Economic Dispatch Optimization of a grid with Wind Storage

Economic Dispatch Optimization of a grid with Wind & Storage, available at $54.99, has an average rating of 4.98, with 34 lectures, based on 164 reviews, and has 2288 subscribers.

You will learn about IMPORTANT: Use this code at checkout (remove spaces): F4C E32D C4BC C0 05F EFA9 and get access to the online course ALGORITHMIC ENERGY?ECONOMICS Understand the fundamentals of economic dispatch and its importance in electricity grid management. Learn to define input data, build models, and formulate mathematical representations for economic dispatch in Python/Pyomo. Gain practical skills in solving economic dispatch models with storage and CO2 constraints using Python and GAMS. Develop the ability to model and solve economic dispatch problems that include renewable energy sources such as wind. Explore advanced concepts by modeling economic dispatch in a 24-bus grid, including understanding power system topology and reliability. Obtain practical experience in debugging, optimizing, and exporting model results to Excel for further analysis. This course is ideal for individuals who are Professionals working in the energy sector looking to enhance their skills in electricity grid management and renewable integration. or Quantitative developers who want to apply their technical skills to the field of energy system modeling and optimization, leveraging their expertise in data analysis and mathematical modeling. or Data scientists and analysts interested in applying their skills to energy system modeling and optimization. or Students studying electrical engineering, energy systems, or related fields who want to understand economic dispatch models. or Researchers focusing on energy economics, optimization, or renewable energy technologies. or Policy makers and regulatory professionals aiming to gain a deeper understanding of economic dispatch and its implications for renewable energy. or PhD candidates or Anyone with a general interest in energy management, renewable energy, and the technical aspects of electricity grid operation. It is particularly useful for Professionals working in the energy sector looking to enhance their skills in electricity grid management and renewable integration. or Quantitative developers who want to apply their technical skills to the field of energy system modeling and optimization, leveraging their expertise in data analysis and mathematical modeling. or Data scientists and analysts interested in applying their skills to energy system modeling and optimization. or Students studying electrical engineering, energy systems, or related fields who want to understand economic dispatch models. or Researchers focusing on energy economics, optimization, or renewable energy technologies. or Policy makers and regulatory professionals aiming to gain a deeper understanding of economic dispatch and its implications for renewable energy. or PhD candidates or Anyone with a general interest in energy management, renewable energy, and the technical aspects of electricity grid operation.

Enroll now: Economic Dispatch Optimization of a grid with Wind & Storage

Summary

Title: Economic Dispatch Optimization of a grid with Wind & Storage

Price: $54.99

Average Rating: 4.98

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

IMPORTANT: Use this code at checkout (remove spaces): F4C E32D C4BC C0 05F EFA9 and get access to the online course ALGORITHMIC ENERGY?ECONOMICS

Understand the fundamentals of economic dispatch and its importance in electricity grid management.

Learn to define input data, build models, and formulate mathematical representations for economic dispatch in Python/Pyomo.

Gain practical skills in solving economic dispatch models with storage and CO2 constraints using Python and GAMS.

Develop the ability to model and solve economic dispatch problems that include renewable energy sources such as wind.

Explore advanced concepts by modeling economic dispatch in a 24-bus grid, including understanding power system topology and reliability.

Obtain practical experience in debugging, optimizing, and exporting model results to Excel for further analysis.

Who Should Attend

Professionals working in the energy sector looking to enhance their skills in electricity grid management and renewable integration.

Quantitative developers who want to apply their technical skills to the field of energy system modeling and optimization, leveraging their expertise in data analysis and mathematical modeling.

Data scientists and analysts interested in applying their skills to energy system modeling and optimization.

Students studying electrical engineering, energy systems, or related fields who want to understand economic dispatch models.

Researchers focusing on energy economics, optimization, or renewable energy technologies.

Policy makers and regulatory professionals aiming to gain a deeper understanding of economic dispatch and its implications for renewable energy.

PhD candidates

Anyone with a general interest in energy management, renewable energy, and the technical aspects of electricity grid operation.

Target Audiences

Professionals working in the energy sector looking to enhance their skills in electricity grid management and renewable integration.

