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added portfolio configuration, updated single site config
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@bpulluta
bpulluta committed Aug 30, 2024
1 parent 162dd4f commit ea88205
Showing 1 changed file with 131 additions and 72 deletions.
203 changes: 131 additions & 72 deletions reoptjl/views.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1402,23 +1402,14 @@ def create_custom_table_excel(df, custom_table, calculations, output):
base_percent_format = {'num_format': '0%', 'align': 'center', 'valign': 'center', 'border': 1, 'font_size': 10}
base_currency_format = {'num_format': '$#,##0.00', 'align': 'center', 'valign': 'center', 'border': 1, 'font_size': 10}

# Formula formats using a medium-dark orange
formula_color = '#FF8C00'
formula_format = workbook.add_format({'bg_color': '#FFE599', 'align': 'center', 'valign': 'center', 'border': 1, 'font_color': formula_color, 'font_size': 10, 'italic': True})
formula_percent_format = workbook.add_format({'bg_color': '#FFE599', 'num_format': '0%', 'align': 'center', 'valign': 'center', 'border': 1, 'font_color': formula_color, 'font_size': 10, 'italic': True})
formula_currency_format = workbook.add_format({'bg_color': '#FFE599', 'num_format': '$#,##0.00', 'align': 'center', 'valign': 'center', 'border': 1, 'font_color': formula_color, 'font_size': 10, 'italic': True})
# Formula formats using dark blue background
formula_color = '#F8F8FF'
formula_format = workbook.add_format({'bg_color': '#0B5E90', 'align': 'center', 'valign': 'center', 'border': 1, 'font_color': formula_color, 'font_size': 10, 'italic': True})
formula_percent_format = workbook.add_format({'bg_color': '#0B5E90', 'num_format': '0%', 'align': 'center', 'valign': 'center', 'border': 1, 'font_color': formula_color, 'font_size': 10, 'italic': True})
formula_currency_format = workbook.add_format({'bg_color': '#0B5E90', 'num_format': '$#,##0.00', 'align': 'center', 'valign': 'center', 'border': 1, 'font_color': formula_color, 'font_size': 10, 'italic': True})

# Message format to match formula style
message_format = workbook.add_format({
'bg_color': '#FFE599',
'align': 'center',
'valign': 'center',
'border': 1,
'font_color': formula_color,
'bold': True,
'font_size': 12,
'italic': True
})
message_format = workbook.add_format({'bg_color': '#0B5E90', 'align': 'center','valign': 'center','border': 1,'font_color': formula_color, 'bold': True, 'font_size': 12, 'italic': True })

# Combine row color with cell format, excluding formulas
def get_combined_format(label, row_color, is_formula=False):
Expand Down Expand Up @@ -1462,7 +1453,7 @@ def get_combined_format(label, row_color, is_formula=False):
else:
worksheet.write(row_num + 1, col_num + 1, value, cell_format)

worksheet.merge_range(len(df.index) + 2, 0, len(df.index) + 2, len(df.columns), "Values in orange are formulas. Do not input anything.", message_format)
worksheet.merge_range(len(df.index) + 2, 0, len(df.index) + 2, len(df.columns), "Values in white are formulas. Do not input anything.", message_format)

