The Economic Feasibility of
Bio-energy Generation for
Peak Demand of Electricity
Xiaolan Liu
Texas Tech University
Economics of Alternative Energy Sources and Globalization
Nov. 2009 Orlando, Florida
The Economic Feasibility of
Bio-energy Generation for
Electricity at Peak Demand
 Introduction and Problem statement
 Study Objectives
 Methods and Procedures
 Model Development and Results
 Conclusions
I. Introduction and
Problem statement

On average, 1,570,000 tons of cotton gin waste
(CGW) is produced; approximately equals to 4,791
million kwh of electricity annually;
 Compare to the national structure, the market
demand for bio-fuel in Texas is more pronounced at
industry sector, which account for 72% of total
biomass energy consumption;
 Strong intents exist to obtain bio-energy from
biomass at Texas.
Regional Concentration of Texas Cotton Planting
(Source: USDA/TASS)
I. Introduction and
Problem statement

Because of the natural of agricultural waste, the idea of
converting it to bio-fuels faces a long-standing difficulty
of commercialization:




(cont.)
unstable supply (dependents on weather and crop market
price);
limited scale and low efficiency;
relative higher costs of biomass transport and conversion
facilities.
As a result, the usually low selling price and unstable
profits of bio-energy production restrict its scale, and
lead some undesirable features for many investors.
II. Study Objectives
The overall objective is to analyze economic
feasibility of electricity generation from CGW for
peak load demand. Four specific objectives:
 establish the appropriate area, locations and
collectable volume of CGW;
 Estimate the variability and distribution of CGW;
 Determine the economic models for optimal
production scales;
 Conduct relative economic analysis: sensitivity
analysis, cost/benefit analysis, rate of return, risk
analysis etc.
III. Methods and Procedures
 Grouped CGW

Based on cotton production and maps of GIS, the
locations and volumes of CGW for each gin are
identified: 79 gins from 16 counties with total
average of CGW around 850,000 tons annually;

CGW from ginners are grouped within 10 miles
radius area from a possible location of a bioenergy plant based on the closest rule. 19 groups
are identified, and 13 of them with average CGW
above 20,000 tons annually.
Locations of Gins and selected groups
J
E
B
L
F
Q
G
C
M
H
I
N
Alternative Technologies and Possible Scenarios
Mobile
END
III. Methods and Procedures (cont.)
 Variations and distribution of CGW supply



Variation is an un-ignorable feature of crop residues, and is
important to determine the possible firm scales and related
risk and costs;
The main factors related the variation of CGW supply are
weather and cotton price because not only market risk exists,
but also cotton production is heavily influenced by the
incidence of dry weather in study region;
MCMC method was used to estimate the parameters. With
specified joint distribution, values of the unknown parameters
from their conditional (posterior) distribution are sampled
given those stochastic nodes that have been observed.
III. Methods and Procedures (cont.)
 Economic Models for Optimal Scale


