Vicki Ngan, Zana Cranmer, and Erin BakerMechanical and Industrial Engineering, University of Massachusetts—Amherst

Developing a Framework for the Value of Offshore Wind Energy

Abstract Methods

Future Work

AcknowledgementsThis work is supported in part by the National Science Foundation under NSF award number 1460461 and Joint Global Change Research Institute (JGCRI). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation.

The value of offshore wind energy can differ depending on space, time, and the policy context. In order to explore the value of offshore wind energy, we consider three main climate policy scenarios: no policy, carbon cap, and carbon tax. These scenarios will be run using GCAM-USA (Global Climate Assessment Model-USA Version), a technologically-detailed model which integrates the economy and the climate. The model will numerically analyze the effects of different parameters, such as spatial factors and cost of integration, and provide the cost of achieving the policy goals and global changes in temperature. The global changes in temperature will be used in damage functions to determine the cost of damages from climate change. We will examine cases with and without offshore wind energy.

•  Run GCAM-USA under different scenarios and parameters.

•  Compare the global value of offshore wind with local costs for environmental, ecological, and other factors with other models.

The problem: The value of offshore wind energy depends on a collection of scenarios and parameters considered in a variety of contexts. For example, when an offshore wind farm is permitted and built, it may displace non-carbon emitting sources in a carbon cap policy scenario. CO2 emissions and abatement levels remain because of the cap, and the value of offshore wind is the difference in abatement costs. Our Goal is to develop a framework for understanding the value of offshore wind in terms of mitigating climate change. The overall cost of climate changes is used to determine cost and is composed of two main factors: the cost of abatement and the level of climate damages where C(µ,R) is the cost of abatement as a function of the level of abatement, µ, and the vector of energy produced with different technologies to accomplish that abatement, R, and D(µ) is the cost associated with the damages from climate change as a function of the level of abatement. The value of offshore wind is the difference between two scenarios and can then be represented as:

where µ* and R* represent the abatement and set of technologies in the economy under a scenario without offshore wind, µw and Rw represent abatement and

technology given that offshore wind technology is available, 𝜙 is a climate

policy, and 𝜓 is a set of parameters.

For example, in the case of an emissions cap, the level of abatement would stay the same with or without offshore wind and the value would be:

Resource Curves: We have all the resource curves for each coastal U.S. state ready to be inputted and analyzed by GCAM.

Results

Global Climate Assessment Model (GCAM-USA)

𝐶(𝜇,𝑅)+𝐷(𝜇)

Fig. 1: Clockwise, from the upper left: United States bathymetry distribution, U.S. administrative areas, distance from U.S. shoreline (nm), and U.S. offshore wind resource at 90 m above the surface. These spatial parameters are taken into account in our analysis. Administrative areas are territories where U.S. has control. (Bjerkseter et al)

We reviewed literature to obtain the capital and operation & maintenance costs of different offshore wind turbines structures as well as offshore wind resource areas for each coastal state where offshore wind farms are possible (Shwartz et al, Mone et al, Myhr et al, Bjerkseter et al, Green et al). Monopile, jacket, and WindFloat turbine structures were studied because they are appropriate structures for our analysis. WindFloat can be used below 100 meter (m) water depth so estimates can start at 60m water depth. From literature, we calculated the levelised cost of energy (LCOE) values and annual energy different technologies, in $/MWh. A simple LCOE formula was used: We created resource curves for each wind speed bin (fig. 4). The bins are listed to the right in Table 1. There are two cases of a set of resource curves created for each coastal state: •  The high cost case consists of high capital cost and high operations &

maintenance costs estimates. For this case, we looked at the lower wind speed in the wind speed bin (e.g. 7m/s in a 7-7.5m/s wind speed bin), which provides higher LCOE estimates. Similarly, the low cost case consists of low capital and low operations and maintenance cost estimates. For this case, we looked at the higher wind speed in the wind speed bin (e.g. 7.5m/s in a 7-7.5m/s wind speed bin), which provides lower LCOE estimates.

•  For both cost cases, we did sensitivity, considering three cases: a high, medium, and low fit case, which include assumptions about the specific farm location and characteristics (Table 1).

