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Course Detail

Course Name Micro Grids
Course Code 23AID464
Program B.Tech in Artificial Intelligence and Data Science
Credits 3
Campus Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati

Syllabus

Unit 1

Introduction to Microgrids:Introduction – Overview of Microgrid architecture and components – Advantages and challenges of Microgrids – Applications of Microgrids in different industries

Unit 2

Microgrid Control :Control strategies for Microgrids – Energy management systems for Microgrids – Microgrid stability analysis and control – Microgrid protection and islanding

Unit 3

Microgrid Optimization :Microgrid modeling for optimization – Linear and nonlinear programming for Microgrid optimization – Convex optimization for Microgrid optimization

Unit 4

Advanced Topics in Microgrid Optimization and Control :Distributed optimization for Microgrids – Stochastic optimization for Microgrids – Artificial intelligence and machine learning for Microgrid control and optimization – Integration of renewable energy sources in Microgrids

Objectives and Outcomes

Course Objectives

  • To enable students to identify and analyse the benefits and hurdles of Microgrids in various industries and applications.
  • To equip students with the ability perform comparative analysis of different control strategies and energy management systems.
  • To develop the skillset towards applying optimization techniques for modelling and analysing Microgrids to achieve optimal performance.
  • To develop the knowledge and proficiency for incorporating AI and ML-based to Microgrid optimization and control problems

 

Course Outcomes

After completing this course, students will be able to

CO1

Gain a critical comprehension of Microgrid technology, comprising its structure, constituents, and usage.

CO2

Acquaint themselves with various control tactics and energy management systems for Microgrids.

CO3

Utilize optimization methods to design and evaluate Microgrids for attaining optimal performance

CO4

Assess and incorporate advanced areas in Microgrid optimization and control, involving artificial intelligence and machine learning, to enhance Microgrid effectiveness and dependability.

CO-PO Mapping

PO/PSO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

PSO3

CO

CO1

2

2

–

1

1

1

2

  

–

–

  

2

1

–

1

CO2

2

3

2

2

2

1

2

  

–

–

1

2

2

–

1

CO3

2

3

2

2

2

1

1

  

–

–

1

2

3

–

3

CO4

2

3

3

2

2

1

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–

–

1

2

3

1

3

Evaluation Pattern

Evaluation Pattern

Assessment

Internal/External

Weightage (%)

Assignments (minimum 2)

Internal

30

Quizzes (minimum 2)

Internal

20

Mid-Term Examination

Internal

20

Term Project/ End Semester Examination

External

30

Text Books / References

Text Books / References

Nikos Hatziargyriou, Hassan Bevrani, and Jacob Ostergaard, “Microgrids: Control and Operation”,2017

  1. Chowdhury, S. P. Chowdhury, P. Crossley, “Microgrids and Active Distribution Networks”,2009

Jizhong Zhu, “Optimization of Power System Operation”,2015

Weerakorn Ongsakul and Vo Ngoc Dieu, “Artificial Intelligence in Power System Optimization”,2013

Haitham Abu-Rub, Mariusz Malinowski, and Kamal Al-Haddad, “Power Electronics for Renewable Energy Systems, Transportation, and Industrial Applications”,2014

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