Graduate Intern - Synchrophasor Research - Engineering Analytics & Modeling

 

Dominion Energy is committed to providing reliable, affordable, and increasingly clean energy that powers our customers every day. If you want to work for a purpose-driven company that values safety and collaboration, we’re looking for you. You won’t just find a job here; you’ll find your career. Review the position below and apply today.

 

We offer a hybrid 3-2 work schedule (three days in the office, two days of teleworking) to accommodate the need for flexibility.

 



 

 

We Strive to Drive Power Innovation Through Data

The nature of the grid is in flux – we on the Engineering Analytics & Modeling team see this challenge and aim to rise to the occasion. Our team mandate is simple: we aim to bridge the gap between real-world system data and generate innovative capabilities and provide consumable business intelligence to help steer the future of how we monitor and manage the grid. Our team is responsible for the capture, utilization, and application of time-series data, including Synchrophasors and SCADA. Our work is to steward this data and ensure its analysis and outputs influence our business and the industry to adapt and meet the demands of shifting energy landscape. We seek the curious, the self-motivated, and those driven by the pursuit of driving change.

Job Summary: We Seek Joint Expertise in Power, Statistics, and Modeling

Our team comprises a critical part of the Strategic Initiatives team on the Electric Transmission organization. We focus on addressing a broad spectrum of transformational and innovative projects with a focus on solving problems through real grid measurements, data analytics, and custom developed modeling and software capabilities built on the backbone of our time series data. A successful candidate will be able to:

• Support the development of an array of analytical tools and capabilities aimed at observing, diagnosing, and surfacing system behavior that can help improve grid management.  
• Explore new research areas for time-series analysis in power systems.
• Develop and evaluate new system evaluation methodologies built on the premise of improving our observability of dynamic system behaviors through the creative use of signal analysis. 

Required Knowledge, Skills, Abilities & Experience

The knowledge, skills, abilities and experiences required for entry into this job include the following: 

• Engineering principles: A strong engineering background, preferably in electric power systems. Other engineering backgrounds may be considered if presented in combination with a strong electric power background.
• Power system fundamentals: steady-state and dynamic stability analysis (e.g. PV curve, transient stability, eigen analysis). The candidate should be able to extend this theoretical knowledge to large-scale, practical problems to identify implementable solutions.
• Data Science & Data Analysis: The candidate should possess strong software development skills and frequently use code to solve analytical and automation problems. More specifically, the candidate must demonstrate a high degree of proficiency in developing tools and performing analysis using Python and the common Python scientific computing libraries. 
• Real-world measurement data analysis: experience analyzing and modeling of electric power systems (e.g. inertia estimation, mode estimation, model validation, state estimation). 
• Operational model analysis: This includes the application of signal processing techniques to power system analysis (Fourier transform, SVD, time-frequency analysis). 
• Autonomy and self-starting: We are spearheading a brand-new industry capability; we are seeking candidates with the ability to operate independently while aligning to the joint mission of our team. 
• Critical thinking: Our team requires an ability to think abstractly, pursue unknown unknowns, and the motivation to persevere even in failure. What we are solving for is new and challenging – we require out of the box thinkers. 

Education Requirements

• Candidates must have completed a Bachelor’s degree in an engineering discipline and be working on a Master’s or preferably a Ph.D. degree from an ABET accredited Engineering program at a four year or post-graduate college or university.
• Must have an anticipated graduation date of Winter 2026 or later. 
• Candidates must have a 3.2 m