As a flexible source of renewable energy, hydropower balances energy demands with resource availability. To fully utilize hydropower energy capabilities, dam operators must understand the system constraints for generating electricity, including maximum capacity and maintenance schedules.
PNNL researchers, in support of the U.S. Department of Energy’s Water Power Technologies Office, are working to understand how changing hydrologic regimes impact hydropower investments and operational decisions. Hydrologic regime refers to the varying state and characteristics of a body of water that repeatedly go through phases, such as seasons.
System modeling
PNNL developed a scalable, physics-based modeling framework to better understand and evaluate hydropower investments and operational decisions in the face of changing hydrologic regimes. The framework explores the relationship between changing water temperature regimes in rivers, electric power generation from hydropower, thermoelectric plant cooling and discharge, and water quality and habitat needs for sensitive species.
Numerical modeling
PNNL uses a variety of modeling tools to understand and predict watershed-river-reservoir systems. These tools can help identify how variations affect hydropower energy generation and flexibility, and how those operations will affect the environment.
These numerical models simulate a range of river and estuarine flows, water quality problems, and hydrodynamic conditions in 1 and 2 dimensions over time and space. The Modular Aquatic Simulation System 1D simulates 1 dimensional flow and transport (cross-sectional averages) in branched channel systems and includes capabilities to simulate various hydraulic structures. The Modular Aquatic Simulation System 2D model simulates 2 dimensional distributions of depth-averaged velocities, water surface elevations, bed shear stress, and water quality constituents.
Researchers are also evaluating how climate changes affect weather patterns that lead to increased or decreased water flow in regions. PNNL is assessing how the chemical, physical, and biological characteristics of water affects water quality, including pH, temperature, and dissolved oxygen. These water variables are important for river health.
Another tool—the Distributed Hydrology Soil Vegetation Model (DHSVM)—numerically represents, with high spatial resolution, the effects of local weather, topography, soil type, and vegetation on hydrologic processes within watersheds. PNNL, in partnership with the University of Washington, operates an open source data hub for DHSVM.
PNNL is also developing new approaches to evaluate flow forecasts for hydropower and grid operations, examining how climate change and forestry practices impact hydropower infrastructure and operations design.
PNNL uses a variety of modeling tools to help identify how variations affect hydropower energy generation and flexibility, and how those operations will affect the environment.
Information technology and data assimilation
PNNL developed an integrative approach to assess hydropower and environmental opportunities on a river basin scale. The approach emphasizes sustainable, small hydropower and related renewable energies, while simultaneously identifying opportunities for environmental improvements in a given basin. By approaching the environmental effects of hydropower at the scale of a river basin, energy and environmental planners can understand the regional impacts of hydropower and identify sustainable solutions.
Data fusion and analytics
Collaborative management of water resources requires a broad suite of data that extends beyond basic information about water quantity and quality. Related topics, such as water infrastructure, land use impacts, and aquatic ecosystem health are also important.
Using advanced techniques, such as remote sensing, data fusion, and machine learning, PNNL researchers are connecting water, ecological, and infrastructure data to provide realistic information about hydropower at the regional, watershed, and local scales. A new ongoing effort, in collaboration with Stanford University, is investigating new paradigms about data access at the watershed-scale to improve fusion and analysis of data to inform watershed decision makers.
