Last updated 11/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.92 GB | Duration: 6h 38m
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
rerequisites: 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
Homepage
Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
Links are Interchangeable - No Password - Single Extraction
