Electricity customers are lining up to generate their own clean, affordable solar energy, but to get it to them, solar developers must navigate the impediments of a congested and outdated electricity grid.
For this episode of the Local Energy Rules podcast, host John Farrell speaks with Yochi Zakai, attorney with Shute, Mahaly, and Weinberger representing Interstate Renewable Energy Council (IREC). The two discuss hosting capacity analysis and how publicly shared grid information can help solar developers, electric customers, and others make more informed decisions.
Listen to the full episode and explore more resources below — including a transcript and summary of the conversation.
Episode Transcript
Expensive Electric Accommodations
Electric distribution grids were built as top-down avenues for delivering electricity from large, centralized power plants. Now, as distributed generation and energy storage become more popular, utilities are having to accommodate the two-way flow of electricity. To do so, the utility often needs to upgrade the distribution system. This is especially true in areas where there is a lot of distributed energy development.
“The grid was built for this one way flow of electricity. But as more customers decide to install generation in their homes, the way that the distribution grid operates is also going to change.”
Solar developers looking to connect their new generation source to the grid may trigger the need for a system upgrade. In most cases, whoever triggers a grid upgrade must pay the upgrade costs — which can be severe. Larger solar gardens are more likely to trigger upgrades. If a developer is surprised by these costs, and building their solar garden is no longer feasible, they may be forced to drop their plans entirely. Hosting capacity analysis can provide key grid information proactively for individuals hoping to plug in.
Hosting Capacity Analysis
In a hosting capacity analysis, utilities compile information about the electric grid and publish it online for the use of developers and other stakeholders. The resulting map has pop-ups with data on various localized grid conditions: how much generating capacity that section of the grid can still handle, the voltage of the line, and the existing generation on that part of the grid.
This information, which Zakai calls “geeky grid data,” helps customers and solar developers make decisions.
“The studies produce a wealth of information that developers can use to cite and design the systems so they don’t trigger upgrades. And in some cases they can even make the grid more reliable.”
Utilities in seven states are required to publish hosting capacity maps. Some utilities even publish this information voluntarily. Zakai says that generally, hosting capacity analysis is most common in states with robust distributed energy development, including Hawaii, Massachusetts, and New York.
California’s hosting capacity analysis process, called integration capacity analysis, provides more useful information than the hosting capacity maps published in other states. This is thanks, in part, to a petition from Zakai and the Interstate Renewable Energy Council (IREC). IREC asked the state of California to consider all kinds of interconnecting loads, including electric vehicle chargers, electric heat, and solar generating power, when implementing its integration capacity analysis. In January 2021, the California commission filed its petition to make changes to the analysis and its resulting map.
In California, grid users also uniquely share the cost of grid upgrades, rather than the typical ‘cost-causer pays’ model used in other states.
Automating & Simplifying the Interconnection Process
It is not possible to automate all new grid interconnections, says Zakai. Still, hosting capacity analysis could simplify many of the steps within this process. California is the first state in the country to try using hosting capacity analysis to reduce the complexity of the interconnection process.
“Hosting capacity analysis can be used to automate and increase the precision of some of the most problematic technical review processes that the utilities use when they evaluate new grid connections. Last fall, California became the first state in the country to make a final decision to use the hosting capacity analysis to automate some of these processes.”
Thanks to new rules adopted by the California Public Utilities Commission, solar developers can use the public hosting capacity maps to design and site projects with more certainty. As developers make more informed proposals, utilities will not waste resources reviewing projects that will never get built.
For concrete examples of how cities can take action toward gaining more control over their clean energy future, explore ILSR’s Community Power Toolkit.
Explore local and state policies and programs that help advance clean energy goals across the country, using ILSR’s interactive Community Power Map.
This is episode 135 of Local Energy Rules, an ILSR podcast with Energy Democracy Director John Farrell, which shares powerful stories of successful local renewable energy and exposes the policy and practical barriers to its expansion.
Local Energy Rules is Produced by ILSR’s John Farrell and Maria McCoy. Audio engineering for this episode is by Drew Birschbach.
