High‑Level NEM PyPSA Model

A concise, five‑node PyPSA model of the Australian National Electricity Market (NEM), designed for learners and stakeholders exploring energy transition scenarios.

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Table of Contents

• High‑Level NEM PyPSA Model
  • Table of Contents
  • Overview
  • Features
  • Directory Structure
  • Prerequisites
  • Usage
  • Customization & Scenarios
  • View Quarto rendered HTML

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Overview

The High‑Level NEM model provides a baseline representation of the NEM’s five regions (one bus per region) at 30-minute resolution, using actual 2024 generation and demand data. It enables:

• Analysis of current dispatch and grid topology
• Exploration of high‑renewable scenarios with storage
• Visualisation of network topology, generation mix, dispatch and curtailment
• Streamlit app for interactive scenario analysis

Scenario dispatch example

Intended Audience:

• Students and professionals learning power system modelling
• Energy analysts and policy researchers interested in the Australian grid
• Anyone curious about the electricity transition and renewable integration

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Features

• 5‑bus network representing NSW, QLD, SA, TAS, VIC
• 30-min time series for loads and generator availability
• Baseline vs. Future Scenarios: easily swap in higher VRE/storage capacities
• Optimisation via linear programming (HiGHS/Gurobi)
• Interactive & static plots of dispatch, imports/exports, and curtailment
• Exportable to NetCDF for external tools

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Directory Structure

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├─ High-level_NEM.qmd                  # Main Quarto analysis/report
├─ data/                               # Input datasets (already cleaned)
│  ├─ inputs/                          # Assumption tables
│  └─ nemweb/clean/                    # NEMWeb extracts
├─ scripts/                            # Data prep & rendering helpers
├─ results/
│  └─ scenarios/                       # Scenario netCDF + summaries
└─ docs/                               # Built site & assets for html report

Prerequisites

• Python 3.8+
• PyPSA
• pypsa, pandas, numpy, matplotlib, cartopy, datetime, great_tables
• (Optional) Gurobi solver (requires license)

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3. Input Data

   • Components: generators.csv, buses.csv, loads.csv, lines.csv, links.csv, storage_units.csv
   • Time series: loads_t.p_set.csv, generators_t.p_max_pu.csv

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Usage

1. Run the Quarto document

   Open High-level_NEM.qmd in Quarto and run cells, or render to HTML by running quarto_to_html_render.py, or:

2. Walk through sections

   • Data Import & Preprocessing
   • Network Construction (buses, loads, generators, storage, lines/links)
   • Optimisation (Solve the network)
   • Visualisation (senario analysis) (plot_dispatch examples for static & interactive plots (plotly) and the great_tables package for variable renewable curtailment analysis)

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Customization & Scenarios

• Adjust capacities: edit generators.csv or use the scenario function to scale VRE/storage.
• Temporal window: call plot_dispatch(n, time="YYYY-MM-DD", days=N, regions=[…]).
• Solver choice: switch between HiGHS (default) and Gurobi in the optimisation cell.

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Key References:

• TU Berlin: Data Science for Energy System Modelling
• PyPSA Documentation and Components
• PyPSA Earth Documentation
• GitHub PyPSA Sources
• PyPSA-PH: High-Resolution Open Source Power System Model for the Philippines
• 2024 Integrated System Plan (ISP)
• Open Electricity

View Quarto rendered HTML

A High-level Open Source Model for the Australian National Electricity Market (NEM)

Note: I'm rendering a single html file (with asssociated assets), which I'm renaming and moving to the docs/ directory via the qaurto_to_html_render.py script. I'm not creating a full website and my project type is not Website Project.

Using quarto render High-level_NEM.qmd --to html will render the Quarto document to HTML in the root directory - however I need it to be in the docs/ directory so that it can be served by GitHub Pages.