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8 Building a Multidisciplinary Research Community
Pages 128-136

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From page 128...
... Each of these disciplines in turn could have subdisciplines that would be appropriate for specific problems -- for example, optimization, nonlinear dynamics, machine learning, or databases. And while some multidisciplinary teams form naturally through mutual collaborations, a more strategic approach will be required to build a more effective multidisciplinary community to address the challenges described in this report.
From page 129...
... Ensemble forecasting based on this assumption produces probabilistic forecasts rather than specific predictions of wind velocity or cloud cover that would affect renewable energy resources. So while we may continue to work toward more precise forecasts, as desired by the power systems community and many others, multidisciplinary research teams can recognize that wind models for the grid must be treated probabilistically, and that they will produce results with uncertainty and can have larger variance than we desire.
From page 130...
... While PSERC has contributed in important ways to building a multidisciplinary research community that supports the electric grid, its mix of expertise does not extend to many of the areas of importance to developing the analytical and mathematical capabilities that will be needed for the next-generation grid. For example, computational tools to address the ac optimal power flow problem discussed throughout this report are likely to require fundamental advances in optimization that are typically outside the domain boundaries of PSERC.
From page 131...
... These centers have a strong focus on the mathematical sciences, as described in DOE Program Announcement 12-698: "These science and engineering challenges must be abstracted into an interrelated set of mathematical research challenges that require new integrated, iterative processes across multiple mathematical disciplines." The Mathematics Climate Research Network (MCRN) , which started in 2010 with support from the NSF, provides another model for fostering multidisciplinary collaborations between mathematicians and scientists in another discipline.
From page 132...
... Recommendation 9: The Department of Energy should sponsor additional efforts to create synthetic data libraries to facilitate studies of, and tool building for, the reliability and control of the future electric grid. RECOMMENDATION FOR SOFTWARE LIBRARIES In addition to having access to synthetic data, one must be able to simulate portions of the grid so as to study the various behaviors in steady-state or faulted conditions, under heavy or light loads, and so on.
From page 133...
... Recommendation 11: In view of the importance of its efforts to coordinate power grid research at the national laboratories, the Department of Energy should broaden this coordination to include academic and industry researchers. Recommendations 9 and 10, about creating libraries of synthetic data and providing access to software tools, should be well aligned.
From page 134...
... Thus all power engineering research roadmaps as adopted by DOE, NSF, the Electric Power Research Institute, and other research agencies call for new analytics for the planning and operation of the fast evolving power grid. Analytics usually refers to the suite of computer-based tools that is used by power engineers for designing the transmission and distribution systems and developing real-time monitoring and control systems for them.
From page 135...
... Computer models developed at this center using synthetic data could also be tested later on in a secure facility using CIP data. The center should foster a cross-disciplinary, multi-institutional approach to the analytical problems of the next-generation grid on a scale not normally possible in individual institutions.


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