SAN DIEGO--(BUSINESS WIRE)--Lawrence Livermore National Laboratory (LLNL), XENDEE Corporation, and a team that includes the University of Michigan - Dearborn, with the support from White Sands Missile Range, have been awarded a contract by the United States Department of Defense (DoD) to develop a new and innovative approach to energy management and analytics. The new platform will integrate multi-source data and convert it to actionable information for energy efficiency, resiliency, and security enhancement as part of the Facility Energy Saving and Security Technology (FEST) project.
“The FEST project integrates a number of techniques ranging from machine learning, optimization, modeling and simulation, to techno-economic analysis, all building upon LLNL’s partnership with XENDEE and the University of Michigan, so that military facilities are able to save on their energy usage and at the same time be more resilient against unknowns,” said Emma Stewart, Ph.D., Associate Program Leader of the Cyber and Infrastructure Resilience Program at Lawrence Livermore National Laboratory.
As part of this contract, the Project Team will develop resiliency, security, and efficiency metrics along with technology that transcends existing utility data and asset management systems to enable enduring infrastructure, energy efficiency, and optimized full-spectrum planning and operations. Additionally, the XENDEE platform will be connected to the FEST platform and enable more efficient and accurate Microgrid design and decision-making in the DoD space.
“The Integration of XENDEE technology with the LLNL FEST platform will enable multiple value streams for Microgrid powered DoD facilities,” said Michael Stadler, Ph.D., CTO of XENDEE. “FEST will provide reliable data, planning and operational guidance that will support the performance characteristics, fight-through capabilities of critical facilities and increase the efficacy of decision-making within a mission critical environment.”
The project includes development of solutions that integrate a large volume of multi-source data, including smart meter data for energy system recovery from man-made and natural extreme events, and help planners and operators make decisions before, during, and after the events. The tools developed through this project will help enable optimal planning and operation of military facilities by leveraging operational data from distributed energy resources on site in addition to flexible load in buildings. Moreover, new knowledge visualization technologies treating energy as a mission critical commodity will be developed to help facility energy managers prioritize capital investments (i.e. planning) and optimally manage installations to save energy demands and costs (i.e., operation).
“The ability to recast big data from smart meters, devices and distributed energy resources into a cognitive co-processor integrated with algorithmic optimization in economic, physical, and cyber domains is the next-step in developing a smarter energy solution for the U.S. Military,” said Adib Nasle, CEO of XENDEE. “We are proud to embark on this endeavor with Lawrence Livermore National Laboratory to enhance the effectiveness of DoD energy management systems.”
About Lawrence Livermore National Laboratory: Lawrence Livermore National Laboratory has a mission of strengthening the United States’ security through development and application of world-class science and technology to enhance the nation’s defense, reduce the global threat from terrorism and weapons of mass destruction, and respond with vision, quality, integrity and technical excellence to scientific issues of national importance.
About XENDEE: XENDEE develops world-class Microgrid decision support software that helps designers and investors optimize and certify the resilience and financial performance of projects with confidence. The XENDEE Microgrid platform enables a broad audience; from business decision makers to scientists, with the objective of supporting investments in Microgrids and maintaining electric power reliability when integrating sources of renewable generation.
