optimise_heat

Find the financially optimum household heating system replacement with no BER upgrade. The choice is between replacing current system or installing a heat pump.

optimise_heat(
  hli,
  tech_old,
  installation_time,
  house_type,
  storeys,
  construction_year,
  region,
  floor_area,
  eta,
  params,
  include_rebound = FALSE
)

Arguments

hli

the current W/Km2 not upgraded

tech_old

new ber kWh/m2/year

installation_time

year of installation of old tech

house_type

seai house type

storeys

1 or 2+

construction_year

integer

region

region

floor_area

total floor area (m2)

eta

heterogeneous disruption parameter

params

current parameter values

include_rebound

default FALSE

Value

data frame (2 or 1 rows)

Details

The function is used for example in the case of a heating system failure, where a household does not consider heat loss efficiency upgrades.

If current system is a heat pump, choose between a heat pump swap or reversion to gas.

Examples

params <- scenario_params(sD,2026)
optimise_heat(2.3,"oil",2000,"terraced",2,1980,"Munster",90,0.12,params)
#> # A tibble: 2 × 10
#>   tech_old tech_new  new_cost old_cost savings upgrade_cost heating_sys_cost
#>   <chr>    <chr>        <dbl>    <dbl>   <dbl>        <dbl>            <dbl>
#> 1 oil      oil           2024     2332  -0.132            0             2268
#> 2 oil      heat_pump     1965     2332  -0.157            0            12894
#> # ℹ 3 more variables: grant_type <chr>, upgrade_grant <dbl>,
#> #   heat_pump_grant <dbl>
optimise_heat(2.3,"oil",2000,"terraced",2,1980,"Munster",90,0.0,params)
#> # A tibble: 2 × 10
#>   tech_old tech_new  new_cost old_cost savings upgrade_cost heating_sys_cost
#>   <chr>    <chr>        <dbl>    <dbl>   <dbl>        <dbl>            <dbl>
#> 1 oil      oil           2024     2332  -0.132            0             2268
#> 2 oil      heat_pump     1965     2332  -0.157            0            12894
#> # ℹ 3 more variables: grant_type <chr>, upgrade_grant <dbl>,
#> #   heat_pump_grant <dbl>
params <- scenario_params(sD,2026)
optimise_heat(2.4,"oil",2000,"terraced",2,1980,"Munster",90,0.12,params)
#> # A tibble: 2 × 10
#>   tech_old tech_new  new_cost old_cost savings upgrade_cost heating_sys_cost
#>   <chr>    <chr>        <dbl>    <dbl>   <dbl>        <dbl>            <dbl>
#> 1 oil      oil           2093     2426  -0.138            0             2299
#> 2 oil      heat_pump     3913     2426   0.613            0            13166
#> # ℹ 3 more variables: grant_type <chr>, upgrade_grant <dbl>,
#> #   heat_pump_grant <dbl>
optimise_heat(5.4,"oil",2000,"terraced",2,1980,"Munster",90,0.12,params)
#> # A tibble: 2 × 10
#>   tech_old tech_new  new_cost old_cost savings upgrade_cost heating_sys_cost
#>   <chr>    <chr>        <dbl>    <dbl>   <dbl>        <dbl>            <dbl>
#> 1 oil      oil           4150     5256  -0.210            0             3223
#> 2 oil      heat_pump     6987     5256   0.329            0            21323
#> # ℹ 3 more variables: grant_type <chr>, upgrade_grant <dbl>,
#> #   heat_pump_grant <dbl>