Exhaust gas recirculation in gas power: the economics?

This data-file explores an alternative design for a combined cycle gas turbine, re-circulating exhaust gases (including CO2) after the combustion stage, back into the turbine’s compressor and combustion zones. The result is to increase the concentration of CO2 and thus improve the economics of carbon capture (chart below).

The data-file draws on costs data and operating parameters from several detailed technical papers, and model the economics. Even with EGR technology, it will still be challenging to decarbonize a conventional gas turbine for less than $100/ton (at which point blue hydrogen becomes competitive).

A short note is presented on the first tab, explaining the background, the theory and our main conclusions. You can stress-test the numbers and input assumptions in the model.

Blue hydrogen from methane reforming: the economics?

This data-file captures the economics of hydrogen production via reforming natural gas: either steam-methane reforming (SMR) or auto-thermal reforming (ATR), which yields blue hydrogen if purified CO2 is sequestered.

Costs are drawn from technical papers in the final three tabs of the data-file, which also include our notes on blue hydrogen and an explanation of operating parameters in the model.

ATR is preferable to SMR as a decarbonization technology, eliminating 90% versus 60% of respective CO2 emissions relative to natural gas (chart below).

We find that blue hydrogen production may be competitive with CCS. Please downlaod the data-file to stress-test sensitivities to capex costs, opex costs, gas efficiency, gas prices, power prices, CO2 prices and fiscal regimes.

 

Capturing CO2 with the Amine process: the economics?

This data-file models the economics of capturing CO2 from exhaust flues using the amine process, in order to calculate what CO2 price is necessary to earn a passable IRR. This is one of three components of a CCS project, alongside CO2 transport and CO2 sequestration.

Input assumptions can be flexed in cells H5:35 of the model, in order to stress test the sensitivity to gas prices, energy efficiency, amine costs, water costs, capex, G&A and fiscal conditions. Our own base case estimates are informed by five tabs of cost data and technical papers.

CO2 costs are extremely sensitive to CO2 concentrations, rising exponentially if it is necessary to capture CO2 from more diffuse sources. The typical CO2 concentrations of different exhaust streams is tabulated in another of our data-files, here.

 

Combined heat and power: the economics?

This data-file models the energy economics of a combined heat and power installation, to provide electricity and heating behind the meter, in lieu of purchasing electricity from the grid. The economics are strong, especially for larger units.

CO2 emissions can also be reduced by 5-30%compared to purchasing power from the grid, due to high efficiency capturing and using exhaust heat in CHPs.

Economic sensitivities can be stress-tested, including to power prices, gas prices, thermal efficiencies and system sizes (examples below).

The full model also contains granular cost data, c100 rows of operational data for CHP systems and our full notes from the technical literature.

Organic Rankine Cycles: the energy economics?

This data-file captures the economics of an Organic Rankine Cycle engine to recover low-grade waste heat (at 70-200C) from an industrial facility, or in the geothermal industry. A CO2 price of $50-75/ton could greatly accelerate adoption and improve the efficiency of industrial facilities.

Our model draws on past project data and technical papers to estimate costs (in $/kW) and thermal efficiency levels (in %). This shows how economics vary with facility size (below) and how large an industrial facility needs to be to be able to install an Organic  Rankine Cycle heat recovery system.

Notes on the industry are also tabulated, including how the technology works, total market size, capacity and leading companies ranked by past project deliveries.

Transporting green hydrogen as ammonia: the economics?

This data-file models the costs of converting green hydrogen into ammonia, transporting the ammonia in an LPG tanker, then converting then converting the ammonia back into hydrogen through ammonia cracking.

We model what hydrogen price is required, (in $/kg), to earn a 10% IRR on the investment, the energy intensity of the process (in kWH/kg) and the overally energy efficiency (in %), based on technical papers and recent guidance from Air Products (which aims to start up a 230kTpa project in 2025).

With some generous assumptions, a large-scale green hydrogen and ammonia value chain may be able to reach consumers in developed world countries at a cost of $10/kg, although this looks ambitious, and additional costs may be incurred or returns may be diluted.

Afforesting deserts: energy economics?

We model the economics of afforesting deserts by desalinating and distributing sufficient water for trees to grow. This could increase the global land available for new forest growth by a factor of 5x.

The best case economics are achievable in the Permian, where 10% IRRs are achievable at $30/ton CO2 prices, total costs are 60% lower than current produced water disposal costs, and the CO2 savings could be sufficient to make entire upstream operations to ‘Net Zero’.

Economics are more challenging for desalinating sea-water and distributing it inland. If 50T of water are required by T of CO2 capture in forests, equivalent to adding 100mm of annual rainall, then costs may be passable. But to grow forests in the Sahara would likely require well over 300T of water per T of CO2 and the energy economics become impossible.

The data-file also contains useful workings from our recent research report, Green deserts: a final frontier for forest carbon?

Alternative truck fuels: how economic?

This data-file compares different trucking fuels — diesel, CNG, LNG, LPG and Hydrogen — across 35 variables. Most important are the economics, which are fully modelled, in the 2020s in the US, in the 2020s in Europe and incorporating deflation in the 2040s.

Hydrogen still screens as an expensive alternative. We estimate full cycle freight costs will be c30% higher for hydrogen vehicles than diesels in Europe, and as much as 2x higher in the US. The data-file contains a breakdown of hydrogen truck concepts and their operating parameters.

Natural Gas can be close to competitive. On an energy-equivalent basis, $3/mcf gas is 4x more economical than $3/gal diesel. However, the advantages are offset by higher vehicle costs, operational costs and logistical costs. Mild environmental positives of gas are also offset by mild operational challenges.

Hydrogen storage: the economics?

This model captures the costs of storing hydrogen, which appear to be much higher than storing natural gas or diesel fuel.

We estimate a $2.50/kg storage spread may be needed to earn a 10% IRR on a $500/kg storage facility, while costs could be deflated to $0.5/kg if nearby salt caverns are available and projects are large and efficient.

Costs could also be deflated modestly at a high-utilization but low-speed vehicle fuelling station, in the transportation sector.

The model hinges on costs of tanks and compressors, where costs are bounded based on technical papers and online sources. Detailed notes and input data are tabulated in backup tabs behind the model.

Please download the data-file to stress test the economics.

Liquid pipelines: the energy economics?

This model captures the energy economics of a pipeline carrying oil or water. Specifically, we have modelled energy requirements using simple fluid mechanics, and modelled costs using past projects and technical papers, which are tabulated in the data-file.

Our conclusions show the requisite costs, energy and CO2 intensities of different pipelines (below).

You can stress test the economics directly in the model, by varying pipeline tariffs, capex costs, energy costs, CO2 prices, maintenance costs, pipeline diameter, pipeline distance, pipeline elevation, pipeline materials, fluid viscosity and compressor efficiencies.