Hybrid horizons: industrial use of batteries?

Gas and diesel engines can be particularly inefficient when idling, or running at 20-30% loads. At these levels, their fuel economy can be impaired by 30-80%. This is the rationale for hybridizing engines with backup batteries: the engines are always run at efficient, 80-100% loads, including to charge up the batteries, which can better cover lower intensity energy needs.

Hybrid passenger cars are the best known example, since Toyota re-introduced them in the late 1990s. c25-30% energy savings are achieved, including through engine down-sizing and regenerative breaking

Industrial applications are also increasingly taking hold as battery costs come down, achieving even higher, 30-65% energy savings. This data-file summarizes a dozen examples, from oil and gas, marine, construction and even the machinery at LNG plants.

Value in Use: CO2 intensities of household items?

We estimate costs and carbon intensities per use for twenty low-utilisation household objects: the average is $13 per use and 1.3kg of CO2, respectively. Both are high numbers.

The biggest determinant is the number of uses per item.  We fear that once purchased by a consumer, the average item on our list will be used just c20 times in its entire lifetime.

More extensive “sharing” will be enabled by drone delivery technologies, potentially saving $150bn of annual sales and 15MTpa of CO2 emissions across these 20 items items alone. Across the entire US economy the savings could reach $1trn and 100MT per year.

Distribution Costs: Ships, Trucks, Trains and Delivery Vans?

This data-file breaks down the financial and carbon costs associated with a typical US consumer’s purchasing habits. It covers container-ships, trucks, rail freight, cars and last-mile delivery vans; based on the ton-miles associated with each vehicle and its fuel economy.

We estimate the distribution chain for the typical US consumer costs 1.5bbls of fuel, 600kg of CO2 and $1,000 per annum.

The costs will increase 20-40% in the next decade, as the share of online retail doubles to c20%. New technologies are needed in last-mile delivery.

Please download the model to for a full breakdown of the data, and its sensitivity to oil prices, consumption patterns, international trade and exciting new delivery technologies.

Drones attack military fuel economy?

This data-file quantifies the fuel economies of typical military vehicle-types, as $1.7 trn per annum of global military activity consumes c0.7Mbpd of total oil demand on our estimates, which are also included in the data-file.

Military drones  are transformational. Almost all the incumbent military vehicles in our data-file have fuel economies below 1 mpg. But the Reaper and Predator drones, famous for their deployment in recent conflicts, have achieved 3mpg and 8mpg respectively. But small, next-generation electric drones will achieve well above 1,000 mpg-equivalent.

Swarms of small-scale electric drones could emerge as the most devastating military weapon of the 21st century, according to a book we read last year on the topic, arguing that “A swarm of armed drones is like a flying minefield…they are so numerous that they are impossible to defeat… each one presents a target just 4-inches across… and shooting down a $1,000 drone with a $5,000 missile is not a winning strategy”. Our notes on the book are included in the data-file.

 

Container ships versus trucks and trains

This data-file tabulates c10 examples for the fuel economy of container vessels, which is a function of their size and speed.

The most efficient container ships are 2x more efficient than typical trains and 20x more efficient than typical trucks.

We calculate that moving goods from overseas to the developed world’s c1bn consumers accounts for c0.5% of global CO2 emissions (c50% in ships, c50% in trucks). These calculations are also shown in the data-file.

Drone Delivery: the Energy Economics

This data-file quantifies the energy economics of drone-deliveries, after Amazon and Google both announced breakthrough progress in this space in 2019.

15 commercial drones are evaluated in the ‘drones’ tab, which tabulates their energy consumption as a function of weight, velocity, flight times and costs (chart above).

The equations of flight are then modeled out fully for Amazon’s Prime Air concept in the ‘AmazonCalculation’ tab; for a full comparison against trucks.

We conclude that drone delivery could use c90% less energy, c99% less cost and c90% lower carbon than is typical in current last-mile deliveries.

