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U.S. Electric Power Generation Planning under Endogenous Learning-by-Searching Technology Change

U.S. Electric Power Generation Planning under Endogenous Learning-by-Searching Technology Change. Tuesday, October 11, 2011 Session 31: Electricity Demand Modeling and Capacity Planning USAEE/IAEE North American Conference, Washington DC

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U.S. Electric Power Generation Planning under Endogenous Learning-by-Searching Technology Change

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  1. U.S. Electric Power Generation Planning under Endogenous Learning-by-Searching Technology Change Tuesday, October 11, 2011 Session 31: Electricity Demand Modeling and Capacity Planning USAEE/IAEE North American Conference, Washington DC Nidhi R. Santen, Massachusetts Institute of Technology (nrsanten@mit.edu) Mort D. Webster, Massachusetts Institute of Technology David Popp, Syracuse University/National Bureau of Economic Research

  2. Introduction (1 of 2) EIA, AER 2009; EIA 2011

  3. Introduction (2 of 2) Government Makes Environmental Policies Electric UtilitiesBuild Power Plants Using Available Generation Technologies Two main policy pathways to reduce cumulative power sector emissions “Now v. Later” “Adoption v. Innovation” 1. Constraining Regulations 2. Production Support New or Improved Generation Technologies Direct R&D Support CO2Emissions Increased Demand for Technologies Power System Technology R&D (Public and Private) NaturalEnvironment

  4. Research Question and Outline • Research Question: • What is the socially optimal balance of inter-temporal regulatory policy and • technology-specificR&D expenditures for the U.S. electricity generation sector, given a • specific cumulative climate target?” • Outline for Today’s Presentation • Overview of existing electricity sector planning models’ capabilities • Introduction of the current modeling framework • Snapshots from first results • Future research • Summary

  5. 1. Overview of Existing Numerical Power Generation Expansion Models (1 of 2) • Top-Down v. Bottom-up Models • Top-Down: Use Average Costs and Assume Capacity Factors • Bottom-Up: Use Specific Costs (e.g., Capital, O&M, Fuel) and Solve for Capacity Factors • Rigorously studying emissions potentials from the power sector requires modeling operational details of the physical system (more easily resolved in bottom-up models). Including Operational Realism Matters! Results Preview – More Detail Results Preview – Less Detail

  6. 1. Overview of Existing Numerical Power Generation Expansion Models (2 of 2) Common Methods to Model Technology Change and Learning Dynamics Decision Variables: Capacity Additions 1. (Exogenous) Fixed Trend: CapCostt,g = CapCostt-1,g*(1+ α) 2. (Endogenous) Learning-by-Doing: CapCostt,g = InitialCapCostg/ (CapitalStockt,g)LBDCoeff Decision Variables: Capacity Additions + R&D Investments 3. (Endogenous) Learning-by-Searching: CapCostt,g = InitialCapCostg / (KnowledgeStockt,g)LBSCoeff KnowledgeStockt,g= δΣ1:t-1R&D$t,g+ R&D$t,g 4. 2-Factor Learning Curves (2FLC): CapCostt,g = InitialCapCostg / [(CapitalStockt,g)LBDCoeff2 * (KnowledgeStockt,g)LBSCoeff2] KnowledgeStockt,g= δΣ1:t-1R&D$t,g+ R&D$t,g

  7. 2. Modeling Framework for this Research • Generation Planning Inputs Environmental Policy • Generation Planning Model • Generation Planning Model Generation Technology Costs ($/MWh) Electricity Demand (MW/time) Generation Technology Availability (Year) • R&D$ • New Power Plant Additions(GW)Production (GWh) Technology Change Module “Innovation Possibilities Frontier” ht = aRD$bHΦ Learning by Researching Learning by Experience Knowledge Stock (H) • CO2 Emissions (Million Metric Tons) Ht,g= (1-δ)Ht-1,g + ht,g • New Knowledge (h)

