Demonstration of the Available Metrics#

Binder

For a complete list of metrics and their documentation, please see the API Metrics documentation.

This demonstration will rely on the results produced in the "How To" notebook and serves as an extension of the API documentation to show what the results will look like depending on what inputs are provided.

A Jupyter notebook of this tutorial can be run from examples/metrics_demonstration.ipynb locally, or through binder.

from pprint import pprint
from functools import partial

import pandas as pd
from pandas.io.formats.style import Styler

from wombat.core import Simulation, Metrics
from wombat.utilities import plot

# Clean up the aesthetics for the pandas outputs
pd.set_option("display.max_rows", 30)
pd.set_option("display.max_columns", 10)
style = partial(
  Styler,
  table_attributes='style="font-size: 14px; grid-column-count: 6"',
  precision=2,
  thousands=",",
)

Table of Contents#

Below is a list of top-level sections to demonstrate how to use WOMBAT's Metrics class methods and an explanation of each individual metric.

If you don't see a metric or result computation that is core to your work, please submit an issue with details on what the metric is, and how it should be computed.

Setup#

The simulations from the How To notebook are going to be rerun as it is not recommended to create a Metrics class from scratch due to the large number of inputs that are required, and the initialization is provided in the simulation API's run method.

To simplify this process, a feature has been added to save the simulation outputs required to generate the Metrics inputs and a method to reload those outputs as inputs.

sim = Simulation("COREWIND", "morro_bay_in_situ.yaml")

# Both of these parameters are True by default for convenience
sim.run(create_metrics=True, save_metrics_inputs=True)

# Load the metrics data
fpath = sim.env.metrics_input_fname.parent
fname = sim.env.metrics_input_fname.name
metrics = Metrics.from_simulation_outputs(fpath, fname)

# Delete the log files now that they're loaded in
sim.env.cleanup_log_files()

# Alternatively, in this case because the simulation was run, we can use the
# following for convenience convenience only
metrics = sim.metrics

Common Parameter Explanations#

Before diving into each and every metric, and how they can be customized, it is worth noting some of the most common parameters used throughout, and their meanings to reduce redundancy. The varying output forms are demonstrated in the availability section below.

frequency#

project

Computed across the whole simulation, with the resulting DataFrame having an empty index.

annual

Summary of each year in the simulation, with the resulting DataFrame having "year" as the index.

monthly

Summary of each month of the year, aggregated across years, with the resulting DataFrame having "month" as the index.

month-year

computed on a month-by-year basis, producing the results for every month of the simulation, with the resulting DataFrame having "year" and "month" as the index.

by#

windfarm

Aggregated across all turbines, with the resulting DataFrame having only "windfarm" as a column

turbine

Computed for each turbine, with the resulting DataFrame having a column for each turbine

Availability#

There are two methods to produce availability, which have their own function calls:

Here, we will go through the various input definitions to get time-based availability data as both methods use the same inputs, and provide outputs in the same format.

Inputs:

  • frequency, as explained above options: "project", "annual", "monthly", and "month-year"

  • by, as explained above options: "windfarm" and "turbine"

Below is a demonstration of the variations on frequency and by for time_based_availability.

style(metrics.time_based_availability(frequency="project", by="windfarm"))
  windfarm
0 0.91
style(metrics.production_based_availability(frequency="project", by="windfarm"))
  windfarm
0 0.91

Note that in the two above examples, that the values are equal. This is due to the fact that the example simulation does not have any operating reduction applied to failures, unless it's a catastrophic failure, so there is no expected difference.

