Research Project Description
Post-Master Research Assistant for Hydropower Cost Data Collection and Analysis
Oak Ridge National Laboratory
Oak Ridge, TN
This position requires a Master’s degree; the successful applicant will work collaboratively with senior engineers, economic analysts, hydrologists, and environmental scientists at ORNL to reduce hydropower cost and advance innovative technologies in the Environmental Sciences Division’s Water Power group. Examples of work efforts include hydraulic, mechanical, and economic analyses of existing and proposed hydroelectric machine components, systems, and hydropower project designs. The current focus is for the Hydropower Cost Modeling Project, and the range of work includes following tasks:
Task 1: Search and collect cost data from public sources, including communication with Public Utility Companies, Federal Energy Regulatory Commission (FERC) staff, etc.
Task 2: Fill, review and verify the cost data for data transferred from industry-data providers. Such providers include commercial data gatherers, hydropower developers, consultants, facility owners, equipment suppliers, etc.
Task 3: Categorize the collected cost data, using prepared Excel cost data templates, for different types of projects and different data analysis purposes. The project categories will include non-powered dam, constructed canal/conduit projects, new site development, existing plant upgrades and expansions, redevelopment at aged plant, and pumped-storage hydropower projects.
Task 4: Organize and assemble industry-survey responses from two surveys sent in July-August to collect information on cost reduction potential and hydropower local economic and job impacts.
Task 5: Perform regression analysis using collected data.
Task 6: Write report sections and other publications.
The successful candidate must have a master's degree or equivalent in in civil or mechanical engineering. Experience in use of regression analysis and other statistical methods is needed, as is experience in creating and using Excel-based datasets. The successful candidate must have good communication skills, as the position requires sharing information and data among members of a diverse group of project scientists.