Estimating Delivered Biomass Fuel Supply Costs and the Levelized Cost of Electricity (LCOE) for New Biomass Fueled Power Plants:

Two case studies from Cote d'Ivoire and New South Wales, Australia

Using @RISK and RISKOptimizer to find Optimal SolutionsWilliam Strauss, PhDNovember 12, 2019

Dr. William Strauss, President

Named one of the most influential leaders in the

biomass sector in 2016 and 2017 by Argus Media. Recipient of the 2012

International Excellence in Bioenergy Award.

A leader in the industry for more than two decades.

John Swaan, Pellet Plant Operations

John is a leading expert on pellet plant operations. He is

the founder of Pacific BioEnergy and producer of the

first bulk shipment of wood pellets from North America to

Europe (1998). Recipient of the 2014 International

Founders Award.

Senior Members of the FutureMetrics Team

Annette Bossler, Market Intelligence Expert

Annette provides detailed market intelligence on

renewable energy policies around the world with a

strong focus on Japan. Annette speaks, reads, and writes fluent

Japanese. She was the 2013 recipient of the President’s

Award from the Maine International Trade Center.

Laurenz Schmidt, Technology Specialist

Laurenz is a globally respected expert in

thermodynamics and mass and energy flow dynamics in the renewable energy sector. He reviews and analyzes all advanced pellet technologies.

Yoshinobu Kusano, Japan Policy Advisor

Yoshi is one of the most highly respected

participants in the Japanese biomass sector. He was a key member of

Sumitomo’s pellet trading operations. He provides

valued insights into Japanese policy developments.

Wood pellets are upgraded solid fuel made from woody biomass.

Global trade in pellets was about 35.4 million metric tonnes in 2018.

Two Major Sectors in the Wood Pellet Industry:

Heating

Power (as replacement for coal)

The use of biomass derived fuel has a strong carbon emissions benefit.

The ability to provide efficient useful energy with renewable solid fuel that is carbon neutral in

combustion is the basis for why the markets exist.

The reason that we care about CO2 emissions

If the trend in the chart below is followed, we will release most of the geologic carbon sequestered over hundreds of

millions of years over a span of about 250 years.

Curve is from a simulation by FutureMetrics

250 Years

The industrial wood pellet sector is supported by various policies to compensate the power generators for the higher cost of pellet fuel versus coal.

The policies are based on a strategy to lower carbon emissions from the power sector.

In order to receive the support, the utility has to prove that the pellet fuel lowers carbon emissions.

This is done through independent third-party certification schemes that prove that the sources of feedstock for making the pellets are sustainable. (For example: FSC, SFI, PEFC, SBP)

That is, the annual supply of pellet feedstock cannot lower the forest’s inventory across the landscape and therefore cannot lower the stock of

carbon held by the forested lands.

Biogenic carbon contained in forest biomass released to atmosphere.

Biogenic carbon released during energy generation recaptured in new growth on forest landscape.

Unlike geologic carbon (hydrocarbon), forest carbon (carbohydrate) is continuously recycled.

As long as the harvest rate does not exceed the growth rate over the managed landscape, the atmosphere does not experience a net increase in CO2 concentration from use of pellets.

Geologic carbon released across the supply chain.

Diagram courtesy of Jennifer Jenkins, Chief Sustainability

Officer, Enviva

It is a continuous and dynamic

process across the managed landscape

Absolute necessary condition:

Removals that are combusted for energy must be equal to or

less than want is sequestered.

Two Case Studies for Using Biomass Derived Fuel for Power Generation

Biomass Power in the Ivory Coast

FutureMetrics was originally retained to provide an analysis of the feasibility of producing pellets as fuel for the biomass power project in Côte d’Ivoire. After initial due diligence and a visit to the proposed development area,

FutureMetrics determined that producing pellets is not recommend due to the characteristics of the project.

The woody biomass fuel in this case is a by-

product of cocoa production.

Cocoa shells.

The problem was to calculate the cost to deliver cocoa shells to a new-build centrally located biomass power plant.

There are 18 aggregation zones producing enough cocoa shells to fuel a 70 MW power plant.

FutureMetrics developed a transportation cost model that quantifies the impact of travel from the 18 aggregation zones to the proposed power station location.

The input parameters to the model include (mean values and variability around the means based on local research and analysis by FutureMetrics):

• The distance from each of the aggregation zones to the power station site;• The estimated road travel speeds over three categories of roads for the loaded and

unloaded trucks; • Assumed fuel cost;• Fuel consumption per kilometer for different speeds and loads;• Truck driver cost per hour;• Loading and unloading time;• Operating 312 days per year (6 days per week);• Operating hours per day - assumed to be 24 with no overtime pay;• Amortized capital cost of the trucks and trailers;

A selection of tables from the modeling showing

expected values…

Calculation of Truck Capital Investment based on Expected Values

Delivered cost per tonne of the fuel (cocoa shells) is a key input into calculating the cost of generation. This includes payment to landowner, harvest cost (mostly

labor), transport to aggregation zone, and transport to the power station.

Probability distributions are estimated for all of the inputs for harvest, aggregation, and transportation models based on local research and analysis.

Each aggregation zone has unique sensitivity to inputs. Here is zone #5 (close to the power station) and zone #17 (distant from the power station).

Note bulk density is number one. More on that on next slide.