In a landmark shift for the U.S. housing finance system, the Federal Housing Finance Agency has issued a directive ordering Fannie Mae and Freddie Mac to formally consider cryptocurrency as an asset in single-family mortgage loan risk assessments.
The move, signed by FHFA Director William J. Pulte on Wednesday, signals a new era of crypto integration into traditional financial infrastructure — this time within the core of American home lending.
The order directs both housing finance giants to develop proposals that include digital assets — without requiring borrowers to liquidate them into U.S. dollars prior to a loan closing.
Pulte said in a post on X that the move aligns with President Donald Trump‘s vision “to make the United States the crypto capital of the world.”
Historically, cryptocurrency has been excluded from underwriting frameworks due to volatility, regulatory uncertainty, and the inability to easily verify reserves. This directive changes that.
Read more CNBC tech news
The decision comes at a time of increasing institutional embrace of crypto across banking, payments, and federal policy.
“Cryptocurrency is an emerging asset class that may offer an opportunity to build wealth outside of the stock and bond markets,” the order states, acknowledging crypto’s growing role in household financial portfolios.
The directive restricts consideration to digital assets that are stored on U.S.-regulated, centralized exchanges and can be clearly evidenced. It also requires Fannie Mae and Freddie Mac to develop internal adjustments to account for crypto’s market volatility and ensure that any risk-weighted reserves comprised of crypto do not compromise underwriting standards.
Under the directive, both enterprises must submit their assessment proposals to the boards of directors for approval and then to the FHFA for final review.
Fannie Mae and Freddie Mac were put under government control in September 2008 as entities that are known as government-sponsored enterprises, or GSEs.
Arevon Energy just brought a massive new battery storage project online in San Diego’s Barrio Logan neighborhood, and it’s built to keep the lights on when the grid gets stressed.
The new Peregrine Energy Storage Project clocks in at 200 megawatts (MW)/400 megawatt-hours (MWh), making it one of the biggest battery storage facilities in the San Diego region. That’s enough stored energy to power around 200,000 homes for two hours during peak demand.
Built for $300 million, Peregrine is the fifth utility-scale energy storage project Arevon has launched in California. It uses lithium iron phosphate (LFP) batteries, which are known for their safety and thermal stability. LFP batteries use iron, phosphate, and lithium to create a strong chemical bond that resists overheating, making them safer than other lithium-ion chemistries. They also have a longer lifespan and are less prone to degradation over time.
The facility created more than 90 construction jobs and is expected to generate over $28 million in property tax revenue over its lifetime.
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Energy storage projects like this are key to making California’s grid more stable and reliable. By soaking up clean energy when demand is low and discharging it when the grid is under strain, Peregrine helps reduce blackouts and avoid spikes in electricity prices.
“The successful completion of Peregrine Energy Storage is a result of the collaborative efforts of the project’s stakeholders and the local community who collectively support California’s renewable energy goals,” said Kevin Smith, CEO of Arevon.
Arevon already operates more than 3.2 gigawatts (GW) of renewable energy projects in California, with another 800 MW under construction. Nationwide, it owns and operates 4.7 GW of solar and storage projects across 17 states.
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The time is finally here: there are actual driverless Tesla Robotaxis on the road, at least in a portion of Austin, Texas, as of this weekend. And thanks to their ridership of exclusively Tesla influencers, almost all of the miles they’ve put under their belt has been filmed or livestreamed, which gives us plenty of footage to discover what’s gone right and what’s gone wrong.
Tesla’s Robotaxi service went live on Sunday around noon, at least for the relatively small number of Tesla influencers who were invited to ride.
It’s a limited launch in several other ways, too – it’s geofenced to somewhere around 30 square miles in South Austin which Tesla spent additional time mapping and testing in, it’s supported by backup teleoperation, it doesn’t operate from 12am-6am or in bad weather, and every car has a “safety monitor” in the passenger seat with access to controls to stop the vehicle.
Because of that decade of promises, a lot of eyes have been on this launch – and also because of the fact that every invited rider is chasing views on social media, so we have a lot of footage just a few days in.