# Demonstrate the by turbine granularity
style(metrics.time_based_availability(frequency="project", by="turbine"))
  WTG_0000 WTG_0001 WTG_0002 WTG_0003 WTG_0004 WTG_0005 WTG_0006 WTG_0007 WTG_0008 WTG_0009 WTG_0100 WTG_0101 WTG_0102 WTG_0103 WTG_0104 WTG_0105 WTG_0106 WTG_0107 WTG_0108 WTG_0109 WTG_0200 WTG_0201 WTG_0202 WTG_0203 WTG_0204 WTG_0205 WTG_0206 WTG_0207 WTG_0208 WTG_0209 WTG_0300 WTG_0301 WTG_0302 WTG_0303 WTG_0304 WTG_0305 WTG_0306 WTG_0307 WTG_0308 WTG_0309 WTG_0400 WTG_0401 WTG_0402 WTG_0403 WTG_0404 WTG_0405 WTG_0406 WTG_0407 WTG_0408 WTG_0409 WTG_0500 WTG_0501 WTG_0502 WTG_0503 WTG_0504 WTG_0505 WTG_0506 WTG_0507 WTG_0508 WTG_0509 WTG_0600 WTG_0601 WTG_0602 WTG_0603 WTG_0604 WTG_0605 WTG_0606 WTG_0607 WTG_0608 WTG_0609 WTG_0700 WTG_0701 WTG_0702 WTG_0703 WTG_0704 WTG_0705 WTG_0706 WTG_0707 WTG_0708 WTG_0709
0 0.90 0.91 0.91 0.90 0.93 0.92 0.91 0.91 0.92 0.92 0.91 0.91 0.90 0.92 0.90 0.90 0.91 0.91 0.90 0.92 0.91 0.92 0.92 0.91 0.92 0.91 0.90 0.91 0.91 0.92 0.90 0.90 0.90 0.90 0.91 0.91 0.91 0.92 0.91 0.91 0.91 0.90 0.92 0.92 0.92 0.91 0.90 0.91 0.91 0.89 0.90 0.90 0.91 0.92 0.92 0.91 0.90 0.90 0.89 0.91 0.91 0.91 0.91 0.91 0.89 0.93 0.91 0.91 0.92 0.91 0.89 0.91 0.90 0.91 0.91 0.91 0.90 0.92 0.91 0.91
# Demonstrate the annualized outputs
style(metrics.time_based_availability(frequency="annual", by="windfarm"))
  windfarm
year  
2002 0.95
2003 0.92
2004 0.92
2005 0.91
2006 0.86
2007 0.89
2008 0.91
2009 0.91
2010 0.87
2011 0.89
2012 0.91
2013 0.92
2014 0.91
2015 0.92
2016 0.90
2017 0.91
2018 0.92
2019 0.92
2020 0.93
2021 0.91
# Demonstrate the month aggregations
style(metrics.time_based_availability(frequency="monthly", by="windfarm"))
  windfarm
month  
1 0.89
2 0.86
3 0.88
4 0.85
5 0.89
6 0.91
7 0.93
8 0.95
9 0.95
10 0.94
11 0.94
12 0.92
# Demonstrate the granular monthly reporting
style(metrics.time_based_availability(frequency="month-year", by="windfarm"))
    windfarm
year month  
2002 1 0.94
2 0.95
3 0.97
4 0.96
5 0.96
6 0.94
7 0.97
8 0.97
9 0.97
10 0.97
11 0.91
12 0.92
2003 1 0.85
2 0.90
3 0.89
4 0.90
5 0.89
6 0.90
7 0.92
8 0.97
9 0.97
10 0.94
11 0.96
12 0.92
2004 1 0.86
2 0.90
3 0.89
4 0.90
5 0.90
6 0.90
7 0.93
8 0.98
9 0.96
10 0.97
11 0.96
12 0.93
2005 1 0.69
2 0.91
3 0.90
4 0.89
5 0.90
6 0.90
7 0.97
8 0.98
9 0.98
10 0.92
11 0.95
12 0.94
2006 1 0.71
2 0.30
3 0.90
4 0.90
5 0.90
6 0.89
7 0.90
8 0.94
9 0.97
10 0.97
11 0.96
12 0.93
2007 1 0.91
2 0.55
3 0.88
4 0.91
5 0.89
6 0.90
7 0.91
8 0.92
9 0.96
10 0.96
11 0.97
12 0.94
2008 1 0.93
2 0.90
3 0.84
4 0.93
5 0.90
6 0.89
7 0.90
8 0.90
9 0.95
10 0.95
11 0.96
12 0.93
2009 1 0.94
2 0.77
3 0.91
4 0.91
5 0.93
6 0.93
7 0.91
8 0.95
9 0.94
10 0.85
11 0.92
12 0.94
2010 1 0.93
2 0.87
3 0.56
4 0.67
5 0.93
6 0.90
7 0.90
8 0.94
9 0.94
10 0.93
11 0.91
12 0.93
2011 1 0.92
2 0.94
3 0.72
4 0.72
5 0.93
6 0.96
7 0.93
8 0.92
9 0.91
10 0.93
11 0.91
12 0.93
2012 1 0.93
2 0.91
3 0.91
4 0.81
5 0.93
6 0.92
7 0.96
8 0.95
9 0.91
10 0.94
11 0.93
12 0.89
2013 1 0.91
2 0.94
3 0.92
4 0.69
5 0.95
6 0.93
7 0.96
8 0.97
9 0.93
10 0.90
11 0.95
12 0.95
2014 1 0.93
2 0.94
3 0.93
4 0.67
5 0.91
6 0.93
7 0.96
8 0.96
9 0.96
10 0.91
11 0.90
12 0.90
2015 1 0.90
2 0.95
3 0.96
4 0.84
5 0.73
6 0.98
7 0.96
8 0.98
9 0.96
10 0.95
11 0.90
12 0.90
2016 1 0.90
2 0.90
3 0.89
4 0.89
5 0.80
6 0.90
7 0.90
8 0.96
9 0.94
10 0.94
11 0.89
12 0.90
2017 1 0.89
2 0.90
3 0.90
4 0.89
5 0.85
6 0.89
7 0.90
8 0.96
9 0.96
10 0.93
11 0.96
12 0.93
2018 1 0.92
2 0.90
3 0.90
4 0.91
5 0.90
6 0.90
7 0.96
8 0.96
9 0.95
10 0.94
11 0.93
12 0.90
2019 1 0.90
2 0.88
3 0.90
4 0.89
5 0.90
6 0.86
7 0.94
8 0.96
9 0.96
10 0.95
11 0.96
12 0.92
2020 1 0.89
2 0.89
3 0.90
4 0.90
5 0.86
6 0.95
7 0.94
8 0.98
9 0.98
10 0.96
11 0.95
12 0.93
2021 1 0.89
2 0.90
3 0.88
4 0.90
5 0.90
6 0.84
7 0.90
8 0.95
9 0.97
10 0.94
11 0.93
12 0.96

Plotting Availability#

As of v0.9, the ability to plot the wind farm and turbine availability has been enabled as an experimental feature. Please see the plotting API documentation for more details.