Quantitative developers who want to apply their technical skills to the field of energy system modeling and optimization, leveraging their expertise in data analysis and mathematical modeling.

Data scientists and analysts interested in applying their skills to energy system modeling and optimization.

Students studying electrical engineering, energy systems, or related fields who want to understand economic dispatch models.

Researchers focusing on energy economics, optimization, or renewable energy technologies.

Policy makers and regulatory professionals aiming to gain a deeper understanding of economic dispatch and its implications for renewable energy.

PhD candidates

Anyone with a general interest in energy management, renewable energy, and the technical aspects of electricity grid operation.

IMPORTANT FOR YOU:

IMPORTANT: Use this code at checkout (remove spaces):

F4C E32D C4BC C0 05F EFA9

and get access to the online course “ALGORITHMIC ENERGY ECONOMICS”

WHAT THIS COURSE IS ABOUT:

This course is designed to provide you with a deep understanding of economic dispatch, a crucial concept in the efficient operation of power systems. You’ll start with a thorough introduction to the basics before diving into the specifics of economic dispatch with storage in a 1-bus grid.  You will learn to define input data, build models, formulate mathematical representations, and solve these models using Python/Pyomo and GAMS, complete with practical debugging techniques.

As you progress, the course will guide you through more complex scenarios, including the integration of CO2 constraints and renewable energy sources such as wind. You’ll gain experience modelling and solving economic dispatch problems in Python and GAMS, both with and without storage. This includes understanding the convexity of objective functions and how to incorporate CO2 constraints effectively. Then, you will focus on economic dispatch in a more complex 24-bus grid, learning about power system topology, reliability test systems, and the per-unit system.

By the end of the course, you will have modelled, solved, and optimized various dispatch scenarios and be able to export your results to Excel for further analysis.

Whether you’re a student, researcher, or industry professional, this course offers valuable insights and skills to enhance your understanding and management of electricity grids with renewables and storage.

There are no prerequisites. The course uses Python, Pyomo, and GAMS, assuming no prior experience. The lectures are easy to follow because the videos are very detailed. Come back here every 6 – 12 months to watch the updated version, as the content is regularly updated.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Economic Dispatch with Energy Storage in a 1-bus grid

Lecture 1: Introduction

Lecture 2: Define the input data in Python

Lecture 3: Define the model

Lecture 4: Mathematical Formulation

Lecture 5: Defining the decision variables

Lecture 6: Defining the objective and constraints

Lecture 7: Solving the model and making necessary plots

Lecture 8: Modelling and solving in GAMS

Lecture 9: Debugging in GAMS

Chapter 3: Economic Dispatch with Storage and CO2

Lecture 1: Solving the model in Python with Storage and CO2

Lecture 2: Convexity of the objective function and the CO2 constraint

Lecture 3: Modelling and solving in GAMS: With Storage and CO2

Lecture 4: Modelling in Pyomo: Without Storage, with CO2

Lecture 5: Modelling in GAMS: Without Storage, with CO2

Chapter 4: Economic Dispatch with Storage, wind and CO2

Lecture 1: Modelling in Python: With storage, wind and CO2

Lecture 2: Modelling in Python: No Storage, with wind & CO2

Lecture 3: Modelling in GAMS

Chapter 5: Conclusions & Code

Lecture 1: GAMS: Sending the results to Excel

Lecture 2: Conclusions

Lecture 3: Pyomo code

Chapter 6: Economic Dispatch with Storage in a 24-bus grid

Lecture 1: Introduction and formulation

Lecture 2: What is a topology of a power system

Lecture 3: What is a reliability test system

Lecture 4: What is the per-unit system

Lecture 5: Modelling in Python

Lecture 6: Modelling in Python: Defining the constants

Lecture 7: Defining the decision variables

Lecture 8: Defining the constraints

Lecture 9: Optimal solution

Lecture 10: Defining more constants

Lecture 11: Solving in GAMS

Chapter 7: Solving without Storage

Lecture 1: Python modelling

Chapter 8: Conclusions

Lecture 1: Overview

Instructors

Researcher – Software Engineering and Energy
Data Science, Optimization, ML applied to Energy

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 7 votes

5 stars: 157 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!