  8. 2. Modeling Framework for this Research • Structural Details • Centralized, social planning (decision-support model) • Representative technologies of the U.S. system • Representative U.S. load duration curve • 50-year planning horizon, 10-year time steps • Objective • Decision Variables (per period) • (1) R&D $ (by Technology) • (2) Carbon Cap • (3) Generation Expansion • (4) Generation Operation • Key Constraints • (1) All traditional generation expansion constraints (e.g., demand balance, reliability, non-cycling nuclear technology, etc.) • (2) Cumulative carbon cap • (3) Cumulative R&D funding account balance Generation Technologies Coal w CCS Gas CT Hydro Other Advanced Coal Gas CC Nuclear Solar Coal Steam Gas Wind

  9. 3. First Results: With and Without Learning-by-Searching (under a Medium Cumulative Emissions Target) No LBS With LBS (NPVLBS < NPVNoLBS)

  10. 3. First Results: Medium v. Strong Cumulative Emissions Target Medium Target Strong Target

  11. 3. First Results: Sensitivity of Innovation Possibilities Parameters (Strong CCS Possibilities under a Medium Emissions Target) Base Case Innovation Possibilities Strong CCS Innovation Possibilities

  12. 4. Future Research • Model optimal generation (carbon cap distribution) and R&D investment decisions under multiple uncertain innovation possibilities using stochastic dynamic programming

  13. Summary • Studying how to balance regulatory efforts and R&D efforts for the electricity generation sector requires a decision model where the capital costs of technology change endogenously with respect to new builds (adoption) and new research (innovation) • Rigorous study of emissions management from the power sector requires operational details of the physical system, embodied within bottom-up type models. • Results confirm both a “tradeoff” and “interaction” between adoption v. innovation for technologies with strong learning potentials (dynamics that are popular in the theoretical literature) • More research needs to be done to 1) understand the sensitivity of innovation parameters on decisions, 2) compare these results with more traditional knowledge stock formulations, and 3) model the effect of uncertainty of returns to research on near-term regulatory and R&D decisions.

  14. Thank You • Source: US EPA E-Grid Database & NPR.org

  15. References Title Slide Photo Credits (from left to right): (1) www.scientificamerican.com (2) http://www.pelamiswave.com(3) Sandia National Labs (4) http://www.metaefficient.com (5) http://img.dailymail.co.uk (6) https://inlportal.inl.gov Barreto, L.and S. Kypreos. (2004). “EndogenizingR&D and market experience in the "bottom-up" energy-systems ERIS model,” Technovation,24(8):615-629. Fischer, C. and R. G. Newell. (2008). “Environmental and technology policies for climate mitigation.” Energy Economics 55: 142-162. Hobbs, B. F. (1995). “Optimization methods for electric utility resource planning.” European Journal of Operational Research 83:1-20. Ibenholt, K. (2002). “Explaining learning curves for wind power,” Energy Policy 30: 1181-1189. Jaffe, A., and M. Trajtenberg. (2002). Patents, citations, & innovations: a window on the knowledge economy. MIT Press: Cambridge, MA, 478pp. Johnstone, N., Hascic, I, and D. Popp. (2010). “Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts,” Environmental Resource Econ, 45: 133-155. Messner, S. (1997). “Endogenized technological learning in an energy systems model,” J Evol Econ 7: 291-313. Miketa, A. and L. Schrattenholzer. (2004). “Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes.” Energy Policy, 32(15):1679-1692. Popp, D. (2002). “Induced Innovation and Energy Prices.” American Economic Review 92(1): 160-180. Popp, D. (2006). “ENTICE-BR: Backstop Technology in the ENTICE Model of Climate Change.” Energy Economics28(2): 188-222. Popp, D. (2006b). “They Don't Invent Them Like They Used To: An Examination of Energy Patent Citations Over Time.” Economics of Innovation and New Technology 15(8): 753-776.

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