Increasing the percentage of wind power in the United States electricity generation mix would facilitate the transition towards a more sustainable, low-pollution, and environmentally-conscious electricity grid. However, this effort is not without cost. Wind power generation is time-variable and typically not synchronized with electricity demand (i.e., load). In addition, the highest-output wind resources are often located in remote locations, necessitating transmission investment between generation sites and load. Furthermore, negative public perceptions of wind projects could prevent widespread wind development, especially for projects close to densely-populated communities. The work presented in my dissertation seeks to understand where it’s best to locate wind energy projects while considering these various factors. First, in Chapter 2, I examine whether energy storage technologies, such as grid-scale batteries, could help reduce the transmission upgrade costs incurred when siting wind projects in distant locations. For a case study of a hypothetical 200 MW wind project in North Dakota that delivers power to Illinois, I present an optimization model that estimates the optimal size of transmission and energy storage capacity that yields the lowest average cost of generation and transmission ($/MWh). I find that for this application of storage to be economical, energy storage costs would have to be $100/kWh or lower, which is well below current costs for available technologies. I conclude that there are likely better ways to use energy storage than for accessing distant wind projects. Following from this work, in Chapter 3, I present an optimization model to estimate the economics of accessing high quality wind resources in remote areas to comply with renewable energy policy targets. I include temporal aspects of wind power (variability costs and correlation to market prices) as well as total wind power produced from different farms. I assess the goal of providing 40 TWh of new wind generation in the Midwestern transmission system (MISO) while minimizing system costs. Results show that building wind farms in North/South Dakota (windiest states) compared to Illinois (less windy, but close to population centers) would only be economical if the incremental transmission costs to access them were below $360/kW of wind capacity (break-even value). Historically, the incremental transmission costs for wind development in North/South Dakota compared to in Illinois are about twice this value. However, the break-even incremental transmission cost for wind farms in Minnesota/Iowa (also windy states) is $250/kW, which is consistent with historical costs. I conclude that for the case in MISO, building wind projects in more distant locations (i.e., Minnesota/Iowa) is most economical. My two final chapters use semi-structured interviews (Chapter 4) and conjoint-based surveys (Chapter 5) to understand public perceptions and preferences for different wind project siting characteristics such as the distance between the project and a person’s home (i.e., “not-in-my-backyard” or NIMBY) and offshore vs. onshore locations. The semi-structured interviews, conducted with members of a community in Massachusetts, revealed that economic benefit to the community is the most important factor driving perceptions about projects, along with aesthetics, noise impacts, environmental benefits, hazard to wildlife, and safety concerns. In Chapter 5, I show the results from the conjoint survey. The study’s sample included participants from a coastal community in Massachusetts and a U.S.-wide sample from Amazon’s Mechanical Turk. Results show that participants in the U.S.-wide sample perceived a small reduction in utility, equivalent to $1 per month, for living within 1 mile of a project. Surprisingly, I find no evidence of this effect for participants in the coastal community. The most important characteristic to both samples was the economic benefits from the project – both to their community through increased tax revenue, and to individuals through reduced monthly energy bills. Further, participants in both samples preferred onshore to offshore projects, but that preference was much stronger in the coastal community. I also find that participants from the coastal community preferred expanding an existing wind projects rather than building an entirely new one, whereas those in the U.S.-wide sample were indifferent, and equally supportive of the two options. These differences are likely driven by the prior positive experience the coastal community has had with an existing onshore wind project as well as their strong cultural identity that favors ocean views. I conclude that preference for increased distance from a wind project (NIMBY) is likely small or non-existent and that offshore wind projects within 5 miles from shore could cause large welfare losses to coastal communities. Finally, in Chapter 6, I provide a discussion and policy recommendations from my work. Importantly, I recommend that future research should combine the various topics throughout my chapters (i.e., transmission requirements, hourly power production, variability impacts to the grid, and public preferences) into a comprehensive model that identifies optimal locations for wind projects across the United States.
| Identifer | oai:union.ndltd.org:cmu.edu/oai:repository.cmu.edu:dissertations-1742 |
| Date | 01 December 2016 |
| Creators | Lamy, Julian V. |
| Publisher | Research Showcase @ CMU |
| Source Sets | Carnegie Mellon University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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