Facilitating the Development and Integration of Low-Carbon Energy Technologies
Creators: name: Fertig, Emily
Publisher: name: Carnegie Mellon University
Year: 2013
Subjects: subject: 160510 Public Policy
subject: FOS: Political science
subject: FOS: Political science
subject: 99999 Engineering not elsewhere classified
subject: FOS: Other engineering and technologies
subject: FOS: Other engineering and technologies

description: Climate change mitigation will require extensive decarbonization of the electricity sector. This thesis addresses both large-scale wind integration (Papers 1-3) and development of new energy technologies (Paper 4) in service of this goal. Compressed air energy storage (CAES) could be paired with a wind farm to provide firm, dispatchable baseload power, or serve as a peaking plant and capture upswings in electricity prices. Paper 1 presents a firm-level engineering-economic analysis of a wind/CAES system with a wind farm in central Texas, load in either Dallas or Houston, and a CAES plant whose location is profit-optimized. Of a range of market scenarios considered, the CAES plant is found to be profitable only given the existence of large and infrequent price spikes. Social benefits of wind/CAES include avoided construction of new generation capacity, improved air quality during peak demand, and increased economic surplus, but may not outweigh the private cost of the CAES system nor justify a subsidy. Like CAES, pumped hydropower storage (PHS) ramps quickly enough to smooth wind power and could profit from arbitrage on short-term price fluctuations exacerbated by large-scale wind. Germany has aggressive plans for wind power expansion, and Paper 2 analyzes an investment opportunity in a PHS facility in Norway that practices arbitrage in the German spot market. Price forecasts given increased wind capacity are used to calculate profit-maximizing production schedules and annual revenue streams. Real options theory is used to value the investment opportunity, since unlike net present value, it accounts for uncertainty and intertemporal choice. Results show that the optimal investment strategy under the base scenario is to wait approximately eight years then invest in the largest available plant. Paper 3 examines long-distance interconnection as an alternate method of wind power smoothing. Frequency-domain analysis indicates that interconnection of aggregate regional wind plants across much of the western and mid-western U.S. would not result in significantly greater smoothing than interconnection within a single region. Time-domain analysis shows that interconnection across regions reduces the magnitude of low-probability step changes and doubles firm power output (capacity available at least 92 % of the time) compared with a single region. An approximate cost analysis indicates that despite these benefits, balancing wind and providing firm power with local natural gas turbines would be more cost-effective than with transmission interconnection. Papers 1 and 3 demonstrate the need for further RD&amp;D (research, development, and deployment) of low-carbon energy technologies. Energy technology development is highly uncertain but most often modeled as deterministic, which neglects the ability both to adapt RD&amp;D strategy to changing conditions and to invest in initially high-cost technologies with small breakthrough probabilities. Paper 4 develops an analytical stochastic dynamic programming framework in which RD&amp;D spending decreases the expected value of the stochastic cost of a technology. Results for a two-factor cost model (which separates RD&amp;D into R&amp;D and learning-by-doing) applied to carbon capture and sequestration (CCS) indicate that given 15 years until large-scale deployment, investment in the RD&amp;D program is optimal over a very broad range of initial mitigation costs ($10-$380/tCO2). While the NPV of the program is zero if initial mitigation cost is $100/tCO2, under uncertainty the program is worth about $7 billion. If initial mitigation cost is high, the program is worth most if cost reductions exogenous to the program (e.g. due to private sector activity) are also high. Factors that promote R&amp;D spending over learning-by-doing include more imminent deployment, high initial cost, lower exogenous cost reductions, and lower program funds available.