Demonstration of the Available Metrics

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: Running a simulation to gather the results

• Common Parameters: Explanation of frequently used parameter settings

• Availability: Time-based and energy-based availability

• Capacity Factor: Gross and net capacity factor

• Task Completion Rate: Task completion metrics

• Equipment Costs: Cost breakdowns by servicing equipment

• Service Equipment Utilization Rate: Utilization of servicing equipment

• Vessel-Crew Hours at Sea: Number of crew or vessel hours spent at sea

• Number of Tows: Number of tows breakdowns

• Dispatch Summary: Mobilization and charter period breakdown

• Labor Costs: Breakdown of labor costs

• Equipment and Labor Costs: Combined servicing equipment and labor cost breakdown

• Emissions: Emissions of servicing equipment based on activity

• Component Costs: Materials costs

• Fixed Cost Impacts: Total fixed costs

• OpEx: Project OpEx

• Process Times: Timing of various stages of repair and maintenance

• Request Summary: Total number of submitted, canceled, and completed repair and maintenance tasks

• Power Production: Energy production, in GWh, MWh, or kWh

• H2 Production: Produced Hydrogen, in kg/hr or t/hr

• Net Present Value: Project NPV calculator

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.

electrolyzer

Computed for each electrolyzer, with the resulting DataFrame having a column for each electrolyzer. This option is only available for the availability and capacity factor metrics.

Availability

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

• energy: actual power produced divided by potential power produced

  • wombat.core.post_processor.Metrics.production_based_availability()

• time: The ratio of all non-zero hours to all hours in the simulation, or the proportion of the simulation where turbines are operating

  • wombat.core.post_processor.Metrics.time_based_availability()

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", "turbine", or "electrolyzer"

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
time_availability	0.91

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

	windfarm
energy_availability	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
time_availability	0.91	0.90	0.88	0.90	0.89	0.90	0.91	0.91	0.91	0.90	0.92	0.91	0.91	0.92	0.90	0.90	0.92	0.92	0.93	0.91	0.92	0.91	0.90	0.92	0.91	0.91	0.92	0.90	0.90	0.91	0.91	0.90	0.91	0.90	0.91	0.90	0.91	0.91	0.91	0.90	0.91	0.91	0.89	0.91	0.92	0.90	0.89	0.90	0.91	0.91	0.90	0.92	0.91	0.91	0.89	0.91	0.91	0.91	0.91	0.91	0.92	0.90	0.89	0.92	0.91	0.91	0.91	0.90	0.90	0.92	0.90	0.91	0.91	0.90	0.90	0.90	0.91	0.91	0.91	0.91

# Demonstrate the annualized outputs
style(metrics.time_based_availability(frequency="annual", by="windfarm"))

	windfarm
year
2002	0.95
2003	0.90
2004	0.90
2005	0.91
2006	0.91
2007	0.90
2008	0.90
2009	0.92
2010	0.86
2011	0.90
2012	0.91
2013	0.93
2014	0.91
2015	0.93
2016	0.90
2017	0.91
2018	0.92
2019	0.90
2020	0.91
2021	0.89

