ENZ_HIP

Gas network capex

Inventory

Electricity

Renewables quantity

Rooftop solar

Renewable cost red.

Results in short-medium-term elevation in renewable LCOEs

Battery cost reduction

Extent to which GWAP/TWAP penalty varies with increasing penetration of renewable technology

Exogenously specify how much coal is replaced bv biomass

Renewable init. cost

Gas vs. elect cost drivers

Exogenously specify how much coal is replaced bv biomass

Gas Connection costs recovered from consumers - only relevant for price-switching mode

Elec Nwk cost scen

This allows for a specific PLANNED end date for the pipelines to be specified. This results in appropriate accelerated depreciation of any new assets that are commissioned in the intervening years for the purposes of determining pipeline revenue recovery.

Pipe Tx end date

Specifies whether accelerated depreciation for existing assets at the start of the period (being the actual approach the ComCom has adopted) is used.

Green gas

Parameter: Sc_BioCH4_Uptake_ResComPer

Parameter: Sc_GreenGas_Alloc_Pipe

Parameter: Sc_rLPG_cost_supply

Parameter: Sc_rLPG_uptake

Sc_GreenGas_Alloc_LPG

Carbon capture and storage

Parameter: Sc_CCS_GasProd

CCUS urea

CCUS methanol

CCUS geothermal

Misc industrial

Tiwai exit from

Tiwai green anodes from

Green anodes price sens.

Extent to which Steel moves to green steel. Specify electric arc furnace start year and emissions reduction level

Steel exit from

Ability to specify carbon prices, above which Glenbrook will implement green steel functionality (ie, using H2)

Parameter: "Sc_Cement". Extent to which Cement moves to lower-emissions production using biomass and/or tire-derived fuel (TDF).

Exogenous specification of when Kinleith pulp & paper mill implements High Efficiency Recovery Boiler

Waitara Valley methanol plant

Urea decarb. H2

Urea decarb. compression

HFC scenario

Exogenous scenarios of extent to which off-road liquid fuels move to electrification

Demand efficiencies & load mngt

Applies to Space heating and water heating efficiencies

Parameter name: Sc_App_Eff

Food processing eff.

Parameter: Sc_Petro_Eff

Parameter: Sc_Load_Man

Demand projections

Exogenous exit (S or C), flat demand (F) or modelled (M). Seperate parameters for res,com, industry boiler heat and furnace heat

Exogenous exit year (S for steady decrease or C for cliff-edge), flat demand (F) or modelled (M).

Petrochem WTP

New elec load H2

New elec load data centres

Exogenous scenarios of when gas-fired cogen plant switch to boiler-only operation

Specifies whether the future gas demand from petrochem is by assumption (A) or Modelled (M). If by assumption, the parameters below specify the scenarios for when the petrochem trains will close, or whether Waitara Valley re-opens. If modelled, the HIP module closes the trains in such a way that there is enough gas for all remaining pipeline gas users over the period of the projection, for the underlying scenario of petrochem reserve development (specified below).

Policy options

Gas conn ban

Gas ind. consent ban

Parameter: Sc_Subsidy
Value of zero means no subsidy. Current scenario options have the subsidy set to 25%, all of which start immediately. Who receives the subsidy is specified by the scenario numbers as follows:
1) Res appliances
2) Com appliances
3) Ind boiler heat
4) Res & Com
5) Res, Com & Ind

Consent cost red.

due to consenting timelines

Parameter: Sc_ConBan_Coal_Ind

Emissions pricing

Exogenous carbon price

Rate of IA reduction

ETS auction vol & prices

Electricity Allocation Factor

Int. travel in ETS from

Value of 'E' means use the exogenously specified carbon prices in parameters below. Otherwise, - for modelling the ETS type ‘ETS’ - for emissions optimisation, specify the targeting scenario #

Economic drivers

Value of 'E' means use the exogenously specified carbon prices in parameters below. Otherwise, - for modelling the ETS type ‘ETS’ - for emissions optimisation, specify the targeting scenario #

Parameter: Sc_FuGas_Disc_Rate

Value of 'E' means use the exogenously specified carbon prices in parameters below. Otherwise, - for modelling the ETS type ‘ETS’ - for emissions optimisation, specify the targeting scenario #

Threshold pipeline demand

Decom. cost profile

Capex & opex
Consumer connection
System growth
Asset replacement and renewal
Asset relocations
Total reliability, safety and environment
Non-network assets
Decommissioning
Service interruptions, incidents and emergencies
Routine and corrective maintenance and inspection
Asset replacement and renewal
Non-network opex
Decommissioning/Disconnection
Compressor fuel

Project gas conns

Gas network opex

Dx WACC

Tx WACC

Financial model to project GNB revenue requirements

Cost of debt

Change in cost of debt

Elec distribution capex

Inflation

Elec distribution opex

Elec dist. opex /ICP

Connections & sys. growth

Dist. capex recovered

Building Blocks Allowable Revenue

From the FOR sheet row 4911

Source: ? (see His_31.xls)
Total generation by fuel

Source: MBIE energy tables
By sector
HIP For sheet around row 655

Implied heat rate

Historical coal demand

Historical gas demand

Historical liquid fuel dem.

