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OwlSim : Revolutionizing National Energy Policies Through Technology

OwlSim : Revolutionizing National Energy Policies Through Technology. COMP 410 in Collaboration with Citizens for Affordable Energy. Overview. Introduction COMP 410 Project Motivation Project Objectives Simulation Framework Energy Model and Plans Demo Conclusion Questions.

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OwlSim : Revolutionizing National Energy Policies Through Technology

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  1. OwlSim: Revolutionizing National Energy Policies Through Technology COMP 410 in Collaboration with Citizens for Affordable Energy

  2. Overview • Introduction • COMP 410 • Project Motivation • Project Objectives • Simulation Framework • Energy Model and Plans • Demo • Conclusion • Questions

  3. Introduction

  4. About COMP 410 • “Software Engineering Methodology” • Design class satisfying computer science Bachelors of Science Decree capstone requirement • Typically taken by juniors and seniors • Warm-up project during first 3 weeks, then semester-long project … with a real customer! • Student driven – no problem sets or lectures

  5. Our Customer:Citizens for Affordable Energy • CFAE is a national not-for-profit membership association • Goal is to educate citizens and policymakers about non-partisan national energy solutions • Leadership • John Hofmeister, Founder and CEO • Karen Hofmeister, Executive Director

  6. Project Motivation • U.S. has no long-term national energy policy • CFAE believes this could be disastrous • CFAE wants a software tool that can simulate the long term effects of policies (or lack thereof) • Shifting policy landscape mandates a very flexible simulator

  7. Our Mission • Create a mathematical model of the U.S. electric power generation and distribution • Make a set of policies and assumptions for the next 50 years of the energy industry • Develop a simulation framework to evaluate the effects of the plans based on the assumptions and the model • Make the results accessible to the public • Make the framework flexible and extensible to allow domain experts to change our assumptions and models

  8. Simulation Framework

  9. Theoretical Design • Modeling complex systems with mathematical functions • Functions represented as modular “circuit elements” with inputs and outputs • Functional modules can be “composited” • Encapsulate components of model • Allows composite modules with other modules inside. • Arbitrarily complicated models can be created • Diagram!

  10. Capabilities • Supports many simultaneous users • Scales with load • Basic use case • View model, plan, precomputed results • Authenticated use case • Edit plan, recompute results, save results • Expert Authenticated use case (if working) • System Administration use case (if working) • Publish results (if working)

  11. Energy model and Plans

  12. High-Level Model

  13. Worst-Case Plan

  14. Average-Case Plan

  15. Best-Case Plan

  16. Comparison with Other Models

  17. Demo

  18. Demo • Connecting through web • Explain GUI • Basic use case • View model, plan, precomputed results • Authenticated use case • Edit plan, recompute results, save results • Expert Authenticated use case (if working) • System Administration use case (if working) • Publish results (if working)

  19. Conclusion

  20. Acknowledgements • Citizens for Affordable Energy, John Hofmeister, Karen Hofmeister • People we consulted for help • Microsoft for sponsorship • Steven Wong, Scott Rixner • TAs: • Team leads and all our hard-working students

  21. Questions

  22. Questions • Can’t this be done in Excel? • Yes, simple test model could be done in Excel, but professionals could develop more complicated models. Excel doesn’t have centralized mechanism for managing results. Visualizing model, others can edit, maintainability, portability. Results is not only objective – also export. • How can you develop model without domain knowledge? • Talked to experts, used recognized resources (EIA etc.). Simple but reasonable model. • How did you choose Azure over other clouds? • Had past support from Microsoft

  23. Questions • What was biggest technical challenge? • What was biggest non-technical challenge? • HTML5, other technology choices • Did you use curve fitting on historical data?

  24. Backup

  25. References • EIA etc.

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