# Demonstrate the month aggregations
style(metrics.time_based_availability(frequency="monthly", by="windfarm"))

	windfarm
month
1	0.88
2	0.85
3	0.88
4	0.85
5	0.88
6	0.91
7	0.93
8	0.95
9	0.96
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	0.95
	2	0.95
	3	0.90
	4	0.97
	5	0.96
	6	0.93
	7	0.97
	8	0.97
	9	0.98
	10	0.96
	11	0.93
	12	0.91
2003	1	0.58
	2	0.90
	3	0.90
	4	0.90
	5	0.90
	6	0.90
	7	0.91
	8	0.96
	9	0.98
	10	0.96
	11	0.94
	12	0.92
2004	1	0.70
	2	0.90
	3	0.89
	4	0.90
	5	0.89
	6	0.90
	7	0.93
	8	0.97
	9	0.95
	10	0.96
	11	0.95
	12	0.83
2005	1	0.82
	2	0.75
	3	0.90
	4	0.90
	5	0.90
	6	0.89
	7	0.96
	8	0.97
	9	0.97
	10	0.94
	11	0.94
	12	0.94
2006	1	0.89
	2	0.73
	3	0.90
	4	0.90
	5	0.90
	6	0.90
	7	0.90
	8	0.94
	9	0.97
	10	0.98
	11	0.96
	12	0.93
2007	1	0.91
	2	0.67
	3	0.88
	4	0.91
	5	0.90
	6	0.90
	7	0.90
	8	0.91
	9	0.97
	10	0.94
	11	0.96
	12	0.92
2008	1	0.92
	2	0.69
	3	0.88
	4	0.93
	5	0.90
	6	0.89
	7	0.88
	8	0.89
	9	0.95
	10	0.94
	11	0.95
	12	0.94
2009	1	0.96
	2	0.90
	3	0.85
	4	0.92
	5	0.93
	6	0.93
	7	0.90
	8	0.93
	9	0.94
	10	0.95
	11	0.94
	12	0.94
2010	1	0.92
	2	0.77
	3	0.57
	4	0.66
	5	0.93
	6	0.91
	7	0.91
	8	0.93
	9	0.94
	10	0.91
	11	0.92
	12	0.93
2011	1	0.92
	2	0.74
	3	0.92
	4	0.76
	5	0.94
	6	0.94
	7	0.93
	8	0.91
	9	0.91
	10	0.92
	11	0.91
	12	0.93
2012	1	0.93
	2	0.90
	3	0.90
	4	0.79
	5	0.93
	6	0.92
	7	0.97
	8	0.94
	9	0.92
	10	0.92
	11	0.92
	12	0.88
2013	1	0.89
	2	0.89
	3	0.87
	4	0.91
	5	0.96
	6	0.94
	7	0.97
	8	0.97
	9	0.94
	10	0.91
	11	0.94
	12	0.96
2014	1	0.92
	2	0.92
	3	0.91
	4	0.73
	5	0.95
	6	0.89
	7	0.97
	8	0.96
	9	0.97
	10	0.92
	11	0.90
	12	0.90
2015	1	0.90
	2	0.90
	3	0.94
	4	0.89
	5	0.87
	6	0.97
	7	0.96
	8	0.97
	9	0.96
	10	0.96
	11	0.89
	12	0.89
2016	1	0.90
	2	0.90
	3	0.90
	4	0.77
	5	0.84
	6	0.90
	7	0.89
	8	0.98
	9	0.95
	10	0.94
	11	0.90
	12	0.90
2017	1	0.89
	2	0.90
	3	0.90
	4	0.79
	5	0.86
	6	0.90
	7	0.89
	8	0.97
	9	0.96
	10	0.95
	11	0.96
	12	0.94
2018	1	0.91
	2	0.89
	3	0.90
	4	0.86
	5	0.86
	6	0.90
	7	0.94
	8	0.97
	9	0.97
	10	0.94
	11	0.94
	12	0.91
2019	1	0.91
	2	0.90
	3	0.89
	4	0.84
	5	0.67
	6	0.89
	7	0.94
	8	0.97
	9	0.96
	10	0.95
	11	0.95
	12	0.92
2020	1	0.90
	2	0.90
	3	0.91
	4	0.87
	5	0.77
	6	0.90
	7	0.94
	8	0.96
	9	0.96
	10	0.97
	11	0.96
	12	0.92
2021	1	0.90
	2	0.91
	3	0.90
	4	0.89
	5	0.64
	6	0.84
	7	0.90
	8	0.92
	9	0.97
	10	0.94
	11	0.94
	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)

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", "turbine", and "electrolyzer"

Example Usage:

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

  Net capacity factor: 50.41%
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: 96.30%
Unscheduled Task Completion Rate: 95.61%
    Overall Task Completion Rate: 95.87%

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	2,375,359,367.74

# 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	128,272,674.82	187,191,980.82	25,563,194.69	25,563,214.73	25,563,205.38	25,563,202.19	25,563,205.64	25,563,200.90	25,563,198.81	120,929,228.93	1,760,023,060.82

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	0.87	0.93	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.95	1.00

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	693,884.26

# Annual project totals, broken out by vessel
style(metrics.vessel_crew_hours_at_sea(frequency="annual", by_equipment=True))

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[16], line 2
      1 # Annual project totals, broken out by vessel
----> 2 style(metrics.vessel_crew_hours_at_sea(frequency="annual", by_equipment=True))

