Economics of Alternative Energy Sources and Globalization Nov. 2009 Orlando, Florida

The Economic Feasibility of Bio-energy Generation for Peak Demand of Electricity

Xiaolan Liu

Texas Tech University

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 (cont.)

Because of the natural of agricultural waste, the idea of converting it to bio-fuels faces a long-standing difficulty of commercialization:

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 bio-energy 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

Mobile

Alternative Technologies and Possible Scenarios

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 sd MC error 2.5% median 97.5% start sample b[1] 2.576 6.029 0.02035 -9.562 2.605 14.56 501 88500 b[2] 4.257 3.295 0.01108 -2.36 4.269 10.84 501 88500 b[3] -0.5866 0.5413 0.001818 -1.669 -0.5888 0.5039 501 88500 b[4] 0.668 0.5919 0.001962 -0.5 0.6565 1.894 501 88500 tau 12.95 9.774 0.05568 1.525 10.49 38.17 501 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;

50% of total electricity OWN needs can be provided by the process of bio-energy production.

Transportation cost is $20 per ton of CGW;

Supplement (penalty) cost is $140 per MWe;

Variable cost is $5.5 per MWe generated;

Establishment expense: gasification $ 2.8 MM/Mwe ($185,000 /MWe annually);

Results of Economic Model for GasificationPerformance 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 $ 882,917

MWP (Mwe/yr) 9227.3 9227.3 MWSP (Mwe/yr) 9764.3 9764.3 FC $ 1,806,393 $ 1,806,393 HOUR (hour/yr) 7000 7000 Capacity (Mwe/hr) 9.76 9.76 Note: Model1 with Penalty term, Model2 with Transportation term.

BACK

Model Sensitivity AnalysisDual 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 1338 353

MWSP 9764 1338 353

Optimal Results of Selected Groups

Optimal Results of Economic Models for Selected Groups ($/year, MWe/year)

Group G Group M Group L Group R Sum

Expected Profits $ 935,115 $ 841,827 $ 573,571 $ 262,523 $ 6,327,150

Production Capacity 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

Fixed Cost $ 2,006,589 $ 1,806,393 $ 1,230,911 $ 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

BACK

Income Statement of Standalone 100 tpd Bio-oil Plant

Net Sale Bio-oil $ 4,547,510

Char $ 263,670

Others

Total Sales

$ 4,811,180

Cost of Goods Sold

Labor & Overhead $ 561,700

23% Transport Cost $ 27,720

1%

Electricity $ 558,000

23% Consumables $ 240,559

10%

Maintenance $ 280,000

11% Total CGS

$ 1,667,979

Gross Profit/Loss

$ 3,143,201

Operating Expenses

SG&A $ 131,160

5% Cost of Money $ 491,880

20%

Property Tax $ 168,000

7% Total Operating Expense

$ 791,040

Net Profit / Loss

$ 2,352,161

ROI 96%

Break Even Bio-oil Price

$ 0.59

20% ROI Bio-oil Price $ 0.72

Assumptions: Bio-oil Sale Price 1.22 $/gal

Char Sale Price 47 $/ton Transportation Cost 0.14 $/ton/mile,

Results of Economic Model for Bio-oil / 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 bio-energy 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.