1

MEMORANDUM

Subject: Application of Monte Carlo process to the determination of proposed volume

requirements for 2014 standards NPRM

From: David Korotney

To: EPA Air Docket EPA

- HQ

- OAR

- 2013

- 0479

This memorandum includes all inputs and outputs for the Monte Carlo process that w as

used to develop ranges and point estimates for the proposed 2014 volume requirements for the

Renewable Fuel Standard (RFS) program in the Notice of Proposed Rulemaking (NP RM).

A. Standard D istributions

The Monte Carlo analyses were based on three standard distributions. These three

distributions are defined as follows:

Table A

- 1

Defining Characteristics of the Three Standard Distributions

Normal Mean = 0.8

St Dev = 0.2

Skewed (Weibull) Shape factor = 0.5

Scale factor = 1.7

Half

- normal Mean = 0

St Dev = 0. 4

The steps taken to develop a randomly generated distribution of volumes for each type of

renewable fuel (e.g. biodiesel, domestic advanced ethanol, etc) were as follows:

1. A flat distribution was used to generate random numbers between zero and one.

These randomly chosen values were used to represent values on the cumulative

distribution curves shown above (abscissa).2

2. For each randomly generated value on the cumulative distribution, a correspo n ding

volume surrogate 1 was determined from one of t he three standard distributions

(ordinate)

3. The volume surrogate determined for the chosen standard distribution was scaled to fit

the chosen range for t he type of renewable fuel in question. To do this, t he chosen

distribution as defined in Table A

- 1 above was shifted and rescaled such that the 5th

percentile of the standard distribution corresponded to the low end of the designated

volume range, and the 95th percentile of the standard distribution co r responded to the

high end of the designated volume range.

The results of this shifting and rescaling are shown for all input volumes in Sections B and D

below.

To demonstrate how the three standard distr ibutions affect the means, modes, and percentiles

differently, the three distributions can be shifted and scaled to align their 5th and 95th

percentiles. The results are shown below.

Figure A

- 1

Aligned Standard Distributions

1 So

- called because it has not yet been converted into a volume representing a specific type of renewable fuel, such

as biodiesel or sugarcane ethanol.3 Table A

- 2

Aligned Standar d Distributions

Mean Percentiles

Mode

5th 50th 95th

Normal 0.607 0.224 0.607 0.988 0.607

Skewed (Weibull) 0.542 0.224 0.503 0.988 0.409

Half

- normal 0.519 0.224 0.470 0.988 0.200

B

. Input v alues and d istributions for c ellulosic b io fuel

Table B

- 1

Inputs for Cellulosic Biofuel Production (mill ethanol

- equivalent gallons)

Company Distribution Low end of range High end of range

Abengoa Half

- normal 0 18

DuPont Half

- n ormal 0 2

INEOS Bio Skewed 2 5

KiOR Skewed 0 9

Poet Half

- normal 0 6

CNG/LNG Producers Normal 35 5 4

Edeniq Half

- n ormal 0 7

Ensyn Normal 0 5456

C

. Results of aggregation of cellulosic biofuel sources7

D

. Input v alues and d istributions for t otal r enewable f uel and a dvanced b iofuel

Table D

- 1

Inputs for Total Renewable Fuel and Advanced Biofuel (million ethanol

- equivalent gallons)

Distribution Low end of range High end of range

E85 consumption Half

- normal 100 3 0 0

Available volumes of biomass

based diesel Half

- normal 1,920 2,400

Available volumes of non

ethanol advanced biofuel Normal 24 132

Available volumes of non

ethanol non

- advanced

renewable fuel Normal 1 25

Available volumes of domestic

ethanol advanced biofuel Normal 28 142

Available volumes of imported

sugarcane ethanol Normal 300 80089

E

. Results of aggregation for total renewable fuel10

F

. Results of aggregation for advanced biofuel11

G

. Results of aggregation for non

- advanced ethanol (corn

- ethanol)12