Demonstration of the Metrics To-Date¶

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.

In [1]:

from pprint import pprint

import pandas as pd

from wombat.core import Simulation
from wombat.core.library import load_yaml

pd.set_option("display.float_format", '{:,.2f}'.format)
pd.set_option("display.max_rows", 1000)
pd.set_option("display.max_columns", 1000)

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.

In [2]:

simulation_name = "dinwoodie_base"

sim = Simulation(simulation_name, "DINWOODIE", "base.yaml")
sim.run()

# For convenience only
metrics = sim.metrics

Availability¶

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

energy: production_based_availability
time: 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.

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

by options:

windfarm: computed across all turbines
turbine: computed for each turbine

In [3]:

# Project total at the whole windfarm level
total = metrics.time_based_availability(frequency="project", by="windfarm")
print(f"Project total: {total * 100:.1f}%")

Project total: 89.7%

In [4]:

# Project total at the turbine level
metrics.time_based_availability(frequency="project", by="turbine")

Out[4]:

	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	0.87	0.97	0.86	0.92	0.97	0.90	0.80	0.68	0.97	0.79	0.87	0.83	0.97	0.85	0.97	0.78	0.85	0.96	0.97	0.98	0.97	0.92	0.97	0.86	0.89	0.87	0.68	0.98	0.97	0.97	0.73	0.97	0.97	0.87	0.81	0.94	0.85	0.75	0.97	0.97	0.75	0.97	0.82	0.84	0.97	0.83	0.80	0.89	0.97	0.84	0.96	0.91	0.97	0.97	0.92	0.97	0.89	0.92	0.82	0.97	0.96	0.97	0.89	0.97	0.44	0.92	0.97	0.97	0.85	0.96	0.97	0.84	0.97	0.93	0.78	0.96	0.85	0.98	0.97	0.93	0.97

In [5]:

# Project annual totals at the windfarm level
metrics.time_based_availability(frequency="annual", by="windfarm")

Out[5]:

	year	windfarm
0	2003	0.96
1	2004	0.88
2	2005	0.90
3	2006	0.94
4	2007	0.94
5	2008	0.87
6	2009	0.85
7	2010	0.86
8	2011	0.89
9	2012	0.89

In [6]:

# Project monthly totals at the windfarm level
metrics.time_based_availability(frequency="monthly", by="windfarm")

Out[6]:

	month	windfarm
0	1	0.90
1	2	0.89
2	3	0.88
3	4	0.88
4	5	0.89
5	6	0.89
6	7	0.90
7	8	0.90
8	9	0.91
9	10	0.91
10	11	0.91
11	12	0.90

In [7]:

# Project month-by-year totals at the windfarm level
# NOTE: This is limited to the first two years for cleanliness of the notebook
metrics.time_based_availability(frequency="month-year", by="windfarm").head(24)

Out[7]:

	year	month	windfarm
0	2003	1	0.98
1	2003	2	0.97
2	2003	3	0.97
3	2003	4	0.96
4	2003	5	0.96
5	2003	6	0.97
6	2003	7	0.98
7	2003	8	0.95
8	2003	9	0.96
9	2003	10	0.96
10	2003	11	0.94
11	2003	12	0.93
12	2004	1	0.92
13	2004	2	0.90
14	2004	3	0.87
15	2004	4	0.85
16	2004	5	0.87
17	2004	6	0.87
18	2004	7	0.86
19	2004	8	0.87
20	2004	9	0.87
21	2004	10	0.88
22	2004	11	0.89
23	2004	12	0.88

Capacity Factor¶

Here, we will go through the various input definitions to get capacity factor data. The inputs are very similar to that of the availability calculation.

which options:

net: net capcity factor, actual production
gross: gross capacity factor, potential production

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

by options:

windfarm: computed across all turbines
turbine: computed for each turbine

In [8]:

# Project total at the whole windfarm level
cf = metrics.capacity_factor(which="net", frequency="project", by="windfarm")
print(f"  Net Capacity Factor: {cf:.2f}%")

cf = metrics.capacity_factor(which="gross", frequency="project", by="windfarm")
print(f"Gross Capacity Factor: {cf:.2f}%")

  Net Capacity Factor: 0.43%
Gross Capacity Factor: 0.48%

In [9]:

# Project total at the turbine level
metrics.capacity_factor(which="net", frequency="project", by="turbine")

Out[9]:

