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| Srl | Item |
| 1 |
ID:
104888
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| Publication |
2011.
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| Summary/Abstract |
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. We present 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. With 2008 hourly prices and load in Houston, the economically optimal CAES expander capacity is unrealistically large - 24 GW - and dispatches for only a few hours per week when prices are highest; a price cap and capacity payment likewise results in a large (17 GW) profit-maximizing CAES expander. Under all other scenarios considered the CAES plant is unprofitable. Using 2008 data, a baseload wind/CAES system is less profitable than a natural gas combined cycle (NGCC) plant at carbon prices less than $56/tCO2 ($15/MMBTU gas) to $230/tCO2 ($5/MMBTU gas). Entering regulation markets raises profit only slightly. Social benefits of CAES paired with wind include avoided construction of new generation capacity, improved air quality during peak times, and increased economic surplus, but may not outweigh the private cost of the CAES system nor justify a subsidy.
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| 2 |
ID:
162347
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| Summary/Abstract |
Uncertainty in technological learning is a crucial factor in planning research, development, and demonstration (RD&D) strategies. Nevertheless, most previous work either models technological change as deterministic or accounts for uncertainty without fully capturing the recourse feature of the problem. This paper improves upon these approaches by developing a real options-based stochastic dynamic programming method for valuing and planning low-carbon energy RD&D investment and is the first of its kind to disaggregate the effects of R&D and learning-by-doing. This simplified model captures the relevant features of the problem and provides general insights on RD&D strategy under technological uncertainty. Results indicate that imminent deployment, high cost, lower exogenous cost reductions, and lower program funds all promote R&D spending over learning-by-doing, since under these circumstances a breakthrough, rather than slow and consistent cost reductions, will render the program successful.
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| 3 |
ID:
097340
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| Publication |
2010.
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| Summary/Abstract |
We present the first frequency-dependent analyses of the geographic smoothing of wind power's variability, analyzing the interconnected measured output of 20 wind plants in Texas. Reductions in variability occur at frequencies corresponding to times shorter than 24 h and are quantified by measuring the departure from a Kolmogorov spectrum. At a frequency of 2.8×10-4 Hz (corresponding to 1 h), an 87% reduction of the variability of a single wind plant is obtained by interconnecting 4 wind plants. Interconnecting the remaining 16 wind plants produces only an additional 8% reduction. We use step change analyses and correlation coefficients to compare our results with previous studies, finding that wind power ramps up faster than it ramps down for each of the step change intervals analyzed and that correlation between the power output of wind plants 200 km away is half that of co-located wind plants. To examine variability at very low frequencies, we estimate yearly wind energy production in the Great Plains region of the United States from automated wind observations at airports covering 36 years. The estimated wind power has significant inter-annual variability and the severity of wind drought years is estimated to be about half that observed nationally for hydroelectric power.
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