Thesis subject

Digital Twins for Smart Grids (MSc)

Smart grids symbolize the transition from conventional electricity grids, where electricity flows one-way from generators to consumers, to interconnected, resilient and flexible grids that ensure a bidirectional flow of electricity and data between power plants and end-users, and all points in between.
In such complex systems, Information and Communication Technology (ICT)infrastructures are essential in order to build, monitor and manage those systems. Digital twins are an emerging technology for connecting the real world to their virtual counterparts and can help tackle the integration, operation and management challenges in smart grids.

Short description

Smart grids involve large ecosystems connecting producers of energy such as conventional power plants and wind and solar farms with consumers including factories, vehicles, offices, farms/greenhouses and households. The ‘smart’ component involves advanced metering, transmission, distribution, optimization and management of such ecosystems, and greatly relies on ICT infrastructures. Building and managing such systems is a big challenge, and is a prerequisite for achieving certain goals for those systems in the first place, including energy efficiency, resiliency and sustainability.
Digital twins are an emerging technology which has three elements: models of real entities, data from the real world connecting to the models, and additional decision making components such as artificial intelligence. Digital twins can be used to build virtual counterparts for (parts of) smart grids to understand the complex underlying structures and processes, as well as to help with optimization and decision making.
The goal of this thesis is to survey the literature for existing digital twin approaches for smart grids, in a domain of your choice (e.g. agri-food, environmental science or social/business sciences). Based on the insights gained from literature, we also would like to design and build a digital twin to tackle a challenge (e.g. sustainability or energy efficiency) in the chosen domain.

Objectives

  1. Review previous work on the application of digital twin approaches for smart grids in the domain of your choice
  2. Construct and evaluate a digital twin for (parts of) a smart grid for achieving a goal such as sustainability or energy efficiency

Tasks

The work in this thesis entails:

  • To collect full-text articles or PDFs from primary studies and literature reviews on digital twin approaches for smart grids in the domain of your choice
  • To assess the challenges and solutions available in the literature
  • Design and implement a digital twin for (parts of) a smart grid for achieving a goal such as sustainability or energy efficiency
  • Evaluate the effectiveness of the digital twin in a realistic scenario and setting

Literature

  • Huxoll, N., Aldebs, M., Baboli, P.T., Lehnhoff, S. and Babazadeh, D., 2021, September. Model Identification and Parameter Tuning of Dynamic Loads in Power Distribution Grid: Digital Twin Approach. In 2021 International Conference on Smart Energy Systems and Technologies (SEST) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/document/9543095
  • Palensky, P., Cvetkovic, M., Gusain, D., & Joseph, A. (2021). Digital twins and their use in future power systems. Digital Twin, 1(4), 4. https://digitaltwin1.org/articles/1-4
  • Onile, A. E., Machlev, R., Petlenkov, E., Levron, Y., & Belikov, J. (2021). Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review. Energy Reports, 7, 997-1015. https://www.sciencedirect.com/science/article/pii/S2352484721000913

Requirements:

  • Courses: Programming in Python (INF-22306),(Optional), Data Science Concepts (INF-34306) or Machine Learning (FTE-35306)
  • Required skills/knowledge: basic data analytics/machine learning and willingness to learn new software tools, interest about smart grids

Key words: Smart Grids, Digital Twins, Sustainable Energy Transition

Contact person(s)
Dr. Tarek Alskaif ( tarek.alskaif@wur.nl)
Önder Babur (onder.babur@wur.nl)