File /opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages/wombat/core/post_processor.py:1030, in Metrics.vessel_crew_hours_at_sea(self, frequency, by_equipment, vessel_crew_assumption)
   1024         return pd.DataFrame(at_sea.sum()[["duration"]]).T.rename(
   1025             columns={"duration": "Total Crew Hours at Sea"}
   1026         )
   1027 additional_cols = frequency.group_cols
   1028 total_hours = (
   1029     total_hours.drop(columns=frequency.drop_cols)
-> 1030     .groupby(group_cols)[["N"]]
   1031     .sum()
   1032 )
   1034 columns = additional_cols + columns
   1035 group_cols.extend(additional_cols)

File /opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages/pandas/core/frame.py:9190, in DataFrame.groupby(self, by, axis, level, as_index, sort, group_keys, observed, dropna)
   9187 if level is None and by is None:
   9188     raise TypeError("You have to supply one of 'by' and 'level'")
-> 9190 return DataFrameGroupBy(
   9191     obj=self,
   9192     keys=by,
   9193     axis=axis,
   9194     level=level,
   9195     as_index=as_index,
   9196     sort=sort,
   9197     group_keys=group_keys,
   9198     observed=observed,
   9199     dropna=dropna,
   9200 )

File /opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1329, in GroupBy.__init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)
   1326 self.dropna = dropna
   1328 if grouper is None:
-> 1329     grouper, exclusions, obj = get_grouper(
   1330         obj,
   1331         keys,
   1332         axis=axis,
   1333         level=level,
   1334         sort=sort,
   1335         observed=False if observed is lib.no_default else observed,
   1336         dropna=self.dropna,
   1337     )
   1339 if observed is lib.no_default:
   1340     if any(ping._passed_categorical for ping in grouper.groupings):

File /opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages/pandas/core/groupby/grouper.py:1043, in get_grouper(obj, key, axis, level, sort, observed, validate, dropna)
   1041         in_axis, level, gpr = False, gpr, None
   1042     else:
-> 1043         raise KeyError(gpr)
   1044 elif isinstance(gpr, Grouper) and gpr.key is not None:
   1045     # Add key to exclusions
   1046     exclusions.add(gpr.key)

KeyError: 'agent'

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

Dispatch Summary

The number of mobilizations and the average length of their charter during the simulation for each piece of servicing equipment. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.dispatch_summary().

Inputs:

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

Example Usage:

# Project Total
style(metrics.dispatch_summary(frequency="project"))

	N Mobilizations	Average Charter Days
agent
Anchor Handling Tug	57	26.07
Cable Laying Vessel	49	47.94
Crew Transfer Vessel 1	1	7,303.00
Crew Transfer Vessel 2	1	7,303.67
Crew Transfer Vessel 3	1	7,303.00
Crew Transfer Vessel 4	1	7,303.00
Crew Transfer Vessel 5	1	7,303.00
Crew Transfer Vessel 6	1	7,303.00
Crew Transfer Vessel 7	1	7,303.00
Diving Support Vessel	162	6.95
Heavy Lift Vessel	132	44.72

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: $42,482.56/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	50,979,075.04	50,979,075.04

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	24,063,170.40	73,560.00
Repair	630,096,493.80	169,838.47
Crew Transfer	8,557,043.70	11,317.50
Site Travel	92,561,878.11	124,324.18
Mobilization	120,910,000.00	134,064.00
Weather Delay	1,445,365,249.03	613,411.21
No Requests	76,934,676.16	387,632.83
Not in Shift	27,849,931.58	108,305.34

# 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	2,253,170.40	2,253,170.40	21,810,000.00	24,063,170.40	73,560.00
Repair	0	8,137,603.15	8,137,603.15	621,958,890.65	630,096,493.80	169,838.47
Crew Transfer	0	355,121.83	355,121.83	8,201,921.87	8,557,043.70	11,317.50
Site Travel	0	3,925,833.50	3,925,833.50	88,636,044.61	92,561,878.11	124,324.18
Mobilization	0	0.00	0.00	120,910,000.00	120,910,000.00	134,064.00
Weather Delay	0	22,416,100.81	22,416,100.81	1,422,949,148.22	1,445,365,249.03	613,411.21
No Requests	0	10,775,300.57	10,775,300.57	66,159,375.60	76,934,676.16	387,632.83
Not in Shift	0	3,115,944.79	3,115,944.79	24,733,986.79	27,849,931.58	108,305.34

