PyPSA Introduction

PyPSA Introduction#

PyPSA stands for Python for Power System Analysis.

PyPSA is an open source Python package for simulating and optimising modern energy systems that include features such as

  • conventional generators with unit commitment (ramp-up, ramp-down, start-up, shut-down),

  • time-varying wind and solar generation,

  • energy storage with efficiency losses and inflow/spillage for hydroelectricity

  • coupling to other energy sectors (electricity, transport, heat, industry),

  • conversion between energy carriers (e.g. electricity to hydrogen),

  • transmission networks (AC, DC, other fuels)

PyPSA can be used for a variety of problem types (e.g. electricity market modelling, long-term investment planning, transmission network expansion planning), and is designed to scale well with large networks and long time series.

Compared to building power system by hand in linopy, PyPSA does the following things for you:

  • manage data inputs

  • build optimisation problem

  • communicate with the solver

  • retrieve and process optimisation results

  • manage data outputs

Major Dependencies#

  • pandas for storing data about network components and time series

  • numpy and scipy for linear algebra and sparse matrix calculations

  • matplotlib and cartopy for plotting on a map

  • networkx for network calculations

  • linopy for handling optimisation problems

Structure#

Component

Description

Network

Container for all components.

Bus

Node where components attach.

Carrier

Energy carrier or technology (e.g. electricity, hydrogen, gas, coal, oil, biomass, on-/offshore wind, solar). Can track properties such as specific carbon dioxide emissions or nice names and colors for plots.

Load

Energy consumer (e.g. electricity demand).

Generator

Generator (e.g. power plant, wind turbine, PV panel).

Line

Power distribution and transmission lines (overhead and cables).

Link

Links connect two buses with controllable energy flow, direction-control and losses. They can be used to model:

  • HVDC links
  • HVAC lines (neglecting KVL, only net transfer capacities (NTCs))
  • conversion between carriers (e.g. electricity to hydrogen in electrolysis)

StorageUnit

Storage with fixed nominal energy-to-power ratio.

GlobalConstraint

Constraints affecting many components at once, such as emission limits.

Store

Storage with separately extendable energy capacity.

not used in this course

LineType

Standard line types.

Transformer

2-winding transformer.

TransformerType

Standard types of 2-winding transformer.

ShuntImpedance

Shunt.

Warning

Per unit values of voltage and impedance are used internally for network calculations. It is assumed internally that the base power is 1 MW.

Environment#

Google Colab#

You can run the examples without a local Python installation using Google Colab (colab.google) which provide an online Python environment. This requires a Google account. Click on the rocket in the top right corner and launch “Colab”. If that doesn’t work download the .ipynb file and import it in Google Colab.

conda#

The conda environment specification can be downloaded here:

fneum/gasunie-workshop

Run

conda env create -f environment.yaml

Activate

conda activate gasunie

pip#

The pip environment specification can be downloaded here:

fneum/gasunie-workshop

Run

pip install -r requirements.txt
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