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

4 Research Question and Outline Research Question: What is the socially optimal balance of inter-temporal regulatory policy and technology-specific R&D expenditures for the U.S. electricity generation sector, given a specific cumulative climate target?” Outline for Today’s Presentation 1.Overview of existing electricity sector planning models’ capabilities 2.Introduction of the current modeling framework 3.Snapshots from first results 4.Future research 5.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 – Less Detail Results Preview – More 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: CapCost t,g = CapCost t-1,g *(1+ α) 2. (Endogenous) Learning-by-Doing: CapCost t,g = InitialCapCost g / (CapitalStock t,g ) LBDCoeff Decision Variables: Capacity Additions + R&D Investments 3. (Endogenous) Learning-by-Searching: CapCost t,g = InitialCapCost g / (KnowledgeStock t,g ) LBSCoeff KnowledgeStock t,g = δΣ 1:t-1 R&D$ t,g + R&D$ t,g 4. 2-Factor Learning Curves (2FLC): CapCost t,g = InitialCapCost g / [(CapitalStockt,g) LBDCoeff2 * (KnowledgeStock t,g ) LBSCoeff2 ] KnowledgeStock t,g = δΣ 1:t-1 R&D$ t,g + R&D$ t,g

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

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 Steam Gas Wind Advanced Coal Gas CC Nuclear Solar Coal w CCS Gas CT Hydro Other

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

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.

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

 Barreto, L. and S. Kypreos. (2004). “Endogenizing R&D and market experience in the "bottom-up" energy-systems ERIS model,” Technovation, 24(8):  Fischer, C. and R. G. Newell. (2008). “Environmental and technology policies for climate mitigation.” Energy Economics 55:  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:  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:  Messner, S. (1997). “Endogenized technological learning in an energy systems model,” J Evol Econ 7:  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):  Popp, D. (2002). “Induced Innovation and Energy Prices.” American Economic Review 92(1):  Popp, D. (2006). “ENTICE-BR: Backstop Technology in the ENTICE Model of Climate Change.” Energy Economics 28(2):  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): References Title Slide Photo Credits (from left to right): (1) (2) (3) Sandia National Labs (4) (5) (6)