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,
    },
    "Cable Laying Vessel": {
        "transit": 4,
        "maneuvering": 7,
        "idle at site": 0.2,
        "idle at port": 0.2,
    },
    "Anchor Handling Tug": {
        "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))

		duration	distance_km	emissions
agent	category
Anchor Handling Tug	idle at port	5,119.44	0.00	1,279.86
	idle at site	33,257.92	0.00	33,257.92
	maneuvering	2,075.45	0.00	6,226.34
	transit	25,597.19	inf	102,388.77
Cable Laying Vessel	idle at port	3,240.95	0.00	648.19
	idle at site	48,426.70	0.00	9,685.34
	maneuvering	1,313.90	0.00	9,197.29
	transit	16,204.74	inf	64,818.97
Crew Transfer Vessel 1	idle at port	3,046.91	0.00	761.73
	idle at site	32,211.60	0.00	16,105.80
	maneuvering	1,235.23	0.00	3,705.70
	transit	15,234.56	inf	60,938.23
Crew Transfer Vessel 2	idle at port	3,071.72	0.00	767.93
	idle at site	32,164.74	0.00	16,082.37
	maneuvering	1,245.29	0.00	3,735.88
	transit	15,358.61	inf	61,434.43
Crew Transfer Vessel 3	idle at port	3,122.58	0.00	780.65
	idle at site	32,853.14	0.00	16,426.57
	maneuvering	1,265.91	0.00	3,797.74
	transit	15,612.92	inf	62,451.69
Crew Transfer Vessel 4	idle at port	3,048.00	0.00	762.00
	idle at site	31,334.40	0.00	15,667.20
	maneuvering	1,235.68	0.00	3,707.03
	transit	15,240.01	inf	60,960.03
Crew Transfer Vessel 5	idle at port	3,089.85	0.00	772.46
	idle at site	32,588.95	0.00	16,294.48
	maneuvering	1,252.64	0.00	3,757.93
	transit	15,449.25	inf	61,797.00
Crew Transfer Vessel 6	idle at port	3,082.42	0.00	770.60
	idle at site	32,529.21	0.00	16,264.60
	maneuvering	1,249.63	0.00	3,748.89
	transit	15,412.08	inf	61,648.33
Crew Transfer Vessel 7	idle at port	3,091.13	0.00	772.78
	idle at site	32,267.80	0.00	16,133.90
	maneuvering	1,253.16	0.00	3,759.48
	transit	15,455.66	inf	61,822.64
Diving Support Vessel	idle at port	11,818.85	0.00	2,363.77
	idle at site	5,502.97	0.00	1,100.59
	maneuvering	4,791.43	0.00	33,539.99
	transit	59,094.27	123,716.57	236,377.07
Field Support Vessel	idle at port	0.00	0.00	0.00
	idle at site	0.00	0.00	0.00
	maneuvering	0.00	0.00	0.00
	transit	0.00	0.00	0.00
Heavy Lift Vessel	idle at port	6,679.58	0.00	3,339.79
	idle at site	118,853.40	0.00	118,853.40
	maneuvering	2,707.94	0.00	18,955.55
	transit	33,397.88	inf	400,774.56

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 subassembly

  • False: (default) Aggregated to the sum of all subassemblies

• by_action

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

  • False: (default) Aggregated as the sum of all actions

• by_task

  • True: Computed by each repair and maintenance task for each subassembly

  • False: (default) Aggregated as the sum of all tasks

• include_travel

  • True: Include intrasite and port-to-site travel costs in the repair summary.

  • False: (default) Exclude travel from the cost summary

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

• travel: Any travel to and from the site between shifts or traveling to the system for initial repair, including crew transfer time.

