After Tax Savings and Updates to Results Table Spreadsheet

[Bill-Becker]

This PR adds a bunch of year one after-tax cost outputs and one capital cost output that were added to REopt.jl#after-tax-savings (to be merged into the main branch), and then adds a select number of those outputs along with some updates to the results table spreadsheet to calculate a modified after-tax payback. See the attached example comparison_table.xlsx doc with a highlight of the additions and updates to the table.

comparison_table Example for PR with Notes.xlsx

Not related to the above, this also includes a fix for cooling load:

• Added a new vector type monthly_fraction in the simulated_load function in julia_src/http.jl.

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(For reference, this is what GitHub Copilot autogenerated for the PR)

This pull request includes several changes to the docker-compose.yml, julia_src/Manifest.toml, julia_src/http.jl, and reoptjl/custom_table_config.py files. The changes primarily focus on updating configurations, modifying commands, and enhancing the table configurations with new fields and calculations.

Key Changes:

Configuration Updates:

• Updated the docker-compose.yml file to use a new command for the Julia project instantiation and execution.

Dependency Management:

• Modified the julia_src/Manifest.toml file to include new repository information for the REopt dependency.

Code Enhancements:

• Added a new vector type monthly_fraction in the simulated_load function in julia_src/http.jl.

Table Configuration Enhancements:

• Added new fields and updated existing ones in the reoptjl/custom_table_config.py file to include after-tax costs and savings, as well as additional financial metrics. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

[bpulluta]

If you want this to show the formula I would reformulate this, like this for example:

• Set these to empty strings

        "label"         : "Total Electric Costs ($)",
        "key"           : "total_electric_utility_costs",
        "bau_value": lambda df: "",
        "scenario_value": lambda df: ""

• Define the values in the table:

        "label"         : "Lifecycle Export After Tax ($)",
        "key"           : "lifecycle_export_after_tax",
        "bau_value": lambda df: "outputs.Financial.lifecycle_export_benefit_after_tax_bau",
        "scenario_value": lambda df: "outputs.Financial.lifecycle_export_benefit_after_tax"

        "label"         : "CHP Lifecycle Standby Cost After Tax($)",
        "key"           : "chp_lifecycle_export_after_tax",
        "bau_value": lambda df: "outputs.CHP.lifecycle_chp_standby_cost_after_tax_bau",
        "scenario_value": lambda df: "outputs.CHP.lifecycle_chp_standby_cost_after_tax"

• Add it as a formula like this:

"name": "Total Electric Costs ($)",
"formula": lambda col, bau, headers: f'={col}{headers["Lifecycle Export After Tax ($)"] + 2}+{col}{headers["CHP Lifecycle Standby Cost After Tax($)"] + 2}'

didnt focus too much on accuracy but you get the point, same with the others