# Demonstrate the granular monthly reporting
plot.plot_farm_availability(sim=sim, which="energy", farm_95_CI=True)
../_images/519f4946a0ee14cc6921a4a716b4b0d102f36c16bcfb1539605dd96dce426eae.png

Capacity Factor#

The capacity factor is the ratio of actual (net) or potential (gross) energy production divided by the project's capacity. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.capacity_factor().

Inputs:

  • which

    • "net": net capacity factor, actual production divided by the plant capacity

    • "gross": gross capacity factor, potential production divided by the plant capacity

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by, as explained above, options: "windfarm" and "turbine"

Example Usage:

net_cf = metrics.capacity_factor(which="net", frequency="project", by="windfarm").values[0][0]
gross_cf = metrics.capacity_factor(which="gross", frequency="project", by="windfarm").values[0][0]
print(f"  Net capacity factor: {net_cf:.2%}")
print(f"Gross capacity factor: {gross_cf:.2%}")
  Net capacity factor: 51.49%
Gross capacity factor: 55.59%

Task Completion Rate#

The task completion rate is the ratio of tasks completed aggregated to the desired frequency. It is possible to have a >100% completion rate if all maintenance and failure requests submitted in a time period were completed in addition to those that went unfinished in prior time periods. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.task_completion_rate().

Inputs:

  • which

    • "scheduled": scheduled maintenance only (classified as maintenance tasks in inputs)

    • "unscheduled": unscheduled maintenance only (classified as failure events in inputs)

    • "both": Combined completion rate for all tasks

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

Example Usage:

scheduled = metrics.task_completion_rate(which="scheduled", frequency="project").values[0][0]
unscheduled = metrics.task_completion_rate(which="unscheduled", frequency="project").values[0][0]
combined = metrics.task_completion_rate(which="both", frequency="project").values[0][0]
print(f"  Scheduled Task Completion Rate: {scheduled:.2%}")
print(f"Unscheduled Task Completion Rate: {unscheduled:.2%}")
print(f"    Overall Task Completion Rate: {combined:.2%}")
  Scheduled Task Completion Rate: 96.52%
Unscheduled Task Completion Rate: 95.73%
    Overall Task Completion Rate: 96.03%

Equipment Costs#

Sum of the costs associated with a simulation's servicing equipment, which excludes materials, downtime, etc. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.equipment_costs().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by_equipment

    • True: Aggregates all equipment into a single cost

    • False: Computes for each unit of servicing equipment

Example Usage:

# Project total at the whole wind farm level
style(metrics.equipment_costs(frequency="project", by_equipment=False))
  equipment_cost
0 2,343,067,603.30
# Project totals at servicing equipment level
style(metrics.equipment_costs(frequency="project", by_equipment=True))
  Anchor Handling Tug Cable Laying Vessel Crew Transfer Vessel 1 Crew Transfer Vessel 2 Crew Transfer Vessel 3 Crew Transfer Vessel 4 Crew Transfer Vessel 5 Crew Transfer Vessel 6 Crew Transfer Vessel 7 Diving Support Vessel Heavy Lift Vessel
0 128,598,611.96 147,096,378.95 25,563,166.94 25,563,203.53 25,563,214.54 25,563,194.51 25,563,175.63 25,563,170.44 25,563,179.56 146,620,281.87 1,741,810,025.38

Service Equipment Utilization Rate#

Ratio of days when the servicing equipment is in use (not delayed for a whole day due to either weather or lack of repairs to be completed) to the number of days it's present in the simulation. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.service_equipment_utilization().

Inputs:

  • frequency, as explained above, options: "project" and "annual"

Example Usage:

# Project totals
style(metrics.service_equipment_utilization(frequency="project"))
  Anchor Handling Tug Cable Laying Vessel Crew Transfer Vessel 1 Crew Transfer Vessel 2 Crew Transfer Vessel 3 Crew Transfer Vessel 4 Crew Transfer Vessel 5 Crew Transfer Vessel 6 Crew Transfer Vessel 7 Diving Support Vessel Heavy Lift Vessel
0 0.85 0.92 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.89 0.97

Vessel-Crew Hours at Sea#

The number of vessel hours or crew hours at sea for offshore wind power plant simulations. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.vessel_crew_hours_at_sea().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by_equipment

    • True: Aggregates all equipment into a single cost

    • False: Computes for each unit of servicing equipment

  • vessel_crew_assumption: A dictionary of vessel names (ServiceEquipment.settings.name, but also found at Metrics.service_equipment_names) and the number of crew onboard at any given time. The application of this assumption transforms the results from vessel hours at sea to crew hours at sea.

