A Weather-Readiness Assessment Model to Determine the Resilience of Utilities Businesses in Dealing with Weather Volatility

In the recent years, there has been an increasing trend of weather volatility negatively impacting electric utilities operations and leading to significant economic losses. Also weather influences several parts of the new smart grid ecosystem with direct and indirect impact to overall business performance of utilities, energy supply sustainability of utilities, the utilities role in greenhouse gas emissions, utilities prudent use of critical natural resources such as water, land, etc. and conservation of local ecosystems. Weather-based decision-making for utilities is gaining significance not only in generation and energy trading but also in operations where, in the past few years, extreme weather events have severely impacted the overall service reliability of utilities. In this context, electric utilities want to ensure that they are ready to deliver reliable services and can make day-to-day operational decisions which are sensitive to weather volatility across their business processes. So a weather-readiness model is proposed here that will help utilities assess current status of their systems with respect to weather volatility. This model will identify specific areas of focus on, to incorporate the influence of weather variables and measure how these variables impact their business goals of providing reliable and affordable power, maintaining service levels and limiting operational costs. It includes an assessment of the utility's operational preparedness to keep outages to a minimum during a severe weather event and optimize the time and number of resources required for restoration and recovery efforts.

The proposed weather-readiness assessment model for utilities will allow them to map all parts of their business impacted by weather so that they can enhance the quality of service to their customers and improve their overall business performance. This can be achieved by identifying simple weather integration points in their operations and decision-support systems in the short-term as well as identify opportunities for building advanced weather-based predictive solutions that can have a positive impact on their performance in the long-term.