To understand the fit cases, the “high fit” cases for CA (fig. 4) will be explained. For the wind speed bin of 9.5m/s – 10m/s, the entry in the upper left cell of Table 1 (266.2) reflects a distance from shore of 0-3 nautical miles (nm) and a depth of between 0-30m. The area estimate (the 266.2) for this fit case takes into account the longest distance and deepest water depth within the aforementioned bins, 3nm and 30m respectively. These wind resource areas were converted into annual energy production. The x-axis on the resource curves (fig. 4) are the cumulative energy values that are available up that associated LCOE value and the y-axis are the calculated LCOE values.

GCAM-USA is an integrated assessment model that is commonly used to examine the effect of technology and policy on climate. The model links economic, energy, climate, and land use systems and runs through 2100 in five year time steps. From GCAM-USA, we can obtain climate outputs that can be used in a damage function to find damages in each region, time period, and scenario.

Fig. 3: Illustration of concepts, from left to right: WindFloat, TLB B, TLB X3, Hywind II, SWAY, Jacket, Monopile and onshore reference. (Bjerkseter et al)

Fig. 2: Base case capital expenditure

quantification per MW for each

concept in the reference scenario.

(Bjerkseter et al)

Fig. 5: Agriculture and land use model (left) and energy/economy model (right) that is incorporated into GCAM-USA. (Calvin et al)

CitationsBjerkseter, C. & Agotnes, A. (2013). Levelised Costs of Energy for Offshore Floating Wind Turbine Concepts. Department of Mathematical Sciences and Technology. Calvin, K., Clarke, L., Kyle, P., Wise, M., Hartin, C., Patel, P. (2015). Introduction to the Global Climate Change Assessment Model (GCAM), Joint Global Change Research Institute Green, J., Bowen, F., Wan, Y. (2007). Electrical Collection and Transmission Systems for Offshore Wind Power. National Renewable Energy Laboratory NREL/CP-500-41135 Mone, C., Smith, A., Maples B., & Hand, M. (2015). 2013 Cost of Wind Energy Review. National Renewable Energy Laboratory NREL/TP-5000-63267 Myhr, A., Bjerkseter, C., Agotnes, A., & Nygaard, T. (2014) Levelised cost of energy for offshore floating wind turbines in a life cycle perspective. Renewable Energy 66 714-728 Shwartz, M., Heimiller, D., Haymes, S., & Musial, W. (2010). Assessment of Offshore Wind Energy Resources for the United States. National Renewable Energy Laboratory NREL.TP-500-45889

𝑉(𝜙,𝜓)≡𝐶(𝜇↑∗ , 𝑅↑∗ ,𝜙,𝜓)+𝐷(𝜇↑∗ ,𝜙,𝜓)−𝐶(𝜇↑𝑤 , 𝑅↑𝑤 ,𝜙,𝜓)−𝐷(𝜇↑𝑤 ,𝜙,𝜓)

𝑉(𝐶𝑎𝑝,𝜓)=𝐶(𝜇↑∗ , 𝑅↑∗ ,𝐶𝑎𝑝,𝜓)−𝐶(𝜇↑∗ , 𝑅↑𝑤 ,𝐶𝑎𝑝,𝜓)

Fig. 4:. California resource curve for a wind speed bin of 9.5-10 m/s. There are six sensitivity cases for each wind speed bin and each state. The three curves in different shades of blue marks a set of high capital cost curves and the three curves in different shades of orange marks a set of low capital cost curves. As the graph proceeds to the right, the LCOE values increase because of deeper water depths and longer distances from shore.

Table 1: Combination of wind speeds, distance from shore, and water depth that were used for sensitivity cases. Each wind speed bin has 9 combinations of water depth and distance from shore. (Shwartz et al)

00.51

1.52

2.53

3.54

4.5

0 0.5 1 1.5

LCOE(1975$/G

J)

EJ/yr

California,WindSpeed9.5-10m/s

LowFit

MidFit

HighFit

LowFit

MidFit

HighFit

Background

PorNolioofU.S.EnergyUsagein2100(BAU)

CSPCSP_storageGen_IIIGen_II_LWRPVPV_storagebiomass(IGCC)biomass(conv)coal(IGCC)coal(convpul)coalcogengas(CC)gas(steam/CT)

Fig. 5: Portfolio of U.S. energy usage in 2100 without offshore wind. This will be compared to cases with offshore wind.

sLCOE= capitalcost∗capitalrecoveryfactor+fixedO&𝑀/

8760 ∗ capacity factor for wind speed