Graduate Intern: Real-Time Microgrid Modeling & HIL

NLR; Golden (CO); On-site; USD 45,000 - 71,000; Full time

Job summary

National Laboratory of the Rockies (NLR) seeks a graduate-level intern to advance modeling, real-time simulation, and hardware-in-the-loop testing of distribution systems and microgrids. You will set up CHIL interfaces, run HIL experiments, and help analyze high-resolution data from simulations.

The role emphasizes grid operation for islanded and connected modes, Python-based data analysis, and collaboration with NLR researchers.

Qualifications

• Currently enrolled in or recently graduated from a Master’s program in Electrical Engineering (Power Systems).
• PhD candidate status considered for longer internship timelines.
• Experience with power system modeling, dynamics, and control (OpenDSS or similar).
• Experience programming commercial relays and microgrid controllers.

Responsibilities

• Set up CHIL interfaces using commercial microgrid controllers and relays for real-time simulation models of microgrids.
• Conduct Hardware in the Loop (HIL) experiments with real-time simulation models of distribution feeders and microgrid controllers.
• Implement sequence of operations for grid-connected and islanded operation of utility-scale microgrids in commercial controllers.
• Analyze high-resolution data from real-time simulations and HIL experiments using Python.

Skills

Power systems knowledge

Python programming

Problem solving

Communication skills

Education

Master’s degree in Electrical Engineering (Power Systems)

PhD candidate in Electrical Engineering (Power Systems)

Tools

OpenDSS

RTDS simulator

SEL RTAC

SEL relays

Job description

National Laboratory of the Rockies (NLR) seeks a graduate-level intern to advance modeling, real-time simulation, and hardware-in-the-loop testing of distribution systems and microgrids. You will set up CHIL interfaces, run HIL experiments, and help analyze high-resolution data from simulations.

The role emphasizes grid operation for islanded and connected modes, Python-based data analysis, and collaboration with NLR researchers.