Example Usage:

# Project totals by component
style(metrics.component_costs(frequency="project", by_category=False, by_action=False))

	total_cost
subassembly
anchor	61,584,851.68
array cable	183,462,260.22
ballast pump	68,553.03
drive_train	124,320,508.11
electrical system	25,022,562.23
export cable	6,576,737.37
generator	279,197,684.41
hydraulic pitch system	33,469,421.43
mooring lines	62,961,169.36
power converter	1,224,850,923.40
rotor_blades	76,941,993.99
supporting_structure	80,911,386.89
transformer	1,254,246.88
yaw_system	21,129,171.58

# 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
subassembly	action
anchor	delay	0	669,999.39	20,175,180.01	20,845,179.40
	repair	10,054,000	986,289.50	29,699,382.78	40,739,672.28
array cable	delay	0	3,171,721.84	108,531,407.10	111,703,128.94
	repair	6,920,000	1,841,055.76	62,998,075.52	71,759,131.28
ballast pump	delay	0	6,314.04	33,608.83	39,922.87
	repair	12,000	2,630.16	14,000.00	28,630.16
drive_train	delay	0	763,408.29	86,911,322.29	87,674,730.58
	repair	3,772,000	211,485.87	32,662,291.67	36,645,777.54
electrical system	delay	0	652,446.68	14,258,692.25	14,911,138.93
	repair	878,000	171,964.98	9,061,458.33	10,111,423.31
export cable	delay	0	166,759.50	5,706,251.67	5,873,011.17
	maintenance	9,000	19,726.20	675,000.00	703,726.20
generator	delay	0	4,694,643.98	191,892,610.92	196,587,254.90
	maintenance	2,148,000	941,597.28	5,012,000.00	8,101,597.28
	repair	5,121,960	549,457.88	68,837,414.35	74,508,832.23
hydraulic pitch system	delay	0	3,524,766.71	22,391,459.83	25,916,226.55
	repair	1,123,000	876,528.22	5,553,666.67	7,553,194.88
mooring lines	delay	0	1,294,765.72	17,254,318.40	18,549,084.12
	repair	8,264,000	1,377,536.84	34,770,548.40	44,412,085.24
power converter	delay	0	4,560,275.63	863,847,408.34	868,407,683.96
	repair	9,048,000	1,602,906.80	345,792,332.64	356,443,239.43
rotor_blades	delay	0	1,472,229.21	42,697,058.51	44,169,287.72
	repair	5,597,856	432,796.64	26,742,053.63	32,772,706.27
supporting_structure	delay	0	4,077,285.25	58,421,241.24	62,498,526.49
	maintenance	1,156,000	1,266,860.40	15,990,000.00	18,412,860.40
transformer	delay	0	148,400.19	789,914.93	938,315.12
	maintenance	19,000	24,986.52	133,000.00	176,986.52
	repair	114,000	3,945.24	21,000.00	138,945.24
yaw_system	delay	0	327,892.01	14,766,607.63	15,094,499.64
	repair	147,000	81,005.28	5,806,666.67	6,034,671.94

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"))

	OpEx
year
2002	110,260,676.98
2003	117,395,980.30
2004	129,951,305.86
2005	113,965,336.87
2006	123,682,482.01
2007	118,271,499.48
2008	136,126,599.55
2009	125,355,903.30
2010	136,195,274.71
2011	134,360,755.92
2012	123,002,003.28
2013	120,796,258.15
2014	121,776,353.93
2015	119,520,714.63
2016	111,894,138.91
2017	135,458,997.79
2018	116,566,101.27
2019	138,207,024.76
2020	116,081,433.67
2021	131,853,417.42

style(metrics.opex("annual", by_category=True))

	operations	port_fees	equipment_cost	total_labor_cost	materials_cost	OpEx
year
2002	0.00	0.00	106,092,966.13	2,359,110.85	1,808,600	110,260,676.98
2003	0.00	0.00	111,737,809.87	2,409,370.43	3,248,800	117,395,980.30
2004	0.00	0.00	124,660,800.73	2,513,105.13	2,777,400	129,951,305.86
2005	0.00	0.00	109,180,922.38	2,304,714.49	2,479,700	113,965,336.87
2006	0.00	0.00	118,001,320.93	2,546,909.07	3,134,252	123,682,482.01
2007	0.00	0.00	113,660,827.31	2,345,232.16	2,265,440	118,271,499.48
2008	0.00	0.00	129,774,780.57	2,722,918.98	3,628,900	136,126,599.55
2009	0.00	0.00	119,302,091.40	2,574,371.91	3,479,440	125,355,903.30
2010	0.00	0.00	130,417,910.32	2,786,392.40	2,990,972	136,195,274.71
2011	0.00	0.00	129,317,635.26	2,716,620.66	2,326,500	134,360,755.92
2012	0.00	0.00	118,214,058.86	2,484,604.42	2,303,340	123,002,003.28
2013	0.00	0.00	115,318,188.82	2,576,493.33	2,901,576	120,796,258.15
2014	0.00	0.00	115,981,027.55	2,566,154.38	3,229,172	121,776,353.93
2015	0.00	0.00	114,323,549.14	2,574,521.50	2,622,644	119,520,714.63
2016	0.00	0.00	107,542,303.23	2,464,895.68	1,886,940	111,894,138.91
2017	0.00	0.00	129,623,099.07	2,730,478.72	3,105,420	135,458,997.79
2018	0.00	0.00	112,072,122.62	2,426,078.65	2,067,900	116,566,101.27
2019	0.00	0.00	132,777,132.51	2,756,412.25	2,673,480	138,207,024.76
2020	0.00	0.00	110,875,669.84	2,286,963.82	2,918,800	116,081,433.67
2021	0.00	0.00	126,485,151.20	2,833,726.22	2,534,540	131,853,417.42

Process Times

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

Inputs:

• include_incompletes

  • True: include requests that have been submitted, but not completed.

  • False: only include requests that have been completed.

Example Usage:

style(metrics.process_times())

		time_to_completion	process_time	downtime	time_to_start	N
subassembly	task
anchor	anchor replacement	19,914.53	11,182.72	11,181.31	9,249.83	19
	major anchor repair	15,758.76	7,190.71	7,189.30	8,873.27	21
array cable	array cable major repair	40,378.53	24,651.69	0.00	17,218.47	45
	array cable replacement	43,352.09	31,372.39	0.00	12,934.49	28
ballast pump	minor ballast pump repair	1,723.65	385.51	379.80	1,457.23	12
drive_train	main shaft major repair	497,812.13	1,799.79	1,794.28	496,459.88	29
	main shaft minor repair	51,610.66	22,811.48	22,556.74	34,660.74	328
	main shaft replacement	32,166.84	8,215.31	8,212.72	24,568.73	14
electrical system	power electrical system major repair	456,054.33	593.64	590.00	455,467.58	27
	power electrical system major replacement	14,092.13	1,055.25	1,053.17	13,126.63	6
	power electrical system minor repair	74,675.26	32,112.76	31,595.48	52,487.50	513
export cable	export cable subsea inspection	10,080.54	2,554.08	0.00	8,349.74	18
generator	annual turbine inspection	1,345,333.74	157,716.94	156,193.84	1,199,167.02	1,432
	direct drive generator major repair	686,462.58	3,646.33	3,639.28	682,866.49	33
	direct drive generator major replacement	42,648.89	16,593.87	16,589.79	26,805.15	17
	direct drive generator minor repair	139,814.07	68,580.22	67,785.06	87,536.58	829
hydraulic pitch system	major pitch system repair	74,440.38	59,206.70	59,000.60	20,371.23	265
	major pitch system replacement	390.21	383.50	383.05	7.41	1
	minor pitch system repair	226,367.68	128,041.11	126,992.92	118,901.34	1,211
mooring lines	buoyancy module replacement	12,203.69	9,400.30	9,364.76	3,269.11	51
	marine growth removal	55,190.74	39,567.95	39,423.00	19,366.62	196
	mooring line major repair	20,323.01	7,434.66	7,432.59	13,316.51	24
	mooring line replacement	17,774.67	9,387.43	9,385.35	8,535.29	18
power converter	power converter major repair	6,677,757.69	21,462.38	21,388.63	6,659,746.13	379
	power converter minor repair	138,860.89	69,208.21	68,498.14	83,843.95	788
	power converter replacement	286,726.98	81,468.65	81,399.02	209,331.84	127
rotor_blades	blades major repair	309,315.00	803.09	799.61	308,518.25	21
	blades major replacement	5,694.17	4,256.65	4,256.65	1,646.17	2
	blades minor repair	133,876.55	71,680.75	71,082.92	72,083.62	687
supporting_structure	structural annual inspection	1,615,852.88	161,216.01	160,033.98	1,459,421.92	1,440
	structural subsea inspection	1,548,408.23	22,931.95	22,386.82	1,532,501.98	719
transformer	oss annual inspection	11,577.45	6,595.66	6,547.26	6,054.47	38
	oss major repair	369.23	285.19	274.49	94.99	1
	oss minor repair	1,012.00	671.66	667.68	454.26	7
yaw_system	yaw system major repair	153,621.61	471.56	469.91	153,195.61	9
	yaw system major replacement	12,340.48	1,097.64	1,096.32	11,244.66	2
	yaw system minor repair	34,690.92	14,471.45	14,305.23	24,422.50	220