Median hydrology gen

A.k.a EEUD breakdown. Decomposes industrial to dairy, meat, food, wood, cement & line, steel, petro, other industry. Assumptions are in EEUD_Ex tab of His

Decomposes industrial to dairy, meat, food, wood, cement & line, steel, petro, other industry.
Assumptions are in EEUD_Ex tab of His

"For" sheet row 705
OTA2201,BEN2201,HAY2201 (representative)

OTA2201,BEN2201,HAY2201 (representative)

Space heat, Water heat, Cooking, Space cooling, Lighting, Refrigeration, Wash n dry, Electronics Proc heat, Motive power (non-tpt), Pump n Irrig

Decomposes industrial to dairy, meat, food, wood, cement & line, steel, petro, other industry.
Assumptions are in EEUD_Ex tab of His

Population divers

Space heat, Water heat, Cooking, Space cooling, Lighting, Refrigeration, Wash n dry, Electronics Proc heat, Motive power (non-tpt), Pump n Irrig

Δ demand from growth

For sheet approx row 914

Derived from MBIE price data. Source: Bottom of Residential Sales Based Electricity section of Yr tab of His_33

Diesel-PJ-based projection, assuming no electrification

Res, Com, Mining, Construction, OthInd., Dairy, Meat, Ind, Wood, Ag., Fishing, OthAg., Other, Metals, OthFood, Cement

New builds

Building turnover

Years to retrofit buildings

Total buildings

Building retrofits

"For" sheet C1114
Factor to translate building energy intensity improvements to water heating

"For" sheet row 1101

Building energy demand

Res, Com, Mining, Construction, OthInd., Dairy, Meat, Ind, Wood, Ag., Fishing, OthAg., Other, Metals, OthFood, Cement

LnW

Res, Com, Mining, Construction, OthInd., Dairy, Meat, Ind, Wood, Ag., Fishing, OthAg., Other, Metals, OthFood, Cement

Milksolid production

Meat production

Log & pulp price

Gas to biomass fuel factor

Dairy
Wood
Ag.
Other
Other
Refining
Cement
Steel

Res, Com, Mining, Construction, OthInd., Dairy, Meat, Ind, Wood, Ag., Fishing, OthAg., Other, Metals, OthFood, Cement

Steel fuel consumption

Steel % from EAF

Steel % from green H20

Steel produced

Steel elec. consumption

Steel cogen

Inventory emissions

Industrial allocation

Al. elect. demand

Other non-ferrous metals

Cement fuel use

Continues as at the 2016-2020 average

Cement fuel & gas split

Cement E.I.

IPPU emissions

Non-Tpt energy emissions

Cement emissions

Al. emissions

Petro refining emissions

Range name Iter_GasDem_Ar_In

Motunui and Waitara Valley total fuel required for full production, and proportion of gas used for energy

Methanol exo gas dem.

Methanol elec. demand

Methanol production

Gas and electricity requirements for feedstock, compression and co-generation. Atomic weights of molecules. Key reference source: https://www.mbie.govt.nz/dmsdocument/11988-ballance-agri-nutrients-accelerating-renewable-energy-and-energy-efficiency-submission-pdf

Electrolyser efficiency 65%
Electrolyser capacity factor 90%
Gas compressor efficiency 60%
Electric compressor efficiency 90%

Assumes constant demand

Production of H feedstock

Gas demand (PJ)
Biomass demand (PJ)
Electricity demand (GWh) - including amount supplied by cogen

From the FOR sheet row 4911

Gas demand (PJ)
Biomass demand (PJ)
Electricity demand (GWh) - including amount supplied by cogen

Food proc. growth

Food proc. fuel

LPG demand

From the FOR sheet row 4911

Δ due to efficiency

Food fuel pre-switch

Food coal cost

Food biomass cost

Prop'n, if switching, going to electricity - prior to biomass resource constraints
=MAX(0,MIN(1,1-(AVERAGE(elec_cost_diff)+SQbias)/(variation_factor*2)))
SQBias (a.k.a. variation factor) = 5. Price has to be greater than this to switch.

Food elec. cost

ETS module

Carbon price

Boiler heat capex

Appliance cost breakdowns for heating, cooking, boilers, furnaces.
Specifies the COP, COP improvement rate, EOL saving, capex improvement rate, installation and maintenance costs for Res, Com, Ind.

