economic analysis of three case studies. Modular PSH refers to both the compactness of the project design and the proposed nature of product fabrication and performance. A modular project is assumed to consist of pre-fabricated standardized components and equipment, tested and assembled into modules before arrival on site. This technology strategy could enable m-PSH projects to deploy with less substantial civil construction and equipment component costs. The concept of m-PSH is technically feasible using currently available conventional pumping and turbine equipment, and may offer a path to reducing the project development cycle from inception to commissioning.

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

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 percent 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.

Water Quality Projects Summary for the Mid-Columbia and Cumberland River Systems

................................................................................................................................................. x 1. BACKGROUND AND INTRODUCTION ........................................................................................... 1 2. MID-COLUMBIA RIVER SYSTEM .................................................................................................... 1 2.1 TDG UPTAKE PREDICTION DEVELOPMENT ..................................................................... 3 2.1.1 METHODOLOGY ........................................................................................................ 3 VALIDATION AND PERFORMANCE ....................................................................... 4 2.1.2 4 2.2 IMPLEMENTATION OF TAILRACE TDG DEVELOPMENT INTO RIVERWARE ............ 6 2.2.1 OVERVIEW OF MID-COLUMBIA OPTIMIZATION MODEL ................................ 6 2.2.2 RIVERWARE’S TDG METHODS ............................................................................... 6 2.2.3 PRIEST RAPIDS TAILRACE TDG OPTIMIZATION................................................ 6 2.2.4 SIGNIFICANCE OF RESULTS ................................................................................... 8 2.3 TDG TRANSFER PREDICTION ............................................................................................... 9 2.3.1 METHODOLOGY ........................................................................................................ 9 2.3.2 VALIDATION AND PERFORMANCE ..................................................................... 11 2.3.3 FUTURE WORK FOR TDG TRANSFER ................................................................. 14 3. CUMBERLAND RIVER SYSTEM .................................................................................................... 16 3.1 DECISION SUPPORT SYSTEM DEVELOPMENT ............................................................... 16 3.1.1 OVERVIEW ................................................................................................................ 17 3.1.2 METHODOLOGY ...................................................................................................... 17 3.2 IMPLEMENTATION OF DECISION SUPPORT SYSTEM ................................................... 18 3.2.1 CE-QUAL-W2 ............................................................................................................. 18 3.2.2 NONLINEAR AUTOREGRESSIVE NETWORK (NARX) MODEL ....................... 19 3.3 DECISION SUPPORT SYSTEM OPTIMIZATION ................................................................ 21 3.3.1 OLD HICKORY AND CORDELL HULL OPTIMIZATION RESULTS .................. 22 4. CONCLUSION .................................................................................................................................... 24 5. REFERENCES ..................................................................................................................................... 25

Predicting Total Dissolved Gas Travel Time in Hydropower Reservoirs

AbstractLarge spills at hydropower facilities can produce environmentally unfavorable supersaturated total dissolved gas (TDG) conditions. Enhanced coordination between adjacent hydropower faciliti...

Total dissolved gas prediction and optimization in RiverWare

................................................................................................................................................ ix 1. BACKGROUND AND INTRODUCTION ........................................................................................... 1 2. METHODOLOGY ................................................................................................................................. 4 2.1 TDG PREDICTION ..................................................................................................................... 5 2.2 DATA PROCESSING AND ASSUMPTIONS ........................................................................... 7 2.2.1 Data Collection and Preliminary Filtering ..................................................................... 7 2.2.2 Project Specific Data Filtering and Assumptions .......................................................... 9 2.3 CALIBRATION AND VALIDATION ....................................................................................... 9 2.4 RESULTS .................................................................................................................................. 11 2.5 RIVERWARE OPTIMIZATION .............................................................................................. 22 3. FUTURE WORK ................................................................................................................................. 24 4. CONCLUSION .................................................................................................................................... 25 5. REFERENCES ..................................................................................................................................... 26