Abstract
Peer-to-peer energy trading within microgrid (MG) communities emerges as a key enabler of the future transactive distribution system and the transactive electricity market. Energy trading within MGs refers to the idea that the surplus energy of one MG can be used to satisfy the demand of another MG or a group of MGs that form an MG community. These communities can be dynamically established through time, based on the variations of demand and supply of the interconnected MGs. In many modern MGs, Electric Vehicles (EVs) have been considered as a viable storage option due to their ease of use (plug-and-play) and their growing adoption rates by drivers. On the other hand, the dynamic nature of EVs escalates the uncertainty in the transactive distribution system. In this paper, we study the problem of energy trading among MGs and EVs with the aim of power loss minimization where there is uncertainty. We propose a novel Bayesian Coalition Game (BCG) based algorithm, which allows the MGs and EVs to reduce the overall power loss by allowing them to form coalitions intelligently. The proposed scheme is compared with a conventional coalitional game theory-based approach and a Q-learning based approach. Our results show significant improvement over other compared techniques.
Highlights
The power grid has experienced major transformation since the mid-2000s, and it is continuing to evolve [1]
A transactive distribution system is composed of several microgrids (MGs) with generation capacity that can satisfy some portion of the demand of the other MGs by allowing peer-to-peer energy trading through a transactive energy market and using an underlying communication technology [2]
The future transactive energy systems will be built on peer-to-peer energy trading and MG
Summary
The power grid has experienced major transformation since the mid-2000s, and it is continuing to evolve [1]. Minimizing the distance between interconnections reduces power loss, which can be realized by coalitions formed by near-by MG communities. To the best of our knowledge, none of these studies considered the effect of uncertainty arising from the integration of EVs. In this paper, we propose a Bayesian Coalitional Game theory (BCG). Approach [8] to form coalitions to effectively address uncertainty in the varying charging/discharging locations of EVs. In our approach, each MG and EV agent assumes a prior belief function over the type of other agents (either fixed MG or moving EV). As the agents interact through the iterations of BCG, they update their belief estimations, which, in the end, reach coalitions that minimize power loss.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
