[LDES-coremodel] Wind class, ITC and PTC, and remaining cost to-dos

[Julia Szinai]

Hi Sarah et al,
Here are a couple of things I've promised.

1. This google doc
<https://docs.google.com/document/d/1YInHwhHxFy4rMweN4OCT1NqBUhiiD2F2GirKd9Z6aWk/edit?usp=sharing>
is a table of the key parameters that comprise a SWITCH scenario, from the
load forecast to the costs and generator list. I need to do some
preliminary SWITCH runs for a separate project that has an upcoming
conference deadline, so I've listed what I plan to use for the baseline run
for that SWITCH analysis. Some of these parameters will definitely change
and be refined for the CEC project's baseline analysis (like the time
sampling strategy). The highest priority items left on my to-do list to
start doing some runs are highlighted in yellow: updated overnight costs
and fuel costs. I'm working on finishing updates on the items in green.

2. Regarding the wind class to use for overnight costs in the ATB, I'm fine
with using what the current ATB calls 'class 5' if that is the most common
class of speeds in the WECC region. I can't tell from the SWITCH
documentation what the average wind speeds or classes were assumed in each
load zone but I've copied the relevant info from previous supplemental
materials on how the hourly capacity factors were calculated for
candidate wind generators. See below:

"Hourly wind turbine output is obtained from the 3TIER wind power output
dataset produced for the Western Wind and Solar Integration Study (WWSIS)
(3TIER 2010). 3TIER models the historical 10-minute power output from
Vestas V-90 3 MW turbines in a 2-km by 2-km grid cells across the western
United States over the years 2004-2006 using the Weather Research and
Forecasting (WRF) mesoscale weather model. Each of these grid cells
contains ten turbines, so each grid cell represents 30 MW of potential wind
capacity. The Vestas V-90 3 MW turbine has a 100 m hub height.

Grid cells were selected by 3TIER using the following criteria:

1.     Wind projects that already exist or are under development

2.     Sites with the high wind energy density at 100 m within 80 km of
existing or planned transmission networks

3.     Sites with high degree of temporal correlation to load profiles near
the grid point

4.     Sites with the highest wind energy density at 100 m (irrespective of
location)


All of the grid cells in the 3TIER dataset (> 30,000) within WECC are
aggregated into 3,311 onshore and 48 offshore wind farms. Many of the grid
cells are very near each other; adjacent wind points are aggregated if
their area is within the corner-to-corner distance of each other, 2.8 km.
Wind points with standard deviations in their average SCORE-lite power
output greater than 3 MW are aggregated into different wind farms. Offshore
and onshore wind points are aggregated separately. The 10-minute SCORE-lite
power output for each wind point is averaged over the hour before each
timestamp, and then these hourly averages are again averaged over each
group of aggregated grid cells to create the hourly output of 3,311 onshore
(875 GW) and 48 offshore (6 GW) wind farms. The onshore wind farms are then
put through the site selection process (Section 2.10.12: Site Selection of
Variable Renewable Projects), resulting in 1,527 sites with 466 GW of
potential capacity."

3. These reports from LBNL on Wind and Solar cost forecasts may be helpful
in how to adjust the overnight costs to account for the PTC and ITC
incentives.

Wind:
https://emp.lbl.gov/publications/2018-wind-technologies-market-report
see p85 in PDF:
https://eta-publications.lbl.gov/sites/default/files/wtmr_final_for_posting_8-9-19.pdf

Solar:
https://emp.lbl.gov/utility-scale-solar
see p11 in PDF:
https://emp.lbl.gov/sites/default/files/lbnl_utility_scale_solar_2019_edition_final.pdf


Hope this helps. Have a good weekend all!
Best,
Julia

-- 
*Julia Szinai*
PhD Candidate | Energy & Resources Group | University of California,
Berkeley
Graduate Student Researcher | Lawrence Berkeley National Lab
NSF InFEWS Fellow
Energy & Resources Group, MS '17
Goldman School of Public Policy, MPP '17
University of California, Berkeley
jszinai at berkeley.edu
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