Economics Of Power Stations Using Data Science

Economics & Data Analysis (Python & Optimisation/ pyomo) applied to Power Stations

What you'll learn

Theory of Power Station Economics

Calculating wind patterns for wind farms using Python

Technical characteristics of Power stations

How electricity generators determine the wholesale price, using Python

Modelling the Hydroelectric power plants, using Python

Costs, Revenues & Subsidies for Power Stations

Capital Costs, Levelized Cost of Electricity - explanation & examples

Optimization (pyomo): Optimal strategy of Power Stations on spot & wholesale electricity markets

Data analysis on electricity generation datasets

Part of the giannelos dot com official certificate for high-tech projects.

Requirements

The only prerequisite is to take the first course of the "giannelos dot com" program , which is the course "Data Science Code that appears all the time at workplace".

Description

What is the course about:This course teaches everything about the most important part of Electricity systems: Power Stations, also known as electricity generation units, or simply "units". We begin with an in-depth presentation of the Theory of Power Station technologies going through Hydro Electric power stations, which we also model on Python, and also wind farms - and we compare offshore versus onshore farms in terms of investment - and also tidal/geothermal / biomass units as well as we model fundamental techno economics of wind farms such as the development of wind patterns using Python.We also discuss, in-depth, the technical characteristics of power stations, such as capacity factor, ramp rate, efficiency, minimum stable generation, installed capacity accounting for transmission and distribution losses, dispatchability and flexibility among others.We move on by developing a Python executable file, from scratch, which models the operation of electricity generators and show how they dynamically affect the wholesale electricity price. We can use this application for studying the interaction between wholesale electricity price, merit order and marginal generation costs, which we define and view in practice, using Python.We then proceed with the Economics of Power Stations., starting with fundamental costs, such as Capital Costs, and Levelized Cost of Electricity for different electricity generation types; we develop the LCOE, and we plot it and explain it.We proceed to the Revenue, and specifically - subsidies for electricity generation units. We analyse contracts for difference, and the Renewables Obligation scheme - we build the model from scratch in Excel and Python.We also use Pyomo and perform optimization to determine the optimal strategy of power stations in spot electricity markets and wholesale electricity markets with the objective being to maximize the revenue. Finally, we learn about how to perform Data Analysis on all possible structures of datasets used for Power Stations and generally electricity generation.  Who:I am a research fellow at Imperial College London, and I have been part of high-tech projects at the intersection of Academia & Industry for over 10 years, prior to, during & after my Ph.D. I am also the founder of the giannelos dot com program in data science.Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Masters of Engineering (M. Eng.) in Power Systems and Economics. Important:Prerequisites: The course Data Science Code that appears all the time at Workplace.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 explain everything in detail.

Overview

Section 1: Introduction

Lecture 1 Overview

Section 2: Software Installation

Lecture 2 Python installation

Section 3: Theory of Electricity Generation Assets (Power Stations)

Lecture 3 Analysis

Lecture 4 Key Electricity Infrastructure Assets

Lecture 5 Hydroelectric units: Reservoir & Run of River

Lecture 6 Python: Modelling of technoeconomics of Hydro units

Lecture 7 Wind units

Lecture 8 Calculating wind patterns & placing them in the dataframe, using Python

Lecture 9 Onshore and Offshore wind units: comparison

Lecture 10 Coal and Oil units

Lecture 11 Gas units

Lecture 12 Carbon Capture and Storage units

Lecture 13 Nuclear units

Lecture 14 Biomass units

Lecture 15 Geothermal units

Lecture 16 Tidal units

Lecture 17 solar PV units

Lecture 18 Concentrated Solar Power units

Section 4: Technical characteristics of Electricity Generation Assets

Lecture 19 Installed capacity of generators accounting for t&d losses

Lecture 20 Technological Maturity

Lecture 21 Capacity factor

Lecture 22 Availability factor

Lecture 23 Ramp rate of power plants

Lecture 24 Start-up time of electricity generators

Lecture 25 Minimum Stable Generation

Lecture 26 Efficiency of a power station

Lecture 27 Dispatchability of power stations

Lecture 28 Flexibility of electricity generators

Lecture 29 Baseload and Peaking units

Lecture 30 Emissions intensity of a unit

Section 5: Python: How electricity generators determine the wholesale electricity price

Lecture 31 Introduction to merit order

Lecture 32 Electricity price in centralized wholesale markets

Lecture 33 Description and Receiving user input on Marginal Costs and Capacities

Lecture 34 Determining the generation technology that sets the wholesale price.

Lecture 35 Making the merit order plot

Lecture 36 Sensitivity analysis

Lecture 37 Creating a responsive/interactive merit order plot via Plotly

Lecture 38 Making the executable file

Lecture 39 Running the executable file

Lecture 40 Explaining the code that produced the graphical user interface (tkinter package)

Section 6: Economics of Power Stations. Part 1: Costs

Lecture 41 Capital Costs & Lead times

Lecture 42 Introduction to LCOE (part 1)

Lecture 43 Introduction to LCOE (part 2)

Lecture 44 Plotting the LCOE

Lecture 45 Explanation of LCOE

Lecture 46 Barplot for the LCOE

Section 7: Economics of Power Stations. Part 2: Revenue

Lecture 47 Subsidies: Contracts for Difference, Renewables OC

Lecture 48 Python Applications

Section 8: Optimization: Market Strategy for an Electricity Generation company

Lecture 49 Install Pyomo

Lecture 50 Install Solvers

Lecture 51 Introduction - Description of the case study

Lecture 52 Developing the Mathematical Formulation (concrete & abstract)

Lecture 53 Loading the input parameters from a text file.

Lecture 54 Abstract model definition, instantiation & optimal solution

Lecture 55 Investigating the Optimal Solution

Lecture 56 Duality theory & Strategy in the Spot Electricity Market

Lecture 57 The mathematics behind the solver finding the optimal solution.

Section 9: Data analysis on electricity generation

Lecture 58 Processing & cleaning raw data on Python

Lecture 59 Per type, per bus, total system generation capacity

Lecture 60 Categorizing the data per generator type, year, bus etc using Python

Lecture 61 Replace existing generation types with new ones in the Generation dataset

Section 10: Bonus

Lecture 62 Extras

Enterpreneurs,Economists,Quants,Members of the highly googled giannelos dot com program,Investment Bankers,Academics, PhD Students, MSc Students, Undergrads,Postgraduate and PhD students.,Data Scientists,Energy professionals (investment planning, power system analysis),Software Engineers,Finance professionals