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We can do better by making renewables predictable.
Take your time and walk through the presentation deck for yourself.
Slideshow PDF file with speaker notes
We are entering the Ion Age. The way we run our vehicles and supply predictable renewable power is changing rapidly and significantly.
"EVs" produce range anxiety, they are too unpredictable and too expensive.
EVs are advancing, but they come with a high degree of waste. Expensive parts, replacing batteries and wasted time waiting to charge are the norm as owners sit idle and fear long distance drives that will never come because of significant limitations.
Charging stations are coming online but not fast enough or in the right places to open up all of the driving landscape. Supercharging is available, but still slower than it takes to fill the gas/petrol tank of a ICE (Internal Combustion Engine) vehicle. Drivers get slightly better utility and some time back when waiting to charge but it it nothing compared to filling up a gas tank.
The third iteration moves the focus to driving by improving the speed of charging to be faster than an ICE vehicle can have it's gas tank filled. Overall reducing cost and moving infrastructure elastically to where demand meets supply. We now virtualize charging into slices of battery on demand, green generation, and a universal system that brings the cost of EV ownership far below that of prior EV iterations and ICE vehicles of every category. Our process produces far less waste because the energy storage can be spun-up or down in reaction to demand nearly instantly. We can scale charging to multiple nodes in places where capacity is available now, whether in Boston or Hong Kong. To scale well, we have hardware and applications designed for the next age of humanity, the Ion Age. Self-contained units of power providing well-defined service on-demand to the whole of the modern distributed ecosystem that is predictable renewables for consumer use, whether in EVs or spot power situations.
The key element enabling this is dynamic capacity planning and elastic load alerting for the managed clusters of battery banks. Do we need to scale now? If so, do we scale out because we need more resources to serve an avalanche of demand? Or do we scale in, because SXSW has passed and our retail EV customers no longer needs to use so many resources in the Austin area?
Our project is motivated to reduce waste by "right-sizing" energy consumption by linking it more
directly to demand. Not many have seen the inside of a large Data Center, but these are colossal
energy-hungry ventures[1] on a scale that is difficult to appreciate
without seeing one in person.
Everything in a data center is planned around watt usage. Over half of the wattage goes to server
load and most of the rest to cooling equipment. If we can better predict and scale compute resource
load more efficiently we have the opportunity to effect change in the United States alone at about
the 9.1 TWhr (Terawatts per hour) level and, perhaps, slow the projected rise in consumption by 2020
to 13.9 TWhr [2].
Looking for solutions
Our inferential goals are to understand how better to do capacity planning and alerting for renewable-based, managed clusters of battery resources. We learned what makes a good alert threshold, such that we can predictably recommend actions that will keep renewble power highly available under various demand scenarios ranging from low to high. Effectively, we want to investigate the elasticity of supply and demand on renewable generated power resources so that we can make ongoing dynamic recommendations about the proper scale for a given set of inputs. Benefits include:
Learning what a low usage state is for power and provide information on the scale (how many battery nodes) that should be sent to a locale on a dynamic basis
Reduction of energy usage by fitting the supply and demand more appropriately over time
The ability to prescriptively maintain power supply availability with the minimum amount of renewable resources
Locations around the world
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the image above to view our interactive map
Locations by country
We started by analyzing a small subset of data provided to us to scale easily in and out.
This data provided us insight into the nature of a completely container-based approach.
We started by analyzing a general container metric which gave around 3,000 instances (batteries)
running on a foundation.
Our limited model illustrates the possibility of reducing energy usage by a terawatt in the near future.
FindingsOur data allowed us to group high and low utilization applications. We will be able to segregate the high usage services, with common patterns, through anti-affinity rules while letting the others fill in the gaps. Moreover, these pattern identification methods allow us to predict scaling needs before thresholds are met. This will allow us to scale out, along with scaling in as demand fades. The capability to reduce capacity will cut down on future electricity consumption.
