5. Availability curves#

This section details capacity factors used across all TIMES-NZ generation types.

5.1. Dispatchable#

For dispatchable plants, the capacity factor estimate is considered an annual upper bound. However, they are free to generate at different times of year depending on system needs.

Table 80 Dispatchable plant capacity factor assumptions#
Plant Type	Capacity Factor (%)
Biogas	75
Diesel (peakers)	2
Huntly Rankine units	37
Natural gas (CCGT)	65
Natural gas (OCGT)	33

We note that this is a limitation for CCGT plants, as this method allows them too much flexibility in how quickly they might begin or halt generation.

5.2. Baseload#

Geothermal and cogeneration plants are considered “baseload”. This means that their generation within the model is fixed at every time of year and day according to the below assumptions

Table 81 Baseload plant capacity factors#
Plant Type	Capacity Factor (%)
Biomass (cogeneration)	55
Coal (cogeneration)	55
Natural gas (cogeneration)	55
Geothermal (cogeneration)	80
Geothermal (electricity)	88

In the model, these plants will not flex generation up or down depending on demand.

5.3. Solar#

Solar availability factors are generated in the stage-3 electricity workflow from NIWA typical meteorological year (TMY) weather files using the PVWatts model in NREL PySAM, then converted into TIMES-NZ timeslices. The workflow is implemented in solar_prepare_epw.py, solar_run_hourly_profiles.py, and solar_build_curves.py, and the resulting curves are consumed in stage 4 by renewable_curves.py.

Note

All solar availability curves are considered fixed, rather than upper bounds. This means solar will always generate at the listed output in the model, and not flex depending on demand. In stage 4 the rows are written as NCAP_AF with LimType = FX, so the model does not treat solar as a spillable upper bound in the way it treats wind and some hydro constraints.

Data source and preprocessing. The weather input is the MBIE October 2024 present-climate TMY3 release, stored in the repo as data_raw/external_data/niwa/tmy3_epw.tar.gz. The preparation step extracts the EPWs into stage-3 storage and validates that the files contain the expected 18 climate zones, 8760 rows, and NIWA EPW hour fields. EPW format details are documented by SAM[1]. MBIE’s weather-files page identifies this TMY3 release as the current weather-file source for building-energy modelling[2].

PVWatts configuration. Solar simulations use PySAM’s Pvwattsv8 model via the project dependency nrel-pysam = "^7.1" in pyproject.toml, which corresponds to >=7.1.0,<8.0.0. Each solar archetype is simulated as a 1 kW DC system, so the hourly PVWatts output can be used directly as generation_kw_per_kw. We assume that all utility-scale plants in the generation stack are single-axis tracking, and that commercial and industrial distributed solar is bifacial. The following PVWatts settings are applied:

Table 82 Configured solar PVWatts archetypes#
`Tech_TIMES`	Configured description	Array type	Key differences from other archetypes
`SolarDistSmall`	Residential rooftop PV	`fixed_roof_mount`	`Bifaciality = 0`, `Albedo = 0.2`
`SolarDistBifacial`	Commercial fixed bifacial rooftop PV	`fixed_roof_mount`	`Bifaciality = 0.65`, `Albedo = 0.3`
`SolarTrack`	Utility-scale single-axis backtracking bifacial PV	`single_axis_backtracking`	`Bifaciality = 0.65`, `Albedo = 0.2`

All three archetypes currently use:

SystemCapacityKW = 1.0
TiltDeg = 30
AzimuthDeg = 0
LossesPercent = 9.03
InvEffPercent = 96
ModuleType = standard
DcAcRatio = 1.2
Gcr = 0.4
UseWeatherFileAlbedo = False

LossesPercent uses the standard PVWatts/SAM system-losses input[3]. In this workflow it is set to 9.03% so the average generation across regions aligns with the existing EECA Solar Power Calculator.

Conversion to TIMES timeslices. For each solar archetype \(s\), climate zone \(z\), and hour \(h\), PVWatts returns hourly generation \(g_{s,z,h}\) in kW from the 1 kW DC system. The zone-level TIMES availability factor for timeslice \(\tau\) is:

\[AF_{s,z,\tau} = \frac{1}{|H_\tau|}\sum_{h \in H_\tau} g_{s,z,h}\]

where \(H_\tau\) is the set of hours mapped to that TIMES timeslice. In the implementation this is the mean of generation_kw_per_kw over all rows in the timeslice.