	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	0.41	0.46	0.41	0.44	0.46	0.42	0.38	0.33	0.46	0.37	0.41	0.40	0.46	0.41	0.46	0.38	0.41	0.46	0.46	0.44	0.46	0.41	0.43	0.41	0.32	0.47	0.47	0.47	0.47	0.40	0.47	0.42	0.46	0.35	0.46	0.47	0.46	0.42	0.39	0.45	0.40	0.35	0.46	0.46	0.37	0.46	0.39	0.40	0.46	0.39	0.38	0.43	0.46	0.41	0.46	0.43	0.46	0.46	0.44	0.46	0.42	0.44	0.39	0.46	0.46	0.46	0.42	0.46	0.21	0.44	0.46	0.47	0.41	0.46	0.46	0.40	0.47	0.44	0.38	0.46	0.41	0.47	0.46	0.45	0.46

In [10]:

# Project annual totals at the windfarm level
metrics.capacity_factor(which="net", frequency="annual", by="windfarm")

Out[10]:

	windfarm
year
2003	0.40
2004	0.41
2005	0.44
2006	0.46
2007	0.48
2008	0.45
2009	0.40
2010	0.37
2011	0.45
2012	0.44

In [11]:

# Project monthly totals at the windfarm level
metrics.capacity_factor(which="net", frequency="monthly", by="windfarm")

Out[11]:

	windfarm
month
1	0.57
2	0.47
3	0.46
4	0.38
5	0.36
6	0.30
7	0.31
8	0.34
9	0.43
10	0.48
11	0.53
12	0.52

In [12]:

# Project month-by-year totals at the windfarm level
# NOTE: This is limited to the first two years for cleanliness of the notebook
metrics.capacity_factor(which="net", frequency="month-year", by="windfarm").head(24)

Out[12]:

		windfarm
year	month
2003	1	0.63
	2	0.37
	3	0.38
	4	0.52
	5	0.34
	6	0.31
	7	0.30
	8	0.29
	9	0.27
	10	0.41
	11	0.53
	12	0.54
2004	1	0.52
	2	0.49
	3	0.46
	4	0.35
	5	0.26
	6	0.34
	7	0.24
	8	0.37
	9	0.52
	10	0.51
	11	0.44
	12	0.44

Task Completion Rate¶

Here, we will go through the various input definitions to get the task completion rates. The inputs are very similar to that of the availability calculation.

which options:

scheduled: scheduled maintenance only (classified as maintenace tasks in inputs)
unscheduled: unscheduled maintenance only (classified as failure events in inputs)
both:

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

In [13]:

# Project total at the whole windfarm level
total = metrics.task_completion_rate(which="scheduled", frequency="project")
print(f"  Scheduled Task Completion Rate: {total * 100:.0f}%")

total = metrics.task_completion_rate(which="unscheduled", frequency="project")
print(f"Unscheduled Task Completion Rate: {total * 100:.0f}%")

total = metrics.task_completion_rate(which="both", frequency="project")
print(f"    Overall Task Completion Rate: {total * 100:.0f}%")

  Scheduled Task Completion Rate: 95%
Unscheduled Task Completion Rate: 100%
    Overall Task Completion Rate: 100%

In [14]:

# Project annual totals at the windfarm level
metrics.task_completion_rate(which="both", frequency="annual")

Out[14]:

	year	Completion Rate
0	2003	0.99
1	2004	0.95
2	2005	0.99
3	2006	0.98
4	2007	0.98
5	2008	1.01
6	2009	1.02
7	2010	1.02
8	2011	1.01
9	2012	1.03

In [15]:

# Project monthly totals at the windfarm level
metrics.task_completion_rate(which="both", frequency="monthly")

Out[15]:

	year	Completion Rate
0	1	0.85
1	2	0.99
2	3	1.00
3	4	1.03
4	5	1.01
5	6	1.05
6	7	1.00
7	8	1.03
8	9	1.00
9	10	1.01
10	11	0.95
11	12	1.01

In [16]:

# Project month-by-year totals at the windfarm level
# NOTE: This is limited to the first two years for cleanliness of the notebook
metrics.task_completion_rate(which="both", frequency="month-year").head(24)

Out[16]:

	year	month	Completion Rate
0	2003	1	0.84
1	2003	2	1.14
2	2003	3	0.93
3	2003	4	1.00
4	2003	5	1.00
5	2003	6	1.03
6	2003	7	0.97
7	2003	8	1.00
8	2003	9	0.95
9	2003	10	0.93
10	2003	11	1.03
11	2003	12	0.98
12	2004	1	0.51
13	2004	2	1.08
14	2004	3	1.07
15	2004	4	1.15
16	2004	5	1.11
17	2004	6	1.04
18	2004	7	0.97
19	2004	8	1.02
20	2004	9	1.11
21	2004	10	1.04
22	2004	11	0.91
23	2004	12	1.10

Equipment Costs¶

Here, we will go through the various input definitions to get the equipment cost data.

