Optimization (pyomo) for Energy Investments using Python

Focused View

Dr. Spyros Giannelos

4:11:37

380 View

1 - Overview

1 - Overview.mp4

00:37

2 - Analysis.mp4

02:14

2 - Installation of Python Pyomo & Solvers

3 - Anaconda & Python Installation.mp4

02:18

3 - LP.pptx

4 - Pyomo installation.mp4

00:57

4 - all possible ways by which we can install pyomo.zip

4 - documentation for pyomo including installation guidelines.zip

4 - pyomo-install.pdf

5 - Solvers Gurobi Ipopt GLPK installation.mp4

04:24

5 - To-install-the-GLPK-solver.pdf

5 - link with all possible ways to install the glpk solver.zip

3 - OPTIMIZATION MODEL pyomo Investments

6 - Description of the case.mp4

07:38

6 - LP.pptx

6 - slides.pdf

7 - Defining the Concrete Mathematical Optimization Model.mp4

02:34

7 - analysis.zip

8 - Defining the input parameters for the concrete model.mp4

02:15

8 - analysis.zip

9 - Defining the decision variables for the concrete model.mp4

05:58

10 - Defining the constraints & the objective function for the concrete model.mp4

05:21

11 - Setting the Solver & Getting the Optimal Solution to the concrete model.mp4

09:29

12 - Conducting sensitivity analysis for the concrete model.mp4

03:19

13 - Comparing the performance of the solvers for the concrete model.mp4

02:15

14 - Defining the Abstract Mathematical Optimization Model.mp4

01:04

15 - Defining the input parameters variables & constraints for the abstract model.mp4

01:12

16 - Defining an abstract objective function.mp4

01:45

17 - Solving two nonlinear instances of the abstract model.mp4

05:46

18 - Conducting sensitivity analysis on an instance of the abstract model.mp4

01:19

4 - OPTIMIZATION MODEL pyomo Investment

19 - Define the model & input parameters.mp4

15:11

19 - LP.pptx

19 - analysis.zip

20 - Defining the decision variables.mp4

08:15

21 - Defining the constraints.mp4

19:36

22 - Defining the objective function.mp4

10:22

23 - Solving the model & analysing the output.mp4

17:24

24 - Validating the solution.mp4

12:54

24 - myfile-lp.zip

5 - OPTIMIZATION MODEL pyomo Investment

25 - Description of the Consultancy case.mp4

07:28

25 - LP.pptx

25 - slides.pdf

26 - Defining the concrete model input parameters & decision variables.mp4

02:24

26 - analysis.zip

27 - Defining the constraints & the objective function of the concrete model.mp4

01:38

28 - Optimal solution to the concrete model.mp4

01:33

29 - Visualization of the optimal solution to the concrete model.mp4

02:19

30 - Conducting sensitivity analysis on the concrete model.mp4

02:16

31 - Defining the abstract model its inputs variables & constraints.mp4

01:04

32 - Defining the abstract objective function.mp4

02:17

33 - Instantiating the abstract model & solving the instance.mp4

03:41

34 - Visualization & sensitivity analysis & Elements of a Successful Consultancy.mp4

03:07

6 - OPTIMIZATION MODEL pyomo Investment strategy

35 - Description of the consultancy case.mp4

02:06

35 - LP.pptx

35 - slides.pdf

36 - Formulating the problem mathematically.mp4

05:08

36 - analysis.zip

36 - slides.pdf

37 - Defining input parameters variables & constraints for the concrete model.mp4

02:36

38 - Defining the constraints & the Objective Function for the concrete model.mp4

01:22

39 - Solving the concrete model via the GLPK solver.mp4

04:00

40 - Defining the Abstract optimization model.mp4

01:40

41 - Abstract constraints & Abstract objective function.mp4

01:03

42 - Solving the abstract optimization problem.mp4

02:30

43 - Generalized formulation for abstract models.mp4

07:14

43 - slides.pdf

44 - Bringing the externallysourced data into a form readable by Pyomo.mp4

04:31

44 - input-params.zip

44 - slides.pdf

45 - Generalized formulation for constraints & objective function for abstract model.mp4

02:50

46 - Passing data while instantiating the model & solving it.mp4

01:50

47 - Obtaining the optimal solution to the abstract model & making a second instance.mp4

02:20

48 - Index sets abstract arrays & decision variables for the abstract model.mp4

03:04

7 - OPTIMIZATION MODELpyomo Energy Investments

49 - Defining the model the decision variables & input parameters.mp4

11:01

49 - analysis.zip

50 - Defining the objective and the constraints.mp4

07:10

51 - Solving the model & reading the optimal solution.mp4

04:30

52 - Plotting the optimal solution.mp4

14:48

52 - average thermal power plant utilization in india.zip

52 - electricity generation by fuel in india.zip

52 - irena database.zip

8 - Bonus

53 - Discount-Coupons-october-2022.pdf

53 - Extras.html

53 - my personal website with great offers for you.zip

Description

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

Who is this for?

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

More details

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.
â€‹
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.

â€‹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.Â Just follow what is shown on screen, because we go slowly and explain everything in detail.
Who this course is for:
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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Python

Dr. Spyros Giannelos

Instructor's Courses

Dr. Spyros Giannelos, is a Research Scientist, leading energy projects using Mathematical Optimization & Data Science. Specifically such projects have been around energy investments with a focus on electricity. He holds a Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization from Imperial College London. His research interests include energy investments, optimization, data science, machine learning and quantitative finance.

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