Office of the Under Secretary for Science and Energy Energy and Big Data Ellen Williams, Senior Advisor to the Secretary of Energy Virginia Summit on Science, Engineering and Medicine Big Data in Industry December 5, 2014
Projecting the Energy Future Industrialisation and growing power demand increase the world s appetite for primary energy: BP Energy Outlook http://www.bp.com/statisticalreview 3
Clean, Secure and Affordable Energy Clean Reduced GHG emissions Reduced health hazard emissions Reduced environmental degradation Clean Meeting all three goals will require: An all of the above energy mix with more low carbon energy sources Greatly improved energy efficiency Technical transformations that increasingly make change economically attractive Secure Reduced Energy Imports Withstand natural disasters Withstand attacks Local economies Secure Affordable Affordable Direct economic costs Indirect costs Sustainability
Big data in energy system networks Example: Rail Shipments of Coal to Power plants Between 2013 and 2014 average train speeds are down about 20%, and average dwell times for shunted trains are up 20%, even though overall shipping volume is up only 4%. A number of coal fired Power Plants are below the preferred 30 day stockpile of coal on site
Big data in energy system networks System response is non linear with respect to total traffic, because the system is optimized based on different delivery criteria for different types of freight. Systems are highly instrumented, with feedback and controls fit for purpose data management.
Example: The Electric Power Grid Big data in energy system networks
Big data in energy system networks Example: Manufacturing Manufacturing is responsible for about 25% of U.S. Primary Energy Use. About 2/3 of that energy is lost as waste heat, and within the manufacturing processes themselves about 40% of energy is lost as waste heat.
Manufacturing efficiency and big data Advanced Controls, Sensors, Models & Platforms Encompass factory and supply chain use of sensing, instrumentation, monitoring, control, and optimization Enable hardware, protocols and models for advanced industrial automation Leverage High Performance Computing for High Fidelity Process Models Significantly reduce energy consumption and GHG emissions & improve operating efficiency 20% to 30% potential Increase productivity and competitiveness Smart factories will be interconnected with supply chain, distribution, and business systems. c Couple with innovation in manufacturing techniques such as roll-to-roll manufacturing and additive manufacturing
High Fidelity Models in Manufacturing Physical simulations Oak Ridge National Laboratory s Titan High performance computer has a highly parallel, CPU GPU hybrid architecture that enables simulations of unrivaled detail. It s speed, 27 Petaflops, allows optimization by simulations covering hundreds of combinations of operational and design variables. Design Application: Computational fluid dynamics evaluation of transonic fluid compression rotor reveals the formation of vortices that can cause stall and degrade efficiency. Future: Operational feedback based on simulation and analysis in real time c
High Fidelity Models in Oil & Gas Slide 11 Exploration by seismic imaging Historically, the design of seismic measurements was limited because the outputs that could only be interpreted with limited post processing. Now, more complex measurement designs can be used, where the information returned requires advanced algorithms and computer processing for interpretation
High Fidelity Models in Oil & Gas Slide 12 Exploration by seismic imaging Computational power technically available doubles roughly every year, enabling more powerful analysis: Elastic modeling Full Waveform Inversion Higher frequencies and resolution Integration of rock physics with geophysical analysis Slow, manual processes being automated & run iteratively to achieve dramatic improvements in cycle time There are clear pathways to further improvements in Seismic Imaging including potential for 4D imaging.
Data needs in well development Monitoring the rock environment Growing application: real time seismic imaging using passive sensors of response to drilling operations. Driving interest in improving fraccing in shale. 14b
In energy systems, Big Data spans from physical modeling and analysis, through operational control, to supply chains and customer response. Scenarios: energy mix in 2035 Because of the advances in computational and sensor capabilities, there are extensive opportunities for significant improvements in energy efficiency, integration of diverse energy sources, and implementation of physical approaches that are only feasible with intense instrumentation and controls. Examples shown are only a part of the range of Big Data applications in Energy Systems. Gains across virtually every part of the energy system are available to help deliver Clean, Secure and Affordable Energy. Source: IEA World Energy Outlook 2011 14b