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

by_equipment options:

True: computed across all equipment used
False: computed for each piece of equipment

In [17]:

# Project total at the whole windfarm level
total = metrics.equipment_costs(frequency="project", by_equipment=False)
print(f"Project total: ${total / metrics.project_capacity:,.2f}/MW")

Project total: $554,807.68/MW

In [18]:

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

Out[18]:

	Crew Transfer Vessel 1	Crew Transfer Vessel 2	Crew Transfer Vessel 3	Field Support Vessel	Heavy Lift Vessel
0	6,393,260.42	6,393,260.42	6,393,260.42	2,660,000.00	111,314,062.50

In [19]:

# Project annual totals at the windfarm level
metrics.equipment_costs(frequency="annual", by_equipment=False)

Out[19]:

	year	equipment_cost
0	2003	11,324,406.25
1	2004	13,729,687.50
2	2005	12,893,187.50
3	2006	13,874,437.50
4	2007	14,324,437.50
5	2008	11,629,687.50
6	2009	12,905,687.50
7	2010	13,205,687.50
8	2011	13,574,437.50
9	2012	15,692,187.50

In [20]:

# Project monthly totals at the equipment level
metrics.equipment_costs(frequency="monthly", by_equipment=True)

Out[20]:

	month	Crew Transfer Vessel 1	Crew Transfer Vessel 2	Crew Transfer Vessel 3	Field Support Vessel	Heavy Lift Vessel
0	1	543,010.42	543,010.42	543,010.42	0.00	0.00
1	2	495,250.00	495,250.00	495,250.00	0.00	0.00
2	3	542,500.00	542,500.00	542,500.00	0.00	0.00
3	4	525,000.00	525,000.00	525,000.00	0.00	0.00
4	5	542,500.00	542,500.00	542,500.00	0.00	36,028,125.00
5	6	525,000.00	525,000.00	525,000.00	2,670,291.67	0.00
6	7	542,500.00	542,500.00	542,500.00	0.00	35,207,812.50
7	8	542,500.00	542,500.00	542,500.00	0.00	0.00
8	9	525,000.00	525,000.00	525,000.00	0.00	38,954,687.50
9	10	542,500.00	542,500.00	542,500.00	0.00	1,123,437.50
10	11	525,000.00	525,000.00	525,000.00	0.00	0.00
11	12	542,500.00	542,500.00	542,500.00	0.00	0.00

In [21]:

# Project month-by-year totals at the equipment level
# NOTE: This is limited to the two years only
metrics.equipment_costs(frequency="month-year", by_equipment=True).head(24)

Out[21]:

	year	month	Crew Transfer Vessel 1	Crew Transfer Vessel 2	Crew Transfer Vessel 3	Field Support Vessel	Heavy Lift Vessel
0	2003	1	54,760.42	54,760.42	54,760.42	0.00	0.00
1	2003	2	49,000.00	49,000.00	49,000.00	0.00	0.00
2	2003	3	54,250.00	54,250.00	54,250.00	0.00	0.00
3	2003	4	52,500.00	52,500.00	52,500.00	0.00	0.00
4	2003	5	54,250.00	54,250.00	54,250.00	0.00	3,570,312.50
5	2003	6	52,500.00	52,500.00	52,500.00	266,000.00	0.00
6	2003	7	54,250.00	54,250.00	54,250.00	0.00	3,543,750.00
7	2003	8	54,250.00	54,250.00	54,250.00	0.00	0.00
8	2003	9	52,500.00	52,500.00	52,500.00	0.00	2,820,312.50
9	2003	10	54,250.00	54,250.00	54,250.00	0.00	0.00
10	2003	11	52,500.00	52,500.00	52,500.00	0.00	0.00
11	2003	12	54,250.00	54,250.00	54,250.00	0.00	0.00
12	2004	1	54,250.00	54,250.00	54,250.00	0.00	0.00
13	2004	2	50,750.00	50,750.00	50,750.00	0.00	0.00
14	2004	3	54,250.00	54,250.00	54,250.00	0.00	0.00
15	2004	4	52,500.00	52,500.00	52,500.00	0.00	0.00
16	2004	5	54,250.00	54,250.00	54,250.00	0.00	2,970,312.50
17	2004	6	52,500.00	52,500.00	52,500.00	266,000.00	0.00
18	2004	7	54,250.00	54,250.00	54,250.00	0.00	3,120,312.50
19	2004	8	54,250.00	54,250.00	54,250.00	0.00	0.00
20	2004	9	52,500.00	52,500.00	52,500.00	0.00	5,000,000.00
21	2004	10	54,250.00	54,250.00	54,250.00	0.00	451,562.50
22	2004	11	52,500.00	52,500.00	52,500.00	0.00	0.00
23	2004	12	54,250.00	54,250.00	54,250.00	0.00	0.00

Service Equipment Utilization Rate¶

Here, we will go through the various input definitions to get the service equipment utiliztion rates.

