There are many recognised disruptors to the utility sector - electric vehicles (EVs), renewables, transactive energy and bi-directional electricity trading.
As utilities continue to move through the energy transition and integrate these disruptors into the grid and shape business models around them, one thing is key: situational intelligence.
In this context, situational intelligence means the aggregation, translation and visualisation of various data sources into actionable analytics, and delivering these insights to the right person at the right time.
So how does this relate to weather, and what are the key difficulties utilities are facing in developing and distributing weather-based situational intelligence throughout their operations.
We spoke to experts Rob D’Arienzo, Senior Meteorologist at IBM and Kerry Mason, Meteorologist at The Weather Company, to gain insight on how utilities can, and should, better leverage the weather as a tool.
Changes to the distribution grid
In line with disruptive technology development and shifting expectations placed upon utilities, D’Arienzo explains that weather can impact a variety of utility operations.
He says: “Weather impacts almost every line of business within a utility, whether they like it or not. Analytical solutions that are driven by weather, such as outage prediction and energy forecasting are needed to combat a changing climate and grid modernisation. Utilities need to embrace these technologies in order to remain efficient and sustainable.
Especially in Europe, there is more to consider than just the new technologies entering the grid. D’Arienzo says: “The combination of more extreme weather and aging infrastructure, especially in the UK where assets are very old and in poor health, puts utilities at significant risk.
“The risk can traditionally be reduced by effective system hardening but can be greatly optimised by utilising predictive maintenance that can determine when and how an asset will fail due to weather conditions. When these technologies are successfully implemented, utilities can experience fewer outages which greatly reduces maintenance and storm costs, and ultimately increases grid reliability.”
With many countries now implementing aggressive renewable targets, a key challenge will be the integration and optimisation of these technologies.
D’Arienzo explains that accurate renewables forecasting is crucial to achieving this: “Understanding how the weather will impact wind and solar power production and making sure those predictions are effectively shared with the grid operators is key. Using predictive analytics for renewable assets is critical to successfully integrating renewables into the grid.”
Mason agrees, seeing leveraging existing data as an important business decision: “In the case of distribution system operators, it’s not a viable option to replace their ageing overhead infrastructure all at once or to migrate to an underground network - particularly in rural locations. Eventually one or the other will likely happen, but that’s going to occur over long period of time. In the meantime we see solutions that leverage the weather and its impact on their assets as an easy sustainable fix in the interim.”
Improving operational efficiency with weather data
With this in mind, D’Arienzo argues that weather is perhaps one of the greatest disruptors to progression. This, however, is not translating to all utilities.
In Mason’s experience, utilities may not have the capability to translate weather data into actionable meaning - that, or they don’t fully see the potential benefits.
She says: “Most utilities or wind farm operators will already have some sort of weather solution in their operations. Normally there is a dashboard displaying their assets alongside current and forecast weather, however it's up to the user to translate that forecast information into something meaningful. The business decisions will therefore be very subjective and based solely on the experience of staff members. If those people leave the company, or are out of office, it makes the decision making process much harder.”
Even with this subjective experience to hand, human error is always a risk factor, and manually accessing historical data to backup decisions can be a time consuming and inefficient process.
Small variables and errors can lead to significant issues for utilities. Mason exemplifies this: “There is a need to move away from experience-based decisions to objective data-driven decisions. For instance, a distribution system operator might not understand that while a prevailing westerly wind at 50km/h isn’t going to notably impact their assets, an easterly wind at the same speed will. It’s about transforming and displaying that information for them into something they can act on.
“Furthermore, time wasted spent monitoring weather conditions decreases operational efficiency. Leveraging alerts that bring severe weather to the attention of the company saves valuable time- particularly if there are field staff working in those conditions.”
Understanding weather data as valuable
By aggregating various data sources such as outage data and historic weather data, utilities can better calculate the weather variables that will actually impact their assets.
This information, in addition to real-time forecasting and monitoring, can not only help utilities preparing for weather-related outages, but also provide situational intelligence for workforce management.
D’Arienzo breaks down the importance of this to field operations: “Performing tasks high off the ground, in underground vaults and often inches away from high voltage equipment, utility workers are considered one of the top 10 most dangerous professions. They are subject to even more dangers during weather events, especially lighting, high winds and heavy rain.
“Field workers do not consistently have the time or the background to continuously monitor weather conditions as they focus on the job at hand. Field technicians can benefit from targeted weather alerts so that safety, productivity, and collaboration are improved. Safety needs to remain a top priority for utilities as it is an accident waiting to happen.”
One case study D’Arienzo cites as illustrative of the importance of situational intelligence is that of New Brunswick Power in Canada. The electric utility experiences extreme cold weather conditions yearly and turned to The Weather Company to improve its outage prediction, mobilisation and workforce optimisation KPIs.
Of the implementation, D’Arienzo says: “Emergency managers, operations/planning employees, and executives from New Brunswick Power now benefit from an outage forecast that is driven by numerous weather forecasts with a granularity down to 500 square meters.
There’s no one-size fits all solution, however. Mason explains: “What we’re talking about here isn’t one solution - it’s a whole series and selection of solutions which come together to solve different utilities’ unique difficulties.”