Post Doc Research Associate – Energy Storage Analytics and Grid Observability

The Electricity Infrastructure and Buildings Division is one of Pacific Northwest National Laboratory's most innovative teams. We are talented, award-winning administrators, specialists, technicians, managers, scientists and engineers, and we're working at the forefront of some of America's toughest energy challenges. Our mission is to provide multi-disciplinary solutions to energy challenges that have impacts on a national-scale. We employ a systems perspective that addresses technological, economic, regulatory, and market barriers to improve the nation's energy systems from generation to end-use. We provide transparent, reproducible, and unbiased results to guide policy and technological innovation.

Responsibilities
The Electricity Infrastructure and Buildings Division of PNNL is accelerating the transition to a resilient, affordable and secure energy system through basic and applied research. We leverage a strong technical foundation in power and energy systems and in advanced data analytics to drive innovation and transform markets.

The Optimization and Control Group within the EIBD is seeking a Postdoctoral Research Associate to contribute to innovative research in energy storage analytics and grid observability. This position will focus on applying optimization and control algorithms, modeling, machine learning, and dynamic state estimation to address challenges related to energy storage in grid applications. Specific areas of interest include hybrid and long-duration energy storage systems, as well as distribution system observability for enhanced grid monitoring and situational awareness.

The Postdoctoral RA will contribute to cutting-edge research in advanced data analytics and control for energy storage, grid observability, and demand-side resources. The primary focus will be the development and validation of advanced modeling and learning-based control algorithms to maximize the economic and resilience benefits of energy storage and demand-side resources within the power grid. This work will require deep technical expertise in control, optimization, and machine learning, and rich experience in energy storage analytics, building modeling, and distribution simulation tools. The candidate will collaborate effectively with multi-disciplinary research and development teams, including researchers within and outside PNNL. The candidate is expected to implement and test algorithms on Python, and/or MATLAB, Julia, and help summarize the technical findings in presentations and/or peer-reviewed publications.

Qualifications

Minimum Qualifications:

• Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.

Preferred Qualifications:

• PhD student in Optimization and Control, Electrical Engineering, Mechanical Engineering, or related field.
• Strong theoretical foundation in optimization and control.
• Expertise in optimization modeling languages and various linear/nonlinear solvers.
• Expertise in machine learning for power system applications.
• Understanding of electricity transmission and/or distribution simulation tools.
• Understanding of energy system processes, components and their workings, (e.g., generation, transmission, distribution, and consumption).
• Research experience in energy storage and distributed energy resources, thermal energy storage for buildings, and microgrid.
• Expertise in MATLAB, Python, and Julia.
• Experience with co-simulation and HELICS.
• Strong analytical, task management, and communications skills, both oral and written, and able to communicate clearly the goals, parameters, objectives, and outcomes of their research.