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis

In [22]:

# Project totals at the project level
total = metrics.service_equipment_utilization(frequency="project")
total

Out[22]:

	Crew Transfer Vessel 1	Crew Transfer Vessel 2	Crew Transfer Vessel 3	Field Support Vessel	Heavy Lift Vessel
0	0.96	0.96	0.96	0.91	0.99

In [23]:

# Annualized project totals
total = metrics.service_equipment_utilization(frequency="annual")
total

Out[23]:

	Crew Transfer Vessel 1	Crew Transfer Vessel 2	Crew Transfer Vessel 3	Field Support Vessel	Heavy Lift Vessel
year
2003	0.64	0.63	0.65	0.10	0.89
2004	1.00	1.00	1.00	1.00	1.00
2005	1.00	1.00	1.00	1.00	1.00
2006	1.00	1.00	1.00	1.00	1.00
2007	1.00	1.00	1.00	1.00	1.00
2008	1.00	1.00	1.00	1.00	1.00
2009	1.00	1.00	1.00	1.00	1.00
2010	1.00	1.00	1.00	1.00	1.00
2011	1.00	1.00	1.00	1.00	1.00
2012	1.00	1.00	1.00	1.00	1.00

Labor Costs¶

Here, we will go through the various input definitions to get the labor cost data.

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

by_type options:

True: computed across each labor type
False: computed for both labor types used

In [24]:

# Project total at the whole windfarm level
total = metrics.labor_costs(frequency="project", by_type=False)
print(f"Project total: ${total / metrics.project_capacity:,.2f}/MW")

Project total: $0.00/MW

In [25]:

# Project totals for each type of labor
# NOTE: Only salaried labor was defined for thesese analyses
metrics.labor_costs(frequency="project", by_type=True)

Out[25]:

	hourly_labor_cost	salary_labor_cost	total_labor_cost
0	0.00	0.00	0.00

In [26]:

# Project annual totals for all labor
metrics.labor_costs(frequency="annual", by_type=False)

Out[26]:

	year	total_labor_cost
0	2003	0.00
1	2004	0.00
2	2005	0.00
3	2006	0.00
4	2007	0.00
5	2008	0.00
6	2009	0.00
7	2010	0.00
8	2011	0.00
9	2012	0.00

In [27]:

# Project monthly totals for all labor
metrics.labor_costs(frequency="monthly", by_type=False)

Out[27]:

	month	total_labor_cost
0	1	0.00
1	2	0.00
2	3	0.00
3	4	0.00
4	5	0.00
5	6	0.00
6	7	0.00
7	8	0.00
8	9	0.00
9	10	0.00
10	11	0.00
11	12	0.00

In [28]:

# Project month-by-year totals for all labor
# NOTE: This is limited to the first two years only
metrics.labor_costs(frequency="month-year", by_type=False).head(24)

Out[28]:

	year	month
0	2003	1
1	2003	2
2	2003	3
3	2003	4
4	2003	5
5	2003	6
6	2003	7
7	2003	8
8	2003	9
9	2003	10
10	2003	11
11	2003	12
12	2004	1
13	2004	2
14	2004	3
15	2004	4
16	2004	5
17	2004	6
18	2004	7
19	2004	8
20	2004	9
21	2004	10
22	2004	11
23	2004	12

Equipment and Labor Costs¶

Here, we will go through the various input definitions to get the equipment and labor cost data broken out by expense categories.

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

by_category options:

True: computed across as equipment, plus each labor type, plus totals
False: computed as single total

NOTE: For this breakdown the expense category (reason) is distributed across the rows in addition to time.¶

reason definitions:

Maintenance: routine maintenance
Repair: unscheduled maintenance, ranging from inspections to replacements
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 of the operating hours of the windfarm

In [29]:

# Project totals
metrics.equipment_labor_cost_breakdowns(frequency="project", by_category=False)

Out[29]:

	reason	total_cost
0	Maintenance	1,854,617.19
1	Repair	26,326,622.40
2	Mobilization	15,000,000.00
3	Weather Delay	18,286,325.52
4	No Requests	3,365,640.62
5	Not in Shift	34,934,877.60
6	NaN	31,855,500.00

In [30]:

