Optimization (Pyomo) For Energy Investments Using Python
Last updated 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.83 GB | Duration: 4h 12m
Mathematical Optimization Investment models using Python (pyomo)
What you'll learn
Pyomo and Python
Mathematical Optimization models from scratch
Energy Investment problems. Focus: Sustainable Energy. All on Python.
The subtitles are manually created. Therefore, they are fully accurate. They are not auto-generated.
Part of the giannelos dot com official certificate
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 how to apply Mathematical Optimization in order to find the most economical (optimal) investment decisions, with an application to energy. A Mathematical Optimization Model is a type of Data Science Model, which is used for economic analyses.In this case, the course shows how to use such models for analyses of Investments in Energy Infrastructure.Focus is placed on Renewables infrastructures such as Wind Farms, Solar Photovoltaics, and Hydropower units.The idea of this course is that you either have your own consultancy company or you work for a consultancy company, whose clients are companies interested in investing in energy but have not yet decided when to start the construction, which location to select, and they are not sure about how much the cost will be.You will build an Optimization model that will model the specific requirements of the client as accurately as possible, and produce results that you can explain to the client.The clients will provide a number of input data (typically Excel files) that will need to be taken into account. This means that the Optimization model will have to read the input data that the client has provided, which can be done through Python.In this course, the entire process is displayed in detail. 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. Special Acknowledgements:To Himalaya Bir Shrestha who has been contributing to the development of Python scripts for this course and to Medium with insightful posts. 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: Overview
Lecture 1 Overview
Lecture 2 Analysis
Section 2: Installation of Python, Pyomo & Solvers
Lecture 3 Anaconda & Python Installation
Lecture 4 Pyomo installation
Lecture 5 Solvers (Gurobi, Ipopt, GLPK) installation
Section 3: OPTIMIZATION MODEL (pyomo): Investments in Hydro power stations
Lecture 6 Description of the case
Lecture 7 Defining the Concrete Mathematical Optimization Model
Lecture 8 Defining the input parameters for the concrete model
Lecture 9 Defining the decision variables for the concrete model
Lecture 10 Defining the constraints & the objective function for the concrete model
Lecture 11 Setting the Solver & Getting the Optimal Solution to the concrete model
Lecture 12 Conducting sensitivity analysis for the concrete model
Lecture 13 Comparing the performance of the solvers for the concrete model
Lecture 14 Defining the Abstract Mathematical Optimization Model
Lecture 15 Defining the input parameters, variables & constraints for the abstract model
Lecture 16 Defining an abstract objective function
Lecture 17 Solving two nonlinear instances of the abstract model
Lecture 18 Conducting sensitivity analysis on an instance of the abstract model
Section 4: OPTIMIZATION MODEL (pyomo) : Investment in Power Stations & Storage operation
Lecture 19 Define the model & input parameters
Lecture 20 Defining the decision variables
Lecture 21 Defining the constraints
Lecture 22 Defining the objective function
Lecture 23 Solving the model & analysing the output
Lecture 24 Validating the solution
Section 5: OPTIMIZATION MODEL (pyomo): Investment in Onshore Wind Farms
Lecture 25 Description of the Consultancy case
Lecture 26 Defining the concrete model, input parameters & decision variables
Lecture 27 Defining the constraints & the objective function of the concrete model
Lecture 28 Optimal solution to the concrete model
Lecture 29 Visualization of the optimal solution to the concrete model
Lecture 30 Conducting sensitivity analysis on the concrete model
Lecture 31 Defining the abstract model, its inputs, variables & constraints
Lecture 32 Defining the abstract objective function
Lecture 33 Instantiating the abstract model & solving the instance
Lecture 34 Visualization & sensitivity analysis & Elements of a Successful Consultancy!
Section 6: OPTIMIZATION MODEL (pyomo): Investment strategy for a Wind-Turbine Manufacturer
Lecture 35 Description of the consultancy case
Lecture 36 Formulating the problem mathematically
Lecture 37 Defining input parameters, variables & constraints for the concrete model
Lecture 38 Defining the constraints & the Objective Function for the concrete model
Lecture 39 Solving the concrete model via the GLPK solver
Lecture 40 Defining the Abstract optimization model
Lecture 41 Abstract constraints & Abstract objective function
Lecture 42 Solving the abstract optimization problem
Lecture 43 Generalized formulation for abstract models
Lecture 44 Bringing the externally-sourced data into a form readable by Pyomo
Lecture 45 Generalized formulation for constraints & objective function for abstract model
Lecture 46 Passing data while instantiating the model & solving it
Lecture 47 Obtaining the optimal solution to the abstract model & making a second instance
Lecture 48 Index sets, abstract arrays & decision variables for the abstract model
Section 7: OPTIMIZATION MODEL(pyomo): Energy Investments in India
Lecture 49 Defining the model, the decision variables & input parameters
Lecture 50 Defining the objective and the constraints
Lecture 51 Solving the model & reading the optimal solution
Lecture 52 Plotting the optimal solution
Section 8: Bonus
Lecture 53 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
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