antonydesigns

antonydesigns / power_market_simulator

Public

For generating dispatch data for any number of generators using merit order logic and constraint handling. Useful for scenario-building.

14
5
69% credibility
Found Mar 04, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A simulator that models electricity markets by dispatching power plants to meet demand while managing costs, constraints, and ramping, with interactive charts.

How It Works

1
🔍 Discover the Simulator

You stumble upon this cool tool that lets you play with how power plants meet electricity demand in a market.

2
🚀 Start the Example

You load the ready sample with nuclear and hydro plants to see a basic market in action.

3
📊 See the Chart Appear

A beautiful interactive graph pops up in your browser, stacking colorful power outputs to perfectly match the demand line over a week.

4
⚙️ Customize Power Plants

You mix in solar panels, wind farms, gas, or coal, tweaking sizes, costs, and limits to build your energy mix.

5
▶️ Run the Market

You kick off the simulation and watch it balance supply, handle limits, and set prices hour by hour.

6
💾 Save the Schedule

You grab the full results as a simple file to review dispatch plans, costs, and everything.

🎉 Understand Power Markets

You now get how real electricity systems juggle plants, costs, and demand like a pro.

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AI-Generated Review

What is power_market_simulator?

This Python tool simulates power markets by generating dispatch data for fleets of centrally dispatched generating units using merit order logic and constraint handling. Feed it generator bids—nuclear, hydro, gas, solar, wind—with params like marginal costs, ramp rates, startup costs, and real load schedules from ENTSO-E, and it outputs hourly dispatch plans as Pandas DataFrames savable to CSV. Run the example script for instant Plotly charts stacking generator output against load curves, perfect for quick scenario-building in power system simulator market analysis.

Why is it gaining traction?

It nails constraint handling—ramping limits, unit locks, online/offline states—without complex solvers, using straightforward protocols like curtailing non-constrained units or shutting down marginal ones. Developers dig the zero-config start: define units, hit run, get logs and visuals. Pandas/NumPy backbone means dispatch data integrates seamlessly into data pipelines or Jupyter notebooks.

Who should use this?

Power market analysts prototyping dispatch scenarios under varying renewables or constraints. Energy researchers testing merit-order impacts on system costs. Python devs building tools for grid optimization who need fast, flexible generator dispatch logic without full-blown optimization libraries.

Verdict

Grab it for early-stage power market sims—14 stars and 0.7% credibility score signal it's raw, with thin docs beyond the README example, but the core logic delivers reliable dispatch data out of the box. Solid prototype fuel; fork and harden for production.

(198 words)

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