Capacity Expansion Model Developer

National Renewable Energy Laboratory • Golden, CO

2d • Onsite

About The Position

NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development. Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth. At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being. This employee will collect the required data and develop fundamental methods and software tools and interfaces required to support large scale electric system capacity expansion. The developed data sets, data specifications, and tools will help inform future planning, policy and infrastructure development. Develop the generalized frameworks to link capacity expansion models (CEM), such as GenX and ReEDS, and production cost models (PCM), such as Sienna\Ops, including automated methods to translate data inputs and results from one form of model to another (leveraging the Sienna\Data package). Develop automated workflows to estimate maximum power flow transfer constraints between any arbitrary set of aggregated spatial regions (or “zones”) based on a nodal/line-by-line model of the continental US transmission system composed from utility-sourced data with power flow simulation capabilities in Sienna\Ops. Work towards automating transmission network designs to enable generation interconnections and transmission capacity augmentations consistent with results from CEMs and develop methods for automated downscaling of zonal CEM results to a nodal PCM for further reliability analysis (and eventually stability analysis) and confirm costs used for transmission expansion used in the CEM.

Requirements

• Relevant PhD
• Or, relevant Master's Degree and 3 or more years of experience
• Or, relevant Bachelor's Degree and 5 or more years of experience
• Demonstrates broad understanding and wide application of engineering technical procedures, principles, theories and concepts in the field.
• General knowledge of other related disciplines.
• Demonstrates leadership in one or more areas of team, task or project lead responsibilities.
• Demonstrated experience in management of projects.
• Very good writing, interpersonal and communication skills.
• Must meet educational requirements prior to employment start date.
• Domain knowledge of bulk power system modeling, such as capacity expansion, resource adequacy, and/or production cost modeling.
• Demonstrated understanding of power systems and power flow modeling fundamentals.
• Demonstrated experience in optimization modeling, including proficiency with a programming language such as GAMS, Python, and/or Julia.
• Experience with working with large data sets.
• An interest in power system issues.
• Strong written and oral communication and interpersonal skills.
• Ability to work independently and in team settings.

Nice To Haves

• Bulk power system domain knowledge about investment/planning processes, grid operations, markets, regulatory processes, etc.
• Experience with software engineering practices, including version control systems, automated testing frameworks, and continuous integration pipelines.
• Deep familiarity with using and developing Sienna for power systems modeling.
• Demonstrated Experience working with large scale power systems data sets.
• Preference for individuals who are able to obtain and maintain a DOE security clearance at the DOE (Q) and SCI access. SCI access may require a polygraph examination. Eligibility requirements: To obtain a clearance, an individual must be at least 18 years of age; U.S. citizenship is required except in very limited circumstances. See DOE O 472.2A for additional information.

Responsibilities

• Collect the required data and develop fundamental methods and software tools and interfaces required to support large scale electric system capacity expansion.
• Develop the generalized frameworks to link capacity expansion models (CEM), such as GenX and ReEDS, and production cost models (PCM), such as Sienna\Ops, including automated methods to translate data inputs and results from one form of model to another (leveraging the Sienna\Data package).
• Develop automated workflows to estimate maximum power flow transfer constraints between any arbitrary set of aggregated spatial regions (or “zones”) based on a nodal/line-by-line model of the continental US transmission system composed from utility-sourced data with power flow simulation capabilities in Sienna\Ops.
• Work towards automating transmission network designs to enable generation interconnections and transmission capacity augmentations consistent with results from CEMs and develop methods for automated downscaling of zonal CEM results to a nodal PCM for further reliability analysis (and eventually stability analysis) and confirm costs used for transmission expansion used in the CEM.

Benefits

• Benefits include medical, dental, and vision insurance; short- and long-term disability insurance; pension benefits; 403(b) Employee Savings Plan with employer match; life and accidental death and dismemberment (AD&D) insurance; personal time off (PTO) and sick leave; paid holidays; and tuition reimbursement.
• NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component.
• Some positions may be eligible for relocation expense reimbursement.
• Limited-term positions are not eligible for long-term disability or tuition reimbursement.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree