Demonstration of the Available Metrics

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.

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

# 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
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
Power Production: Potential and actually produced power
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("DINWOODIE", "base.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

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/wombat/core/environment.py:503: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
  .resample("H")

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:

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" 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	0.97

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

	windfarm
0	0.97

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"))

	S00T1	S00T2	S00T3	S00T4	S00T5	S00T6	S00T7	S00T8	S00T9	S00T10	S00T11	S00T12	S00T13	S00T14	S00T15	S00T16	S00T17	S00T18	S00T19	S00T20	S00T21	S00T22	S00T23	S00T24	S00T25	S00T26	S00T27	S00T28	S00T29	S00T30	S00T31	S00T32	S00T33	S00T34	S00T35	S00T36	S00T37	S00T38	S00T39	S00T40	S00T41	S00T42	S00T43	S00T44	S00T45	S00T46	S00T47	S00T48	S00T49	S00T50	S00T51	S00T52	S00T53	S00T54	S00T55	S00T56	S00T57	S00T58	S00T59	S00T60	S00T61	S00T62	S00T63	S00T64	S00T65	S00T66	S00T67	S00T68	S00T69	S00T70	S00T71	S00T72	S00T73	S00T74	S00T75	S00T76	S00T77	S00T78	S00T79	S00T80
0	0.94	0.95	0.97	0.96	0.98	0.96	0.97	0.98	0.98	0.97	0.97	0.97	0.98	0.97	0.98	0.96	0.97	0.98	0.98	0.97	0.96	0.97	0.95	0.97	0.97	0.98	0.92	0.97	0.98	0.97	0.97	0.98	0.96	0.97	0.98	0.98	0.97	0.96	0.97	0.95	0.93	0.98	0.95	0.97	0.95	0.95	0.97	0.97	0.96	0.96	0.97	0.98	0.95	0.97	0.98	0.95	0.98	0.97	0.97	0.94	0.98	0.97	0.95	0.98	0.94	0.97	0.97	0.97	0.98	0.97	0.98	0.98	0.94	0.97	0.97	0.96	0.95	0.97	0.97	0.97

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

	windfarm
year
2003	0.99
2004	0.96
2005	0.96
2006	0.96
2007	0.96
2008	0.96
2009	0.96
2010	0.96
2011	0.96
2012	0.96

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

	windfarm
month
1	0.97
2	0.97
3	0.97
4	0.97
5	0.97
6	0.97
7	0.97
8	0.97
9	0.96
10	0.96
11	0.97
12	0.97

# Demonstrate the granular monthly reporting
style(metrics.time_based_availability(frequency="month-year", by="windfarm"))

		windfarm
year	month
2003	1	0.99
	2	0.99
	3	0.99
	4	0.99
	5	0.99
	6	0.99
	7	0.99
	8	0.99
	9	0.99
	10	0.98
	11	0.99
	12	0.98
2004	1	0.96
	2	0.96
	3	0.96
	4	0.96
	5	0.96
	6	0.96
	7	0.97
	8	0.96
	9	0.96
	10	0.96
	11	0.97
	12	0.98
2005	1	0.97
	2	0.96
	3	0.96
	4	0.96
	5	0.96
	6	0.96
	7	0.97
	8	0.96
	9	0.96
	10	0.97
	11	0.96
	12	0.96
2006	1	0.97
	2	0.97
	3	0.96
	4	0.96
	5	0.97
	6	0.96
	7	0.96
	8	0.96
	9	0.97
	10	0.96
	11	0.96
	12	0.96
2007	1	0.96
	2	0.97
	3	0.96
	4	0.96
	5	0.96
	6	0.96
	7	0.97
	8	0.96
	9	0.96
	10	0.97
	11	0.96
	12	0.96
2008	1	0.97
	2	0.97
	3	0.97
	4	0.96
	5	0.96
	6	0.97
	7	0.97
	8	0.97
	9	0.96
	10	0.96
	11	0.95
	12	0.97
2009	1	0.97
	2	0.96
	3	0.96
	4	0.97
	5	0.97
	6	0.96
	7	0.97
	8	0.97
	9	0.96
	10	0.96
	11	0.96
	12	0.97
2010	1	0.96
	2	0.96
	3	0.97
	4	0.96
	5	0.97
	6	0.97
	7	0.97
	8	0.96
	9	0.96
	10	0.97
	11	0.96
	12	0.96
2011	1	0.96
	2	0.97
	3	0.97
	4	0.97
	5	0.96
	6	0.96
	7	0.97
	8	0.97
	9	0.97
	10	0.96
	11	0.96
	12	0.96
2012	1	0.96
	2	0.97
	3	0.97
	4	0.97
	5	0.96
	6	0.97
	7	0.97
	8	0.96
	9	0.96
	10	0.95
	11	0.97
	12	0.97