Example Usage:

# Project total, not broken out by vessel
style(metrics.vessel_crew_hours_at_sea(frequency="project", by_equipment=False))
  Total Crew Hours at Sea
0 687,183.13
# Annual project totals, broken out by vessel
style(metrics.vessel_crew_hours_at_sea(frequency="annual", by_equipment=True))
  Total Crew Hours at Sea Crew Transfer Vessel 1 Crew Transfer Vessel 2 Crew Transfer Vessel 3 Crew Transfer Vessel 4 Crew Transfer Vessel 5 Crew Transfer Vessel 6 Crew Transfer Vessel 7 Heavy Lift Vessel Cable Laying Vessel Anchor Handling Tug Diving Support Vessel
year                        
2002 21,869.28 1,381.51 1,398.29 1,657.32 1,051.05 1,512.76 1,395.56 1,744.53 7,773.80 1,768.23 2,186.22 0.00
2003 32,275.78 2,507.53 2,896.41 2,622.43 2,607.80 2,577.95 2,602.61 2,448.33 7,474.58 1,658.55 4,879.60 0.00
2004 35,314.18 2,570.52 2,404.43 2,618.05 2,527.60 2,717.61 2,614.12 2,396.01 7,982.57 1,479.95 2,733.96 5,269.37
2005 29,546.80 2,178.60 2,301.90 2,273.57 2,165.74 2,505.57 2,596.55 2,197.77 7,589.57 2,056.97 1,711.79 1,968.75
2006 33,986.75 2,465.22 2,484.27 2,456.59 2,453.34 2,341.02 2,392.72 2,441.75 7,593.32 985.63 2,262.55 6,110.36
2007 31,618.50 2,497.28 2,591.61 2,538.20 2,278.13 2,542.29 2,245.61 2,534.19 6,886.80 3,122.62 2,438.96 1,942.83
2008 38,785.44 2,530.01 2,617.95 2,521.40 2,714.58 2,512.52 2,494.31 2,530.57 7,704.90 4,869.62 1,760.62 6,528.95
2009 30,258.77 2,252.29 2,561.00 2,453.57 2,413.33 2,567.98 2,354.75 2,343.44 8,003.79 0.00 3,354.35 1,954.29
2010 36,501.35 2,442.55 2,646.55 2,701.21 2,517.49 2,424.05 2,712.54 2,598.65 7,306.78 0.00 5,150.62 6,000.91
2011 36,940.27 2,517.03 2,361.80 2,530.74 2,484.97 2,469.41 2,236.76 2,458.16 7,803.37 4,394.85 4,347.84 3,335.33
2012 36,153.71 2,383.91 2,685.84 2,474.52 2,409.95 2,449.57 2,603.18 2,463.68 7,267.22 4,112.03 2,349.60 4,954.20
2013 35,496.81 2,397.02 2,090.03 2,382.49 2,308.71 2,205.82 2,211.43 2,377.32 7,918.52 3,832.08 2,856.22 4,917.17
2014 33,771.55 2,270.11 2,310.27 2,449.24 2,516.26 2,230.21 2,369.24 2,333.44 7,632.55 2,123.94 2,563.75 4,972.56
2015 33,522.17 2,274.86 1,783.07 1,963.77 2,139.19 2,129.53 1,854.06 2,135.71 8,252.60 3,989.74 2,088.64 4,910.99
2016 36,974.83 2,457.21 2,624.90 2,537.65 2,288.04 2,414.45 2,380.41 2,339.90 7,838.69 4,264.44 3,530.66 4,298.46
2017 39,757.35 2,757.22 3,125.85 2,722.05 2,910.56 2,797.16 2,710.81 2,637.41 8,073.78 3,618.63 2,917.72 5,486.15
2018 34,501.75 2,206.20 2,141.33 2,404.70 2,272.01 2,292.83 2,608.83 2,196.69 7,704.59 2,580.35 4,130.15 3,964.08
2019 39,904.30 2,596.62 2,525.29 2,581.00 2,577.29 2,569.45 2,567.19 2,485.81 8,189.86 3,634.16 4,783.05 5,394.59
2020 35,531.41 2,406.50 2,380.22 2,636.98 2,323.62 2,374.49 2,203.77 2,546.56 7,537.76 3,761.82 3,021.37 4,338.31
2021 34,472.11 2,373.02 2,600.49 2,639.32 2,632.85 2,552.75 2,623.93 2,593.22 7,791.95 1,132.87 2,425.61 5,106.10

Number of Tows#

The number of tows performed during the simulation. If tow-to-port was not used in the simulation, a DataFrame with a single value of 0 will be returned. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.number_of_tows().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by_tug

    • True: Computed for each tugboat (towing vessel)

    • False: Aggregates all the tugboats

  • by_direction

    • True: Computed for each direction a tow was performed (to port or to site)

    • False: Aggregates to the total number of tows

Example Usage:

# Project Total
# NOTE: This example has no towing, so it will return 0
style(metrics.number_of_tows(frequency="project"))
  total_tows
0 0