headers = {header: idx for idx, header in enumerate(df.index)}

Expand Down Expand Up @@ -1498,7 +1489,7 @@ def get_combined_format(label, row_color, is_formula=False):
if missing_keys:
row_idx = headers.get(calc["name"])
if row_idx is not None:
worksheet.write(row_idx + 1, col - 1, "MISSING DATA", error_format)
worksheet.write(row_idx + 1, col - 1, "MISSING REFERENCE IN FORMULA", error_format)
message = f"Cannot calculate '{calc['name']}' because the required fields are missing: {', '.join(missing_keys)} in the Table configuration provided. Update the Table to include {missing_keys}. Writing 'MISSING DATA' instead."
if message not in logged_messages:
logged_messages.add(message)
Expand Down Expand Up @@ -1627,47 +1618,29 @@ def get_combined_format(label, row_color, is_formula=False):
"bau_value": lambda df: "",
"scenario_value": lambda df: safe_get(df, "inputs.Meta.description", "None provided")
},
{
"label": "Site Location",
"key": "site_lat_long",
"bau_value": lambda df: f"({safe_get(df, 'inputs.Site.latitude')}, {safe_get(df, 'inputs.Site.longitude')})",
"scenario_value": lambda df: f"({safe_get(df, 'inputs.Site.latitude')}, {safe_get(df, 'inputs.Site.longitude')})"
},
{
"label": "Site Address",
"key": "site_address",
"bau_value": lambda df: safe_get(df, "inputs.Meta.address", "None provided"),
"scenario_value": lambda df: safe_get(df, "inputs.Meta.address", "None provided")
},
{
"label": "PV Size (kW)",
"key": "pv_size",
"bau_value": lambda df: safe_get(df, "outputs.PV.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.PV.size_kw")
},
{
"label": "Wind Size (kW)",
"key": "wind_size",
"bau_value": lambda df: safe_get(df, "outputs.Wind.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.Wind.size_kw")
},
{
"label": "CHP Size (kW)",
"key": "chp_size",
"bau_value": lambda df: safe_get(df, "outputs.CHP.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.CHP.size_kw")
"label": "Site Location",
"key": "site_lat_long",
"bau_value": lambda df: f"({safe_get(df, 'inputs.Site.latitude')}, {safe_get(df, 'inputs.Site.longitude')})",
"scenario_value": lambda df: f"({safe_get(df, 'inputs.Site.latitude')}, {safe_get(df, 'inputs.Site.longitude')})"
},
{
"label": "PV Total Electricity Produced (kWh)",
"key": "pv_total_electricity_produced",
"bau_value": lambda df: safe_get(df, "outputs.PV.annual_energy_produced_kwh_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.PV.annual_energy_produced_kwh")
"label": "PV Nameplate capacity (kW), purchased",
"key": "pv_size_purchased",
"bau_value": lambda df: safe_get(df, "outputs.PV.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.PV.size_kw")
},
{
"label": "PV Exported to Grid (kWh)",
"key": "pv_exported_to_grid",
"bau_value": lambda df: safe_get(df, "outputs.PV.electric_to_grid_series_kw"),
"scenario_value": lambda df: safe_get(df, "outputs.PV.electric_to_grid_series_kw")
"label": "PV Nameplate capacity (kW), existing",
"key": "pv_size_existing",
"bau_value": lambda df: safe_get(df, "outputs.PV.existing_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.PV.existing_kw")
},
{
"label": "PV Serving Load (kWh)",
Expand All @@ -1676,16 +1649,10 @@ def get_combined_format(label, row_color, is_formula=False):
"scenario_value": lambda df: safe_get(df, "outputs.PV.electric_to_load_series_kw")
},
{
"label": "Wind Total Electricity Produced (kWh)",
"key": "wind_total_electricity_produced",
"bau_value": lambda df: safe_get(df, "outputs.Wind.annual_energy_produced_kwh_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.Wind.annual_energy_produced_kwh")
},
{
"label": "Wind Exported to Grid (kWh)",
"key": "wind_exported_to_grid",
"bau_value": lambda df: safe_get(df, "outputs.Wind.electric_to_grid_series_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.Wind.electric_to_grid_series_kw")
"label": "Wind Nameplate capacity (kW), purchased",
"key": "wind_size_purchased",
"bau_value": lambda df: safe_get(df, "outputs.Wind.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.Wind.size_kw")
},
{
"label": "Wind Serving Load (kWh)",
Expand All @@ -1694,16 +1661,46 @@ def get_combined_format(label, row_color, is_formula=False):
"scenario_value": lambda df: safe_get(df, "outputs.Wind.electric_to_load_series_kw")
},
{
"label": "CHP Total Electricity Produced (kWh)",
"key": "chp_total_electricity_produced",
"bau_value": lambda df: safe_get(df, "outputs.CHP.annual_electric_production_kwh_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.CHP.annual_electric_production_kwh")
"label": "Backup Generator Nameplate capacity (kW), purchased",
"key": "backup_generator_capacity_purchased",
"bau_value": lambda df: safe_get(df, "outputs.Generator.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.Generator.size_kw")
},
{
"label": "Backup Generator Nameplate capacity (kW), existing",
"key": "backup_generator_capacity_existing",
"bau_value": lambda df: safe_get(df, "outputs.Generator.existing_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.Generator.existing_kw")
},
{
"label": "Backup Generator Serving Load (kWh)",
"key": "backup_generator_serving_load",
"bau_value": lambda df: safe_get(df, "outputs.Generator.electric_to_load_series_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.Generator.electric_to_load_series_kw")
},
{
"label": "CHP Exported to Grid (kWh)",
"key": "chp_exported_to_grid",
"bau_value": lambda df: safe_get(df, "outputs.CHP.electric_to_grid_series_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.CHP.electric_to_grid_series_kw")
