Graduate (Summer) Intern - Grid Optimization (L2O) for Unit Commitment

National Renewable Energy Laboratory • Golden, CO

23h

About The Position

U.S. Independent System Operators (ISOs) rely heavily on Mixed-Integer Linear Programming (MILP) to support critical operational decisions, including Security-Constrained Unit Commitment (SCUC) and Security-Constrained Optimal Power Flow (SCOPF). These tools determine which generators run, how much power they produce, and how to maintain system reliability. While MILP models are computationally efficient and scalable, they require significant simplifications of the physical power system. This creates an accuracy–solvability tradeoff: to ensure fast computation, important nonlinear dynamics and uncertainty effects are simplified or ignored, resulting in economic inefficiencies. This project explores a new paradigm: Learning to Optimize for large-scale Mixed-Integer Nonlinear Programming (MINLP) problems in Unit Commitment. By combining machine learning with structured optimization, the goal is to solve large-scale nonlinear problems efficiently while preserving theoretical guarantees. The intern will contribute to developing scalable learning-based optimization methods for next-generation grid operations. This internship offers hands-on experience at the interface of nonlinear control, optimization, and learning. The student will gain exposure to research-level problem formulation, rigorous stability analysis, and computational implementation — excellent preparation for graduate studies or research-oriented careers in control and dynamical systems.

Requirements

• Minimum of a 3.0 cumulative grade point average.
• Undergraduate: Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution.
• Post Undergraduate: Earned a bachelor’s degree within the past 12 months. Eligible for an internship period of up to one year.
• Graduate: Must be enrolled as a full-time student in a master’s degree program from an accredited institution.
• Post Graduate: Earned a master’s degree within the past 12 months. Eligible for an internship period of up to one year.
• Graduate + PhD: Completed master’s degree and enrolled as PhD student from an accredited institution.
• Please Note: • Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process. • If selected for position, a letter of recommendation will be required as part of the hiring process. • Must meet educational requirements prior to employment start date. Must meet educational requirements prior to employment start date
• Strong Math Background
• Strong background in linear algebra, optimization, and basic probability.
• Basic understanding of power systems or energy systems
• Interest in machine learning and large-scale computational methods.
• Programming & Simulation Skills
• Python (NumPy, SciPy)
• Familiarity with optimization solvers (e.g., Gurobi, CPLEX, IPOPT, or similar) is preferred
• Familiarity with NeuroMANCER library
• Experience developing and test Learning-to-Optimize (L2O) algorithms for large-scale optimization.

Responsibilities

• Assist in formulating Unit Commitment problems as MILP and MINLP models.
• Implement optimization models in Python (e.g., Pyomo, JuMP, or similar tools).
• Develop and test Learning-to-Optimize (L2O) algorithms for accelerating large-scale optimization.
• Perform computational experiments on benchmark power system datasets.
• Participate in weekly research meetings and present progress updates.

Benefits

• Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match; and sick leave (where required by law).
• 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.
• Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.

What This Job Offers

• Career Level: Intern