Labor Costs#

Sum of all labor costs associated with servicing equipment, excluding the labor defined in the fixed costs, which can be broken out by type. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.labor_costs().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by_type

    • True: Computed for each labor type (salary and hourly)

    • False: Aggregates all the labor costs

Example Usage:

# Project total at the whole wind farm level
total = metrics.labor_costs(frequency="project", by_type=False)
print(f"Project total: ${total.values[0][0] / metrics.project_capacity:,.2f}/MW")
Project total: $41,946.12/MW
# Project totals for each type of labor
style(metrics.labor_costs(frequency="project", by_type=True))
  hourly_labor_cost salary_labor_cost total_labor_cost
0 0.00 50,335,348.13 50,335,348.13

Equipment and Labor Costs#

Sum of all labor and servicing equipment costs, excluding the labor defined in the fixed costs, which can be broken out by each category. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.equipment_labor_cost_breakdown().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by_category

    • True: Computed for each unit servicing equipment and labor category

    • False: Aggregated to the sum of all costs

reason definitions:

  • Maintenance: routine maintenance, or events defined as a wombat.core.data_classes.Maintenance

  • Repair: unscheduled maintenance, ranging from inspections to replacements, or events defined as a wombat.core.data_classes.Failure

  • Mobilization: Cost of mobilizing servicing equipment

  • Crew Transfer: Costs incurred while crew are transferring between a turbine or substation and the servicing equipment

  • Site Travel: Costs incurred while transiting to/from the site and while at the site

  • Weather Delay: Any delays caused by unsafe weather conditions

  • No Requests: Equipment and labor is active, but there are no repairs or maintenance tasks to be completed

  • Not in Shift: Any time outside the operating hours of the wind farm (or the servicing equipment's specific operating hours)

Example Usage:

# Project totals
style(metrics.equipment_labor_cost_breakdowns(frequency="project", by_category=False))
  total_cost total_hours
reason    
Maintenance 22,963,339.85 72,164.88
Repair 598,014,490.69 161,559.96
Crew Transfer 8,735,275.34 11,009.75
Site Travel 95,724,108.94 122,323.65
Mobilization 129,660,000.00 145,224.00
Weather Delay 1,424,356,947.79 603,820.81
No Requests 87,712,632.61 404,191.69
Not in Shift 26,236,156.21 105,367.91
# Project totals by each category
style(metrics.equipment_labor_cost_breakdowns(frequency="project", by_category=True))
  hourly_labor_cost salary_labor_cost total_labor_cost equipment_cost total_cost total_hours
reason            
Maintenance 0 2,196,920.01 2,196,920.01 20,766,419.84 22,963,339.85 72,164.88
Repair 0 7,579,402.43 7,579,402.43 590,435,088.26 598,014,490.69 161,559.96
Crew Transfer 0 343,770.13 343,770.13 8,391,505.21 8,735,275.34 11,009.75
Site Travel 0 3,866,589.12 3,866,589.12 91,857,519.82 95,724,108.94 122,323.65
Mobilization 0 0.00 0.00 129,660,000.00 129,660,000.00 145,224.00
Weather Delay 0 21,879,743.43 21,879,743.43 1,402,477,204.36 1,424,356,947.79 603,820.81
No Requests 0 11,448,915.24 11,448,915.24 76,263,717.37 87,712,632.61 404,191.69
Not in Shift 0 3,020,007.76 3,020,007.76 23,216,148.44 26,236,156.21 105,367.91

Emissions#

Emissions (tons or other provided units) of all servicing equipment activity, except overnight waiting periods between shifts. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.emissions().

Inputs:

  • emissions_factors: Dictionary of servicing equipment names and the emissions per hour of the following activities: transit, maneuvering, idle at site, and idle at port, where port is stand-in for wherever the servicing equipment might be based when not at site.

  • maneuvering_factor: The proportion of transit time that can generally be associated with positioning servicing, by default 10%.

  • port_engine_on_factor: The proportion of the idling at port time when the engine is running and producing emissions, by default 25%.

# Create the emissions factors, in tons per hour
emissions_factors = {
    "Crew Transfer Vessel 1": {
        "transit": 4,
        "maneuvering": 3,
        "idle at site": 0.5,
        "idle at port": 0.25,
    },
    "Field Support Vessel": {
        "transit": 6,
        "maneuvering": 4,
        "idle at site": 1,
        "idle at port": 0.5,
    },
    "Heavy Lift Vessel": {
        "transit": 12,
        "maneuvering": 7,
        "idle at site": 1,
        "idle at port": 0.5,
    },
    "Diving Support Vessel": {
        "transit": 4,
        "maneuvering": 7,
        "idle at site": 0.2,
        "idle at port": 0.2,
    },
    "Anchor Handling Vessel": {
        "transit": 4,
        "maneuvering": 3,
        "idle at site": 1,
        "idle at port": 0.25,
    },
}

