Engineering IoT-Based Open MAS for Large-Scale V2G/G2V

• Published in: Systems (Basel) 2023-03, Vol.11 (3), p.157
• Main Authors: Spanoudakis, Nikolaos I., Akasiadis, Charilaos, Iatrakis, Georgios, Chalkiadakis, Georgios
• Format: Article
• Language: English
• Subjects: Algorithms; Architecture; Business models; Case studies; Computer architecture; digital twin; Digital twins; Electric vehicle charging; Electric vehicles; Electricity; Electricity pricing; Energy industry; Energy transfer; Engineering; engineering multiagent systems (EMASs); Engineers; Environment models; Internet of Things; internet of things (IoT); Multiagent systems; open multiagent systems; Smart grid; Systems stability; Traffic control; Vehicle-to-grid
• ISSN: 2079-8954; 2079-8954
• DOI: 10.3390/systems11030157

In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer problem domain. The proposed solution addresses the important non-functional requirement of scalability. To this end, we employed an open multiagent systems architecture, arranging agents as modular microservices that were interconnected via a multi-protocol Internet of Things platform. Our approach allows agents to view, offer, interconnect, and re-use their various strategies, mechanisms, or other algorithms as modular smart grid services, thus enabling their seamless integration into our MAS architecture, and enabling the solution of the challenging V2G/G2V problem. At the same time, our IoT-based implementation offers both direct applicability in real-world settings and advanced analytics capabilities via enabling digital twin models for smart grid ecosystems. We have described our MAS/IoT-based architecture in detail; validated its applicability via simulation experiments involving large numbers of heterogeneous agents, operating and interacting towards effective V2G/G2V; and studied the performance of various electric vehicle charging scheduling and V2G/G2V-incentivising electricity pricing algorithms. To engineer our solution, we used ASEME, a state-of-the-art methodology for multiagent systems using the Internet of Things. Our solution can be employed for the implementation of real-world prototypes to deliver large-scale V2G/G2V services, as well as for the testing of various schemes in simulation mode.