style(metrics.process_times(include_incompletes=False))

		time_to_completion	process_time	downtime	time_to_start	N
subassembly	task
anchor	anchor replacement	19,914.53	11,182.72	11,181.31	9,249.83	17
	major anchor repair	15,758.76	7,190.71	7,189.30	8,873.27	18
array cable	array cable major repair	40,378.53	24,651.69	0.00	17,218.47	40
	array cable replacement	43,352.09	31,372.39	0.00	12,934.49	26
ballast pump	minor ballast pump repair	1,723.65	385.51	379.80	1,457.23	12
drive_train	main shaft major repair	497,812.13	1,799.79	1,794.28	496,459.88	14
	main shaft minor repair	51,610.66	22,811.48	22,556.74	34,660.74	328
	main shaft replacement	32,166.84	8,215.31	8,212.72	24,568.73	14
electrical system	power electrical system major repair	456,054.33	593.64	590.00	455,467.58	13
	power electrical system major replacement	14,092.13	1,055.25	1,053.17	13,126.63	6
	power electrical system minor repair	74,675.26	32,112.76	31,595.48	52,487.50	513
export cable	export cable subsea inspection	10,080.54	2,554.08	0.00	8,349.74	18
generator	annual turbine inspection	1,345,333.74	157,716.94	156,193.84	1,199,167.02	1,432
	direct drive generator major repair	686,462.58	3,646.33	3,639.28	682,866.49	19
	direct drive generator major replacement	42,648.89	16,593.87	16,589.79	26,805.15	17
	direct drive generator minor repair	139,814.07	68,580.22	67,785.06	87,536.58	829
hydraulic pitch system	major pitch system repair	74,440.38	59,206.70	59,000.60	20,371.23	265
	major pitch system replacement	390.21	383.50	383.05	7.41	1
	minor pitch system repair	226,367.68	128,041.11	126,992.92	118,901.34	1,211
mooring lines	buoyancy module replacement	12,203.69	9,400.30	9,364.76	3,269.11	51
	marine growth removal	55,190.74	39,567.95	39,423.00	19,366.62	196
	mooring line major repair	20,323.01	7,434.66	7,432.59	13,316.51	22
	mooring line replacement	17,774.67	9,387.43	9,385.35	8,535.29	18
power converter	power converter major repair	6,677,757.69	21,462.38	21,388.63	6,659,746.13	190
	power converter minor repair	138,860.89	69,208.21	68,498.14	83,843.95	788
	power converter replacement	286,340.55	81,172.15	81,134.82	209,209.36	126
rotor_blades	blades major repair	309,315.00	803.09	799.61	308,518.25	8
	blades major replacement	5,694.17	4,256.65	4,256.65	1,646.17	2
	blades minor repair	133,835.69	71,632.75	71,043.73	72,081.70	686
supporting_structure	structural annual inspection	1,615,852.88	161,216.01	160,033.98	1,459,421.92	1,440
	structural subsea inspection	1,548,408.23	22,931.95	22,386.82	1,532,501.98	584
transformer	oss annual inspection	11,577.45	6,595.66	6,547.26	6,054.47	38
	oss major repair	369.23	285.19	274.49	94.99	1
	oss minor repair	1,012.00	671.66	667.68	454.26	7
yaw_system	yaw system major repair	153,621.61	471.56	469.91	153,195.61	4
	yaw system major replacement	12,340.48	1,097.64	1,096.32	11,244.66	2
	yaw system minor repair	34,690.92	14,471.45	14,305.23	24,422.50	220

Request Summary

Computes the total number of submitted, canceled, incomplete, and completed repair and maintenance request by subassembly and task description. For further documentation, see the API docs here: wombat.core.post_processor.Metrics.request_summary().

