Microgrids can be conceived as integrated operational and technological systems that help in optimizing sustainable power generation, distribution, and consumption. The concept refers to a set of energy consumption units and distributed energy resources operating as a single controllable system that provides power to its local area. Microgrids can have two different modes of operation: the islanded mode and the grid-connected mode. The design of microgrids typically comprise a cyber-physical infrastructure (i.e., including a digital system) for intelligent management and operation.
In the agriculture sector, microgrids can have high potential in improving energy security, sustainability and efficiency, where power demands of farming activities and infrastructure (e.g., greenhouses and in-door farming) can be locally and intelligently matched with low carbon and renewable energy sources. Microgrids are typically managed and operated by a smart cyber-physical system capable of measuring and monitoring a multitude of parameters, such as power usage, power generation, irrigation, and other environmental and operational data from IoT devices. Such system should enable data management and exchange between the physical components and with the involved stakeholders in an efficient, secure and reliable manner. It should provide data-driven support decisions and enable analytics to end-users (e.g. farmers).
This master thesis project will investigate the different cyber-physical system designs that have been used for microgrids operation in prior literature, and propose an intelligent design, integrated with big data systems, for microgrids operation in the agriculture and farming infrastructure. Investigating the possibilities to use a digital twin for managing microgrids could also be part of this thesis.
The work in this master thesis entails:
- To assess the potential needs for microgrids in the agriculture sector.
- To perform a systematic literature review on the different cyber-physical systems design for data collection, processing, analytics and exchange between different systems components in microgrids.
- To investigate the possibilities of big data systems and digital twins for managing microgrids.
- To develop and propose a smart system design for microgrids in the farming infrastructure.
- Moharm, Karim. "State of the art in big data applications in microgrid: a review." Advanced Engineering Informatics 42 (2019): 100945.
- Hirsch, Adam, Yael Parag, and Josep Guerrero. "Microgrids: A review of technologies, key drivers, and outstanding issues." Renewable and sustainable Energy reviews 90 (2018): 402-411.
- Zia, Muhammad Fahad, Elhoussin Elbouchikhi, and Mohamed Benbouzid. "Microgrids energy management systems: A critical review on methods, solutions, and prospects." Applied energy 222 (2018): 1033-1055.
- Ouammi, Ahmed, et al. "Optimal operation scheduling for a smart greenhouse integrated microgrid." Energy for Sustainable Development 58 (2020): 129-137.
- Courses: Programming in Python (INF-22306), Big Data (INF-34306)
- Required skills/knowledge: Programming in Python, big data analytics, interest about sustainability, energy transition and smart grids.
Key words: Smart Systems Design / Software engineering for Sustainability