Computational and data-enabled science and engineering in energy systems

Our research provides solutions by the development, implementation, testing, and application of algorithms and software used to solve large-scale scientific and engineering problems of energy industry from power plants, power systems, atmospheres, oceans, vehicles to others. We extensively employ data assimilation and data driven methods to achieve better solutions. The aim is to provide for innovative products in energy industry. We are primarily interested in developing knowledge and tools for energy industry and applying those tools for the solution of problems in a variety of applications.

COMPUTATIONAL ENERGY SOLUTIONS

Our research is focused for all three weather-driven resources wind, solar, and marine. Using environmental prediction systems that incorporate computing technologies and proprietary methodologies, the aim is to deliver products for relevant, accurate, and critical information necessary for effectively siting, developing, and operating energy projects.

RESEARCH CONTEXT

The way we power and fuel our economies is a critical and immediate challenge that faces the world today. The scale of the challenge requires an unprecedented response. Driven by worldwide population increases and rising standards of living, global energy demand is projected to grow dramatically in the coming decades. Achieving energy security and sustainability in an environmentally responsible manner requires the development of cost-effective energy solutions.

DATA ASSIMILATED AND DRIVEN POWER SYSTEM APPLICATIONS

New England test system (NETS) and New York power system (NYPS) 68 bus power system one line diagram figure used in data assimilation.

DATA ASSIMILATED AND DRIVEN ATMOSPHERES AND OCEAN APPLICATIONS

Turkish Burdur region solar radiation forecast ensemble employing Mesoscale solar data assimilation.

DATA ASSIMILATED AND DRIVEN OPERATION AND MAINTENANCE APPLICATIONS

Novel approach subjective logic input triangle for human input with no statistical knowledge feedback to data-enabled machine learning operation and maintenance algorithm.

DATA ASSIMILATED AND DRIVEN COMPUTATIONAL FLUID DYNAMICS APPLICATIONS

Spanish Techno-park Urban flow simulation employing measurements.

DATA ASSIMILATED AND DRIVEN ELECTRICITY MARKET APPLICATIONS

Canadian electricity market price spike parameter estimation employing data assimilation and machine learning.

VISION

Realizing the full business potential of alternative and renewable energy will require advances in the underlying technologies as well as adaptations in the existing energy infrastructure. High-performance computing capabilities and state-of-the-art numerical simulation models will play a key role in accelerating the business progress necessary to fulfill this potential and is critical to advancing our understanding of the fundamentals of renewable energy business, from the smallest spatial and temporal scales to the integration and design of full systems. Advances in high-performance computing, numerical methods, algorithms, and software design now enable scientists and engineers to solve renewable energy business problems that were once thought intractable. Renewable energy resources and clean energy alternatives are playing a larger role in diversifying the world's energy future. The aim is to identify computational needs and opportunities in energy.

For more information on Data and model driven computational energy solutions., please contact Bahri Uzunoğlu (bahri.uzunoglu@angstrom.uu.se / bahriuzunoglu@computationalrenewables.com) and please visit our laboratory website https://www.researchgate.net/lab/Bahri-Uzunoglu-Lab.