For remaining coal use

A boiler fleet can't be converted overnight. Assumes min 10 years (10% per year)

For remaining coal use

Kinleith upgrade

Wood fuel pre-switch

Wood proc. switching

Effect of demand for service plus energy efficiency on demand relative to previous year

Impr. appliance effic.

For remaining coal use

Wood fuel after switch

Wood emissions

Refridgerants emissions

F-gas projections

Other coal pre-switch

Efficiency improvement

Other coal switching

Years to switch

Other coal remaining

Premium on wholesale $

Other coal cost

Excl. LPG and transport fuels

By sector:
Res
Com
Fishing
OthAg.
Mining
Construction
OthInd

Liquid fuel demand

By sector
Res_switch =IF(RecMarine_Liq_Lag_Flag,
Heavy_truck_prop_vkt_electric-OffRoad_Liq_Lag),
IF(year>=RecMarine_StartYr,
MIN(1,SUM(prev_year_switch,RecMarine_Switch_Rate)),0))
These variables are all in the liquid fuel scenario. Note that RecMarine_Liq_Lag_Flag = false by default.

Tpt module

Prop'n of HT vtk which is from EVs

Res_switch =IF(RecMarine_Liq_Lag_Flag,
Heavy_truck_prop_vkt_electric-OffRoad_Liq_Lag),
IF(year>=RecMarine_StartYr,
MIN(1,SUM(prev_year_switch,RecMarine_Switch_Rate)),0))
These variables are all in the liquid fuel scenario. Note that RecMarine_Liq_Lag_Flag = false by default.

Gas, Coal, Lignite, LPG, BioCH4, Diesel, Petrol, Oil, TDF, Bio_diesel, Jet-A1, Bunker, Wood pellet biomass

For Res, Com, Ind boiler heat (BH) and Ind furnace heat (FH). Applies either a steady decrease to exit year (S) or a sudden drop to zero at exit year (C)
Range "Iter_GasDem_Ar_Out"

% of base demand=IF(historical_year,1,previous_value*(1+efficiency_change))

Part of iteration routine to avoid circular references. Range name Iter_GasDem_Ar_Out

To align with that reported by ComCom
Res & Com 100%
Dairy = 35%
Wood = 65%
Ag = 65%
Ind Other BH = 100%
Ind Other FH = 30%

Small amount by private pipeline

Total Gas Tx (PJ)

Total Gas Dx (PJ)

Gas from new field

=Prev_available_gas + new_gas - prev_demand

Developable gas

Karewa
Toutouwai
Maui East
Other 2

Dynamic petrochem

=1/(1+Sc_FuGas_Disc_Rate)^RTP_ratio*LNG_price

= available_gas/gas_required for non-petro, Bal, Mot1&2, WV

=1/(1+Sc_FuGas_Disc_Rate)^RTP_ratio*LNG_price

=IF(breakeven>LNG_shadow_price,1,0)
Plus additional flag to say whether WV is allowed to reopen

Petro gas demand

Gas demand per ICP

=total_Dx/demand_per_ICP
For Res, Com, Ind

Small amount by private pipeline

=total_Dx/demand_per_ICP
For Res, Com, Ind

Small amount by private pipeline

=total_Dx/demand_per_ICP
For Res, Com, Ind

For modelled demand it assumes a lag of the growth in gas demand for the sector. For exogenous demand it multiplies previous year by the exogenous growth rate
Formula:
=IF(historical_year,historical_demand)*segment_%,
last_year_demand*IF(demand_approach="M",
IF(gas_demand_3_yrs_ago=0,0,gas_demand_2_yrs_ago/gas_demand_3_yrs_ago),
IF(last_year_exogenous_demand>0,exogenous_demand/last_year_exogenous_demand,0)
))

10% for Res, 20% Com

Baseline 2%

=IF(historical_year,historical_LPG,
SUM(LPG_stage_1,
SUM((LPG_in_2022 - LPG_stage_1)*%_staying_with_LPG,
(gas_in_2022-current_year_gas)*%_staying_with_gas)*(1-switch_rate_gas_to_LPG)^(year-2022)))

LPG dem. per ICP

# LPG ICPs

Δ LPG ICPs

Δ LPG connections

Δ LPG disconnections

LPG connections

LPG disconnections

Renewable LPG

PowerGen CCS reduction

Assembles all the CCUS options into an array with capture %, cost and from year

PowerGen CCS cost

Assembles the petrochem efficiency components into an array with % and from year

IPPU emissions after effic.

