Energy Operations Research¶
Forecasting, optimisation, reliability and decision intelligence for renewable energy and BESS.
An open, executable textbook. Every important mathematical technique in it solves a recognisable operational problem from a real renewable-energy business: sharing a congested connection, dispatching a battery, timing a gearbox exchange around crane wind limits, deciding which of 26 turbines to fix first.
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The book
Intuition first, then mathematics, then a picture of what the mathematics means.
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The labs
Every practical chapter has a Google Colab notebook. No local install needed.
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The library
energy_orholds the tested, reusable models that the book and the labs call. -
The visualisations
Manim animations and live in-browser models show why an optimiser behaves as it does.
Start here¶
Two wind farms share a 100 MW connection. Farm A could export 70 MW, farm B 80 MW, and B's megawatt-hours are worth more. Who gets the connection? And what is one more megawatt of network actually worth?
That question is a linear program, and it is the first chapter of this book: 1 · Two wind farms, one connection.
Three tracks, one system¶
The book runs three tracks in parallel. Each modelling chapter is followed by an engineering lab that turns the model into something you could actually run.
| Track | Starts with | Ends with |
|---|---|---|
| Mathematical optimisation | LP, duality, shadow prices | Stochastic, robust and multi-objective portfolio optimisation |
| Energy operations | Dispatch behind a shared limit | Fleet trading, maintenance, logistics and contracts in one model |
| Production engineering | A tested Python package | Observable, latency-budgeted, backtested decision services |
See the full table of contents for the planned curriculum and what is available today.
What this book is not
It is not a replacement for AEMO documentation, and none of its models reproduce NEMDE. Where a chapter approximates a market process, it says so. Synthetic data is always labelled as synthetic.