# Project totals by each category
metrics.equipment_labor_cost_breakdowns(frequency="project", by_category=True)

Out[30]:

	reason	equipment_cost	total_cost
0	Maintenance	1,854,617.19	1,854,617.19
1	Repair	26,326,622.40	26,326,622.40
2	Mobilization	15,000,000.00	15,000,000.00
3	Weather Delay	18,286,325.52	18,286,325.52
4	No Requests	3,365,640.62	3,365,640.62
5	Not in Shift	34,934,877.60	34,934,877.60
6	NaN	31,855,500.00	31,855,500.00

In [31]:

# Project annual totals
# NOTE: This is limited to the first two years
metrics.equipment_labor_cost_breakdowns(frequency="annual", by_category=False).head(10)

Out[31]:

	year	reason	total_cost
0	2003	Maintenance	35,145.83
1	2003	Repair	2,411,268.23
2	2003	Mobilization	1,500,000.00
3	2003	Weather Delay	923,981.77
4	2003	No Requests	1,588,348.96
5	2003	Not in Shift	2,753,312.50
6	2003	NaN	1,985,875.00
7	2004	Maintenance	213,135.42
8	2004	Repair	2,838,408.85
9	2004	Mobilization	1,500,000.00

In [32]:

# Project monthly totals
# NOTE: This is limited to the first two years
metrics.equipment_labor_cost_breakdowns(frequency="monthly", by_category=False).head(10)

Out[32]:

	month	reason	total_cost
0	1	Maintenance	103,723.96
1	1	Repair	477,695.31
2	1	Mobilization	0.00
3	1	Weather Delay	155,002.60
4	1	No Requests	50,604.17
5	1	Not in Shift	150,682.29
6	1	NaN	661,500.00
7	2	Maintenance	105,437.50
8	2	Repair	464,315.10
9	2	Mobilization	0.00

In [33]:

# Project month-by-year totals
# NOTE: This is limited to the first two years
metrics.equipment_labor_cost_breakdowns(frequency="month-year", by_category=False).head(20)

Out[33]:

	year	month	reason	total_cost
0	2003	1	Maintenance	0.00
1	2003	1	Repair	43,604.17
2	2003	1	Mobilization	0.00
3	2003	1	Weather Delay	16,369.79
4	2003	1	No Requests	50,604.17
5	2003	1	Not in Shift	2,460.94
6	2003	1	NaN	49,000.00
7	2003	2	Maintenance	0.00
8	2003	2	Repair	54,085.94
9	2003	2	Mobilization	0.00
10	2003	2	Weather Delay	10,445.31
11	2003	2	No Requests	26,304.69
12	2003	2	Not in Shift	1,859.38
13	2003	2	NaN	51,625.00
14	2003	3	Maintenance	0.00
15	2003	3	Repair	48,981.77
16	2003	3	Mobilization	0.00
17	2003	3	Weather Delay	6,635.42
18	2003	3	No Requests	66,171.88
19	2003	3	Not in Shift	3,591.15

Component¶

Here, we will go through the various input definitions to get the component cost data broken out by various categories.

NOTE: It should be noted that the the component costs will not sum up to the whole project operations costs because of delays that are not associated with any repair or maintenance task, such as no requests needing to be processed.

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

by_category options:

True: computed across each cost category (includes total)
False: computed as single total

by_action options:

True: computed by each of "repair", "maintenance", and "delay"
False: computed as single total

NOTE: For this breakdown the expense category (reason) is distributed across the rows in addition to time.¶

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

In [34]:

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

Out[34]:

	component	total_cost
0	cable	0.00
1	drive_train	0.00
2	electrical_system	0.00
3	electronic_control	0.00
4	gearbox	0.00
5	generator	149,928,671.88
6	hydraulic_system	0.00
7	mechanical_brake	0.00
8	rotor_blades	0.00
9	rotor_hub	0.00
10	sensors	0.00
11	supporting_structure	0.00
12	transformer	0.00
13	yaw_system	0.00

In [35]:

# Project totals by each category and action type
metrics.component_costs(frequency="project", by_category=True, by_action=True)

Out[35]:

	component	action	equipment_cost	total_cost
0	generator	delay	85,075,171.88	85,075,171.88
1	generator	maintenance	1,854,617.19	1,854,617.19
2	generator	repair	26,326,622.40	26,326,622.40

In [36]:

# Project annual totals by category
# NOTE: This is limited to the first two years
metrics.component_costs(frequency="annual", by_category=True, by_action=False).head(28)

