Helping PUCs analyze options to reduce GHG regulatory risk in coal dependent states

1. Helping PUCs analyze options to reduce GHG regulatory risk in coal dependent states Dalia Patiño-Echeverri Nicholas School of the Environment - Duke University CEDM Annual meeting May 16-17, 2011. Pittsburgh, Pennsylvania

2. Motivation • PUCs in coal dependent states need to obey their mandate to protect ratepayers by approving only prudent investments • Upcoming EPA regulations (CATR, Utility toxics (MACT), Coal combustion residuals, 316b) will force investments very soon but, are they prudent? Difficult to know due to • Uncertainty on GHG regulation • Uncertain fuel prices and technology costs • By learning more about PUCs challenge we can perhaps improve the methods of decision making under uncertainty in the electricity industry

3. Objectives • Improve understanding on how PUCs make decisions regarding capital investments under uncertainty • Identify a number of PUCs that can benefit from using an stochastic optimization modeling framework (OptInvest) • Engage in “consultant work” to identify ways to improve OptInvest. The goal is to make it a user friendly tool in the near future • Learn about Best Practices to propose ways in which PUCs can proactively reduce their ratepayers exposure to GHG regulatory risk

4. 1. Understanding how PUCs work Methods • Literature review (Law students and Nicholas School MEM students) • Meetings/Workshops • North Carolina Utilities Commission (April 11) • The seven commissioners attended a 3 hour long meeting • We can’t help them with modeling but they are interested in a workshop on why models can be helpful • Kentucky Public Service Commission (May 9) • We are partners !

5. 1. Understanding how PUCs work Lessons so far • At least two types of PUCs • Reactive • Proactive • Capable of doing own analysis (interested in quantitative modeling) • A tool that integrates their subjective beliefs about scenarios with an optimization framework and NEMS’ forecasts can be useful

6. 2. Engaging in consultant work with OptInvest Methods • Identify an upcoming decision, use the model to provide guidance to PUC • First job: Analyze a rate case for Kentucky Public Service Commission • Want to avoid a common mistake: looking only at short term optimality and then in retrospective regretting decisions

7. OptInvest Assume Federal Policy scenario 2. IECM, NETL retrofit Study Baseline cost & performance of power plants 1. NI NEMS Pricesof: Electricity Fuel SO2, NOx, CO2 3. OPTIMIZATION MODEL Investment, Operation, Emissions Assume Natural Gas and Coal scenarios AssumeTechnologyscenario

8. 3. Identifying best practices to design mechanisms to reduce risk from GHG regulatory uncertainty First example: NC Clean Smokestacks Act • Required emissions reductions from in state coal plants • Reduce NOx emissions 77% by 2009 • Reduce SO2 emissions 73% by 2013 • Install mercury controls by 2018 • Results: • Duke Energy has already met SO2 and NOx requirements • Progress energy met 2009 requirements and is on schedule to meet SO2 regulations

9. Benefits of NC Cleans Smokestacks • Progress and Duke Energy are not affected by EPAs new regs timeline. (They had over a decade to comply)

10. Benefits of NC Clean Smokestacks • Compliance costs fro Duke and Progress have exceeded 2002 estimates by 21% and 30% respectively (but this is low if compared to 59% capital costs increases in the industry • Costs would have been incurred to comply with CATR and MACT • Early investment avoided • Delays from bottlenecks in permittng, engineering and constructions • Electric Power Supply shortages • Cost escalation

11. What can we learn from NC Clean Smokestacks case • How can we judge the decision without looking at the outcome ? • Retrospective modeling (with retrospective subjective probabilities on EPAs rules) • What are the costs and benefits of acting in anticipation of potential rules? • What does this mean for uncertainty on GHG regulations for coal states?

12. Thank you • Dalia.patino@duke.edu