# 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](pypsa-zambia-workshops/05-workshop-python.ipynb) | 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