Advanced Weather Modeling for Outage Prediction and Response Optimization in Utility Operations

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Monday, 5 January 2015: 11:30 AM
224B (Phoenix Convention Center - West and North Buildings)
Lloyd A. Treinish, IBM Research, Yorktown Heights,, NY; and A. Praino, J. P. Cipriani, A. Singhee, H. Wang, and R. Foltman

ADVANCED WEATHER MODELING FOR OUTAGE PREDICTION AND RESPONSE OPTIMIZATION IN UTILITY OPERATIONS Lloyd Treinish*, Anthony Praino, James Cipriani, Amith Singhee, Richard Foltman IBM Thomas J. Watson Research Center

In our continuing work applying advanced weather modeling to enable outage prediction and response optimization for utility operations we examine the application and development of a coupled modeling system. The distribution system of an electric utility, particularly with an overhead infrastructure, can be highly sensitive to local weather conditions. Power outages caused by severe weather events can have major impacts on such operations. The application of an integrated approach to the prediction of specific events or combination of weather conditions that can disrupt that distribution network with sufficient spatial and temporal precision, and lead time has the potential to enable proactive allocation and deployment of resources to minimize time for restoration. To address these problems for a utility in southeastern Michigan, we will present an update on the work in developing an integrated approach to couple weather forecasts as part of a system to enable optimal crew deployment and positioning of appropriate restoration resources prior to an event. The foundational component of this coupled system builds upon the on-going work in applying mesoscale numerical weather prediction (NWP) at the IBM Thomas J. Watson Research Center dubbed “Deep Thunder”. It is derived from a configuration of the WRF-ARW (version 3.4.1) community model, and operates in a nested configuration focused on the utility's service territory, with the highest resolution at one km, utilizing 42 vertical levels. The configuration also includes parameterization and selection of physics options appropriate for the range of geography within the domain from highly urbanized to rural. This includes Thompson double-moment microphysics, Yonsei University non-local-K scheme with explicit entrainment layer and parabolic K profile in the unstable mixed layer for the planetary boundary layer, NOAH land-surface modelling with soil temperature and moisture in four layers, fractional snow cover and frozen soil physics, Grell three-dimensional cumulus parameterization in the outer domain (nine km resolution), and a three-category urban canopy model with surface effects for roofs, walls, and streets. It produces 72-hour forecasts, which are updated twice daily. In addition, it incorporates a diversity of input data sets ranging from NCEP RAP for background conditions, NCEP NAM for lateral boundaries, three-dimensional variational data assimilation from several thousand surface and near-surface observation stations operated by Earth Networks, NOAA and other agencies, and surface conditions derived from remotely sensed observations provided by NASA and USGS. The surface data are particularly important for stable and accurate boundary layer, land surface and urban canopy modelling. NWP-based weather forecasts are a prerequisite to the optimization of weather-sensitive business operations. They are however not sufficient to enable a proactive approach for the optimization of outage planning and resource allocation. The weather model forecast must be coupled to an impact (damage/outage) restoration prediction model. Hence, we will discuss the on-going work, the overall approach to the problem, some specifics of the solution, and lessons that were learned through the development and deployment. We will also discuss the overall effectiveness of our particular approach for these and related applications, issues such as calibration of data and quantifying uncertainty as well as recommendations for future work.