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.

For reference, this is what GitHub Copilot produced for a PR summary:

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This pull request includes various updates across configuration files, Julia scripts, Django migrations, and model definitions to enhance functionality and improve maintainability. Key changes include updates to docker-compose.yml for better dependency management, modifications to Julia scripts and dependencies, and significant additions and refinements to Django models and migrations.

Configuration Updates

• Updated docker-compose.yml to use a more robust Julia command for dependency instantiation and script execution. The command now includes Pkg.instantiate() and script inclusion for better dependency handling.

Julia Code and Dependencies

• Added a new vector type, monthly_fraction, to the vector_types array in julia_src/http.jl for more comprehensive data processing.
• Updated the REopt dependency in julia_src/Manifest.toml to version 0.51.1, including a new git-tree-sha1.

Django Migrations

• Introduced several new fields across multiple models (e.g., FinancialOutputs, ElectricTariffOutputs, ElectricUtilityOutputs) to support additional financial and operational metrics, such as year-one costs and peak grid demand. [1] [2] [3] [4] [5] [6] [7]
• Renamed fields in FinancialOutputs for improved clarity, such as capital_costs_after_incentives_without_macrs to capital_costs_after_non_discounted_incentives_without_macrs.

Django Models

• Added methods to ElectricUtilityOutputs for calculating and saving peak grid demand (calculate_peak_demand and save). These methods dynamically compute peak values based on input series.
• Refactored FinancialOutputs to include new fields for year-one operational costs and savings, both before and after tax, enhancing the granularity of financial reporting. [1] [2]

These updates collectively improve the robustness, clarity, and functionality of the codebase, addressing both backend processing and data modeling requirements.

[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