"label": "Battery power (kW)",
"key": "battery_power",
"bau_value": lambda df: safe_get(df, "outputs.ElectricStorage.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.ElectricStorage.size_kw")
},
{
"label": "Battery capacity (kWh)",
"key": "battery_capacity",
"bau_value": lambda df: safe_get(df, "outputs.ElectricStorage.size_kwh_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.ElectricStorage.size_kwh")
},
{
"label": "Battery Serving Load (kWh)",
"key": "battery_serving_load",
"bau_value": lambda df: safe_get(df, "outputs.ElectricStorage.storage_to_load_series_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.ElectricStorage.storage_to_load_series_kw")
},
{
"label": "CHP capacity (kW)",
"key": "chp_capacity",
"bau_value": lambda df: safe_get(df, "outputs.CHP.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.CHP.size_kw")
},
{
"label": "CHP Serving Load (kWh)",
Expand All @@ -1712,10 +1709,64 @@ def get_combined_format(label, row_color, is_formula=False):
"scenario_value": lambda df: safe_get(df, "outputs.CHP.electric_to_load_series_kw")
},
{
"label": "CHP Serving Thermal Load (MMBtu)",
"key": "chp_serving_thermal_load",
"bau_value": lambda df: safe_get(df, "outputs.CHP.thermal_to_load_series_mmbtu_per_hour_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.CHP.thermal_to_load_series_mmbtu_per_hour")
"label": "Absorption chiller capacity (tons)",
"key": "absorption_chiller_capacity",
"bau_value": lambda df: safe_get(df, "outputs.AbsorptionChiller.size_ton_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.AbsorptionChiller.size_ton")
},
{
"label": "Absorption Chiller Serving Load (ton)",
"key": "absorption_chiller_serving_load",
"bau_value": lambda df: safe_get(df, "outputs.AbsorptionChiller.thermal_to_load_series_ton_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.AbsorptionChiller.thermal_to_load_series_ton")
},
{
"label": "Chilled water TES capacity (gallons)",
"key": "chilled_water_tes_capacity",
"bau_value": lambda df: safe_get(df, "outputs.ColdThermalStorage.size_gal_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.ColdThermalStorage.size_gal")
},
{
"label": "Chilled Water TES Serving Load (ton)",
"key": "chilled_water_tes_serving_load",
"bau_value": lambda df: safe_get(df, "outputs.ColdThermalStorage.storage_to_load_series_ton_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.ColdThermalStorage.storage_to_load_series_ton")
},
{
"label": "Hot water TES capacity (gallons)",
"key": "hot_water_tes_capacity",
"bau_value": lambda df: safe_get(df, "outputs.HotThermalStorage.size_gal_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.HotThermalStorage.size_gal")
},
{
"label": "Hot Water TES Serving Load (MMBtu)",
"key": "hot_water_tes_serving_load",
"bau_value": lambda df: safe_get(df, "outputs.HotThermalStorage.storage_to_load_series_mmbtu_per_hour_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.HotThermalStorage.storage_to_load_series_mmbtu_per_hour")
},
{
"label": "Steam turbine capacity (kW)",
"key": "steam_turbine_capacity",
"bau_value": lambda df: safe_get(df, "outputs.SteamTurbine.size_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.SteamTurbine.size_kw")
},
{
"label": "Steam Turbine Serving Load (kWh)",
"key": "steam_turbine_serving_load",
"bau_value": lambda df: safe_get(df, "outputs.SteamTurbine.electric_to_load_series_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.SteamTurbine.electric_to_load_series_kw")
},
{
"label": "GHP heat pump capacity (ton)",
"key": "ghp_heat_pump_capacity",
"bau_value": lambda df: safe_get(df, "outputs.GHP.size_ton_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.GHP.size_ton")
},
{
"label": "GHP ground heat exchanger size (ft)",
"key": "ghp_ground_heat_exchanger_size",
"bau_value": lambda df: safe_get(df, "outputs.GHP.length_boreholes_ft_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.GHP.length_boreholes_ft")
},
{
"label": "Grid Purchased Electricity (kWh)",
Expand All @@ -1726,8 +1777,8 @@ def get_combined_format(label, row_color, is_formula=False):
{
"label": "Total Site Electricity Use (kWh)",
"key": "total_site_electricity_use",
"bau_value": lambda df: safe_get(df, "outputs.ElectricUtility.electric_to_load_series_kw_bau"),
"scenario_value": lambda df: safe_get(df, "outputs.ElectricUtility.electric_to_load_series_kw")
"bau_value": lambda df: 0,
"scenario_value": lambda df: 0
},
{
"label": "Net Purchased Electricity Reduction (%)",
Expand Down Expand Up @@ -1939,7 +1990,15 @@ def get_combined_format(label, row_color, is_formula=False):
calculations = [
{
"name": "Total Site Electricity Use (kWh)",
"formula": lambda col, bau, headers: f'={col}{headers["PV Serving Load (kWh)"] + 2}+{col}{headers["Wind Serving Load (kWh)"] + 2}+{col}{headers["CHP Serving Load (kWh)"] + 2}+{col}{headers["Grid Purchased Electricity (kWh)"] + 2}'
"formula": lambda col, bau, headers: (
f'={col}{headers["PV Serving Load (kWh)"] + 2}+'
f'{col}{headers["Wind Serving Load (kWh)"] + 2}+'
f'{col}{headers["CHP Serving Load (kWh)"] + 2}+'
f'{col}{headers["Battery Serving Load (kWh)"] + 2}+'
f'{col}{headers["Backup Generator Serving Load (kWh)"] + 2}+'
f'{col}{headers["Steam Turbine Serving Load (kWh)"] + 2}+'
f'{col}{headers["Grid Purchased Electricity (kWh)"] + 2}'
)
},
{
"name": "Net Purchased Electricity Reduction (%)",
Expand Down

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