|Unit Name||SMART GRIDS|
|Unit Duration||12 weeks|
Master of Engineering (Electrical Systems)
Duration: 2 years
|Unit Creator / Reviewer||Ujjwal Datta|
Master total course credit points = 48
(3 credits x 12 ( units )+12 credits ( thesis)
|Mode of Delivery||Online or on-campus.|
10 hours per week
Lecture - 1 hour
Tutorial Lecture - 1 hours
Practical/ Lab - 1 hour ( where applicable )
Personal Study recommended - 7 hours ( guided and unguided)
Unit Description and General Aims
The unit introduces engineers to the principles of smart grid in power system application under various network conditions including smart electricity network, the role of communication in smart grid deployment and develop the understanding of overall smart grid components.
The unit will discuss the basic components of smart grid system and will cover topics of smart grid regulation and market economics, communication technologies and smart transmission/distribution grid. The application of various power electronic devices and the management of energy storage, electric vehicle and demand side management and AMI will also be discussed. The computation tools for smart grid design, adaptive protection, Interoperability standards and software infrastructure will also be highlighted. The understanding of big data and cyber security in smart grid will also be discussed.
After covering the necessary theory, the unit will introduce practical studies involving the simulation of various system conditions using an appropriate software tool and interpreting the results obtained.
On successful completion of this Unit, students are expected to be able to:
- Attain the required theoretical knowledge on smart grids and demonstrate the ability to accurately model smart grid implementation for carrying out different smart grid application studies described in the subsequent outcomes.
Bloom’s Level 6
- Demonstrate the ability to articulate smart grid deployment in the power system and study the effect on system performance.
Bloom’s Level 6
- Demonstrate the ability to simulate the application of power electronics devices, and identify and evaluate the respective parameters.
Bloom’s Level 5
- Establish through the studies the ability of the modelled smart grid system to maintain grid constraints with the integration of electric vehicles and demand side management.
Bloom’s Level 6
- Analyse the challenges of smart grid protection and recommend an adaptive protection for smart grids.
Bloom’s Level 5
- Draw and present conclusions on practical measures to improve the different security threats in smart grid deployment.
Bloom’s Level 5
(e.g. Assessment -2000 word essay (specify topic)
Examination ( specify length and format))
(After Topic 5)
|Weighting (% of total unit marks)||Learning Outcomes Assessed|
Type: Multi-choice test / Group work / Short answer questions / Role Play / Self-Assessment / PresentationTopic examples: smart grid basic info, market regulation and economics, smart transmission/distribution grid.
|After Topic 5||20%||1, 2|
Type: Report / Research / Paper / Case Study / Problem analysis / Project / Professional recommendation
Example: Report (Midterm Project)
Topic examples: Report on the application of energy storage and demand side management in smart grid.
|After Topic 8||25%||1,2,3|
Type: Report (Final Project)
[If a continuation of the midterm, this should complete the report by adding sections on: workings, implementation, results, verification/validation, conclusion/challenges and recommendations/future work. If this is a new report, all headings from the midterm and the final reports must be included.]
Word length: 2000Topic examples: The importance and deployment of adaptive protection in smart grid.
|After Topic 10||35%||4,5,6|
Type: May be in the form of quizzes, class tests, practical assessments, remote labs, simulation software or case studiesExample: Energy management of EV in various grid application; vehicle-to-grid, vehicle-to-home, vehicle-to-building, grid-to-vehicle
Attendance / Tutorial Participation
Example: Presentation, discussion, group work, exercises, self-assessment/reflection, case study analysis, application.
Prescribed and Recommended readings
- Janaka B. Ekanayake et al., Smart Grid: Technology and Applications, 2012, ISBN-13: 978-0470974094
- Salman K. Salman, Introduction to the Smart Grid Concepts, technologies and evolution, 2017, ISBN-13: 978-1-78561-119-3
- Robert C. Qiu and Paul Antoni, Technical Challenges for Smart Grid in Smart Grid using Big Data Analytics: A Random Matrix Teory Approach, 2017, ISBN: 978-1-118-49405-9
- Daphne MahPeter, HillsVictor O.K., LiRichard Balme, Smart Grids: The Regulatory Challenges in Smart Grid Applications and Developments, 2014, ISBN-13: 978-1447162803.