[2.B.8.a Methanol]
[2.B.1 Ammonia Production]
[Hydrogen Production]

LPG retailcosts

Baseline 2%

Gas & LPG switching impact on elect. and biomass demand

Demand pre-switching

Cumulative TJ of fossil fuel switching to electric = Fossil demand if continued according to demand for service factored by efficiency MINUS projected fossil demand (whether projection is via dynamic switching approach or exogenous projection)

=IF(historical_year,historical_LPG,
SUM(LPG_stage_1,
SUM((LPG_in_2022 - LPG_stage_1)*%_staying_with_LPG,
(gas_in_2022-current_year_gas)*%_staying_with_gas)*(1-switch_rate_gas_to_LPG)^(year-2022)))

Existing elec.demand

New elect. demand

Tpt Module

Road electrificity dem.

Rail electricity dem.

Air elecrticity dem.

Marine elecricity dem.

Off-road motive elect. dem.

New demand from datacentres, e-fuels, and the like

Network losses

Demand for flex

Dx demand

Uncontrolled peak factor

Peak mngt potential

Res, com, ag, dairy, aluminium, cement, steel, other industry by energy use.
Year-to-year, within year, within week, within day.
Source: EEUD

Part of iteration routine to avoid circular references. Range name Iter_Dem4Gen_Ar_In and Iter_Dem4Gen_Ar_Out

Δ Cogen

Committed renewable

Committed gen projects

Cost of new renewables

For a given year, this works how much of the cost-supply curve of potential development for a given technology (eg, uSolar, onWind etc,) has already been developed. This pushes the cost of the next increment of development up the assumed cost-supply curve for the technology)

Extent to which LCOE elevated due to global supply constraints or increased carbon costs feeding through to production costs

The capture rate is the ratio of the generation-weighted average price (GWAP) for the technology to the market time-weighted average price (TWAP)

Extent to which LCOE elevated due to global supply constraints or increased carbon costs feeding through to production costs

Cheapest first

Thermal fuel price

IterDiff_PipePrice_Pgen

Ex carbon gas blend price

Currently zero, placeholder for future functionality development

Green gas

Bio CH4 price

Bio CH4 demand

Bio CH4 cost-supply curve

Bio CH4 potential

Calculate amount of existing renewables (from previous year) plus committed renewables and/or exogenous change in cogen

HIP sheet "In"

Range: RenCost_Ar in Ctrl module

Thermal station costs and flex parameters

Calc efficient level of new renew

Calculate the total MW required of firm capacity to meet the post-hydro demand for flex
Range: Iter_FlexDem_Ar_In

Iterate through different capacity factors until find the threshold capacity factor for NEW OCGT

Threshold 'capacity factor' where EXISTING fossi and new OCGTl cheaper than renewable
Bin offset
% of Bin
% of average GWh demand for post-hydro flex to be met by fossil or DR
% of average MW demand for flex at threshold c.f.
MW of fossil + DR required (excl. reserves requirement)
MW of fossil required (excl. reserves requirement)
Total thermal capacity required, incl. reserves requirement (MW)

TCC e3p Rankine
Formula: =IF(
OR(prev_retired>0,
Year>=exo_retirement_year,
revised_thermal+reserve_requiementprev_retired,0)

Available thermal

Shortfall existing thermal

Shortfall new OCGT

Revised thermal

Renew. needed for non-flex

Efficient pseudo-baseload renew.

=MAX(0.01,Total_efficient_pseudo-baseload_renew-pseudo-baseload_existing_renew)

Existing pseudo-baseload renew.

Determine resultant build

Lead times by gen type

% renew. that can be satisfied

Equivalent baseload capacity

Remaining renew. flex capacity

Range: FlexShp_MWVal_Ar

GWh fossil to meet flex

MW of fossil required - prior to demand response & batteries (excl. reserves requirement)

Total fossil GWh

=MAX(0,additional_thermal_MW_required-efficient_renewable_pseudo-baseload)

Ranges: IterDiff_ElecPrice_In and IterDiff_ElecPrice_Out

Revised TWA $/MWh

Merit order position

Minimised by VB optimisation routine

Used for determining threshold break-even capacity factor of renewables and fossil. Refers to previous year’s mix to break circularities

Fossil gen by type

=proportion_renew_type*MIN(renew_required/1000,renew_that_could_be_built)

Threshold capacity factors

Iterate through different capacity factors until find the threshold capacity factor for EXISTING fossil stations

Iterate through different capacity factors until find the threshold capacity factor for NEW OCGT

=proportion_renew_type*MIN(renew_required/1000,renew_that_could_be_built)

By fuel type and cogen, using mean hydrology

By fuel, cogen and station

HIP For sheet around row 5826

Flex equations

Geo. emissions intensity

Existing geo plant capacity

Geothermal generation

Geothermal CCS reduction

Geothermal emissions