Abstract
This study assesses the economic and greenhouse gas (GHG) emissions impacts of a proposed 400-MW wind farm (Big Wind) in Hawaii. Due to its island setting, this project is a hybrid between onshore and offshore wind development. An undersea cable would carry the power from Maui County, which has high-quality wind areas, to the population center of Oahu, which has fewer sites for wind power. The project is additionally motivated by Hawaii’s high electricity rates, which are nearly three times the national average, and a renewable portfolio standard (RPS) mandating that 40 % of the State’s electricity sales be met through renewable sources by the year 2030. Using an economy-wide computable general equilibrium model coupled with a fully dynamic optimization model for the electric sector, we find that the 400- MW wind project increases gross state product by $2.2 billion (in net present value) and average annual per capita income by $60 per year. Although there are potentially near-term welfare losses if there are capital cost overruns, fuel costs are a dominant factor in determining the cost-effectiveness of the project. However, without upgrades to Hawaii’s grid and/or its operations, there is a trade-off between investment in wind energy projects and solar PV. If higher levels of intermittent resources cannot be integrated into the system, higher-cost biofuels serve a more prominent role in meeting the RPS. Without upgrades, wind and solar PV generation are restricted, and hence, reduction in GHG emissions in excess of those present without the Big Wind project is negligible. With upgrades, the project is estimated to reduce GHG emissions by an additional \(\hbox {12 MMTCO}_{2}\) from 2020 to 2040.
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Notes
Renewable fuel/energy types include solar, wind, ocean, geothermal, biomass-based, landfill gas, hydroelectric, CHP/cogeneration, hydrogen, anaerobic digestion, and waste.
For a full description of HELM, refer to Coffman et al. (2012).
To disaggregate labor, we use a sharing process. For renewable energy sources, we use the portion of labor cost from FOM as provided in the JEDI model (NREL 2013). For land-based wind energy, it is 25 %. For thermal generators, we take publicly available data on the number of employees for existing units and multiply it by the industry’s average salary provided in the input–output table. We assume that new units have a similar labor profile as their existing counterparts.
Household electricity demand is estimated from the number of people in a household and typical per capita consumption (6 kWh/day), which is adjusted according to household income and an assumed income elasticity of demand for electricity (0.5). We assume that PV is installed such that households’ electricity use is effectively cancelled out based on the existing Net Metering Agreement.
Most recent tests indicate that existing oil-fired units may be able to burn 100 % bio-oil.
This is a reasonable starting assumption because wind would be the major resource to which the cable provides access. If Oahu pursued importation of LNG, however, the benefits could be bidirectional and this merits further study.
A full description of H-CGE is available by request as well as on authors Web site at www.uhero.hawaii.edu.
Big Wind is a one-time cost, whereas the Hawaii economy’s output is at least $140 billion from 2020 onward. Therefore, on a lifecycle basis, the capital cost of Big Wind is quite small compared to the State’s GSP over the life of Big Wind.
The limit on wind generation means that the 50-MW Oahu unit that is built in the 30 % scenarios is not built under the 20 % scenario.
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Acknowledgments
We thank the two peer reviewers for their constructive comments and feedback. We thank the US Department of Energy, the Hawaii Natural Energy Institute, the University of Hawaii Economic Research Organization and the Center for Global Partnership for support of this project. We also thank Aaron Mann and Sherilyn Wee for the research assistance.
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Coffman, M., Bernstein, P. Linking Hawaii’s Islands with wind energy. Ann Reg Sci 54, 1–21 (2015). https://doi.org/10.1007/s00168-014-0644-y
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DOI: https://doi.org/10.1007/s00168-014-0644-y