The Heat Is On — Is The Grid Ready?
Courtesy of Pacific Northwest National Laboratory.
By Kelsey Adkisson
As yet another heat wave shattered temperature records in the Pacific Northwest in mid-2021, threats of rolling blackouts rippled throughout the region.
These recurring extreme weather threats offer a sobering reminder that aging energy grids weren’t designed to handle the stress of climate change. Nor were they designed to withstand the energy impact from extreme events like heat waves, droughts, or wildfires, which are predicted to become more frequent and intense, according to Pacific Northwest National Laboratory’s (PNNL’s) Nathalie Voisin, a PNNL Earth scientist who is part of a team working on grid resilience in relation to climate change.
“Even under modest climate change projections, threats of power shortfalls will become more common,” said Voisin.
In the Pacific Northwest, which is dependent on hydropower to help generate electricity, more frequent heat waves, water scarcity, and increased wildfire risk put increased pressure on an overburdened power grid. Currently, over 90% of the western United States is facing drought conditions. One year ago, it was 40%.
To relieve some of that pressure, research teams at PNNL are focused on prevention. They are working to predict future drought scenarios and create hydropower and grid contingency plans, implement smart electricity load controls, manage forests to reduce the impact of wildfire, and place new grid infrastructure, like energy storage or microgrids, where they are needed most.
“When we’re talking about power shortfalls, even small steps add up. Shifting large appliance use, like a high amount of dishwashers or washing machines, from afternoon and evening peak hours to the morning or the night, or increasing thermostats a couple degrees in the summer and using ceiling or floor fans can make a difference,” said PNNL’s Dhruv Bhatnagar, an energy systems engineer.
What high temperatures mean for hydropower
The early summer heat wave of 2021 led to a spike in energy demand that left hydroelectric dam operators with a difficult choice: (1) use water to keep up with the surge, leaving less water for late summer, or (2) buy energy on the open market, often at higher prices and from natural gas.
PNNL modelers like Voisin are working to predict these types of events and the impacts to generation and load, including short-term issues like heat waves or longer-term issues like droughts via efforts like the Department of Energy’s HydroWIRES initiative.
PNNL researchers are using advanced modeling to predict droughts and provide grid operators with information for decisions on how to allocate power during extreme events. For instance, to simulate the impact of climate change on the future power grid, researchers used a computer model called GENESYS. Recent results showed that power systems will be affected by multiple stressors simultaneously, and these impacts compound and aren’t just additive.
PNNL is developing drought scenarios to help operators and regulatory agencies with future planning. This includes predicting future drought conditions and the impacts on hydropower and thermoelectric plants, which can then be used to understand the potential impact on grid operations and guide adaptation.
“This information is used to help operators make risk-informed decisions and determine where vulnerabilities may lie. Ultimately, it will help answer the question—given different stressors, will there be enough power to meet the demand and other power grid needs?” said Voisin.
“Will there be enough power to meet the demand?” — Nathalie Voisin, PNNL Earth scientist
Recently, Voisin and her team evaluated how hydropower operations vary seasonally and annually depending on water availability for the Chelan Public Utility District. For example, they demonstrated that even during a dry summer, when hydropower’s overall generation is limited by low water availability, hydropower maintains its flexibility to support the peak load under extreme events. This highlights the need to better consider the range of services that hydropower can provide to address the resilience of the grid under extreme events.
Wildfire and hydropower
During an above-normal fire season, like what is currently occurring in California, there will likely be impacts on the grid, either through intentional shutoffs to reduce fire risk or loss of infrastructure due to the fire itself.
“The idea is not to stop all wildfires but to work in advance to reduce their risk, and predict areas that are more prone to them,” said PNNL’s Mark Wigmosta, a PNNL environmental engineer. Wigmosta’s work focuses on forest thinning and restoration with the goal of less fuel for fires.
“The idea is not to stop all wildfires but to work in advance to reduce their risk” — Mark Wigmosta, PNNL environmental engineer
Reducing fuel load in highly dense forests may leave more water in streams and can lead to higher, longer-lasting snowpack. This may produce more water throughout the summer dry season.
“This may provide a way to get more water into the system, depending on location,” said Wigmosta. Another grid benefit is that weaker fires are likely to burn less energy infrastructure. For example, between 2000 and 2016, wildfires caused at least $700 million in damages to 40 transmission lines in California. Nationwide costs from wildfires are significantly higher.
After fires burn, there is typically an increase in runoff and sedimentation. Sediment flows downstream, builds up in reservoirs, and “isn’t great for infrastructure, including turbines,” said Wigmosta. Prescribed burns or tree thinning can actually increase flow volumes and improve hydropower operations. And, weaker fires will have less of a negative impact on infrastructure and the grid.
