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Webinar: Advancements in Utility-Scale Battery Energy Storage Safety Features, Standards, Practices
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Utility-scale battery energy storage is a critical component to achieving decarbonization objectives while maintaining a reliable and resilient power grid. Deployment of utility-scale battery energy storage capacity is accelerating dramatically in the U.S. However, both local firefighters and people living near proposed energy storage project sites have questions about the safety of these facilities. This presentation by Mike Simpson of AES will address the characteristics of lithium-ion battery energy storage systems (BESS), potential hazards such as thermal runaway, and new approaches to mitigate those risks. These advancements encompass equipment safety approaches; a four-stage approach to effective protection against thermal runaway; advancements in enclosure design; monitoring systems; standardized testing; codes and standards; safety evaluation processes; and passive and active safety measures. The presentation will also provide examples of how clean energy project developers can work together most effectively with local fire departments on hazard mitigation analyses, project site design refinements, and emergency response planning to further enhance energy storage safety.
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Mike Simpson
Director, Innovation Engineering
AES
Mike Simpson, Director ofInnovation Engineering at AES, leads a team of engineers and specialists in thedevelopment of new engineering solutions. He applies a multi-disciplinarybackground in R&D, 19 years’ experience with battery energy storage with 16years’ experience applied to clean energy to generate guidance and integratenew technologies that support a more sustainable, more robust, andcost-effective utility grid. Prior to AES, he worked in renewable energy andenergy efficiency with Fluence, EPRI, NREL, and Rocky Mountain Institute,bringing an early career background in aerospace engineering. He has a B.S. inAerospace Engineering from the University of Colorado and an M.S. in SystemDesign and Optimization from the Georgia Institute of Technology.
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