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Electric utilities spend a large amount of their resources and budget on managing unplanned outages, the majority of which are driven by weather. The weather is the largest contributing factor for power outages faced by the population in the United States and several other countries. A major ongoing effort by utilities is to improve their emergency preparedness process, in order to 1) reduce outage time, 2) reduce repair and restoration costs, and 3) improve customer satisfaction. We present an approach called Outage Prediction and Response Optimization (OPRO) to improve emergency preparedness by combining a) localized and highly accurate weather prediction, b) damage prediction, c) infrastructure health-aware damage hotspot analysis, and d) optimal resource planning. The combination of these capabilities can enable utilities to initiate their storm preparation process 1 to 2 days in advance of the storm and precisely plan their resource schedules and escalation stance. This would be a profound change to the business process of utilities, which today tends to be reactionary once the storm hits. We describe these capabilities and their effectiveness in terms of metrics relevant to a utility, the related use cases, and the overall business process that brings them together in the context of a real U.S. utility.

Efficient, resilient, and safe operation of an electric utility is dependent on the local weather conditions at the scale of its infrastructure. This sensitivity to weather includes such factors as damage to distribution or transmission systems due to relative extremes in precipitation or wind, determining electricity demand and load, and power generation from renewable facilities. Hence, the availability of highly focused weather predictions has the potential to enable proactive planning for the effect of weather on utility systems. Often, such information is simply unavailable. The initial step to address this gap is the application of state-of-the-art physical weather models at the spatial scale of the utility's infrastructure, calibrated to avoid this mismatch in predictability. The results of such a model are then coupled to a data-driven stochastic model to represent the weather impacts. The deployment of such methods requires an abstraction of the weather forecasting component to drive the model coupling.

A number of key technological, social, and business disruptions will drive a new generation of smarter energy applications. The disruptions include the following: 1) large sensor deployments, resulting in a huge increase in data volumes and variety, 2) a move toward clean energy and intermittent renewable energy sources, and 3) a move to highly distributed energy resources. To enable resilient and efficient power delivery, with these disruptions, will require a host of new applications that analyze large amounts and varieties of data in the context of the connected grid and perform analysis, visualization, and control in real-time with very low latency. In this paper, we present a set of capabilities that enable such applications, and a software and hardware platform that combines these capabilities to enable rapid development of a wide array of high-performance and analytics-rich applications. These capabilities include: 1) high-performance time-series ingestion, 2) a flexible data model that spans multiple contexts, 3) high-performance, in-memory analysis of time-varying, hierarchical graphs, 4) data service for co-presenting real-time and static spatiotemporal data for real-time web-based visualization, and 5) a seamless combination of event-based and service-oriented programming models.