Economics Of Energy Markets Using Data Science

A focus on electricity markets using data science

What you'll learn
Data analysis & economics of energy markets step by step.
Analyses on energy markets using Optimization & Data analysis
Software development for electricity markets
The subtitles are manually created so they are fully accurate. They are not auto-generated.
Requirements
We start from scratch and build confidence as we go.
Description
What is the course about:Energy markets, and specifically electricity markets, are transforming in a fundamental way. Data Science has brought an earthquake shaking the fundamentals of how electricity markets are being understood and designed. There is a fundamental evolution in how energy markets are evolving towards the half of the 21st century. This course will help you progress with your career in a very fundamental way, and most importantly will give you vital skills to be able to analyse energy markets.  You will learn to use optimisation, economics and data science - all in one course, giving you necessary skills to advance ahead. Who:I am a research fellow at Imperial College London and I lead industry projects related to energy investments using mathematical optimisation and data science. Specialized in the Data Science aspect of the Green Energy transition, focused on algorithmic design and optimisation methods, using economic principles. Doctor of Philosophy (PhD) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London.Master of Engineering (M. Eng.) degree in Power System Analysis (Electricity) and Economics from National Technical University of Athens.  Important:No pre-requisites and no experience 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.

Overview

Section 1: The Learning Curve of Energy Markets, using Python

Lecture 1 Introduction to Standard Normal Distribution

Lecture 2 Exponential Learning curve, Experience curve, Sigmoid learning curve

Lecture 3 Global aggregate installed capacity in Solar, and Wind, between 2010-2020

Lecture 4 Global aggregate installed capacity across many Renewables technologies

Section 2: The Electricity Market

Lecture 5 Forwards & Futures & the Balancing Mechanism

Lecture 6 System operation withing Frequency and Voltage limits

Lecture 7 Structural Reform of the Electricity Market

Lecture 8 Fundamental Pieces of Legislation

Lecture 9 Different Structures of the Electricity Market

Lecture 10 Dynamics of Electricity Price in a Centralized Wholesale Market

Section 3: Installation of necessary software

Lecture 11 Anaconda and Python

Lecture 12 Pyomo

Lecture 13 Solvers (Gurobi, Ipopt, GLPK)

Section 4: Python Optimization Model: Market Strategy for an Electricity Generation company

Lecture 14 Description of the case study

Lecture 15 Developing the Mathematical Formulation (concrete & abstract)

Lecture 16 Loading the input parameters from a text file.

Lecture 17 Abstract model definition, instantiation & optimal solution

Lecture 18 Investigating the Optimal Solution

Lecture 19 Duality theory & Strategy in the Spot Electricity Market

Lecture 20 The mathematics behind the solver finding the optimal solution.

Lecture 21 Download the entire code

Section 5: A Python model for the Wholesale Electricity Market

Lecture 22 Description and Receiving user input on Marginal Costs and Capacities

Lecture 23 Determining the generation technology that sets the wholesale price.

Lecture 24 Drawing and Interpreting the Merit Order Plot

Lecture 25 Conducting sensitivity analyses on wholesale price, using the Slider Widget

Lecture 26 Creating a responsive/interactive merit order plot via Plotly

Lecture 27 Running the model on command line and producing the executable file

Lecture 28 Running the executable file

Lecture 29 Explaining the code that produced the graphical user interface (tkinter package)

Section 6: Subsidies for electricity generators

Lecture 30 Contracts for Difference Renewables Obligation Certificates: A model on Python

Section 7: Energy Balances using Python

Lecture 31 Processing Energy Balances of countries

Lecture 32 Supply indicators of a country - Energy Production & Supply, Electricity Supply

Lecture 33 Energy demand structure of a country: Total final energy consumption

Section 8: Levelized Cost of Electricity (LCOE)

Lecture 34 Plotting the reduction in LCOE for renewables technologies

Lecture 35 Interpretation of the LCOE plot

Lecture 36 Barplot of min and max LCOE values, per renewable technology

Economists.,Investment bankers.,Data Scientists.,Portfolio Managers with a focus on Energy projects.,Postgraduate and PhD students.,Academics.,Energy Traders & Finance professionals.