# Add in CTVs 2 through 7
for i in range(2, 8):
    emissions_factors[f"Crew Transfer Vessel {i}"] = emissions_factors[f"Crew Transfer Vessel 1"]

style(metrics.emissions(emissions_factors=emissions_factors, maneuvering_factor=0.075, port_engine_on_factor=0.20))
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[22], line 39
     36 for i in range(2, 8):
     37     emissions_factors[f"Crew Transfer Vessel {i}"] = emissions_factors[f"Crew Transfer Vessel 1"]
---> 39 style(metrics.emissions(emissions_factors=emissions_factors, maneuvering_factor=0.075, port_engine_on_factor=0.20))

File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wombat/core/post_processor.py:1532, in Metrics.emissions(self, emissions_factors, maneuvering_factor, port_engine_on_factor)
   1497 """Calculates the emissions, typically in tons, per hour of operations for
   1498 transiting, maneuvering (calculated as a % of transiting), idling at the site
   1499 (repairs, crew transfer, weather delays), and idling at port (weather delays),
   (...)
   1527     equipment definition in ``emissions_factors``.
   1528 """
   1529 if missing := set(self.service_equipment_names).difference(
   1530     [*emissions_factors]
   1531 ):
-> 1532     raise KeyError(
   1533         f"`emissions_factors` is missing the following keys: {missing}"
   1534     )
   1536 valid_categories = ("transit", "maneuvering", "idle at port", "idle at site")
   1537 emissions_categories = list(
   1538     chain(*[[*val] for val in emissions_factors.values()])
   1539 )

KeyError: "`emissions_factors` is missing the following keys: {'Anchor Handling Tug', 'Cable Laying Vessel'}"

Component Costs#

All the costs associated with maintenance and failure events during the simulation, including delays incurred during the repair process, but excluding costs not directly tied to a repair. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.component_costs().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by_category

    • True: Computed across each cost category

    • False: Aggregated to the sum of all categories

  • by_action

    • True: Computed by each of "repair", "maintenance", and "delay", and is included in the MultiIndex

    • False: Aggregated as the sum of all actions

action definitions:

  • maintenance: routine maintenance

  • repair: unscheduled maintenance, ranging from inspections to replacements

  • delay: Any delays caused by unsafe weather conditions or not being able to finish a process within a single shift

Example Usage:

# Project totals by component
style(metrics.component_costs(frequency="project", by_category=False, by_action=False))
  total_cost
component  
cable 155,008,736.00
drive_train 232,961,804.77
electrical_system 1,232,220,694.25
generator 211,733,372.58
hydraulic_system 34,148,490.19
rotor_blades 104,739,475.95
supporting_structure 233,312,846.89
transformer 1,248,167.42
yaw_system 20,718,738.82
# Project totals by each category and action type
style(metrics.component_costs(frequency="project", by_category=True, by_action=True))
    materials_cost total_labor_cost equipment_cost total_cost
component action        
cable delay 0.00 2,692,079.65 92,118,794.60 94,810,874.26
maintenance 0.00 19,726.20 675,000.00 694,726.20
repair 0.00 1,446,429.45 49,494,574.65 50,941,004.10
drive_train delay 0.00 1,010,418.27 159,966,260.66 160,976,678.93
maintenance 0.00 0.00 0.00 0.00
repair 0.00 296,392.32 56,125,047.92 56,421,440.24
electrical_system delay 0.00 4,835,635.68 842,931,113.27 847,766,748.96
maintenance 0.00 0.00 0.00 0.00
repair 0.00 1,662,206.97 331,192,337.85 332,854,544.82
generator delay 0.00 4,886,170.59 133,554,134.46 138,440,305.05
maintenance 0.00 944,227.44 5,026,000.00 5,970,227.44
repair 0.00 471,259.83 49,437,395.83 49,908,655.66
hydraulic_system delay 0.00 3,464,087.89 18,438,889.82 21,902,977.71
maintenance 0.00 0.00 0.00 0.00
repair 0.00 879,103.58 5,567,375.00 6,446,478.58
rotor_blades delay 0.00 1,526,182.16 62,647,399.89 64,173,582.05
maintenance 0.00 0.00 0.00 0.00
repair 0.00 453,483.42 29,338,625.00 29,792,108.42
supporting_structure delay 0.00 6,033,535.95 102,724,725.84 108,758,261.79
maintenance 0.00 1,207,979.85 14,932,419.84 16,140,399.69
repair 0.00 2,285,393.69 62,977,482.01 65,262,875.70
transformer delay 0.00 136,802.29 728,180.80 864,983.08
maintenance 0.00 24,986.52 133,000.00 157,986.52
repair 0.00 2,301.39 12,250.00 14,551.39
yaw_system delay 0.00 313,192.63 12,575,091.59 12,888,284.23
maintenance 0.00 0.00 0.00 0.00
repair 0.00 82,831.78 6,290,000.00 6,372,831.78

Fixed Cost Impacts#

Computes the total costs of the fixed costs categories. For further documentation, see the definition docs, here: wombat.core.data_classes.FixedCosts, or the API docs here: wombat.core.post_processor.Metrics.fixed_costs().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • resolution (also, demonstrated below)