Better technology from buildings to batteries
During peak power demands, like a heat wave, emerging technology offers the potential for consumers to manage or supplement loads. Smart tools, like intelligent load control, automatically manage building energy use during peak electricity demands. PNNL has been working on ways to make buildings more energy efficient, in addition to optimizing the future of hydropower.
Backup or autonomous power sources also offer promise, particularly during emergency situations. Microgrids are self-contained grids that can power key areas, such as hospitals or police stations, during power shortfalls that could occur during extreme events like a wildfire or hurricane. PNNL’s Microgrid Component Optimization for Resilience tool helps streamline the design process for microgrids with the goal of simulating power under a variety of outage conditions.
PNNL is also taking a leadership role in developing new technologies for grid-scale energy storage, which includes a new generation of battery materials and systems and other forms of energy storage. For example, current grid-scale energy storage systems such as pumped storage hydropower use pumps to move water uphill to store renewable energy when demand is low and generate power when demands are high as water flows downhill. PNNL has been working on incremental steps with pumped storage, such as evaluating environmental impacts of newer systems, to enhance future grid resilience or working with international stakeholders to identify strategies to finance and develop new projects. Even concepts like pairing batteries with hydropower are being explored to enhance hydropower’s capabilities and assure reliability during power shortages while reducing environmental impacts.
“Ultimately, we want to prepare for extreme events. Whether it’s through technological innovation, enhancing grid resilience, or supporting long-term planning. We take a holistic approach to tackling these big, long-term challenges to support risk-informed decision-making,” said Voisin.
This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy and Office of Electricity, among other agencies.
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Metro Detroit is about to get a big boost of fast EV chargers, with more than 40 new ChargePoint ports set to come online across multiple sites owned by the Dabaja Brothers Development Group.
The first ultra-fast charging site just opened in Canton, Michigan. It’s owned and operated by Dabaja Brothers, who plan to follow it with additional ChargePoint-equipped locations in Dearborn and Livonia.
“We started this project because we saw a gap in our community – there was almost nowhere to charge an EV in Canton, and a similar lack of charging across metro Detroit,” said Yousef Dabaja, owner/operator at Dabaja Brothers.
Each metro Detroit site will feature ChargePoint Express Plus fast charging stations, which can deliver up to 500 kW to a single port, can fast-charge two vehicles at the same time, and are compatible with all EVs. The stations feature a proprietary cooling system to deliver peak charging speeds for sustained periods, ensuring that charging speed remains consistent.
The stations operate on the new ChargePoint Platform, which enables operators to monitor performance, adjust pricing, troubleshoot issues, and gain real-time insights to keep chargers running smoothly.
Rick Wilmer, CEO at ChargePoint, said, “This initiative will rapidly infill the ‘fast charging deserts’ across the Detroit area, allowing drivers to quickly recharge their vehicles when and where they need to.”
Read more: ChargePoint just gave its EV charging software a major AI upgrade
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Mercedes-Benz High-Power Charging and Starbucks have officially opened their first DC fast charging hub together, off the I-5 in Red Bluff, California.
The 400 kW Mercedes-Benz chargers are capable of adding up to 300 miles in 10 minutes, depending on the EV, and every stall has both NACS and CCS cables – they’re fully open DC fast chargers.
Mercedes-Benz HPC North America, a joint venture between subsidiaries of Mercedes-Benz Group and renewable energy producer MN8 Energy, first announced in July 2024 that it would install DC fast chargers at Starbucks stores along Interstate 5, the main 1,400-mile north-south interstate highway on the US West Coast from Canada to Mexico. Ultimately, Mercedes plans to install fast chargers at 100 Starbucks stores across the US.
Mercedes-Benz HPC opened its first North American charging site at Mercedes-Benz USA’s headquarters in Sandy Springs, Georgia, in November 2023 as part of an initial $1 billion charging network investment. As of the end of 2024, Mercedes had deployed over 150 operational fast chargers in the US, but it hasn’t disclosed an official number of how many chargers are currently online.
Andrew Cornelia, CEO of Mercedes-Benz HPC North America, is leaving the company at the end of the month to become global head of electrification & sustainability at Uber.
Read more: Mercedes-Benz is deploying 400 kW US-made EV fast chargers with CCS and NACS cables
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The race for autonomous driving has three fronts: software, hardware, and regulatory. For years, we’ve watched Tesla try to brute-force its way to “Full Self-Driving (FSD)” with its own custom hardware, while the rest of the automotive industry is increasingly lining up behind NVIDIA.