- Takuro Sato et al., Smart Grid Standards: Specifications, Requirements, and Technologies, 2015, ISBN: 978-1-118-65369-2
- Fereidoon P. Sioshansi, Smart Grid: Integrating Renewable, Distributed and Efficient Energy, 2011, ISBN-13: 978-0123864529
- James A. Momoh, Smart Grid: Fundamentals of Design and Analysis, 2012, ISBN: 978-1-118-15610-0
- Feng Ye, Yi Qian, Rose Qingyang Hu, Smart Grid Communication Infrastructures: Big Data, Cloud Computing, and Security, 2018, ISBN: 978-1-119-24016-7
- Pengwei Du and Ning Lu, ergy Storage for Smart Grids: Planning and Operation for Renewable and Variable Energy Resources (1st ed), 2014, ISBN-13: 978-0124104914
- Panagiotis D. Diamantoulakis, Vasileios M. Kapinas, George K. Karagiannidis, Big Data Analytics for Dynamic Energy Management in Smart Grids, Big Data Research, Volume 2, Issue 3, 2015, Pages 94-101, https://doi.org/10.1016/j.bdr.2015.03.003
- Amr A. Munshi, Yasser A.-R. I. Mohamed, Big data framework for analytics in smart grids, Electric Power Systems Research, Volume 151, 2017, Pages 369-380, https://doi.org/10.1016/j.epsr.2017.06.006
- Fabiano Pallonetto, Mattia De Rosa, Federico Milano, Donal P. Finn, Demand response algorithms for smart-grid ready residential buildings using machine learning models, Applied Energy, Volume 239, 2019, Pages 1265-1282, https://doi.org/10.1016/j.apenergy.2019.02.020
- H. Taherian, M. R. Aghaebrahtmi and S. R. Goldani, "Application of a Customers’ Behavior Learning Machine for Profit Maximization of a Retail Electric Provider in Smart Grid," 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), Sonderborg, Denmark, 2019, pp. 1-6. doi: 10.1109/CPE.2019.8862346
- Articles from https://ieeexplore.ieee.org/Xplore/home.jsp and https://www.sciencedirect.com/ and IEEE Transactions on Smart Grids
One topic is delivered per contact week.
Introduction to Smart Grids and Their Market Economics
- Evolution of smart grids
- The need of policy and regulation in smart grids
- Main regulatory issues (utility disincentives, pricing inefficiencies, and cybersecurity and privacy) and challenges in the electricity market
- Electricity market structure
- Incentivizing market policy and ancillary services
Infrastructure of Smart Grids
- Technical challenges of smart grids
- Composition of the smart grid (Standards adaptation, technical components, technical perspective, conceptual reference model perspective)
- Pathways for designing smart grids
Smart Transmission/Distribution Grids
- Distributed management systems
- SCADA systems and smart grid vision
Applications of Power Electronics and Energy Storage Systems
- FACTS application in smart grids
- Distributed energy resources and their applications
- Energy storage systems
- Power system reliability with intelligent operation strategies
- Operation of large-scale battery storage in the energy market
Demand Side Management and AMI
- Demand side management and response in a smart grid
- The role of smart metering in smart grids
- Use case: smart home and building automation
Electric Vehicles (EVs) Integration and Its Grid Impact
- Intro to electric drive vehicle and the challenges to EV adoption by customers and utility
- Smart grid technologies for EVs load management
- EV flexibility in the grid integration
- Grid support from EVs (vehicle-to-grid, vehicle-to-building, vehicle-to-home etc.)
Measurement and Communication Technologies
- Communication technologies and standards for smart grids
- Multiagent systems (implementation, specification, technique)
- Internet of things (IoT) and the association with smart grids
Computation Tools for Smart Grid Design and Adaptive Protection
- Computational challenges and protection systems under smart grid environment
- Architecture of smart grid protection systems
- Smart adaptive protection for microgrids and distribution networks
Interoperability Standards and Software Infrastructure
- Type and characteristics of interoperability standards for smart grid
- Standards development organizations (SDOs) and key interoperability standards
- Software Architecture in smart grids
- IT challenges, essential smart grid software platform
Artificial Intelligence in Smart Grids
- Big data characteristics and application in smart grids
- Cloud computing implementation in the smart grids
- Application of machine learning algorithms for performance measurements
Cyber Security in Smart Grids
- Information security for the smart grid
- Cyber infrastructure and security of power systems
- Smart grid cyber-security standards and challenges
- Security schemes for AMI private networks and public networks
Project and Revision
In the final week, total course contents will be briefly discussed. Students will have an opportunity to review the contents covered so far. Opportunity will be provided for a review of student work, to clarify any outstanding issues, and to work on finalizing the major assessment report.
The Australian Engineering Stage 1 Competency Standards for the Professional Engineer, approved as of 2013. This table is referenced in the mapping of graduate attributes to learning outcomes and via the learning outcomes to student assessment.
|Stage 1 Competencies and Elements Competency|
|1.||Knowledge and Skill Base|
|1.1||Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.|
|1.2||Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.|
|1.3||In-depth understanding of specialist bodies of knowledge within the engineering discipline.|
|1.4||Discernment of knowledge development and research directions within the engineering discipline.|
|1.5||Knowledge of engineering design practice and contextual factors impacting the engineering discipline.|
|1.6||Understanding of the scope, principles, norms, accountabilities and bounds of sustainable engineering practice in the specific discipline.|
|2.||Engineering Application Ability|
|2.1||Application of established engineering methods to complex engineering problem solving.|
|2.2||Fluent application of engineering techniques, tools and resources.|
|2.3||Application of systematic engineering synthesis and design processes.|
|2.4||Application of systematic approaches to the conduct and management of engineering projects.|
|3.||Professional and Personal Attributes|
|3.1||Ethical conduct and professional accountability.|
|3.2||Effective oral and written communication in professional and lay domains.|
|3.3||Creative, innovative and pro-active demeanour.|
|3.4||Professional use and management of information.|
|3.5||Orderly management of self and professional conduct.|
|3.6||Effective team membership and team leadership.|
Additional resources or files: N/A