Example Usage:

style(metrics.request_summary())

		total_requests	canceled_requests	incomplete_requests	completed_requests
subassembly	task
anchor	anchor replacement	19	0	2	17
	major anchor repair	21	0	3	18
array cable	array cable major repair	45	0	5	40
	array cable replacement	28	0	2	26
ballast pump	minor ballast pump repair	12	0	0	12
drive_train	main shaft major repair	29	0	15	14
	main shaft minor repair	328	0	0	328
	main shaft replacement	14	0	0	14
electrical system	power electrical system major repair	27	0	14	13
	power electrical system major replacement	6	0	0	6
	power electrical system minor repair	513	0	0	513
export cable	export cable subsea inspection	18	0	0	18
generator	annual turbine inspection	1,435	3	0	1,432
	direct drive generator major repair	35	2	14	19
	direct drive generator major replacement	17	0	0	17
	direct drive generator minor repair	829	0	0	829
hydraulic pitch system	major pitch system repair	265	0	0	265
	major pitch system replacement	1	0	0	1
	minor pitch system repair	1,211	0	0	1,211
mooring lines	buoyancy module replacement	51	0	0	51
	marine growth removal	196	0	0	196
	mooring line major repair	24	0	2	22
	mooring line replacement	18	0	0	18
power converter	power converter major repair	498	119	189	190
	power converter minor repair	789	1	0	788
	power converter replacement	128	1	1	126
rotor_blades	blades major repair	21	0	13	8
	blades major replacement	2	0	0	2
	blades minor repair	687	0	1	686
supporting_structure	structural annual inspection	1,440	0	0	1,440
	structural subsea inspection	719	0	135	584
transformer	oss annual inspection	38	0	0	38
	oss major repair	1	0	0	1
	oss minor repair	7	0	0	7
yaw_system	yaw system major repair	9	0	5	4
	yaw system major replacement	2	0	0	2
	yaw system minor repair	220	0	0	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)	106,053,048,099.00

# Project totals, in MWh, at the wind farm level
style(metrics.power_production(frequency="project", units="mwh"))

	windfarm
Project Energy Production (MWh)	106,053,048.10

# Project totals, in GWh, at the wind farm level
style(metrics.power_production(frequency="project"))

	windfarm
Project Energy Production (GWh)	106,053.05

Hydrogen Production

Computes the total hydrogen production for the electrolyzer(s). For further documentation, see the API docs here: wombat.core.post_processor.Metrics.h2_production().

Inputs:

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

• by: Similar to the common definition, but with electrolyzer specific inputs.

  • "total": total H2 produced for all electrolyzers

  • "electrolyzer": H2 production for all and each electrolyzer

• units

  • "kgph": kilograms per hour (kgph)

  • "tph": metric tonnes hours (tph)

Example Usage:

Please note, this will raise an error since this analysis does not feature a modeled electrolyzer.

# Project totals, in kWh, at the wind farm level
style(metrics.h2_production(frequency="project", by="total", units="kgph"))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[38], line 2
      1 # Project totals, in kWh, at the wind farm level
----> 2 style(metrics.h2_production(frequency="project", by="total", units="kgph"))

File /opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages/wombat/core/post_processor.py:2322, in Metrics.h2_production(self, frequency, by, units)
   2286 """Calculates the hydrogen production for the simulation at a project, annual,
   2287 or monthly level that can be broken out by electrolyzer.
   2288 
   (...)   2319     If :py:attr:`units` is not one of "kg" or "tn".
   2320 """
   2321 if self.electrolyzer_rated_production.size == 0:
-> 2322     raise ValueError("No electrolyzers available to analyze.")
   2323 frequency = _check_frequency(frequency, which="all")
   2325 by = by.lower().strip()

ValueError: No electrolyzers available to analyze.

Net Present Value

Calculates 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"))

	OpEx
year
2002	110,260,676.98
2003	117,395,980.30
2004	129,951,305.86
2005	113,965,336.87
2006	123,682,482.01
2007	118,271,499.48
2008	136,126,599.55
2009	125,355,903.30
2010	136,195,274.71
2011	134,360,755.92
2012	123,002,003.28
2013	120,796,258.15
2014	121,776,353.93
2015	119,520,714.63
2016	111,894,138.91
2017	135,458,997.79
2018	116,566,101.27
2019	138,207,024.76
2020	116,081,433.67
2021	131,853,417.42