Now that we know Tesla’s new AI5 chip is delayed and won’t be in vehicles until 2027, it’s worth comparing the two most dominant “self-driving” chips today: Tesla’s latest Hardware 4 (AI4) and NVIDIA’s Drive Thor.
Here’s a table comparing the two chips with the best possible specs I could find. greentheonly’s teardown was particularly useful. If you find things you think are not accurate, please don’t hesitate to reach out:
| Feature / Specification | Tesla AI4 (Hardware 4.0) | NVIDIA Drive Thor (AGX / Jetson) |
|---|---|---|
| Developer / Architect | Tesla (in-house) | NVIDIA |
| Manufacturing Process | Samsung 7nm (7LPP class) | TSMC 4N (custom 5nm class) |
| Release Status | In production (shipping since 2023) | In production since 2025 |
| CPU Architecture | ARM Cortex-A72 (legacy) | ARM Neoverse V3AE (server-grade) |
| CPU Core Count | 20 cores (5× clusters of 4 cores) | 14 cores (Jetson T5000 configuration) |
| AI Performance (INT8) | ~100–150 TOPS (dual-SoC system) | 1,000 TOPS (per chip) |
| AI Performance (FP4) | Not supported / not disclosed | 2,000 TFLOPS (per chip) |
| Neural Processing Unit | 3× custom NPU cores per SoC | Blackwell Tensor Cores + Transformer Engine |
| Memory Type | GDDR6 | LPDDR5X |
| Memory Bus Width | 256-bit | 256-bit |
| Memory Bandwidth | ~384 GB/s | ~273 GB/s |
| Memory Capacity | ~16 GB typical system | Up to 128 GB (Jetson Thor) |
| Power Consumption | Est. 80–100 W (system) | 40 W – 130 W (configurable) |
| Camera Support | 5 MP proprietary Tesla cameras | Scalable, supports 8MP+ and GMSL3 |
| Special Features | Dual-SoC redundancy on one board | Native Transformer Engine, NVLink-C2C |
The most striking difference right off the bat is the manufacturing process. NVIDIA is throwing everything at Drive Thor, using TSMC’s cutting-edge 4N process (a custom 5nm-class node). This allows them to pack in the new Blackwell architecture, which is essentially the same tech powering the world’s most advanced AI data centers.
Tesla, on the other hand, pulled a move that might surprise spec-sheet warriors. Teardowns confirm that AI4 is built on Samsung’s 7nm process. This is mature, reliable, and much cheaper than TSMC’s bleeding-edge nodes.
When you look at the compute power, NVIDIA claims a staggering 2,000 TFLOPS for Thor. But there’s a catch. That number uses FP4 (4-bit floating point) precision, a new format designed specifically for the Transformer models used in generative AI.
Tesla’s AI4 is estimated to hit around 100-150 TOPS (INT8) across its dual-SoC redundant system. On paper, it looks like a slaughter, but Tesla made a very specific engineering trade-off that tells us exactly what was bottling up their software: memory bandwidth.
Tesla switched from LPDDR4 in HW3 to GDDR6 in HW4, the same power-hungry memory you find in gaming graphics cards (GPUs). This gives AI4 a massive memory bandwidth of approximately 384 GB/s, compared to Thor’s 273 GB/s (on the single-chip Jetson config) using LPDDR5X.
This suggests Tesla’s vision-only approach, which ingests massive amounts of raw video from high-res cameras, was starving for data.
Based on Elon Musk’s comments that Tesla’s AI5 chip will have 5x the memory bandwidth, it sounds like it might still be Tesla’s bottleneck.
Here is where Tesla’s cost-cutting really shows. AI4 is still running on ARM Cortex-A72 cores, an architecture that is nearly a decade old. They bumped the core count to 20, but it’s still old tech.
NVIDIA Thor, meanwhile, uses the ARM Neoverse V3AE, a server-grade CPU explicitly designed for the modern software-defined vehicle. This allows Thor to run not just the autonomous driving stack, but the entire infotainment system, dashboard, and potentially even an in-car AI assistant, all on one chip.
Thor has found many takers, especially among Tesla EV competitors such as BYD, Zeekr, Lucid, Xiaomi, and many more.
Electrek’s Take
There’s one thing that is not in there: price. I would assume that Tesla wins on that front, and that’s a big part of the project. Tesla developed a chip that didn’t exist, and that it needed.
It was an impressive feat, but it doesn’t make Tesla an incredible leader in silicon for self-driving.
Tesla is maxing out AI4. It now uses both chips, making it less likely to achieve the redundancy levels you need to deliver level 4-5 autonomy.
Meanwhile, we don’t have a solution for HW3 yet and AI5 is apparently not coming to save the day until 2027.
By then, there will likely be millions of vehicles on the road with NVIDIA Thor processors.
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