Out[36]:

	year	component	materials_cost	equipment_cost	total_cost
0	2003	cable	0.00	0.00	0.00
1	2003	drive_train	0.00	0.00	0.00
2	2003	electrical_system	0.00	0.00	0.00
3	2003	electronic_control	0.00	0.00	0.00
4	2003	gearbox	0.00	0.00	0.00
5	2003	generator	2,410,000.00	8,234,526.04	10,644,526.04
6	2003	hydraulic_system	0.00	0.00	0.00
7	2003	mechanical_brake	0.00	0.00	0.00
8	2003	rotor_blades	0.00	0.00	0.00
9	2003	rotor_hub	0.00	0.00	0.00
10	2003	sensors	0.00	0.00	0.00
11	2003	supporting_structure	0.00	0.00	0.00
12	2003	transformer	0.00	0.00	0.00
13	2003	yaw_system	0.00	0.00	0.00
14	2004	cable	0.00	0.00	0.00
15	2004	drive_train	0.00	0.00	0.00
16	2004	electrical_system	0.00	0.00	0.00
17	2004	electronic_control	0.00	0.00	0.00
18	2004	gearbox	0.00	0.00	0.00
19	2004	generator	4,147,000.00	12,115,885.42	16,262,885.42
20	2004	hydraulic_system	0.00	0.00	0.00
21	2004	mechanical_brake	0.00	0.00	0.00
22	2004	rotor_blades	0.00	0.00	0.00
23	2004	rotor_hub	0.00	0.00	0.00
24	2004	sensors	0.00	0.00	0.00
25	2004	supporting_structure	0.00	0.00	0.00
26	2004	transformer	0.00	0.00	0.00
27	2004	yaw_system	0.00	0.00	0.00

In [37]:

# Project monthly totals
# NOTE: This is limited to the first two months
metrics.component_costs(frequency="monthly", by_category=True, by_action=False).head(28)

Out[37]:

	month	component	materials_cost	equipment_cost	total_cost
0	1	cable	0.00	0.00	0.00
1	1	drive_train	0.00	0.00	0.00
2	1	electrical_system	0.00	0.00	0.00
3	1	electronic_control	0.00	0.00	0.00
4	1	gearbox	0.00	0.00	0.00
5	1	generator	927,500.00	1,576,895.83	2,504,395.83
6	1	hydraulic_system	0.00	0.00	0.00
7	1	mechanical_brake	0.00	0.00	0.00
8	1	rotor_blades	0.00	0.00	0.00
9	1	rotor_hub	0.00	0.00	0.00
10	1	sensors	0.00	0.00	0.00
11	1	supporting_structure	0.00	0.00	0.00
12	1	transformer	0.00	0.00	0.00
13	1	yaw_system	0.00	0.00	0.00
14	2	cable	0.00	0.00	0.00
15	2	drive_train	0.00	0.00	0.00
16	2	electrical_system	0.00	0.00	0.00
17	2	electronic_control	0.00	0.00	0.00
18	2	gearbox	0.00	0.00	0.00
19	2	generator	877,500.00	1,459,445.31	2,336,945.31
20	2	hydraulic_system	0.00	0.00	0.00
21	2	mechanical_brake	0.00	0.00	0.00
22	2	rotor_blades	0.00	0.00	0.00
23	2	rotor_hub	0.00	0.00	0.00
24	2	sensors	0.00	0.00	0.00
25	2	supporting_structure	0.00	0.00	0.00
26	2	transformer	0.00	0.00	0.00
27	2	yaw_system	0.00	0.00	0.00

In [38]:

# Project month-by-year totals
# NOTE: This is limited to the first two months
metrics.component_costs(frequency="month-year", by_category=True, by_action=False).head(28)

Out[38]:

	year	month	component	materials_cost	equipment_cost	total_cost
0	2003	1	cable	0.00	0.00	0.00
1	2003	1	drive_train	0.00	0.00	0.00
2	2003	1	electrical_system	0.00	0.00	0.00
3	2003	1	electronic_control	0.00	0.00	0.00
4	2003	1	gearbox	0.00	0.00	0.00
5	2003	1	generator	33,500.00	112,145.83	145,645.83
6	2003	1	hydraulic_system	0.00	0.00	0.00
7	2003	1	mechanical_brake	0.00	0.00	0.00
8	2003	1	rotor_blades	0.00	0.00	0.00
9	2003	1	rotor_hub	0.00	0.00	0.00
10	2003	1	sensors	0.00	0.00	0.00
11	2003	1	supporting_structure	0.00	0.00	0.00
12	2003	1	transformer	0.00	0.00	0.00
13	2003	1	yaw_system	0.00	0.00	0.00
14	2003	2	cable	0.00	0.00	0.00
15	2003	2	drive_train	0.00	0.00	0.00
16	2003	2	electrical_system	0.00	0.00	0.00
17	2003	2	electronic_control	0.00	0.00	0.00
18	2003	2	gearbox	0.00	0.00	0.00
19	2003	2	generator	53,000.00	120,695.31	173,695.31
20	2003	2	hydraulic_system	0.00	0.00	0.00
21	2003	2	mechanical_brake	0.00	0.00	0.00
22	2003	2	rotor_blades	0.00	0.00	0.00
23	2003	2	rotor_hub	0.00	0.00	0.00
24	2003	2	sensors	0.00	0.00	0.00
25	2003	2	supporting_structure	0.00	0.00	0.00
26	2003	2	transformer	0.00	0.00	0.00
27	2003	2	yaw_system	0.00	0.00	0.00

