Mark Rawson was Chief Operating Officer and Co-Founder at Rhythmos before the company joined Electron in October 2025. He’s now Senior Vice President, North America for Electron, bringing expertise in deploying DER detection and grid analytics at scale across US utilities, through ElectronCompass.
Tell us a bit about your background.
I’ve spent 30 years watching utilities react to energy transitions instead of anticipating them. At the California Energy Commission, I managed $50M in distributed generation research and grid modernization research that shaped state-wide renewable policy.
At SMUD, I ran a $12M research portfolio and led the USA’s largest lithium-ion residential storage deployment. Since 2022, I’ve been COO and co-founder at Rhythmos, seeing it grow by 200%.
What was the core problem Rhythmos set out to solve, and why was the Electron acquisition the logical next step?
Utilities face a fundamental blind spot: they can’t see behind customer meters, making electrification management impossible. At Rhythmos, we built analytics to plug this gap without years-long integrations.
Electron now brings operational expertise and go-to-market infrastructure to scale this across utilities rapidly. More than solving visibility, we’re becoming part of the platform utilities need to modernise their grids and extract grid value from flexible DER like EVs, batteries, and demand response.
You’ve worked with utilities across the USA – can you walk us through how your electric vehicle (EV) detection and DER visibility changes what an engineering or planning team can do differently?
For one of our utility clients, we showed 95% of detected EVs concentrate in one transformer range – evidence, not forecast. We quantified real impacts. Transformers with one EV saw peak utilisation jump 23%, two or more jumped 46%. Instead of waiting up to five years, utilities get this intelligence in eight to twelve weeks and can act immediately.
They know exactly where to target upgrades, enroll customers in managed charging, and design effective rate structures. And, equally important, they know where they do not need to worry about their grid assets degrading.
Rhythmos uses advanced data analytics to detect DERs from AMI data with high accuracy. Can you explain how this works?
We use modern machine learning techniques and disparate data sources to identify Level 2 chargers by customer meter location with greater than 90% accuracy using hourly AMI data utilities already have.
We also extract precise EV charger capacity and link it to specific transformers to show grid impacts. Utilities use this information to enhance grid planning and operations, optimize managed charging programs, and rate design – understanding exactly where flexibility exists and who to target.
Why is the Rhythmos approach so different from others?
Most competitors attempt real-time dispatch, requiring deep IT/OT integrations and placing optimisation liability on utilities. We quantify the spare capacity on their grid assets so they can let aggregators respond appropriately.
It’s simpler to deploy, avoids cloud dependency risks, eliminates indefensible “black box” optimization, and works with existing AMI. Utilities that understand grid constraints make better decisions than utilities trying to control them.
What does a realistic first engagement with a distribution utility look like, and how quickly can you actually show results?
We start with an eight-week sprint. Utilities provide two years of AMI and GIS data; we validate quality and deliver EV detection results that are ingestible to their GIS tool by week eight. Phase two adds Grid Impact Analysis.
By month three, they have actionable intelligence on where EVs are, stressed transformers, and spare capacity – versus other solutions which may require years of integration and millions of dollars to implement.
