1. 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

2. 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

3. 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.

4. Regional Concentration of Texas Cotton Planting (Source: USDA/TASS)

5. 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.

6. 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.

7. 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.

8. Locations of Gins and selected groups

9. Mobile Alternative Technologies and Possible Scenarios END

10. 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.

11. 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

12. IV. Model Development and Results  Model for Estimating CGW Distribution

14. Mean (63) Optimal Point (76) END

15. IV. Model Development and Results (cont.)  Economic Model for Profit Maximization

16. 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);

17. Results of Economic Model for Gasification BACK

18. 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 1338 353 MWSP 9764 1338 353

19. Optimal Results of Selected Groups

20. Results of Economic Model for Bio-oil/Power Generation BACK

22. 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.

23. 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.