Online matching and negotiations algorithms in P2P energy trading platforms
Research area/discipline: Simulation Modelling, Computational Social Simulation
Prerequisites: Programming in Python (INF-22306) and/or Software Engineering (INF-32306)
Peer-to-Peer (P2P) markets, which were originally designed to prioritize the wellbeing of the participating individuals by providing maximum individual freedom, financial independence and privacy, has recently started to emerge in the energy sector and “smart grids”. This evolution in the electricity network was enabled by several ICT advancements.
First of all, recent years have seen a large increase in the number of installed smart metering systems in Europe, which collect energy consumption and production data in real-time.
Second of all, home IoT-based energy management systems (EMS) are becoming widely available. An EMS is an intelligent system that is capable of balancing power usage in a building by measuring and controlling the operation of all connected electric assets.
Third of all, advanced digital platforms, such as distributed ledgers, have shown their disruptive potential in this sector, providing fully decentralized, transparent and verifiable P2P energy trading solutions.
This thesis will investigate and develop fundamental and applicable agents matching and negotiations solutions within an online P2P energy trading platform. The agents represent electricity prosumers (consumers + producer) who want to trade energy directly between each other but might have different availability and preferences.
Depending on the interests and background of the student, the master project draws on tools and techniques from multi-agent decision-making, preference modeling, machine learning, decentralized optimization and automated negotiation. Actual electricity consumption and solar energy generation data for residential prosumers will be made available as part of this thesis.
The work in this master thesis entails:
To perform a literature review on P2P energy trading platforms.
- To develop and compare matching and negotiations solutions within an online P2P energy trading platform.
- To assess the impact of P2P matching algorithms on end-user benefits.
Programming in Python, modeling, interest about smart grids, P2P markets and end-user centered decentralized approaches.
- Parag, Yael, and Benjamin K. Sovacool. "Electricity market design for the prosumer era." Nature energy 1.4 (2016): 1-6.
- van Leeuwen, Gijs, et al. "An integrated blockchain-based energy management platform with bilateral trading for microgrid communities." Applied Energy 263 (2020): 114613.
- Baez-Gonzalez, Pablo, et al. "A Power P2P Market Framework to Boost Renewable Energy Exchanges in Local Microgrids." 2019 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2019.
For more information:
For making an appointment to discuss the thesis topic, please send an email to: Dr.Ir. Tarek AlSkaif | Assistant Professor | Information Technology group (INF) | Wageningen University & Research (WUR) | https://www.wur.nl/en/Persons/Tarek-dr.-T-Tarek-Alskaif.htm | firstname.lastname@example.org