Legislative Policy Conference

1. “Making Hard-Headed Decisions Pay Off”Dale WahlstromCEO, The BioBusiness Alliance of MinnesotaJanuary 14, 2009 Legislative Policy Conference

2. Strategic Flexibility

3. Strategic Flexibility: Renewable Energy

4. Advanced Scenario Planning • Scenario planning or scenario thinking is a strategic planning method to develop flexible long-term plans • Uncover and anticipate hidden weaknesses • Minimize the probability of an unintended consequence • Bring together divergent opinions to focus on a most probable scenario Various Scenario Planning Methods: • Contingency Planning • Sensitivity Analysis • Computer Simulations Enriching Minnesota’s Future through the Biosciences

5. Why is it Important to Do This Kind of Modeling? System Dynamics Modeling • All planning is based on models – mental or simulated • Planning in the business and policy worlds relies heavily on the use of mental models • Mental models are difficult to surface, share and test for completeness and accuracy • Goal: integration of various mental models into one shared model • Overall – it is a cost-effective way of reducing errors and increasing the odds of being successful Enriching Minnesota’s Future through the Biosciences

6. Base Case Results: Share of Renewables 20.50% 8.84% Enriching Minnesota’s Future through the Biosciences

7. Thank You!Dale Wahlstromdwahlstrom@biobusinessalliance.org 952-746-3847651 276 5735www.biobusinessalliance.org

8. BACK UP SLIDES

9. Finally: Review Linking What-How-Whom Know What • To be successful, Collaborative Knowledge Teams must integrate across lines of experience and trends in technical applications around specific market, economic and/or societal challenges. • Clusters of Knowledge & Competency are formed when the Know-How, Know-What, and Know-Whom are linked throughout a region. Know Whom Know How Cluster of Knowledge & Competency Enriching Minnesota’s Future through the Biosciences

10. Model Interface Enriching Minnesota’s Future through the Biosciences

11. Model Specifics • Minnesota Energy Divided into 5 Sectors • Electricity • Transportation • Industrial • Commercial • Residential • Overall Objectives and Measures: • Share of Renewable Fuels by 2025 • Jobs • GSP • Carbon Emissions Enriching Minnesota’s Future through the Biosciences

12. Renewable Energy Analysis Team • Core Team: 20 Experts from Across Minnesota • Feedback Sessions Throughout the State • Michael Sparby, AURI • Bruce Stockman, MN Corn Growers • Mike Bull and Lise Trudeau, Dept. of Commerce • Mike Youngerberg, MN Soybean Growers • Vernon Eidman, UofM • Kate VandenBosch, UofM • Elaine Hoffman, Bemidji State • Bruce Jones and John Frey, MN State U at Mankato • Cecil Massie, 6 Solutions LLC • Mark Willers, MinWind Energy • MaryJo Zidwick, Cargill • Rolf Nordstrom and Brendan Jordan, Great Plains Institute • Richard Magnusson, MN Wheat Growers • Ralph Groschen, MN Dept of Ag • Shalini Gupta, Izaac Walton League • Greg Chamberlain, Xcel Energy Enriching Minnesota’s Future through the Biosciences

13. Modeling Process • To use a model, you need a process • Discussion to capture the diversity of opinions • Debate the issues until the team reaches agreement on a possible scenario (this becomes the “base case”) • Input the data and run the scenario • Analyze outcomes to understand the behavior Enriching Minnesota’s Future through the Biosciences

14. Understanding the Model Results • Three Reoccurring Options when Reviewing Model Results • Results are true and showing insights or unintended consequences that we wouldn’t have expected to see using traditional analysis tools • Results are skewed by incorrect data • Results are skewed by incorrect model structure • Validation and testing needed - never really ends • Includes • Reviewing with experts • Tracking against historical data • Sensitivity analysis Enriching Minnesota’s Future through the Biosciences

15. Current, Unconstrained Base Case Assumptions • The base case developed by the team suspends reality and assumes an unconstrained environment • This means that the challenges in the following areas are resolved: • Funding • Workforce • Research and Technology • Construction Materials and Feedstock Availability • Feedstock Storage and Distribution • Regulatory Requirements, etc. • Land and Water Use • An unconstrained model is not reality Enriching Minnesota’s Future through the Biosciences

16. Current, Unconstrained Base Case Assumptions • All energy units converted to BTUs • Annual 0.5% growth rate in Minnesota energy demand • Nuclear is not replaced • Wind turbine capacity doubles during the 25 year period • Percentage of wind generation utilized – 37.5% • Corn available for ethanol production – 35% • Corn Yield Growth Rate – 2% • Cellulosic ethanol technology becomes viable by 2008 • Available Biomass = 300 trillion BTUs Enriching Minnesota’s Future through the Biosciences

17. Current, Unconstrained Base Case Assumptions Wind Targets (RPS) • 2010 – 11% • 2012 – 15% • 2016 – 21% • 2020 – 22.5% • 2025 – 25% Biomass • Industrial – 20% by 2025 • Commercial – 20% by 2025 • Residential – 5% by 2025 • Electricity – 5% by 2025 • Ethanol Targets • 2000-2010 – 10% • 2013-2020 – 20% • 2020-2030 – 30% • Biodiesel Targets • 2000 – 0% • 2005 – 2% • 2010 – 5% • 2015-2030 - 20% • Solar Target – 0.1% by 2025 • Hydrogen Target – 0% Enriching Minnesota’s Future through the Biosciences

18. Current, Unconstrained Base Case Assumptions • Time to Construct Plants: • Ethanol and Biodiesel – 3 years • Cellulosic Ethanol – 5 Years • Wind Lead Time – 2 Years • Solar – 1 Year • Hydrogen – 5 Years • Biomass: • Commercial/Industrial – 2 Years • Residential Biomass – 1 Year • Electricity Biomass – 4 Years Enriching Minnesota’s Future through the Biosciences

19. Base Case Results: Share of Renewables Enriching Minnesota’s Future through the Biosciences

20. Base Case Results: Net Renewable Electricity Jobs Enriching Minnesota’s Future through the Biosciences

21. Base Case Results: Wind Turbines Enriching Minnesota’s Future through the Biosciences

22. Base Case Results: Transportation Jobs Enriching Minnesota’s Future through the Biosciences

23. Base Case Results: Ethanol Jobs Enriching Minnesota’s Future through the Biosciences

24. Base Case Results: CO2 Enriching Minnesota’s Future through the Biosciences

25. Base Case Results: CO2 Enriching Minnesota’s Future through the Biosciences

26. Conclusions • With food and energy demand increasing, even with our most optimistic projections, we can’t keep up with need…. • Doubtful that humans will respond in time with conservation methods to avert a crisis • Energy and food production and consumption will be distributed • We believe the agricultural community is our primary hope • Given the known constraints on workforce, construction materials, funding, feedstock distribution, etc., we now need discussions with our communities on how to resolve the constraints in order to achieve our goals for Minnesota • We believe that each community can benefit if they “think globally, but act locally”…… start with what can be done now. Enriching Minnesota’s Future through the Biosciences

27. Systems dynamic modeling • Dale’s four lessons to keep in mind: • Benchmark • Look forward • Make informed decisions, prioritized • Track if the prioritized decisions are being implemented • KEY AREAS for you to cover:  • -Use tools like System Dynamics Modeling and Strategic Flexibility (it is the process and not just a tool) •             -Focus on what you know and don’t know and make investments where you know and explore what you don’t know •             (There was a third point here that I missed but think it might be in the few lines above under the “Dale cover” line.)