The objective of this potential FOA is to enhance grid reliability and resilience in the age of big data. The electric power industry is witnessing an explosive growth in the volume, variety, and velocity of utility data. Specifically, the tsunami of data generated by the Advanced Metering Infrastructure (AMI) as well as sensors with fast-streaming data sets (e.g., phasor measurement units) have been challenging the traditional methods of data acquisition, use, and storage in utilities. However, the availability of utility-specific data analytics products and services for taming this data deluge has been very limited to date.

Advanced analytics are critical for harnessing the power of big data that are generated by sensors at unprecedented rates to address challenges related to:

1. variable system dynamics caused by renewable integration,
2. increased vulnerability to climatic extremes, and
3. increased infrastructure decentralization and interdependency. Advanced analytics that extract actionable information from sensor data need to consider not only systems’ physics but also human factors to be effective.

Demonstrations and direct partnerships with data providers and power sector utilities would be strongly encouraged since they ensure that the analytics can meaningfully support planning and operations decision.

The FOA is anticipated to focus on a single Topic Area, Sensor Data Analytics Demonstrations. This Topic Area would seek applications to conduct Research and Development and Demonstration (RD&D) activities that achieve the following FOA objectives:

• Design and validate case studies that consider both rural and urban uses of sensor data for enhancing the resilience and reliability of the grid.
• Develop scalable methodologies for enabling distribution system protection using senor data.
• Develop novel methods for harnessing big data to enable automated event detection and classification.
• Establish innovative approaches for generating and recording contextual information associated with historical data to enable automated data labeling.
• Develop new approaches for capturing contextual information associated with data generated by grid sensors.
• Develop rigorously validated approaches to enable uncertainty-informed load forecasting with variable (moving) prediction time horizons as well as computationally efficient online learning; a focus on extreme weather and climate conditions is encouraged.
• Develop technology that integrates human factors and/or cognitive science considerations to enhance effectiveness of decision support systems related sensor data analytics.
• Develop uncertainty-informed data-integration methods to enable integrating data from multiple sensors and sources with variable degrees of data veracity.
• Projects would culminate in a field demonstration – this should be no less than 15% of the project scope.
• Projects would have a distribution focus but projects that look at both transmission and distribution would not be excluded.

This Notice of Intent is issued so that interested parties are aware that DOE may issue a FOA on this topic. Any information contained in this NOI is subject to change.

No Concept Papers or Full Applications are being accepted currently.