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: 46.02%
Gross capacity factor: 47.67%

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: 67.98%
Unscheduled Task Completion Rate: 99.89%
    Overall Task Completion Rate: 97.40%

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	123,427,472.83

# Project totals at servicing equipment level
style(metrics.equipment_costs(frequency="project", by_equipment=True))

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/wombat/core/post_processor.py:731: FutureWarning: <class 'pandas.core.arrays.string_.StringArray'>._reduce will require a `keepdims` parameter in the future
  costs = costs.fillna(costs.max(axis=0)).T

	Crew Transfer Vessel 1	Crew Transfer Vessel 2	Crew Transfer Vessel 3	Field Support Vessel	Heavy Lift Vessel
0	6,390,497.04	6,390,503.70	6,390,499.95	2,981,921.79	101,274,050.35

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"))

	Crew Transfer Vessel 1	Crew Transfer Vessel 2	Crew Transfer Vessel 3	Field Support Vessel	Heavy Lift Vessel
0	1.00	1.00	1.00	0.93	0.93

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	162,354.27

# 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	Field Support Vessel	Heavy Lift Vessel
year
2003	8,478.97	1,724.49	1,865.73	1,800.24	1,257.00	1,831.50
2004	18,211.24	3,968.24	4,034.95	4,079.22	675.00	5,453.83
2005	14,599.89	4,103.75	4,105.22	4,093.24	0.00	2,297.68
2006	14,870.92	4,200.98	4,181.99	4,206.45	646.00	1,635.50
2007	17,012.87	4,195.19	4,186.96	4,203.73	648.00	3,779.00
2008	19,645.46	4,250.25	4,264.70	4,192.51	1,222.00	5,716.00
2009	17,281.67	4,295.97	4,265.25	4,291.66	518.05	3,910.75
2010	16,896.87	4,215.22	4,133.47	4,165.99	82.95	4,299.24
2011	17,862.94	4,137.48	4,161.98	4,142.72	1,105.00	4,315.75
2012	17,493.43	4,214.25	4,165.23	4,223.70	791.50	4,098.75

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: $0.00/MW

# Project totals for each type of labor
# NOTE: this simulation relies on using a fixed labor cost, so this is still $0
style(metrics.labor_costs(frequency="project", by_type=True))

	hourly_labor_cost	salary_labor_cost	total_labor_cost
0	0	0	0

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	2,200,843.75	30,183.00
Repair	36,391,793.40	63,034.33
Crew Transfer	1,234,583.33	10,279.00
Site Travel	0.00	0.00
Mobilization	9,500,000.00	32,904.00
Weather Delay	37,164,495.12	36,940.25
No Requests	27,649,439.17	27,839.82
Not in Shift	9,286,318.06	116,957.16

# 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	0	0	2,200,843.75	2,200,843.75	30,183.00
Repair	0	0	0	36,391,793.40	36,391,793.40	63,034.33
Crew Transfer	0	0	0	1,234,583.33	1,234,583.33	10,279.00
Site Travel	0	0	0	0.00	0.00	0.00
Mobilization	0	0	0	9,500,000.00	9,500,000.00	32,904.00
Weather Delay	0	0	0	37,164,495.12	37,164,495.12	36,940.25
No Requests	0	0	0	27,649,439.17	27,649,439.17	27,839.82
Not in Shift	0	0	0	9,286,318.06	9,286,318.06	116,957.16

