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Revised 23 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/discom-debt-and-revenue-models

Watch the walkthrough

Watch on YouTube: youtu.be/u9zelV_adSM

Why it is useful

• Who benefits: students, policy analysts and journalists who want to see why distribution company debt grows and which levers change it, with the consumer bill trade-off shown next to the debt.
• What it replaces: a hand-built spreadsheet of the cost-of-supply versus revenue gap. The calculation is a short, readable loop of about forty lines.
• Evidence from the run: on the default inputs, doing nothing takes debt from 40,000 to 175,288 crore rupees over seven years, and only the most aggressive scenario brings it down, at a 50.4 percent rise in the household bill.
• Every input can be changed from the command line, so you can test your own what-ifs in seconds.

What it is for

discom_debt_calculator.py models the yearly economics: energy billed after losses, revenue collected, cost of supply, state subsidy actually received, interest on the debt, and the resulting closing debt. It compares five scenarios and prints a debt trajectory for the best one plus a household bill table.

It is a conceptual scenario calculator, not a forecast. Its own header says it uses flat cost growth, a single blended tariff and no demand response, and that it is no substitute for a regulatory tariff filing or audited accounts. The default company is illustrative. The repository is archived.

Who it is for

• Students and analysts learning why electricity distribution companies accumulate debt
• Writers who want a transparent model to illustrate policy trade-offs
• Non-programmers who want an AI assistant to explain each line

What you need

Get the code and an AI assistant

You do not have to be a programmer. Get the repository, give it to an AI assistant, and ask in plain language. Any of these routes works:

git clone https://github.com/herrrickshaw/discom-debt-and-revenue-models.git
cd discom-debt-and-revenue-models
claude      # or: gemini

Install

1. Get the code (the repository is archived, so you can clone it but not push)

   git clone https://github.com/herrrickshaw/discom-debt-and-revenue-models.git
   cd discom-debt-and-revenue-models

Quick start: commands and what you should see

Run the default illustrative company

python3.13 discom_debt_calculator.py

A header of inputs, a five-scenario table, a year-by-year trajectory for the best scenario, a household bill table and a short interpretation.

Change the inputs

python3.13 discom_debt_calculator.py --debt 50000 --acs 7.2 --arr 6.1 --loss 18

The same report for a company with a wider cost-revenue gap.

Get machine-readable output

python3.13 discom_debt_calculator.py --json

Inputs and year-by-year rows for every scenario as JSON.

Sample outputs

Real output from a run on the date shown in the sources line; trimmed to fit.

Default inputs: five scenarios

python3.13 discom_debt_calculator.py
  Opening debt: ₹40,000 Cr | Energy input: 60,000 MU
  ACS ₹6.8/kWh vs ARR ₹6.2/kWh (gap ₹0.60/kWh) | AT&C loss 15.0% | collection 98.0%
  Interest 9.5% | horizon 7 yrs

  Scenario                      Final debt ₹Cr   Δ vs today ₹Cr Outcome
  Status Quo (no action)               175,288         +135,288  ▲ debt grows
  Tariff hike only (5%/yr)             108,496          +68,496  ▲ debt grows
  Loss + collection reform             139,081          +99,081  ▲ debt grows
  Combined (UDAY-style)                 79,852          +39,852  ▲ debt grows
  Aggressive turnaround                 36,496           -3,504  ▼ debt cut

Debt in crore rupees after seven years, with the 40,000 crore starting point.

Best scenario, year by year (rows 1, 2, 4, 5, 7 of 7)

   Yr    ARR    ACS    Gap  Loss%   OpGap ₹Cr     Debt ₹Cr
    1   6.57   7.07   0.50   13.0        -447       44,247
    2   6.97   7.35   0.39   11.0      +1,199       47,251
    4   7.83   7.96   0.13    7.0      +4,438       49,035
    5   8.30   8.27  -0.02    5.0      +6,280       47,413
    7   9.32   8.95  -0.37    1.0     +11,073       36,496

The cost-revenue gap closes in year 5, and the operating gap turns positive from year 2.

Household bill at 250 units a month

  Scenario                       Bill yr0   Bill yr7   Increase
  Status Quo (no action)       ₹   1,550 ₹   1,550       0.0%
  Tariff hike only (5%/yr)     ₹   1,550 ₹   2,181      40.7%
  Loss + collection reform     ₹   1,550 ₹   1,550       0.0%
  Combined (UDAY-style)        ₹   1,550 ₹   2,040      31.6%
  Aggressive turnaround        ₹   1,550 ₹   2,331      50.4%

The bill is 250 units times the blended tariff. Operational reform leaves the bill unchanged because it only changes losses and collection.

A harsher company (50,000 crore, cost 7.2, revenue 6.1, losses 18%)

python3.13 discom_debt_calculator.py --debt 50000 --acs 7.2 --arr 6.1 --loss 18
  Opening debt: ₹50,000 Cr | ACS ₹7.2/kWh vs ARR ₹6.1/kWh | AT&C loss 18.0%
  Status Quo (no action)               239,657         +189,657  ▲ debt grows
  Combined (UDAY-style)                147,672          +97,672  ▲ debt grows
  Aggressive turnaround                106,051          +56,051  ▲ debt grows

No built-in scenario brings debt down here.

Python 3.14 start-up crash

python3.14 discom_debt_calculator.py
$ python3.14 discom_debt_calculator.py
  File ".../argparse.py", line 1750, in _check_help
    raise ValueError('badly formed help string') from exc
ValueError: badly formed help string
(cause: help="AT&C loss (%)" has a bare percent sign; Python 3.13 runs fine)

A bare percent sign in an argparse help string. Newer Python versions check help strings when the option is defined.

Ways to use it

Sample prompts for your AI assistant

Copy, paste and edit the parts in [brackets].

• Claude: Read discom_debt_calculator.py and explain in plain language how closing debt is computed each year.
• Gemini: Run it with --horizon 3 --collection 90 and tell me why every scenario still grows the debt.
• ChatGPT: I pasted the five-scenario table. Does the script's interpretation line that operational levers cut debt without the tariff burden match the numbers?
• GitHub (Copilot Chat): Change the help string for --loss so the script starts on Python 3.14, and re-run it.
• Claude: Add a scenario where the state subsidy grows with energy cost, and show how it changes the table.

Limits and honest caveats

Sources: Cloned github.com/herrrickshaw/discom-debt-and-revenue-models (archived) into a clean folder and ran discom_debt_calculator.py with Python 3.13 (and Python 3.14 to see the crash) on 23 October 2026, using default inputs and the flags shown. The Dirichlet script and the Colab notebook were not run. The repository was not modified.

About this article: Researched, written and edited by Umashankar Triplicane Dwarakanathan, with AI research assistance; every figure is meant to trace to the primary source cited. See the Editorial Policy for how sourcing, AI use and corrections work.