Enabling Advanced Weather Modelling and Data Assimilation for Utility Distribution Operations

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Tuesday, 4 February 2014: 11:30 AM
Room C114 (The Georgia World Congress Center )
Lloyd Treinish, IBM, Yorktown Heights, NY; and J. P. Cipriani, A. P. Praino, and R. Foltman

Handout (3.3 MB)

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. Hence, the ability to predict 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 (people and equipment) to minimize time for restoration.

To address these problems for a utility in southeastern Michigan, we are developing an approach to couple weather forecasts to optimal crew deployment to enable positioning of appropriate restoration resources prior to an event. The first 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.

Impacts on the utility operations from severe weather vary from convective, summer thunderstorms to winter wind and snow/ice events. Therefore, to converge on an optimal model configuration for consistent, year-around operations that are computationally tractable, retrospective analyses were conducted on ten events that occurred in 2012 and 2013, which were representative of the weather events that led to disruption of the overhead network. Numerical experiments as hindcasts were performed to evaluate the model, whose results were compared to both conventional observations as well as reported utility outages. The validation methodology was developed focused on the utility's needs. It was built with the Model Evaluation Tools (MET version 4.0) community verification package. Temperature, wind, dew point and precipitation data from the aforementioned set of in situ surface observations were compared to the forecast data as well as the data from the NOAA/NCEP North American Model.

Although such NWP-based weather forecasts are only a prerequisite to the optimization of weather-sensitive business operations, we will focus herein on the analysis of the weather component. A companion presentation will cover the impact (damage/outages) and restoration prediction effort. 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. This will include a discussion of the retrospective analysis and the results of operational forecasting that began in July 2013. In addition to the evaluation, we will present how the forecasts are being used, including the deployment of customized visualizations. 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.