    • "high": Computed across the most granular cost levels

    • "medium": Computed for each general cost category

    • "low": Aggregated to a single sum of costs

pprint(metrics.fixed_costs.hierarchy)
{'operations': {'annual_leases_fees': ['submerge_land_lease_costs',
                                       'transmission_charges_rights'],
                'environmental_health_safety_monitoring': [],
                'insurance': ['brokers_fee',
                              'operations_all_risk',
                              'business_interruption',
                              'third_party_liability',
                              'storm_coverage'],
                'labor': [],
                'onshore_electrical_maintenance': [],
                'operating_facilities': [],
                'operations_management_administration': ['project_management_administration',
                                                         'marine_management',
                                                         'weather_forecasting',
                                                         'condition_monitoring']}}

Example Usage:

# Project totals at the highest level
# NOTE: there were no fixed costs defined in this example, so all values will be 0, so
#       this will just be demonstrating the output format
style(metrics.project_fixed_costs(frequency="project", resolution="low"))
  operations
0 0.00
# Project totals at the medium level
style(metrics.project_fixed_costs(frequency="project", resolution="medium"))
  operations_management_administration insurance annual_leases_fees operating_facilities environmental_health_safety_monitoring onshore_electrical_maintenance labor
0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
# Project totals at the lowest level
style(metrics.project_fixed_costs(frequency="project", resolution="high"))
  project_management_administration marine_management weather_forecasting condition_monitoring brokers_fee operations_all_risk business_interruption third_party_liability storm_coverage submerge_land_lease_costs transmission_charges_rights operating_facilities environmental_health_safety_monitoring onshore_electrical_maintenance labor
0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

OpEx#

Computes the total cost of all operating expenditures for the duration of the simulation, including fixed costs. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.opex().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by_category

    • True shows the port fees, fixed costs, labor costs, equipment costs, and materials costs in addition the total OpEx

    • False shows only the total OpEx

Example Usage:

style(metrics.opex("annual"))
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wombat/core/post_processor.py:1963: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  port_fees = port_fees.fillna(0)
  OpEx
year  
2002 107,289,133.53
2003 112,751,347.73
2004 123,125,511.53
2005 116,885,385.41
2006 120,195,423.36
2007 123,193,962.58
2008 131,191,003.55
2009 107,749,179.18
2010 120,801,815.77
2011 132,977,351.78
2012 127,130,652.21
2013 127,375,328.24
2014 114,190,807.70
2015 120,487,497.25
2016 134,291,651.27
2017 128,659,451.22
2018 121,100,295.57
2019 130,598,049.26
2020 123,959,092.54
2021 119,522,427.74
style(metrics.opex("annual", by_category=True))
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wombat/core/post_processor.py:1963: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  port_fees = port_fees.fillna(0)
  operations port_fees equipment_cost total_labor_cost materials_cost OpEx
year            
2002 0.00 0 103,256,208.26 2,214,725.28 1,818,200.00 107,289,133.53
2003 0.00 0 106,813,627.43 2,397,448.30 3,540,272.00 112,751,347.73
2004 0.00 0 117,690,265.66 2,410,545.87 3,024,700.00 123,125,511.53
2005 0.00 0 112,396,160.93 2,293,304.48 2,195,920.00 116,885,385.41
2006 0.00 0 116,275,265.44 2,433,385.92 1,486,772.00 120,195,423.36
2007 0.00 0 118,381,014.55 2,469,012.04 2,343,936.00 123,193,962.58
2008 0.00 0 126,020,082.09 2,757,141.46 2,413,780.00 131,191,003.55
2009 0.00 0 103,142,036.05 2,226,903.13 2,380,240.00 107,749,179.18
2010 0.00 0 116,457,730.09 2,533,113.67 1,810,972.00 120,801,815.77
2011 0.00 0 127,795,005.99 2,763,505.79 2,418,840.00 132,977,351.78
2012 0.00 0 122,789,673.77 2,596,998.44 1,743,980.00 127,130,652.21
2013 0.00 0 122,087,509.59 2,610,562.65 2,677,256.00 127,375,328.24
2014 0.00 0 109,191,775.10 2,452,012.59 2,547,020.00 114,190,807.70
2015 0.00 0 115,064,111.51 2,548,313.74 2,875,072.00 120,487,497.25
2016 0.00 0 128,646,965.04 2,748,186.24 2,896,500.00 134,291,651.27
2017 0.00 0 123,265,217.10 2,629,294.12 2,764,940.00 128,659,451.22
2018 0.00 0 116,052,322.39 2,465,289.18 2,582,684.00 121,100,295.57
2019 0.00 0 124,399,037.91 2,668,231.35 3,530,780.00 130,598,049.26
2020 0.00 0 118,211,211.85 2,638,448.70 3,109,432.00 123,959,092.54
2021 0.00 0 115,132,382.56 2,478,925.18 1,911,120.00 119,522,427.74

Process Times#

Computes the total number of hours spent from repair request submission to completion, performing repairs, and the number of request for each repair category. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.process_times().