Rational producers are assumed to maximize
profit given their limited resources and available
inputs and opportunities;
With the estimated PDF of CGW, expected
profits could be obtained for bio-energy outputs
given fixed cost, possible transportation costs
and variable costs for labor, storage and other
operating costs.
BACK
IV. Model Development and Results
 Model for Estimating CGW Distribution
Table 1. Estimated Results of MCMC Model
Node mean
b[1] 2.576
b[2] 4.257
b[3] -0.5866
b[4] 0.668
tau 12.95
sd
6.029
3.295
0.5413
0.5919
9.774
MC error
0.02035
0.01108
0.001818
0.001962
0.05568
2.5% median 97.5%
-9.562 2.605 14.56
-2.36 4.269 10.84
-1.669 -0.5888 0.5039
-0.5
0.6565 1.894
1.525 10.49 38.17
start
501
501
501
501
501
sample
88500
88500
88500
88500
88500
Mean
(63)
Optimal
Point
(76)
END
IV. Model Development and Results
(cont.)
 Economic Model for Profit Maximization
Summary of Assumptions
 Sale prices of electricity at MWP, MWSP, OWN and IC
are $120, $65, $45, and $30 per MWe;
 Establishment expense: gasification $ 2.8 MM/Mwe
($185,000 /MWe annually);
 Variable cost is $5.5 per MWe generated;
 Supplement (penalty) cost is $140 per MWe;
 Transportation cost is $20 per ton of CGW;
 50% of total electricity OWN needs can be provided
by the process of bio-energy production.
Results of Economic Model for Gasification
Performance Distributions of Economic Models
Performance Distribution of Model 1
Prob
Mwe
IC
OWN
Surpl.
VC
Revenue
Profits
0.05
68350
41984
7374
0
$
375,925
$
3,333,318
$ 1,151,053
0.05
68350
42699
6659
0
$
375,925
$
3,322,593
$ 1,140,328
0.15
68350
43089
6269
0
$
375,925
$
3,316,741
$ 1,134,476
0.25
68350
43673
5686
0
$
375,925
$
3,307,988
$ 1,125,723
0.25
55780
32148
4640
0
$
306,790
$
2,915,203
$
802,073
0.15
42185
19684
3509
0
$
232,018
$
2,490,390
$
452,032
0.05
34870
12978
2901
0
$
191,785
$
2,261,812
$
263,687
0.05
20330
0
1338
0
$
111,815
$
1,802,179
$
-115,976
Performance Distribution of Model 2
Prob
Mwe
IC
OWN
Trans.
VC
Revenue
Profits
0.05
68350
41984
7374
0
$
375,925
$
3,333,318
$ 1,151,053
0.05
68350
42699
6659
0
$
375,925
$
3,322,593
$ 1,140,328
0.15
68350
43089
6269
0
$
375,925
$
3,316,741
$ 1,134,476
0.25
68350
43673
5686
2.00E-03
$
375,925
$
3,307,988
$ 1,125,723
0.25
68350
44718
4640
12570
$
375,925
$
3,292,304
$ 1,097,469
0.15
68350
45849
3509
26165
$
375,925
$
3,275,340
$ 1,066,910
0.05
68350
46458
2901
33480
$
375,925
$
3,266,212
$ 1,050,467
0.05
68350
47667
1691
48020
$
375,925
$
3,248,070
$ 1,017,785
Model1
Model2
Expected Profits
$
841,827 $
MWP (Mwe/yr)
9227.3
MWSP (Mwe/yr)
9764.3
FC
$
1,806,393 $
HOUR (hour/yr)
7000
Capacity (Mwe/hr)
9.76
Note: Model1 with Penalty term, Model2 with Transportation term.
882,917
9227.3
9764.3
1,806,393
7000
9.76
BACK
Model Sensitivity Analysis
Dual Price (shadow price, $/unit): the amount of
E(π) would improve as the constraints are increased
by one unit. Hour 92.6; MWP 89.3; MWSP 34.3
Ranges of Objective Coefficient
MWP [100, 145]; MWSP [46, 89]
FC [-1.1045, -0.8728]
Changes of Electricity Supply (Mwe/yr): how far
either increasing or decreasing the amount of outputs
without changing its dual price.
Current
MWP
9227
MWSP 9764
↑
1338
1338
↓
353
353
Optimal Results of Selected Groups
Optimal Results of Economic Models for Selected Groups ($/year, MWe/year)
Group G
Expected Profits
$
Production Capacity
935,115
Group M
$
841,827
Group L
$
573,571
Group R
$
262,523
Sum
$ 6,327,150
75,925
68,350
46,575
21,315
514,360
MWP
10,250
9,227
6,288
2,878
69,439
MWSP
10,846
9,764
6,654
3,045
73,480
$ 1,806,393
$ 1,230,911
Fixed Cost
$
2,006,589
$
563,325
$ 13,593,800
HOUR (hour/yr)
7000
7000
7000
7000
7000
Plant Scale
10.85
9.76
6.65
3.05
73.48
Average CGW (ton)
74,501
67,067
45,698
20,914
504,702
Note: Sum is the aggregation of 13 groups with average CGW above 20,000 tons annually.
Results of Economic Model for Bio-oil/Power Generation
Income Statement of Standalone 100 tpd Bio-oil Plant
Net Sale
Bio-oil
Char
Others
Total Sales
Cost of Goods Sold
Labor & Overhead
Transport Cost
Electricity
Consumables
Maintenance
Total CGS
Gross Profit/Loss
Operating Expenses
SG&A
Cost of Money
Property Tax
Total Operating Expense
$
$
4,547,510
263,670
$
$
$
$
$
$
561,700
27,720
558,000
240,559
280,000
1,667,979
3,143,201
131,160
491,880
168,000
Net Profit / Loss
Break Even Bio-oil Price
20% ROI Bio-oil Price
Assumptions:
Bio-oil Sale Price
Char Sale Price
Transportation Cost
23%
1%
23%
10%
11%
$
$
$
$
$
4,811,180
5%
20%
7%
$
791,040
$
$
2,352,161
0.59
$
1.22
47
0.14
ROI
96%
0.72
$/gal
$/ton
$/ton/mile,
BACK
Results of Economic Model for Biooil / Electric Power Generation (cont.)
 Gas turbine-based CHP system
$0.86 MM / MWh of fixed costs,
$4.9 / MWh variable costs and
$25 / ton federal subsidy
negative profits obtained for the
process from bio-oil to electricity;
 50% lower cost of boiler and $ 25 /
ton federal subsidy
 electricity production is barely
operated at $120/MW.
V. Conclusions
Locations and collectable volume of biomass are
successfully established;
The estimated variability distribution of CGW is
reasonable for addressing risk in the process of bioenergy production;
Grouped gasification with certain plant scale is a
profitable way to generate electricity for peak load
needs, self consumption and incidental sale;
Bio-oil processing seems profitable, but capital
intensity for power plants leads economic unviable for
electricity generation from bio-oil in the study region.