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Title: 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 qualitymore » constraints at specified locations. Here, 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.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1];  [2]; ORCiD logo [3]
  1. Vanderbilt Univ., Nashville, TN (United States)
  2. Lipscomb Univ., Nashville, TN (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1413612
Alternate Identifier(s):
OSTI ID: 1409861
Grant/Contract Number:
AC05-00OR22725; EE0002668
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 53; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
13 HYDRO ENERGY; genetic algorithms; artificial neural networks; CE-QUAL-W2; hydropower optimization; water quality; adaptive optimization

Citation Formats

Shaw, Amelia R., Sawyer, Heather Smith, LeBoeuf, Eugene J., McDonald, Mark P., and Hadjerioua, Boualem. Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model. United States: N. p., 2017. Web. doi:10.1002/2017WR021039.
Shaw, Amelia R., Sawyer, Heather Smith, LeBoeuf, Eugene J., McDonald, Mark P., & Hadjerioua, Boualem. Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model. United States. doi:10.1002/2017WR021039.
Shaw, Amelia R., Sawyer, Heather Smith, LeBoeuf, Eugene J., McDonald, Mark P., and Hadjerioua, Boualem. Tue . "Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model". United States. doi:10.1002/2017WR021039.
@article{osti_1413612,
title = {Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model},
author = {Shaw, Amelia R. and Sawyer, Heather Smith and LeBoeuf, Eugene J. and McDonald, Mark P. and Hadjerioua, Boualem},
abstractNote = {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. Here, 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.},
doi = {10.1002/2017WR021039},
journal = {Water Resources Research},
number = ,
volume = 53,
place = {United States},
year = {Tue Oct 24 00:00:00 EDT 2017},
month = {Tue Oct 24 00:00:00 EDT 2017}
}

Journal Article:
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This content will become publicly available on October 24, 2018
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