Preparing for risk. Weather readiness

With global warming increasing the energy of the Earth system, extreme events are becoming more frequent and potentially more damaging. Energy utilities need to assess how an increase in hazard intensity and a reduction in return periods will affect their assets. In turn, they need to consider whether to reinforce structures, develop proactive plans and build in redundancy and resilience to be able to maintain or restore functionality with minimal downtime to energy services, so as citizens for instance are not left without electricity (for long).

This is particularly important in terms of power provision because when a hazard hits an area, power reliability is a basic need for a rapid aid response – like for a hospital or critical communications. It is also important to ensure reduction of flow on effects, such as lack of cooling or heating to vulnerable communities, and of possible rebuild costs after the disaster has ended. Best practices and protocols must account for many variables, including the safety of personnel moving into impacted areas (GE-Digital, 2020).

The Weather-Readiness Assessment is a method that uses analytical tools to assess a utility’s preparation to tackle the impact of weather volatility on its operations. The entire system is evaluated (generation, transmission, distribution, demand and all related infrastructure, Figure 1) to determine its current state, aiming to assess its vulnerability or preparedness of the current design concerning weather volatility, and identifies expected damage, approximate crew and funding requirements, and helps to define specific actions to ensure operational resilience during severe weather (Dubus et al. 2018).

Transmission & Distribution

Figure 1. Business outcomes driving Weather-Readiness Assessment. Source: Adapted from Dubus et al. 2018

Being weather-ready means that utilities are not ‘reacting’ to every weather event but instead have clarity of how weather impacts each of their functions and proactively figure out what they need to do to keep their operations resilient during severe weather events that can leave operations at risk. Therefore, utilities can control how they monitor, plan for, and respond to severe weather events (Dubus et al., 2018).

Otherwise, without a clear picture of how impending weather events will impact the grid, utilities must rely on costly numbers of contingency crews to restore power quickly, or risk leaving customers without power for extended periods (GE digital, 2020). However, in many cases, vulnerability and risk to a hazard are strongly determined by local conditions. There is a trend toward more site-specific detailed forecast information. Research is ongoing at national agencies, universities and private enterprises where they are looking into ways to use modern computer technology to improve forecast accuracy and precision for energy systems.

The role of W&CSs

In terms of weather readiness, W&CSs can help energy systems to:

Identify the overall influence of weather and climate hazards on the entire system, be it energy generation, demand or transmission.
Measure how weather variables impact their business goals for reliable energy provision
Inform staff management in particular safety measures while responding to outages caused by extreme events.
Inform energy infrastructure upgrades or reinforcement needs
Unlock opportunities to coordinate and optimize requirements for electricity grid management
Reduce operational costs by using reliable forecasts of energy generation and demand.
Inform how to rebuild damaged facilities in a reliable, sustainable and hazard resilient way

Benefits of using W&CSs to inform energy sector decision making:

Operational and planning level
- Improves generation efficiency and enhances service quality to the end-users
- Increases/Secures customer satisfaction by providing timely information about renewable energy sources.
- Provides competitive advantage if best practices are implemented, as seasonal forecasts and climate projections allow to plan new development of generation plants and suitable energy mixes for a given location.
- Enhances performance by forecasting energy generation and demand
- Reduces costs associated with hazard impacts and weather volatility. If weatherproof measures are implemented that sends a positive message on how prepared the sector is to handle weather volatility, enhancing customer trust.
- Helps to develop action plans in place to deal with extreme events damage to energy infrastructures, which can be constantly improved as data and better models become available.
Reduced safety risks
- Reduced deployment & field labour costs, by allowing to identify maintenance windows for renewable plants, and transmission lines.
- Reduced cancellation of planned work by using forecasts to inform planning
- Improved CAIDI
- Improved field personnel positioning decisions before and during extreme weather events, such as in the case of forest fires affecting transmission lines
- Improved uptime of critical power supply (e.g. to hospitals), with consequent customer satisfaction.

Assessing readiness

These assessments require multiyear climate data to understand weather variations and patterns in specific geographical locations. A situational awareness application focused on supporting severe weather events might also need highly granular sub-second weather records such as lightning data that is updated in real-time. Data recorded by utilities in multiple locations can be used on the operational side to support machine learning algorithms to understand how specific weather variables such as temperatures and wind speeds play out in that location during extreme weather conditions (Dubus et al., 2018).

A post-mortem analysis of a severe weather or climate event, such as a heat wave or cold wave, usually just requires a simple time-series of weather variables (usually just temperatures) to understand the times when the utility demand was most stressed.

However, sophisticated decision-support applications such as one used to predict asset damage from an incoming storm event would require more granular data. Thus, the same weather data in the earlier application can be regressed with data regarding the location and performance of utility assets during similar weather conditions in the past. These ‘historical patterns’ data are then combined with non-weather variables such as vegetation and land use data to identify how a utility service territory will hold up during forecasted stormy weather based on historical behaviour. This allows utilities to predict and simulate severe weather impact on their facilities and predict service interruptions to their end-users. The same data can also drive automated applications which can optimize system decisions such as reconfiguring power through alternative transmission or distribution network pathways based on the availability of network bandwidth which is not impacted by an incoming storm. This ensures that the degree to which service level performance is compromised is kept to a minimum and customer satisfaction level can be maintained by constant communication and prompt restoration (Dubus et al., 2018, GE-Digital, 2020).

Case studies

Several case studies examine practices, procedures, and experiences of utilities during extreme weather events with the goal of understanding and conveying what went right and wrong during the build-up, restoration, and ramp-down phases. The World Energy Council provides a set of examples covering a wide range of extreme events, whereas DSTAR focuses on major storms with a perspective from stakeholder engagement. The investigation includes detailed utility surveys, interviews with company managers, vendors, and consultants, and reviews of reports, proceedings, and papers. The result is a comprehensive discussion of many important aspects of storm restoration with an emphasis on best practices and lessons learned from past experiences.

Preparing for risk. Weather readiness

The role of W&CSs

Assessing readiness

Case studies

References