Fixed Cost Impacts¶

Here, we will go through the various input definitions to get the fixed cost data

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis

resolution options:

high: computed across the lowest itemized cost levels
medium: computed across overarching cost levels
low: computed as single total

In [39]:

# The resolution hierarchy for fixed 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']}}

In [40]:

# Project totals at the highest level
metrics.project_fixed_costs(frequency="project", resolution="low")

Out[40]:

	operations
0	16,000,080.00

In [41]:

# Project totals at the medium level
metrics.project_fixed_costs(frequency="project", resolution="medium")

Out[41]:

	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	16,000,080.00

In [42]:

# Project totals at the lowest level
metrics.project_fixed_costs(frequency="project", resolution="high")

Out[42]:

	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	16,000,080.00

In [43]:

# Project annualized totals at the medium level
metrics.project_fixed_costs(frequency="annual", resolution="medium")

Out[43]:

	year	labor
0	2003	1,600,008.00
1	2004	1,600,008.00
2	2005	1,600,008.00
3	2006	1,600,008.00
4	2007	1,600,008.00
5	2008	1,600,008.00
6	2009	1,600,008.00
7	2010	1,600,008.00
8	2011	1,600,008.00
9	2012	1,600,008.00

Process Times¶

There are no inputs for the process timing as it is a slow calculation, so aggregation is left to the user for now. The results corresond to the number of hours required to complete any of the repair or maintenance activities.

In [44]:

# Project totals at the project level
total = metrics.process_times()
total

Out[44]:

	time_to_completion	process_time	downtime	N
category
annual service	4,262,671.75	65,194.50	56,590.75	419.00
major repair	81,616.83	1,617.50	1,256.50	22.00
major replacement	518,860.50	15,277.50	518,860.50	64.00
manual reset	606,657.98	130,290.00	13,463.50	5,286.00
medium repair	15,102.59	11,756.50	7,572.50	202.00
minor repair	97,823.15	48,695.50	5,250.50	2,040.00

Power Production¶

Here, we will go through the various input definitions to get the power production data.

frequency options:

project: computed across the whole simulation
annual: computed on a yearly basis
monthly: computed across years on a monthly basis
month-year: computed on a month-by-year basis

by_turbine options:

True: computed for each turbines
False: computed for the whole windfarm

In [45]:

# Project total at the whole windfarm level
total = metrics.power_production(frequency="project", by_turbine=False)
total

Out[45]:

	windfarm
Project Energy Production (kWh)	9,001,355,968.50

In [46]:

# Project totals at the turbine level
metrics.power_production(frequency="project", by_turbine=True)

Out[46]:

	windfarm	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
Project Energy Production (kWh)	9,001,355,968.50	107,648,074.50	121,862,554.50	108,545,358.00	115,582,440.50	121,498,227.00	110,684,220.00	98,773,519.50	86,266,014.00	121,090,393.50	98,278,773.00	108,253,191.00	105,014,271.00	121,391,810.00	107,783,308.00	122,070,781.00	98,771,873.00	107,585,524.50	121,037,791.50	122,830,072.00	121,679,370.00	114,810,583.50	121,888,160.50	108,344,876.00	111,933,539.00	108,821,716.50	85,355,986.50	122,602,253.50	122,277,689.00	121,108,753.00	90,747,151.00	121,538,637.50	121,847,805.00	109,362,448.00	101,526,570.00	118,034,699.00	106,319,687.50	92,098,572.50	121,902,836.00	121,258,811.00	96,533,082.50	121,427,126.00	103,375,828.50	104,669,155.50	122,002,582.00	103,837,790.50	100,238,075.50	111,789,492.00	122,054,455.50	107,250,160.00	120,537,318.50	113,435,686.00	122,044,561.00	122,017,771.50	115,627,282.00	121,759,995.50	111,153,907.00	115,810,026.00	103,802,030.50	121,741,656.50	120,408,230.50	121,364,747.50	110,311,047.50	121,690,606.00	54,580,838.00	116,883,550.00	121,313,591.50	122,615,135.00	106,711,468.50	121,292,899.00	121,725,785.00	105,140,010.00	122,485,041.50	114,965,531.50	99,102,445.50	120,326,432.00	107,756,572.00	122,564,419.00	120,854,133.50	117,721,292.00	122,005,869.00