To be clear, this is not the first driverless taxi on the road. GM used to operate robotaxis through subsidiary Cruise (more on that in the Take), and Google has its Waymo robotaxis in multiple US cities (it just expanded its service area last week) and is even testing overseas.
But, Tesla is Tesla, and there’s always more attention on what Tesla does. So lets put a little more attention on the various errors that we’ve seen from Robotaxis in the 3 days since launch.
But, soon, errors started creeping in. We added some as updates to that article as they came in, but we thought this article would be better to compile them all (and thanks to r/SelfDrivingCars which compiled several others)
Indecision leads to driving into an oncoming lane
In Tesla Daily’s first Robotaxi ride, the Tesla tries to attempt a left turn one intersection early, gets indecisive, then continues on, driving through an oncoming lane for a time before re-entering a left turn lane ahead. See the whole exchange starting at around 7:08 in this video:
Robotaxi stops in middle of street for about a minute
Dirty Tesla pressed the “pull over” button to get dropped off early, and the car got confused and tried to let him out in the middle of a left turn lane. Support ended up “resuming the ride” and the Robotaxi found a nearby gas station to drop him off at. The whole interaction took about a minute, starting at ~8:58 in the video:
Robotaxi drops rider off in an intersection, stays there for ~55 seconds
Farzad also asked for a slightly early dropoff, and the car stopped quite early… as in, gridlocked in an intersection and leaking out into one lane of traffic. Thanks to wide Texas streets for letting others by, I guess. 38:04 in the video:
Tesla phantom brakes when caught by sun glare
Kim Java had a hard “phantom braking” moment, where the vehicle hits the brakes for no particular reason, while driving into the setting sun. 10:13 in the video:
Safety monitor intervenes, presses “stop in lane” to avoid UPS truck
In what seems to be the first true intervention caught on video, Dave Lee was approaching a parking spot when a UPS truck stopped in the lane and started backing up. The Tesla “safety monitor” in the front seat wisely anticipated the situation and was hovering the “stop in lane” button, then pressed it when it seemed like the car wouldn’t stop on its own. The car then remained in position while the UPS truck backed up, giving it just enough room, but it probably would have been nicer if it backed up a little more. Excellent job by the safety monitor here, really. 28:53 in the video:
The previous day, Dave Lee was getting picked up by a Robotaxi in a parking lot and it hit a curb in the parking lot right at the start of the drive (at 0:39 in the video).
Robotaxi hits a bump too fast, then goes 27 in a 15mph zone
Farzad was heading to a disc golf course on a low-speed street. The Robotaxi handled one speed bump well, but then took another one too fast. It then drove past a 15mph speed limit sign, slowed down for a deer, and then picked speed back up to 27mph. The whole exchange starts around 14:27:
In the same video, starting at 4:56, the car seems not to know what to do about a shopping bag in the road – it brakes, then considers going around it, then just runs it over.
Tesla brakes for nearby police, exterior view
Edward Niedermeyer, a longtime Tesla hater, posted a video from an exterior angle of a Robotaxi behaving strangely nearby police vehicles. The Robotaxi passes by one police vehicle with lights on in a parking lot, then brakes rather hard when it passes by another police car blocking a side intersection, then passes by another at normal speed, then brakes hard for a fourth despite it being in a parking lot behind a curb. Slowing down would be appropriate behavior in this instance, but the braking events seem more sudden than necessary, and inconsistent given the position of the police vehicles involved.
Safety monitor intervenes, hops in drivers seat in parking lot
In what seems to be the second intervention, Dirty Tesla had just gotten out of the taxi and while it was trying to leave the parking lot, it nearly ran into a parked car. The Safety monitor intervened to stop the car, then apparently got out and drove the car away manually (not captured in video).
Super tight squeeze for robotaxi in one of my last drives 🫢
The owner of the parked car asked if it was my car and I told him it was a robotaxi. The robotaxi backed up and then the driver of the parked car left. It looked like the tire touched the parked car. The safety driver… pic.twitter.com/DzNuAQk6Su
Yes, the title is lighthearted. I was going for irony.
The fact is that there are issues with Tesla’s approach to self-driving, and these various videos show them.
