Matching a State’s Renewable Energy Incentives With the Risk/Return Profile of the Renewable Energy Investor

1. Matching a State’s Renewable Energy Incentives With the Risk/Return Profile of the Renewable Energy Investor By Martha J. Goodell Candidate for M.S. in Environment & Resources Certificate in Energy Analysis & Policy The Nelson Institute, University of Wisconsin, Madison As presented at: 32nd USAEE/IAEE North American Conference July 28-31, 2013 - Anchorage, AK

2. Past research on renewable energy incentive policies has evaluated their effectiveness from different perspectives Research Context • RPS effectiveness across the states – (Carley, 2009) • Comparing price vs. quantity instruments – (Menanteau, et al., 2003) • Risks from renewable energy investment incentive instruments - (Wiser and Pickle 2007) • Investment options and strategies for financing specific technologies - (Awerbuch, 2000) • Impact of RPS on electricity prices (Kung, 2012) • RPS policy price caps – (Johnson et al., 2012) Reveals the complexity and interdependency inherent in incentive policy analysis

3. The objective of establishing state level incentives for renewable energy investment is to increase the use of renewable resources in a sustainable way. State incentive system or Sphere of Influence (SOI) Effectiveness of sustained renewable investment Component risks and project profitability Investment patterns (Dinica 2003)

4. How the components of an SOI are structured directly impacts how institutions invest Factors Influencing Renewable Energy Investment • Incentive Legislation (RPS) • State Regulatory Environment • Federal Tax Incentives • Economic Factors Risk Components Profitability Variables A State’s SOI Financing Agents Developer Risk Industry Risk Industry Econ & Stability Cash Flow Debt Payments Developer Equity Project Developers Demand Risks Price Risks Profit Risks Revenue Maintenance Fuel Costs Taxes, Fees

5. Components of an SOI can be evaluated qualitatively and assigned a risk/return “net effect” to understand investment patterns Risk Components Profitability Variables Investment patterns Financing Agents Developer Risk Industry Risk Industry Econ & Stability Cash Flow Debt Payments Developer Equity     Project Developers   Demand Risks Price Risks Profit Risks Revenue Maintenance Fuel Costs Taxes, Fees Theoretical Cost Cap    (Dinica 2003)

6. The grouping of the investment pattern reveals whether the SOI is effective in establishing sustainable renewable energy investments Investment patterns Degrees of sustainability  Entre-preneurial Investment Minimal Investment      Political Investment Optimal Investment   

7. The components of Illinois’ renewable energy incentive structure charted in risk/return space – from 2007 through 2010 Risks Very High High Mod- erate Low No/low Very High Moderate High Return (Data references on last slide)

8. Illinois SOI component examples Risks Electric Service Choice and Relief Act Very High High RPS of 25% by 2025 passed 2007 Mod- erate In State REC Purchase Requirement Low No/low Very High Moderate High Return

9. Illinois’s SOI components, as clustered in high return/ and low to high risk space, results in successful increase in capacity investments 2007 2006-2010 saw an 800% increase in renewable energy capacity after a 70% increase from the same previous time span. (eia.gov, 2012) Risks 2008 Very High 2009 High 2010 Mod- erate 2011 Low 2012 No/low Very High Moderate High Return

10. Illinois SOI components that occurred in 2010 and 2011, halted most new renewable investment Municipal Aggregation, Raid of the ACP fund, and LPPA confusion 2007 2006-2010 saw an 800% increase in renewable energy capacity after a 70% increase from the same previous time span. Risks 2008 Very High 2009 High 2010 Mod- erate 2011 2010 forward – unintended consequences and political dysfunction limits investment Low 2012 No/low Very High Moderate High Return Out of state REC trading allowed

11. Summary • To build a successful renewable energy incentive structure, the risk/return needs of the investor need to be taken into account and grouped in optimal risk/return space to be sustainable • The risk/return framework can be used ex-post to understand: • How components of renewable energy incentives interact • Where one component effect could overweight multiple influences • How a Federal incentive policy could be drafted • The risk/return framework can be used ex-ante to understand: • How future legislative changes and energy variables overlay to influence future expectations • How component variability from the past might impact future investment • Including a States regulatory environment in the analysis is important but can be difficult Next Steps • California and Texas will also be analyzed using the risk/return framework • Additional states will help qualify and improve the analytical framework

12. Assumptions • The analysis included only influential components of the Illinois SOI. • When investors differed in their opinions of risk/return levels, an average was used. • The PTC and ITC aren’t incorporated in the analysis in order to highlight the state specific influence, but these incentives do impact the investment process. • Electricity prices and REC prices aren’t within direct control of a state’s SOI, but might influence an investment decision. • This analysis is not intended to infer that reaching an RPS goal is the only objective or benefit of renewable energy incentive policies. There are many benefits not related to the actual level of investment in renewable energy capacity, but not discussed here.

13. References • 20 ILCS 3855, Illinois Power Agency Act (2007). • 20 ILCS 3501, Illinois Finance Authority Act (2007). • 220 ILCS 5/, Public Utilities Act (1997). • Awerbuch, S. (2000). Investing in photovoltaics: risk, accounting and the value of new technology. Energy Policy, 28, 1023–1035. • Carley, S. (2009). State renewable energy electricity policies An empirical evaluation of effectiveness. Energy Policy, 37(8), 3071–3081. • Dinica, V. (2006). Support systems for the diffusion of renewable energy technologies—an investor perspective. Energy Policy, 34(4), 461–480. • Dinica, V. (2003). Sustained Diffusion of Renewable Energy. Enschede, The Netherlands, Twente University Press. • EIA, (2012) U.S. Energy Information Administration, eia.gov/electricity. Accessed 20 May 2013. • Illinois Power Agency, (2013). Annual report on the costs and benefits of renewable resource procurement in Illinois under the Illinois Power Agency and Illinois Public Utilities Act. Illinois.gov/IPA. • Illinois Power Agency, (2012). Annual report on the costs and benefits of renewable resource procurement in Illinois under the Illinois Power Agency and Illinois Public Utilities Act. Illinois.gov/IPA. • Johnson, S.D., Moyer, E.J., Feasibility of U.S. renewable portfolio standards under cost caps and case study for Illinois. Energy Policy (2012), http://dx.doi.org/10.1016/j.enpol.2012.06.047 • Kung, H. H. (2012). Impact of deployment of renewable portfolio standard on the electricity price in the State of Illinois and implications on policies. Energy Policy, 44(C), 425–430. • Lydersen, Kari. “How Chicago’s municipal aggregation could be a boon for renewable energy.” Midwest Energy News. 15 November, 2012. Web. 5 May, 2013. • Mentaneau, P., Finon, D., & Lamy, M.-L. (2003). Prices versus quantities: choosing policies for promoting the development of renewable energy. Energy Policy, 31, 799–812. • Roberts, David. “How to make Illinois into a clean-energy leader.” Grist. 19 Oct. 2012. Web. 10 Feb. 2013. • Wiser, R., & Pickle, S. (1997). Financing Investments in Renewable Energy: The Role of Policy Design and Restructuring. University of California, Lawrence Berkeley National laboratories, Environmental Energy Technologies Division, 1–107.