The timeslice mapping is the shared project mapping used elsewhere in the electricity workflow:

season: SUM = December-February, FAL = March-May, WIN = June-August, SPR = September-November
day type: WK weekday, WE weekend
time of day: D = 07:00-17:00, P = 18:00, N = all remaining hours, using New Zealand wall-clock time

To assign weekday, weekend, and time-of-day labels consistently, the workflow applies the shared timeslice mapping using the configured TIMES-NZ model base year calendar. The EPW weather files are treated as fixed New Zealand standard time (UTC+12), and each interval-start hour is converted with the Pacific/Auckland timezone before assigning timeslices.

The solar peak timeslice can be much lower than the daytime timeslice, because P is only the 18:00 wall-clock hour, so it captures shoulder-period. SAM notes that its one-axis tracking algorithm assumes a rotation limit of +/-45 degrees from the horizontal[3]. For SolarTrack, the use of single-axis backtracking further reduces late-afternoon output relative to a tracker that follows the sun more aggressively.

Island aggregation and model input. TIMES-NZ uses island-level curves, so the zone-level availability factors are combined with configurable weights from SolarZoneWeights.csv. For island \(i\):

\[AF_{s,i,\tau} = \frac{\sum_{z \in i} w_z AF_{s,z,\tau}}{\sum_{z \in i} w_z}\]

The weights are normalized within each island before aggregation. The current default is equal weighting for every zone within the North Island and every zone within the South Island, so the derived island-level availability factors are simple averages of the zone-level values within each island.

The tables below show the current generated island-level availability factors under the default PVWatts assumptions and default equal zone weights.

Table 83 SolarDistSmall: Residential distributed solar availability factors#
Season	Day Type	Time of Day	North Island	South Island
Autumn	Weekend	Day	32.5%	29.3%
Autumn	Weekend	Night	0.0%	0.1%
Autumn	Weekend	Peak	5.6%	6.1%
Autumn	Weekday	Day	31.6%	28.2%
Autumn	Weekday	Night	0.0%	0.1%
Autumn	Weekday	Peak	4.9%	6.1%
Spring	Weekend	Day	36.9%	39.5%
Spring	Weekend	Night	0.1%	0.3%
Spring	Weekend	Peak	8.9%	13.3%
Spring	Weekday	Day	37.7%	36.0%
Spring	Weekday	Night	0.1%	0.3%
Spring	Weekday	Peak	9.0%	12.1%
Summer	Weekend	Day	43.0%	42.8%
Summer	Weekend	Night	0.5%	1.1%
Summer	Weekend	Peak	20.5%	26.7%
Summer	Weekday	Day	40.6%	39.9%
Summer	Weekday	Night	0.5%	1.0%
Summer	Weekday	Peak	19.7%	24.3%
Winter	Weekend	Day	24.2%	20.7%
Winter	Weekend	Night	0.0%	0.0%
Winter	Weekend	Peak	0.0%	0.0%
Winter	Weekday	Day	24.5%	21.9%
Winter	Weekday	Night	0.0%	0.0%
Winter	Weekday	Peak	0.0%	0.0%
Annual			15.9%	15.2%

Table 84 SolarDistBifacial: Fixed commercial bifacial solar availability factors#
Season	Day Type	Time of Day	North Island	South Island
Autumn	Weekend	Day	34.4%	31.0%
Autumn	Weekend	Night	0.0%	0.1%
Autumn	Weekend	Peak	6.0%	6.6%
Autumn	Weekday	Day	33.5%	29.8%
Autumn	Weekday	Night	0.0%	0.1%
Autumn	Weekday	Peak	5.3%	6.5%
Spring	Weekend	Day	39.3%	41.8%
Spring	Weekend	Night	0.1%	0.3%
Spring	Weekend	Peak	9.9%	14.5%
Spring	Weekday	Day	40.1%	38.3%
Spring	Weekday	Night	0.1%	0.3%
Spring	Weekday	Peak	10.0%	13.2%
Summer	Weekend	Day	46.0%	45.5%
Summer	Weekend	Night	0.7%	1.3%
Summer	Weekend	Peak	22.8%	29.1%
Summer	Weekday	Day	43.5%	42.5%
Summer	Weekday	Night	0.7%	1.2%
Summer	Weekday	Peak	22.0%	26.6%
Winter	Weekend	Day	25.6%	21.8%
Winter	Weekend	Night	0.0%	0.0%
Winter	Weekend	Peak	0.0%	0.0%
Winter	Weekday	Day	25.9%	23.0%
Winter	Weekday	Night	0.0%	0.0%
Winter	Weekday	Peak	0.0%	0.0%
Annual			16.9%	16.2%

