Technoeconomics of Electricity Grid Assets using Python

Published 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 56 lectures (5h 3m) | Size: 2.3 GB

Economics & Data Analysis (Python & Optimisation/ pyomo) applied to Electricity Grid assets

What you’ll learn
Theory of electricity generation assets
Technical characteristics of Electricity Generation Assets
Python: How electricity generators determine the wholesale electricity price
Economics of Power Stations. Part 1: Costs
Economics of Power Stations. Part 2: Revenue (subsidies)
Optimization: Market Strategy for an Electricity Generation company
Theory & Technoeconomics of Energy Storage using Python

No prerequisites other than simple Python.

What is the course about

We look at the electricity infrastructure assets i.e. Power Stations (all different types) – their technical & economic characteristics, and Energy Storage units as well as other Grid assets.

We use Python and Excel to model the technical characteristics in order to understand them even better. We do this for all different types of technologies.

The idea of this course is that you understand how these assets operate, and then model them in the Data Science model, if necessary for the client.


I am a research fellow and I lead industry projects using mathematical optimization and data science. I have a Ph.D. in Analytics & Mathematical Optimization, from Imperial College London, and specifically, have applied it to Energy investments. Currently he is interested in uncertainty modeling in the context of investments.


No prerequisites and no experience are required.

Every detail is explained, so that you won’t have to search online, or guess. In the end, you will feel confident in your knowledge and skills.

We start from scratch so that you do not need to have done any preparatory work in advance at all. Just follow what is shown on screen, because we go slowly and understand everything in detail.

Who this course is for
Finance professionals
Investment Bankers
Postgraduate and PhD students.
Data Scientists






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