Overview of the workshop and individual learning outcomes

Overview of the workshop and individual learning outcomes#

Agenda#

Start Time

Duration

Trainer

10:00

00:30

Coffee & Setup

10:30

00:30

Overview of the topics and learning objectives

Will

11:00

01:00

Introduction to Modelling and Scenarios

Will

12:00

01:00

Lunch

13:00

01:00

Key Concepts in PyPSA

Katia/Albert

14:00

00:15

Recap of key concepts. What’s unclear?

Will/Katia

14:15

00:45

Python Refresher

Mwiche

15:00

00:45

PyPSA: Modelling Capacity Expansion Scenarios

Katia

15:45

01:00

Exploring [current!] results PyPSA-Zambia

Albert

16:45

00:15

Summary of the day

Will

Individual learning outcomes#

At the end of the training, you will be able to:

  • Use exploratory and normative scenarios approaches to structure a modelling analysis

  • Categorise models and know when to use which sort of model

  • Reflect on the trade-offs and simplifications necessary when translating a real-life energy system into an energy system model.

  • Understand the core building blocks of PyPSA-Zambia

    • Snakemake - is a workflow management tool - which splits a complex process into connected rules, each of which performs a task such as downloading data, or computing a value

    • PyPSA - is an energy system modelling framework for representing a sector-coupled energy system

  • Understand the key functional elements of a PyPSA model - lines, generators, links and how they are combined to represent an energy system

  • Use Python operators, data types (lists, dicts etc.), write functions into scripts which you can execute.

  • Interpret the results from a PyPSA model, understanding the difference between model parameters and variables (inputs and results); what insights can be obtained from the model, and how assumptions and data quality affect the results.

Will not be covering the following in today’s workshop:

  • Critique the choice of (public) data sources, and how these might influence the model results.

  • Understand and describe the different types of data used to parameterise a PyPSA model, including spatial data (raster, vector), timeseries, economic (cost), load/demand, renewable resource and other datasets.

Teaching and Learning Style#

  • Our aim is to give you the knowledge you need to support this project

  • Interactive

    • ask questions

    • be prepared to reflect on and think about the subject

  • If you don’t understand a concept, please stop me and ask

    • there are no silly questions

  • Be nice

    • Listen - let others speak

    • Support one another’s learning