Facility Energy Saving and Securing Technology (FEST) using Multi-Source Data
Vaibhav Donde | Lawrence Livermore National Laboratory
The existing Department of Defense (DoD) utility data and asset management systems such as the Army Meter Data Management System (MDMS) and BUILDER are deployed to reduce energy waste and costs with encouraging results. Primarily this energy savings is derived from increased visibility to energy efficiency choices and losses with improved observability to operators and facilities. However, in most of those data management systems, utility meter data are used for “billing purpose only” instead of “billing and monitoring purposes.” In addition, the meter data (e.g., data in the MDMS) are not well integrated with other measurement data (e.g., data in Utility Monitoring and Control Systems). The economic and technical values of smart meters and the usefulness of “big” smart meter data are not fully realized. The research team, which includes Lawrence Livermore National Laboratory (LLNL), the University of Michigan (UM), and XENDEE, with the support from White Sands Missile Range (WSMR), will demonstrate the Facility Energy Saving and Securing Technology (FEST) using multi-source data with the following capacities:
- Efficiency metric: Integrate a large volume of multi-source data including smart meter data, and convert the integrated data to actionable information to help facility energy mangers prioritize capital investment (i.e., planning) and manage installations to save energy demands and costs (i.e., operation).
- Resiliency metric: Identify and demonstrate use cases of smart meter data for system recovery from man-made and natural extreme events, and help utility operator make decisions before, during, and after the events.
To quantitatively evaluate success, we will use traditional efficiency, reliability/resiliency, and security metrics, such as System Average Interruption Duration Index (SAIDI) and Average Service Availability Index (ASAI). Under each metric, a pair of a base case and an optimal case without/with FEST will be utilized for evaluation, and the variation between the base case and the optimal case will be used as success criteria references.
The team will integrate the multi-source data management and analytic tool Grid-Data-Crossing (GriDXing), Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) and XENDEE’s techno-economic analysis solution for DER adoption as a FEST- based solution to improve the data usability and usefulness in economic, physical, and cyber domains.
FEST will not only enhance the effectiveness of existing systems the DoD and the Services currently use for the collection and handling of facility energy data, but it also provides new capability (resiliency and security enhancement) that is not currently offered by incumbent systems. FEST helps us understand the economic and technical values of smart meters and the usefulness of “big” smart meter data, while maintaining security and minimizing the time and cost for data system maintenance.