Postdoctoral Research Associate - Urban System Visualization, Integration & Optimization

Requisition Id 13620 

Overview: 

The Grid Interactive Controls (GIC) Group in the Electrification and Energy Infrastructure Division (EEID) within the Energy Science and Technology Directorate (ESTD) at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate. The Grid Interactive Controls group specializes in pioneering innovations at the edge of today’s power grid, concentrating on fortifying grid security, enhancing reliability, bolstering resilience and advancing decarbonization. Our primary focus lies in the comprehensive 'Everything-to-Grid' (X2G) strategies, developing cutting-edge solutions in grid-edge integration and control. Our commitment revolves around seamlessly incorporating emerging distributed energy resources, including demand response emerging at the grid edge, providing essential services crucial to its reliable operation. Employing a diverse range of disciplines such as urban modeling, control theory, optimization, economics, game theory, data analytics, and machine learning, the GIC Group delves deeply into understanding intricate grid-edge interactions and operations. Researchers are dedicated to laying the groundwork for optimal X2G integration and utilization. Group initiatives encompass advanced urban system modeling, analysis & visualization, low-cost wireless sensing technologies, and interoperable & scalable control mechanisms tailored for the grid edge. Research promotes grid-interactive efficient buildings as a pivotal component in advancing building-to-grid integration, amplifying their role in electrification of heating and ultimately shaping the future decarbonized grid.

 

Specifically, the urban energy modeling team within the GIC Group has created and simulated a model of every U.S. building. This involves urban-scale building energy modeling at the resolution of individual buildings and at the scale of nations to analyze building codes, energy efficiency, demand response, and climate change impacts toward a sustainable and resilient built environment. These activities involve multi-disciplinary collaboration across ORNL, collaborative partnerships and NDAs with over 20 well-known companies, and significant awards on world-class computational resources. This team achieved world-first simulation of 125.7 million U.S. buildings, public release of 122.9 million building energy models (bit.ly/ModelAmerica), and has scaled EnergyPlus to over 1 million simulations per hour on supercomputers. In partnership with major organizations, the team is actively improving the data and algorithms of the Automatic Building Energy Modeling (AutoBEM, bit.ly/AutoBEM) software suite which involves big data processing, data analytics, machine learning, high-performance computing resources, computer vision, and building science to create, model, simulate, validate, and analyze building performance to create software prototypes (e.g. dashboards, bit.ly/virtual_epb) for actionable use of building data, models, or analysis by specific stakeholders. Over 50 million core-hours have been awarded to release a Model America v2.0 dataset in 2022.

 

Selection will be based on qualifications, relevant experience, skills, and education. The successful candidate should be highly self-motivated and independent in conducting research under general guidance, and is expected to prepare manuscripts for scientific publication and present the work to sponsors and at conferences. This position requires development of innovative software techniques, knowledge of building energy modeling, multi-sector grid edge technology integration, frequent and consistent review of related literatur. It also requires frequent interactions and collaboration with a multi-disciplinary team of researchers from universities, national laboratories, and private industry for the development of technical capabilities, demonstrations, proposals, oral presentations, and publications.

 

As part of the urban energy modeling team, the candidate will assist in the development and implementation of data organization and software development for scalable use and analysis of business-sensitive data by supporting senior R&D staff in 1) the invention or improvement of innovative algorithms for detection or estimation of building characteristics, 2) validation against known data sources, 3) scalable generation/simulation of buildings using OpenStudio and EnergyPlus, and/or 4) use of Artificial Intelligence (AI) techniques to quantify and improve city-to-nation scales of individual building energy models. As part of the R&D, the candidate will be required to appl