Table 85 SolarTrack: Utility-scale tracking solar availability factors#
Season	Day Type	Time of Day	North Island	South Island
Autumn	Weekend	Day	39.5%	35.3%
Autumn	Weekend	Night	0.1%	0.3%
Autumn	Weekend	Peak	12.5%	11.7%
Autumn	Weekday	Day	38.4%	34.0%
Autumn	Weekday	Night	0.1%	0.3%
Autumn	Weekday	Peak	10.5%	11.4%
Spring	Weekend	Day	43.9%	47.2%
Spring	Weekend	Night	0.2%	0.6%
Spring	Weekend	Peak	19.3%	27.1%
Spring	Weekday	Day	45.1%	42.9%
Spring	Weekday	Night	0.3%	0.7%
Spring	Weekday	Peak	19.6%	22.9%
Summer	Weekend	Day	51.1%	50.3%
Summer	Weekend	Night	1.4%	2.4%
Summer	Weekend	Peak	40.4%	45.1%
Summer	Weekday	Day	48.2%	46.7%
Summer	Weekday	Night	1.3%	2.1%
Summer	Weekday	Peak	38.2%	39.7%
Winter	Weekend	Day	29.4%	24.8%
Winter	Weekend	Night	0.0%	0.0%
Winter	Weekend	Peak	0.0%	0.0%
Winter	Weekday	Day	29.7%	26.2%
Winter	Weekday	Night	0.0%	0.0%
Winter	Weekday	Peak	0.0%	0.0%
Annual			19.5%	18.6%

Detailed timeslice values are generated by the workflow and written to data_intermediate/stage_3_scenario_data/electricity/solar_af/timeslices/solar_availability_factors.csv. These generated solar rows replace the static solar rows in RenewableCurves.csv, after which stage 4 converts them into model NCAP_AF records.

5.4. Hydro#

Hydro electricity generation uses a different approach. Here we assume generation follows seasonal patterns, but generation is capable of flexing within these seasonal restrictions. Because average output within a season is fixed, but able to flex within any give timeperiod, the model should, if necessary, lower hydro output when it is not needed. This allows for higher generation at other points in the season while still meeting seasonal constraints.

Table 86 Dispatchable hydro availability assumptions#
Season	North Island	South Island
Summer	45.3%	60.5%
Autumn	49.6%	56.1%
Winter	38.1%	54.0%
Spring	56.9%	64.0%
Annual	47.5%	58.6%

These figures have been extracted from assumptions used for TIMES 2.0. Note that run-of-river hydro is not afforded the same flexibility within the model.

5.5. Wind#

Onshore wind availability curves are based on analysis of the availability of New Zealand’s wind farms since 2020. These are then scaled up slightly to meet a 38% annual average, as we assume future generation may perform better than existing plants as technology improves. We keep the same generation curves for offshore wind, but scaled up across every timeslice to give an annual availability assumption of 50%.

Wind peak modelling

There is a difference between modelled generation during “peak” timeslices, or the highest-demand hour in every day, and the “peak constraint”, which refers to the highest demand in any given year.

In the case of wind, we assume a reasonably high output during peak timeslices, but a lower peak contribution rate. Peak contribution rates are described in more detail in the Technical Parameters section.

Currently, we use the same factors for the North and South Islands.

Table 87 Wind availability factors#
Season	Day Type	Time of Day	Onshore	Offshore
Autumn	Weekend	Day	34.2%	44.9%
Autumn	Weekend	Night	32.6%	42.9%
Autumn	Weekend	Peak	35.8%	47.1%
Autumn	Weekday	Day	33.9%	44.6%
Autumn	Weekday	Night	33.8%	44.5%
Autumn	Weekday	Peak	34.9%	45.9%
Spring	Weekend	Day	43.3%	57.0%
Spring	Weekend	Night	41.1%	54.1%
Spring	Weekend	Peak	43.8%	57.6%
Spring	Weekday	Day	44.6%	58.7%
Spring	Weekday	Night	43.0%	56.5%
Spring	Weekday	Peak	47.2%	62.0%
Summer	Weekend	Day	37.9%	49.9%
Summer	Weekend	Night	35.8%	47.2%
Summer	Weekend	Peak	42.0%	55.3%
Summer	Weekday	Day	36.9%	48.5%
Summer	Weekday	Night	35.8%	47.2%
Summer	Weekday	Peak	39.8%	52.3%
Winter	Weekend	Day	39.2%	51.6%
Winter	Weekend	Night	37.0%	48.7%
Winter	Weekend	Peak	38.2%	50.3%
Winter	Weekday	Day	38.5%	50.6%
Winter	Weekday	Night	38.0%	50.0%
Winter	Weekday	Peak	38.7%	50.9%
Annual			38%	50%