Grid Modeling & Analytics Intern (Power Systems)

Xcel Energy Inc

Denver (CO)

Hybrid

USD 29,000 - 32,000

Full time

5 days ago
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Benefits offered by this job

Pension
401(k) plan
Paid time off (PTO)
Holidays

Job summary

Xcel Energy is seeking interns to support the System Planning Modeling & Analytics Organization, in Denver, CO. The internship focuses on electric distribution modeling, data quality, and planning analyses to support grid modernization and electrification goals.

The program offers hands‑on training, opportunities to present to senior leadership, and collaboration with internal stakeholders. Start date is May 31, 2027 with potential extension based on performance, hybrid in‑person work, and

Qualifications

  • Current student, Junior or higher (as of Fall 2027)
  • Enrolled in ABET accredited college or university and pursuing a degree in Electrical Engineering (plus for power systems engineering program) or Computer Science & Data Analytics
  • Able to work full‑time during the summer (up to 40 hours a week)/ part time during the school year (up to 20 hours a week) and commuting to the designated work location in‑person, per our hybrid work policy.

Responsibilities

  • Participate in validation and analysis of SCADA measurement data.
  • Contribute to utility planning activities.
  • Gain an understanding of GIS, EMS, CIS and other critical utility systems.
  • Power systems modeling, hosting capacity analysis, and time series data analysis.

Skills

Python
Data analysis tools

Education

Electrical Engineering or Computer Science & Data Analytics

Job description

Xcel Energy is seeking interns to support the System Planning Modeling & Analytics Organization, in Denver, CO. The internship focuses on electric distribution modeling, data quality, and planning analyses to support grid modernization and electrification goals.

The program offers hands‑on training, opportunities to present to senior leadership, and collaboration with internal stakeholders. Start date is May 31, 2027 with potential extension based on performance, hybrid in‑person work, and

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