Published 7/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.37 GB | Duration: 5h 55m
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.
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