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,
    },
    "Crew Transfer Vessel 2": {
        "transit": 4,
        "maneuvering": 3,
        "idle at site": 0.5,
        "idle at port": 0.25,
    },
    "Crew Transfer Vessel 3": {
        "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,
    },
}

style(metrics.emissions(emissions_factors=emissions_factors, maneuvering_factor=0.075, port_engine_on_factor=0.20))

		duration	distance_km	emissions
agent	category
Crew Transfer Vessel 1	idle at port	0.00	0.00	0.00
	idle at site	39,274.83	0.00	19,637.41
	maneuvering	0.00	0.00	0.00
	transit	0.00	inf	0.00
Crew Transfer Vessel 2	idle at port	0.00	0.00	0.00
	idle at site	39,334.49	0.00	19,667.24
	maneuvering	0.00	0.00	0.00
	transit	0.00	inf	0.00
Crew Transfer Vessel 3	idle at port	0.00	0.00	0.00
	idle at site	39,368.45	0.00	19,684.23
	maneuvering	0.00	0.00	0.00
	transit	0.00	inf	0.00
Field Support Vessel	idle at port	1,025.64	0.00	512.82
	idle at site	1,401.50	0.00	1,401.50
	maneuvering	415.80	0.00	1,663.20
	transit	5,128.20	inf	30,769.20
Heavy Lift Vessel	idle at port	5,061.60	0.00	2,530.80
	idle at site	9,978.00	0.00	9,978.00
	maneuvering	2,052.00	0.00	14,364.00
	transit	25,308.00	inf	303,696.00

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
turbine	138,788,723.22

# 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
turbine	delay	0	0	46,438,502.73	46,438,502.73
	maintenance	0	0	2,200,843.75	2,200,843.75
	repair	0	0	36,391,793.40	36,391,793.40

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	16,013,230.75

# 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	16,013,230.75

# 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	16,013,230.75

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/3.10.13/x64/lib/python3.10/site-packages/wombat/core/post_processor.py:1955: 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
2003	11,239,073.73
2004	19,800,822.56
2005	18,079,533.96
2006	12,519,911.67
2007	20,407,617.42
2008	23,046,354.23
2009	19,319,078.36
2010	24,323,338.76
2011	23,202,482.06
2012	20,025,490.83

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

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/wombat/core/post_processor.py:1955: 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
2003	1,600,008.00	0	7,284,565.73	0	2,354,500	11,239,073.73
2004	1,604,391.58	0	12,991,430.98	0	5,205,000	19,800,822.56
2005	1,600,008.00	0	12,009,025.96	0	4,470,500	18,079,533.96
2006	1,600,008.00	0	7,287,903.67	0	3,632,000	12,519,911.67
2007	1,600,008.00	0	12,470,109.42	0	6,337,500	20,407,617.42
2008	1,604,391.58	0	15,982,462.64	0	5,459,500	23,046,354.23
2009	1,600,008.00	0	12,029,570.36	0	5,689,500	19,319,078.36
2010	1,600,008.00	0	15,896,330.76	0	6,827,000	24,323,338.76
2011	1,600,008.00	0	14,868,474.06	0	6,734,000	23,202,482.06
2012	1,604,391.58	0	12,607,599.25	0	5,813,500	20,025,490.83

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
annual service	5,395,344.49	80,007.54	125,269.02	4,039,017.49	1,043
major repair	100,102.00	3,093.78	3,075.00	94,804.33	42
major replacement	173,997.66	10,767.80	17,697.05	162,795.94	95
manual reset	1,583,656.13	80,274.99	103,844.89	1,445,380.56	8,478
medium repair	31,340.59	19,757.57	19,860.35	8,232.23	330
minor repair	204,433.50	63,283.37	63,466.80	127,371.50	3,335

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)	9,683,892,169.00

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

	windfarm
Project Energy Production (MWh)	9,683,892.17

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

	windfarm
Project Energy Production (GWh)	9,683.89

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/3.10.13/x64/lib/python3.10/site-packages/wombat/core/post_processor.py:1955: 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
2003	11,239,073.73
2004	19,800,822.56
2005	18,079,533.96
2006	12,519,911.67
2007	20,407,617.42
2008	23,046,354.23
2009	19,319,078.36
2010	24,323,338.76
2011	23,202,482.06
2012	20,025,490.83

Demonstration of the Available Metrics

Contents

Demonstration of the Available Metrics#

Table of Contents#

Setup#

Common Parameter Explanations#

frequency#

by#

Availability#

Capacity Factor#

Task Completion Rate#

Equipment Costs#

Service Equipment Utilization Rate#

Vessel-Crew Hours at Sea#

Number of Tows#

Labor Costs#

Equipment and Labor Costs#

Emissions#

Component Costs#

Fixed Cost Impacts#

OpEx#

Process Times#

Power Production#

Net Present Value#

`frequency`#

`by`#