Example Usage:

style(metrics.process_times())
  time_to_completion process_time downtime time_to_start N
category          
anchor replacement 19,752.50 9,789.29 9,780.88 10,336.92 20
annual turbine inspection 1,394,053.31 175,282.73 173,633.90 1,231,793.11 1,436
array cable major repair 41,023.30 28,060.38 0.00 13,770.04 40
array cable replacement 20,620.41 15,909.33 0.00 5,049.59 18
blades major repair 58,379.28 1,642.23 1,637.70 57,172.78 13
blades major replacement 6,458.32 5,482.34 5,481.43 1,106.37 2
blades minor repair 123,416.85 71,182.93 70,535.33 63,344.59 709
buoyancy module replacement 11,261.67 8,873.02 8,826.76 3,184.03 48
direct drive generator major repair 322,962.19 10,201.30 10,184.07 313,715.27 55
direct drive generator major replacement 6,461.73 3,364.99 3,363.67 3,333.23 5
direct drive generator minor repair 119,371.95 68,598.39 67,913.88 66,171.18 816
export cable subsea inspection 9,976.59 2,841.39 0.00 7,569.47 18
main shaft major repair 221,454.64 7,609.88 7,596.20 215,201.22 50
main shaft minor repair 37,666.07 21,325.86 21,061.70 23,479.95 314
main shaft replacement 21,598.17 10,883.78 10,880.32 12,114.46 17
major anchor repair 16,307.65 7,670.88 7,670.01 8,946.99 26
major pitch system repair 66,814.99 54,248.03 54,047.85 19,251.25 262
major pitch system replacement 597.71 75.50 75.50 522.46 1
marine growth removal 49,908.23 33,043.89 32,868.82 19,342.00 167
minor ballast pump repair 1,994.08 1,011.26 1,002.72 1,218.17 17
minor pitch system repair 262,748.14 130,022.11 128,865.97 157,031.46 1,215
mooring line major repair 19,406.96 8,230.70 8,228.47 11,364.92 26
mooring line replacement 15,573.89 9,659.68 9,658.21 6,059.60 17
oss annual inspection 14,404.54 6,645.91 6,597.64 8,809.71 38
oss minor repair 632.79 368.82 364.62 511.92 7
power converter major repair 7,237,591.43 19,833.56 19,731.78 5,701,626.70 507
power converter minor repair 125,863.61 57,137.48 56,469.86 78,686.54 710
power converter replacement 239,401.71 77,717.91 77,680.89 165,830.98 124
power electrical system major repair 90,972.69 1,429.49 1,423.01 86,518.38 17
power electrical system major replacement 872.10 542.54 540.00 361.25 4
power electrical system minor repair 67,288.35 32,630.11 32,104.01 43,115.42 537
structural annual inspection 1,359,174.51 154,088.18 152,748.77 1,201,099.62 1,406
structural subsea inspection 779,423.27 24,890.19 24,322.06 753,676.60 669
yaw system major repair 168,270.19 239.75 237.00 168,034.44 6
yaw system major replacement 8,546.77 1,253.42 1,253.42 7,709.27 2
yaw system minor repair 33,279.05 14,189.62 13,965.36 24,045.70 220

Power Production#

Computes the total power production for the wind farm. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.power_production().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • by, as explained above options: "windfarm" and "turbine"

  • units

    • "kwh": kilowatt-hours (kWh)

    • "mwh": megawatt-hours (MWh)

    • "gwh": gigawatt-hours (GWh)

Example Usage:

# Project totals, in kWh, at the wind farm level
style(metrics.power_production(frequency="project", by="windfarm", units="kwh"))
  windfarm
Project Energy Production (kWh) 106,293,770,458.50
# Project totals, in MWh, at the wind farm level
style(metrics.power_production(frequency="project", units="mwh"))
  windfarm
Project Energy Production (MWh) 106,293,770.46
# Project totals, in GWh, at the wind farm level
style(metrics.power_production(frequency="project"))
  windfarm
Project Energy Production (GWh) 106,293.77

Net Present Value#

Calcualtes the net present value (NPV) for the project, as \(NPV = (Power * OfftakePrice - OpEx) / (1 + DiscountRate)\).

For further documentation, see the API docs here: wombat.core.post_processor.Metrics.npv().

Inputs:

  • frequency, as explained above, options: "project", "annual", "monthly", and "month-year"

  • discount_rate: The rate of return that could be earned on alternative investments, by default 0.025.

  • offtake_price: Price of energy, per MWh, by default 80.

style(metrics.opex("annual"))
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wombat/core/post_processor.py:1963: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  port_fees = port_fees.fillna(0)
  OpEx
year  
2002 107,289,133.53
2003 112,751,347.73
2004 123,125,511.53
2005 116,885,385.41
2006 120,195,423.36
2007 123,193,962.58
2008 131,191,003.55
2009 107,749,179.18
2010 120,801,815.77
2011 132,977,351.78
2012 127,130,652.21
2013 127,375,328.24
2014 114,190,807.70
2015 120,487,497.25
2016 134,291,651.27
2017 128,659,451.22
2018 121,100,295.57
2019 130,598,049.26
2020 123,959,092.54
2021 119,522,427.74