In [47]:

# Project annual totals for the windfarm
metrics.power_production(frequency="annual", by_turbine=False)

Out[47]:

	windfarm
year
2003	847,795,441.50
2004	862,264,163.00
2005	926,377,047.50
2006	923,660,836.00
2007	1,005,666,408.00
2008	953,597,820.50
2009	846,267,173.50
2010	768,713,430.50
2011	941,257,651.00
2012	925,755,997.00

In [48]:

# Project monthly totals for the windfarm
metrics.power_production(frequency="monthly", by_turbine=False)

Out[48]:

	windfarm
month
1	1,011,225,579.50
2	760,740,883.00
3	828,317,025.00
4	652,647,978.00
5	634,985,385.50
6	515,984,660.00
7	544,902,601.00
8	600,927,882.00
9	745,058,360.00
10	857,886,952.00
11	920,599,360.00
12	928,079,302.50

In [49]:

# Project month-by-year totals for the windfarm
# NOTE: This is limited to the first two years only
metrics.power_production(frequency="month-year", by_turbine=False).head(24)

Out[49]:

		windfarm
year	month
2003	1	110,667,009.50
	2	58,391,902.00
	3	66,318,208.00
	4	90,270,200.50
	5	60,314,461.50
	6	52,732,948.50
	7	53,214,108.00
	8	51,160,175.50
	9	46,240,076.00
	10	73,988,741.00
	11	91,135,362.00
	12	93,362,249.00
2004	1	92,113,370.50
	2	79,299,275.50
	3	82,033,098.00
	4	59,756,310.00
	5	47,073,507.00
	6	58,635,618.50
	7	42,550,087.00
	8	65,255,774.50
	9	89,560,072.00
	10	90,536,295.00
	11	76,000,725.50
	12	79,450,029.50

PySAM-Powered Results¶

For a number of project financial metrics, the PySAM library is utilized.

NOTE: If a "SAM_settings" file is not provided to the simulation, then the following metrics will not be able to be calculated and will raise a `NotImplementedError`.

With the above warning in mind, the appropriate simulation outputs are provided as inputs to PySAM upon initialization to ensure all values are aligned.

Net Present Value (NPV)¶

In [50]:

try:
    npv = metrics.pysam_npv()
    print(f"NPV: ${npv:,.0f}")
except NotImplementedError as e:
    print(e)

No SAM inputs were provided, and 'pysam_npv()' cannot be calculated!

Real Levelized Cost of Energy (LCOE)¶

In [51]:

try:
    lcoe = metrics.pysam_lcoe_real()
    print(f"Real LCOE: ${lcoe:,.2f}/kW")
except NotImplementedError as e:
    print(e)

No SAM inputs were provided, and 'pysam_lcoe_real()' cannot be calculated!

Nominal Levelized Cost of Energy (LCOE)¶

In [52]:

try:
    lcoe = metrics.pysam_lcoe_nominal()
    print(f"Nominal LCOE: ${lcoe:,.2f}/kW")
except NotImplementedError as e:
    print(e)

No SAM inputs were provided, and 'pysam_lcoe_nominal()' cannot be calculated!

After-tax Internal Return Rate (IRR)¶

In [53]:

try:
    npv = metrics.pysam_irr()
    print(f"IRR: {npv:,.1f}%")
except NotImplementedError as e:
    print(e)

No SAM inputs were provided, and 'pysam_irr()' cannot be calculated!

One Data Frame to Rule Them All¶

For this demonstration we will manually load a PySAM settings file and trigger the setup for demonstration purposes, but it should be noted that this practice should be avoided.

In [54]:

SAM_settings = "SAM_Singleowner_defaults.yaml"
metrics.sam_settings = load_yaml(sim.env.data_dir / "windfarm", SAM_settings)
metrics._setup_pysam()

In [55]:

metrics.pysam_all_outputs()

Out[55]:

	Value
Metric
After Tax NPV ($)	24,637,520.25
Nominal LCOE ($/kW)	-0.00
Real LCOE ($/kW)	-0.00
After Tax IRR (%)	13.16

In [56]:

sim.env.cleanup_log_files(log_only=False)

In [ ]: