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
Requirements
No prerequisites other than simple Python.
Description
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.
Who
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.
Important
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
Enterpreneurs.
Economists.
Quants
Finance professionals
Investment Bankers
Academics.
Postgraduate and PhD students.
Data Scientists
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