See how Analytics Workbench (AWB) gives utilities the power to determine EV load growth, usage patterns, and adoption propensity, to help better anticipate and address grid impact, and optimize EV programs. One of many customer intelligence and grid resiliency applications of AWB. Explore first hand with the demo portal. For further reading, explore the whitepaper

The Canary in the Coal Mine: Winter Storms Signal the Criticality of AI and Grid Resiliency

The February 2021 deep freeze in Texas has been called a canary in the coal mine event for the utility industry, signaling a warning that even energy grids that were once believed far from extreme weather impacts are becoming increasingly vulnerable. Texas built its grid to withstand high heat, but with three 500-year flood events since 2015 and two record-breaking cold snaps since 2011, energy generation and delivery systems in the state must now be able to withstand a much broader range of impacts.

Utilities have long been facing a pressing need to upgrade aging infrastructure. Now, the growing severity, frequency and geography of wildfires, superstorms and record-breaking cold and heat have both made the urgency more apparent as well as added another layer of difficulty. At the same time, the risk of cyber and physical attacks looms large. And, the rapidly accelerating pace of EV adoption and other beneficial electrification is placing greater and greater demand on an already fragile grid. In fact, according to the 2021 NREL Electrification Futures Study, gigawatt capacity needs to double if 66% of all cars are EVs by 2050.

Sources of Stress Grow More Complex

Infrastructure upgrade costs are spiraling to keep pace with the growing list of grid stressors — ranging from daily congestion and imbalance to extreme weather and the potential for grid attacks. As a result, utilities are facing unprecedented budgetary and logistical challenges as they seek to modernize grids to be both resilient (i.e. prepared to adapt and to changing conditions and withstand and recover rapidly from disruptions) and reliable (i.e. able to maintain the delivery of electric power when there is routine uncertainty in operating conditions). 

As the events in Texas made clear, certain investments like weatherizing our generating fleet are absolutely essential and must be made now. While retrofitting existing equipment and building new generating infrastructure to withstand severe conditions is expensive, it is far less costly than the price of mass outages. 

But when it comes to managing daily grid congestion and imbalance, particularly in the face of growing beneficial electrification, next steps can be less clear cut.

That’s where Bidgely’s UtilityAI™ Analytics Workbench can help. Leveraging AI-powered insights, Analytics Workbench reveals the best path forward and helps utilities achieve grid optimization more strategically and cost-effectively.

Grid Intelligence at Work: Electric Vehicles

We know, for example, that the proliferation of electric vehicles will require both charging infrastructure and significantly greater electric-grid capacity. Boston Consulting Group estimates that between now and 2030, a model utility with ~2-3 million customers would need to invest between $1,700 and $5,800 in grid upgrades per EV. With experts predicting ~40 million EVs on the road by 2030, that investment could approach $200 billion.

Analytics Workbench provides utilities with data-driven EV insights at both the micro transformer and substation level, as well as the macro regional adoption trend and load forecast level. 

In order to manage EV loads, utilities first need to understand who is conducting charging and where and when it is taking place. Analytics Workbench reveals: 

  • The current adoption patterns and EV load growth in a service territory
  • The frequency of charging sessions and the distribution over time (hourly, 30-min and 15-min intervals) as to when customers are charging 
  • The maximum power draw of the charger at each customer site
  • The grid impacts of EV charging loads at various levels of aggregation (city, substation, feeder) 
  • The grid impacts tied to the proportion of off-peak versus on-peak load 

Because EV consumption is a statistically significant contributor to peak/ load increase at any given hour, Bidgely’s AI-derived loading profiles for EV charging enable utilities to pinpoint substations/feeders that are at risk of reaching capacity, with granular detail that reveals what hours the consumption from EVs is highest compared with other substations. These insights serve as a crucial data input to identify vulnerable infrastructure that may be at risk of failure to inform maintenance and upgrades.

Beyond infrastructure investments, Analytics Workbench enables utilities to more successfully  engage and influence existing and prospective EV owners with personalized education and recommendations to advance behavioral changes — including incentives to take action to shift load, automation of load management actions and the simplification of EV program enrollment and participation. UtilityAI-driven EV customer engagement not only contributes meaningfully to load management — ideally reducing or eliminating the need for congestion- and balancing-related infrastructure spending — it also streamlines marketing and program budgets by making outreach and implementation more effective, which has the potential to free up utility dollars to reinvest in critical grid modernization projects.

AI Delivers Greater Grid ROI

Analytics Workbench offers utilities a valuable tool to enable data-driven grid investments that are both more targeted and impactful. With grid intelligence, utilities are better equipped to build a future-ready generation of smart infrastructure that is more reliable and resilient in response to both daily load challenges and extreme events.

See how Analytics Workbench (AWB) gives utilities the power to determine EV load growth, usage patterns, and adoption propensity, to help better anticipate and address grid impact, and optimize EV programs. One of many customer intelligence and grid resiliency applications of AWB. Explore first hand with the demo portal. For further reading, explore the whitepaper

Categories: Bidgely