Tesla drivers are well acquainted with the current limitations and quirks of FSD as well, many of which were shown off in the clips above. It doesn’t do well with sun glare (neither do you, but you can wear sunglasses and/or flip down the visor for a little help), it sometimes misses speed bumps, it phantom brakes, and it has weird moments of indecision sometimes. C’mon, we’ve all seen it, let’s be honest with ourselves here.
As best I can tell from hundreds of miles away, these vehicles exhibit pretty similar behavior to the FSD in the vehicles I’ve driven. It works pretty well a lot of the time, but most of the time I’m also glad I’m there in the driver’s seat so I can tell it to STOP CHANGING LANES FOR THE 5TH TIME THIS MINUTE FOR PETE’S SAKE.
Tesla’s system also uses only cameras, not LiDAR, and most experts (including Tesla engineers) agree that incorporating multiple sensing modes is the correct path to take (here’s more on that). Tesla is using only cameras because it’s cheaper, and thus more scalable (though LiDAR prices have dropped rapidly).
In particular, LiDAR does better in poor weather than cameras do. We haven’t seen particularly bad weather yet for Robotaxi (there was rain in Austin on the morning of the Robotaxi’s launch – and the launch coincidentally did not happen until afternoon), and Tesla’s FSD system does work in the rain.
But even I, in famously sunny Southern California, have encountered a rainstorm severe enough for FSD to suddenly shut off and tell me to take over. So, in the very conditions that you’d definitely want an enclosed space to keep you safe from the weather, Robotaxi might not work.
So far, the errors we’ve seen above have not caused any sort of damage, either to Tesla occupants or the general public (except for some curb rash, perhaps), but as miles get put on the system, it is inevitable that something will happen.
When something does happen, the public will not respond kindly to it. Recall when GM’s Cruise robotaxi got into an accident in San Francisco – which was actually entirely the fault of a human driver. A human driver struck a pedestrian, who was then pushed into the path of a Cruise vehicle which didn’t have time to stop, and hit the pedestrian as well.
This was largely reported as a self-driving car crash, even though Cruise didn’t cause the accident in the first place. Cruise was, however, responsible for having poor after-crash behavior, as the car didn’t realize the pedestrian was stuck under the vehicle and dragged her on the road for several feet, and then hid this fact from investigators. As a result, its license was pulled in California and it soon shut down elsewhere as well.
We are all aware of how many unpredictable things happen on the road every day, and how many problems are caused by human drivers. Autonomous technology does promise solutions to that, particularly in its theoretical ability to make decisions quickly. But autonomous technology has heretofore not been great at understanding what to do in unexpected situations, like the Cruise issue above.
Waymo has had issues as well, one of which you can see in my own experience with the system, where the car I was in got stuck for several minutes trying and failing to make a left turn into a crowded street. Or this clip where it gets stuck in a parking lot and needs a manual driver.
One pattern I do notice is that a lot of Tesla’s errors seem to happen when the car is dropping off or picking up riders. This could be because parking lots are more complex spaces than roads, or simply because the ability to park is a newer feature for FSD. In my time in Waymos, it also seems the least decisive when trying to find parking or pickup spots.
But the exceptional part about these Tesla issues is that it’s only been three days, and there are reportedly only 10 cars and 20-some riders using the system. Tesla has always said that it could scale its solution to an entire fleet with a single software update, without geofencing, thus turning the entire fleet autonomous overnight.
And Tesla has also always been famous for the “move fast and break things” approach which is so common in Silicon Valley. This is all well and good for tech, but when you’re dealing with thousands of pounds of metal going down the road near pedestrians, things can get serious real quick.
Thankfully, Tesla does seem to be taking a more measured approach than we might have expected, given its inclusion of safety monitors who we’ve already seen avoid two accidents in just the first three days of operation. But that’s not scalable, and while Tesla fans have pointed out that Waymo also started with safety monitors, it didn’t charge fees or take public rides during that testing phase, and Tesla is doing both.
It remains to be seen if Tesla’s approach will be scalable faster than Waymo’s (or MOIA’s, or Zoox, or anyone else’s), but given the first few days of limited operation in Austin, the dream of expanding everywhere overnight does seem unlikely.
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