This information helps the developer know where to focus for minimizing the

risk of unexpected high costs.

The bulk density of the cocoa shells has a significant impact on the capital costs and operating costs.

Lower bulk density = more trucks making more runs for the same tonnes.

Difference of about $1.6 million per year and lower uncertainty with higher bulk density!

If the plant can receive about $80 per megawatt-hour for power, it has a good probability of success.

Given the potential for “regulatory variability”, a higher rate would be preferred.

New-Build Biomass Power Plant in New South Wales, Australia Estimating the optimal location that minimizes delivered biomass fuel cost

(with some constraints)

This analysis is from Part Four of a five-part study for the Government of New South Wales, Australia by FutureMetrics.

Milestone 1 – Overview Milestone 2 – Techno-economic assessment – Co-firing wood pellets at the Bayside coal power station Milestone 3 – Techno-economic assessment – Co-firing wood pellets at the Mt. Piper coal power station• Milestone 4 – Greenfield Development == Optimal location and resulting LCOE for a new-build biomass power stationo Milestone 5 – Final Report == forthcoming.

Milestones 2 and 3 were for existing large utility pulverized coal power stations.

Milestone 4 (this material) was for a new circulating fluid bed boiler power plant using “green” (not upgraded) biomass.

The spatial density and characteristics of available

biomass is an important basis for the analysis.

329 sites represent the sources of biomass in the region. This

includes sawmills and managed forests. Each has different annual sustainable volumes and mixes of

species (hardwood, softwood, etc.).

The sites are dots on the map, and the spatial density of material

(metric tonnes per hectare) is the input for the heat map.

The map also shows the high-voltage transmission lines (blue lines), major roads

(yellow lines), and railways (brown lines).

The problem is to find the location for a new biomass power plant that minimizes the average delivered wood cost but is also within 3 kilometers of a

high voltage power line and is within 5 km a major highway.

All of the spatial information data was geocoded for input into the model.

Either Evolver or RISKOptimizer can solve the problem; but we also want to calculate the LCOE at the optimal location with some uncertainty on harvest and

transportation costs, and power plant characteristics such as efficiency and capacity factor. So RISKOptimizer was used.

Within the latitudes and longitudes that make up NSW, the model was asked to find the minimum delivered fuel cost

subject to several contraints.

Five scenarios were run. Each had a higher minimum number of tonnes per year and maximum haul

distance as added constraints.

Results for the maximum haul distance of 300 km scenario

Location of 300km Max Haul Scenario Power Plant with concentric 60-minute drive time analysis

Green circles show wood supply sites

and relative annual tonnages.

Close up shows location is near road, power line, and rail

Results 100 300 500 600 800Latitude of Optimal Location -37.106 -35.500 -30.476 -30.476 -29.956Longitude of Optimal Location 149.868 149.157 152.997 152.997 152.972Minimum Tonnes per Year 50,000 150,000 250,000 300,000 400,000 Total Tonnes per Year 75,128 168,785 720,523 724,507 804,344 Average Cost per Tonne for Transporation $4.25 $15.90 $26.21 $26.39 $37.20Average Total Cost per Delivered Tonne $45.89 $52.40 $69.15 $69.03 $79.05Weighted Average Distance Biomass is Hauled (km) 38.64 144.57 127.17 130.56 239.17Max Distance Biomass is Hauled (km) 78.49 296.49 493.25 592.86 799.31Total Number of Sites (out of 293) Used in Scenario 18 86 194 214 258Distance to Grid Connection from Optimal Location (km) 1.36 1.99 0.66 3.00 3.00Implied Size of the Power Plant (MW) 10.0 22.4 95.7 96.3 106.9Estimated Annual Delivered Fuel cost per MW of Capacity $345,430 $394,415 $227,431 $228,809 $297,362

Maximum Distance Constraint (km)

Results of Five Optimization Runs (note that 500 and 600 km max haul distance scenarios are in the same location)

Locations of Each of the Five Scenarios in NSW

simtable lat -37.106simtable long 149.8676simtable size 100

Using the “SimTable” function to run the five scenarios in one simulation

Levelized Cost of Electricity for each Scenario

Results use “RiskMean” and “RiskStdDev” functions

Results show the potential variability of the LCOE.

“The decision as to which scenario is best may be simply based on which has the lowest expected LCOE. However, there may be other criteria such as support for the demand for low grade biomass fuel and the associated jobs.

If policy for the reduction in CO2 emissions evolves, there may be monetary benefits associated with generating low carbon power. For this analysis, the social benefits, the carbon benefits, and the political impacts of the different scenarios and their locations are not considered.

If there were monetary impacts as a result of carbon emissions mitigation policy, the curves in the chart above will be shifted leftward to reflect that net benefit per MWh.”

Should they do it?

Provides baseload or on-demand power. Not possible with wind and solar.

And, if there is cost for emitting carbon, at about US$65/AU$95 per tonne, when compared to other generation sources in NSW, biomass power with zero net CO2

emissions from combustion, is competitive.

From the US National Renewable Energy Lab (NREL) and from “Lazard’s Levelized Cost of Energy Analysis, V.12”, November 2018 with US dollar values converted to Australian dollar values using an exchange rate of 0.6856 (October 20, 2019).

Thank youBill Strauss

WilliamStrauss@FutureMetrics.com

Bill enjoying BC powder – March 2, 2019