Subsea Robots: the next generation?

This data-file tabulates over 20 next-generation subsea robots, being pioneered around the industry. Each one is described and categorized, including by technical readiness.

These electric solutions could be very material for offshore economics, improving oilfield decline rates and maintenance costs. Innovations include:

  • Residing subsea for c1-year at a time, by re-charging in subsea “docking” stations. This provides greater availability for lower cost.
  • Increasing autonomy means these robots can be free-swimming, as a communications tether is no longer necessary, improving ranges.
  • More intervention work will be conducted, rather than just inspections.

8 of the concepts in our database have all three of these capabilities above. They are at TRLs 5-6, and should be commercially ready in the early 2020s.

The leading companies are tabulated in the data-file, by Major and Service firm (chart below).

These solutions can save c$0.5-1/boe for a typical offshore oilfield, we estimate: performing inspection tasks 2-6x faster than incumbents, as well as halving costs and eliminating the weather-dependency associated with launching-recovering traditional ROVs.  For full details, please download the data-file.

 

Explaining US gasoline?

This data-file breaks down US gasoline demand, as a function of vehicle miles traveled (urban and rural), GDP growth, gasoline prices and fuel economy across the US vehicle fleet. It contains monthly data on each variable, going back to 2002, so correlations can be explored.

Gasoline demand is modestly slower-than-expected in 2019, rising just +1.0% YoY, which is above the prior 15-year trend for 0.4% pa growth, but below the 1.8% expected basesd on regressions to fuel prices and GDP.

One cause is urban vehicle miles driven, where growth has slowed, defying historical correlations with GDP (strong) and gasoline prices (reasonable). Structural explanations could include the rapid rise of alternative vehicles (e.g., e-scooters), ride-sharing and policy decisions.  

Please download the file to view the data or test your own regressions.

Power Trains? Electric, diesel or hydrogen

This data-file compares diesel trains, electric trains and hydrogen trains, according to their energy consumption, carbon emissions and fuel costs. The data are presented apples-to-apples, per passenger mile, based on worked examples. Seven train routes are compared on 20 metrics overall.

Travelling by train should be 2-15x more fuel-efficient, and 3-20x less carbon intensive than travelling by car.

Electric trains are most efficient and cost-effective. The drawback is that electrifying tracks can cost c$1.4M/km. Nevertheless, we are most positive on the electrification opportunity around railways, particularly using next-generation combustion technologies.

The world’s first hydrogen trains launched in Germany in September-2018. To be cost-competitive with entry-level diesel trains requires c$12/kg hydrogen, $6/gallon diesel and a $50/ton carbon price.

Relative costs and economics can be compared by varying inputs in the file.

 

Electric cars slow the energy transition?

Electric Cars are being overtaken by new electric vehicles, which achieve c3x greater decarbonisation per unit of battery material. This metric matters if one believes that battery materials are a limiting factor in the energy transition. To illustrate our case, our new Excel-file models two scenarios…

In the first scenario, 400kg of battery materials can be used to produce 1 electric vehicle, which displaces 1 gasoline taxi. The calculations show that 28 bbls of oil-equivalent energy and 12T of CO2 emissions are avoided each year.

In the second scenario, 400kg of battery materials can be used to produce 120 electric scooters, which displace 2.5 gasoline taxis. The calculations show that 96bbls of oil-equivalent energy and 37T of CO2 emissions are avoided. I.e., the scooters achieve 3x more decarbonisation than the electric car.

Moreover,  our numbers above only assume that one-in-three scooter trips displaces a car-trip, while the other two-in-three are deemed to be “new demand”. Per mile travelled, the scooters achieve 9x more decarbonisation than putting the same 400kg of battery materials into the electric car.

Please download the data-file to interrogate our assumptions and stress-test your own scenarios. We argue the “electric revolution” goes beyond replacing today’s ground cars with electric ground cars. The opportunities are in new vehicle types.