Distribution Ops & Planning Engineer III
Utilities around the world are transforming their distribution systems to support electrification, distributed energy resources (DER), resilience, decarbonization, and growing operational complexity. As planning and operations become increasingly data-driven, utilities need advanced analytics, high-fidelity modeling, artificial intelligence, and trusted data to maintain a reliable, resilient, and efficient grid.
About the role
The EPRI Distribution Operations and Planning team leads collaborative research that advances the state of the art in distribution system planning and operations. Through the development of innovative methodologies, simulation tools, analytics, and field-validated solutions, the team addresses challenges spanning grid modernization, DER integration, protection, resilience, operations, and emerging technologies.
This position conducts research in advanced power system modeling, simulation, AI, machine learning, and measurement-based analytics. The successful candidate will develop and apply advanced analytical methods that combine power system models with data-driven approaches, leveraging utility data to improve planning, operations, and protection while translating cutting-edge research into practical solutions for the electric utility industry.
Responsibilities
- Makes significant contributions to advanced modeling, simulation, and analytics projects
- Conducts independent research activities with guidance and contributes to development of new tools, methodologies, and research approaches to address industry challenges
- Evaluate emerging technologies and applications through analytical and field-based studies
- Contribute to technical publications, conference papers, webinars, and industry presentations
- Support proposal development and strategic research planning activities
- Engage with utility members to identify challenges, transfer research outcomes, and support technology adoption
- Contribute to the development and evaluation of AI-enabled tools and analytics applications for utility planning and operations
- Develop and apply repeatable processes for measurement data cleaning, quality assessment, and integration
- Conduct model verification and validation activities using utility measurement data
- Assess the applicability, limitations, and performance of emerging AI technologies within utility environments
Requirements
- Strong knowledge of power system fundamentals, specifically distribution systems
- Demonstrated experience developing software tools and analytical applications using Python, C#, Delphi, Java, or similar languages
- Experience with distribution system model development, validation, and verification
- Experience cleaning and utilizing utility measurement data from AMI, SCADA, and other field sources
- Proven ability to conduct power systems analytical studies, with an emphasis on distribution modeling and simulation
- Demonstrated experience with one or more industry modeling and simulation tools such as OpenDSS, CYME, Synergi, Milsoft, PowerFactory, EMTP, PSCAD
- Experience applying AI, machine learning, or advanced analytics techniques to power system applications
- Familiarity with modern AI and data science frameworks such as TensorFlow, PyTorch, Scikit-Learn, LLM-based systems, agentic workflows, or related technologies is desirable
- Experience modeling high penetrations of DER, inverter-based resources, electrification technologies, and flexible loads
Qualifications
- Bachelor’s degree in a technical field or equivalent experience
- Preferred MS or PhD in Electrical Engineering with Power System emphasis
- Minimum 2-3 years of engineering, utility, consulting, research, or equivalent experience preferred
Benefits
- Medical, dental, and vision insurance
- 401k retirement plan
- Short-term and long-term disability (STD/LTD) coverage
- Paid family leave
- Life and accident insurance
- Paid time off (flexible vacation, sick leave, and holiday pay)
Pay
This role is eligible to participate in EPRI’s annual incentive program. The amount of incentive varies and is subject to the terms and conditions of the plan. Salary may vary based on education, experience, skills, abilities, internal equity, and geographic location.