| Project Id
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BITSRMIT024B001259
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| Project Detail
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| Project Title
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AI/ML-based Techniques to Enhance Grid Integration of Renewable Energy and Electric Vehicles
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| Senior Supervision Team (BITS)
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| Supervisor name and Title
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Dr. Alivelu Manga Parimi
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School or Department (or company, if applicable)
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BITS PILANI, HYDERABAD CAMPUS
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Email ID
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alivelu@hyderabad.bits-pilani.ac.in
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| URL for more info
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https://universe.bits-pilani.ac.in/hyderabad/alivelu/profile
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| a) Are you currently supervising a BITS or RMIT HDR student?
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YES
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| Please comment how many you are supervising
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8
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
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| If yes, please elaborate
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| Senior Supervision Team (RMIT)
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| Supervisor name and Title
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Dr. Kazi Hasan
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School or Department (or company, if applicable)
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STEM
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Email ID
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kazi.hasan@rmit.edu.au
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| URL for more info
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https://www.rmit.edu.au/contact/staff-contacts/academic-staff/h/hasan-dr-kazi
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| a) Are you currently supervising a BITS or RMIT HDR student?
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YES
|
| Please comment how many you are supervising
|
9
|
| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
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| Other Supervisors (BITS)
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| Supervisor name and Title
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School or Department (or company, if applicable)
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| Phone Number (Optional)
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Email ID
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| URL for more info
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| Other Supervisors (BITS)
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| Supervisor name and Title
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School or Department (or company, if applicable)
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| Phone Number (Optional)
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Email ID
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| URL for more info
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| Field of Research (For Codes)
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| 400804 | Electrical energy storage | 20.00 |
| 400805 | Electrical energy transmission, networks and syste | 80.00 |
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| Project Description
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The operational aspects of future power systems are expected to be influenced significantly by the increasing grid integration of renewable energy and electric vehicles (EVs). This PhD project will attempt to develop AI-based tools and techniques for increasing the penetration of renewable energy and EVs on future power systems and will investigate the enabling technologies to facilitate high EV integration into the electricity grids. This project aims to explore the following research contents:
• Development of AI/ML-based models for spatiotemporal renewable generation and EV charging load at the distribution substation based on data-driven approaches.
• Articulation of an appropriate simulation model of a distribution network considering correlations among EV, solar PV, and system loads.
• Detailed time-sequential simulation studies with collected historical data on the renewable and EV grid impact assessment.
• Identification and validation of the AI/ML-based mitigation techniques for EV grid integration problems; and
• Provision of recommendations on the planning and operational strategies of the renewable and EV-rich distribution networks.
To achieve the outcomes as per the abovementioned objectives, the following methodology will be followed:
• Collecting historical renewable generation and EV charging data from a wide range of customers over the entire annual cycle
• Developing spatiotemporal renewable generation and EV charging profiles in a representative distribution network
• Performing power system simulation with the spatiotemporal renewable generation and EV data in a combined MATLAB-DIgSILENT power factory software package
• Identifying distribution grid problems, including voltage violation, line overloading, and substation capacity limit violation
• Advanced AI-driven techniques, such as deep learning, hybrid deep learning, and transfer learning will be implemented to ensure accuracy and efficiency.
• Validating the proposed solution approaches considering representative network operational scenarios
• The solution platform will go through rigorous testing and experimentation both in the software platform and in hardware-in-the-loop (HIL) loop facilities
• System stability with the new types of tools and resources (such as demand-side management, electric vehicles, and virtual power plants), and grid-forming (GFM) technologies will be assessed and ensured.
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| Project Deliverable/Outcomes
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Renewable and EV-enabling technologies' contributions to grid loading and system voltage profiles will be quantified. The effectiveness of Al-based enabling technologies at different network conditions will be identified. Furthermore, this research will investigate a combined approach of controlled EV charging for grid benefit, proper utilization of renewable generation for environment-friendly solutions, and execution of demand response through dynamic pricing for customer satisfaction.
This research will also propose smart renewable and EV integration to the grid, which can accomplish all-around achievements. The proposed algorithms will be implemented and verified using MATLAB and DIgSILENT PowerFactory software platforms. Successful implementation of the research will progress the highly desirable adoption of renewable and EV integration for a clean and sustainable future.
The expected outcomes of the project would include:
• Models of spatiotemporal renewable generation and EV charging load at the distribution substation
• Simulation model of a distribution network for renewable generation and EV impact assessment
• Simulation results on the renewable generation and EV grid impact assessment
• Proposition of Al/ML-based mitigation techniques for renewable generation and EV grid integration problems
• Validating the proposed solution approaches hardware-in-the-loop (HIL) loop facilities
• Recommendations on the planning and operations with high renewable generation and EV scenarios
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| Research Impact Themes
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| ADVANCED DIGITAL TECHNOLOGIES AND BUSINESS TRANSFORMATION | ARTIFICIAL INTELLIGENCE AND MODELLING; SUPPLY CHAINS AND OPTIMISATION |
| SUSTAINABLE DEVELOPMENT AND ENVIRONMENT
| CLEAN ENERGY AND SUSTAINABLE TECHNOLOGIES |
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| Which RMIT Sustainable Development Goal (SDG) does your project align to
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AFFORDABLE AND CLEAN ENERGY
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| Which RMIT Enabling Impact Platform (EIP) does your project align to
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SUSTAINABLE TECHNOLOGIES AND SYSTEMS PLATFORM
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| Which RMIT Program code will this project sit under?
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DR220 (ELECTRICALANDELECTRONIC)
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| Student Capabilities and Qualifications
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BSc/BE in EEE, power system knowledge
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Research publications, power system software simulation
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MSc/ME/MS in EEE, Energy
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| Preferred discipline of Student
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| Artificial Intelligence, Deep Learning, Information Extraction & Knowledge Extraction, Machine Learning, Natural Language Processing |
| Computer Science/Information Technology |
| Computing: Computer Science, Computer System Security, Software Engineering, Cyber Security & Cyber Physical Systems |
| Electrical and Electronics Engineering, Power Engineering |
| Energy: Carbon Capture/Sequestration/Storage, Renewables |
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