Abstract
In this paper we put forward an open multi-agent systems (MAS) architecture for the important and challenging to engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer problem domains. To promote scalability, our solution is provided in the form of modular microservices that are interconnected using a multi-protocol Internet of Things (IoT) platform. On the one hand, the low-level modularity of Smart Grid services allows the seamless integration of different agent strategies, pricing mechanisms and algorithms; and on the other, the IoT-based implementation offers both direct applicability in real-world settings, as well as advanced analytics capabilities by enabling digital twins models for Smart Grid ecosystems. We describe our MAS/IoT-based architecture and present results from simulations that incorporate large numbers of heterogeneous Smart Grid agents, which might follow different strategies for their decision making tasks. Our framework enables the testing of various schemes in simulation mode, and can also be used as the basis for the implementation of real-world prototypes for the delivery of large-scale V2G/G2V services.
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Notes
- 1.
More detailed descriptions of the inter- and intra-agent control and a detailed description of the protocols, including the message syntax and semantics, can be found in our online repository: https://github.com/iatrakis/IoT-V2G-G2V.
- 2.
MQTT is an OASIS standard messaging protocol for the Internet of Things, mqtt.org.
- 3.
REpresentational State Transfer (REST) over Hypertext Transfer Protocol (HTTP).
- 4.
Specifically, consumption and production data originate from the ENTSOE platform, and EV data from the MyElectricAvenue project.
References
Akasiadis, C., Pitsilis, V., Spyropoulos, C.D.: A multi-protocol IoT platform based on open-source frameworks. Sensors 19(19), 4217 (2019)
Burke, M.J., Stephens, J.C.: Energy democracy: goals and policy instruments for sociotechnical transitions. Energy Res. Soc. Sci. 33, 35–48 (2017)
Danner, D., Duschl, W., de Meer, H.: Fair charging service allocation for electric vehicles in the power distribution grid. In: e-Energy 2019, pp. 406–408 (2019)
Espe, E., Potdar, V., Chang, E.: Prosumer communities and relationships in smart grids: a literature review, evolution and future directions. Energies 11(10), 2528 (2018)
Hossein Motlagh, N., Mohammadrezaei, M., Hunt, J., Zakeri, B.: Internet of things (IoT) and the energy sector. Energies 13(2), 494 (2020)
International Energy Agency: Global EV outlook: Towards cross-modal electrification (2018)
Käbisch, S., Peintner, D., Heuer, J., et al.: The OpenV2G project. http://openv2g.sourceforge.net/. Accessed 22 Apr 2022
Karfopoulos, E.L., Hatziargyriou, N.D.: A multi-agent system for controlled charging of a large population of electric vehicles. IEEE Trans. Power Syst. 28(2), 1196–1204 (2013)
Ketter, W., Collins, J., Reddy, P.: Power TAC: a competitive economic simulation of the smart grid. Energy Econ. 39, 262–270 (2013)
Lee, Z., Johansson, D., Low, S.H.: ACN-sim: an open-source simulator for data-driven electric vehicle charging research. In: e-Energy 2019, pp. 411–412. ACM (2019)
Liao, J.T., Huang, H.W., Yang, H.T., Li, D.: Decentralized V2G/G2V scheduling of EV charging stations by considering the conversion efficiency of bidirectional chargers. Energies 14(4), 962 (2021)
Lipman, T., Callaway, D., Peffer, T., von Meier, A.: Open-source, open-architecture software platform for plug-in electric vehicle smart charging in California. California Energy Commission (2020)
Mahela, O.P., et al.: Comprehensive overview of multi-agent systems for controlling smart grids. CSEE J. Power Energy Syst. (2020)
Mihaylov, M., Jurado, S., Avellana, N., et al.: NRGcoin: virtual currency for trading of renewable energy in smart grids. In: 11th International Conference on the EU Energy Market (EEM 2014), pp. 1–6. IEEE (2014)
Sarkar, R., Saha, P.K., Mondal, S., Mondal, A.: Intelligent scheduling of V2G, V2V, G2V operations in a smart microgrid. In: e-Energy 2020, pp. 417–418 (2020)
Savaglio, C., Ganzha, M., Paprzycki, M., et al.: Agent-based internet of things: state-of-the-art and research challenges. Futur. Gener. Comput. Syst. 102, 1038–1053 (2020)
Sheppard, C., Jenn, A.: Grid-integrated electric mobility model (GEM) v1.0. US Department of Energy (2021). https://www.osti.gov//servlets/purl/1765949
Spanoudakis, N., Moraitis, P.: Engineering ambient intelligence systems using agent technology. IEEE Intell. Syst. 30(3), 60–67 (2015)
Valogianni, K., Ketter, W., Collins, J., Zhdanov, D.: Sustainable electric vehicle charging using adaptive pricing. Prod. Oper. Manag. 29(6), 1550–1572 (2020)
Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10, 115–152 (1995)
Zambonelli, F.: Key abstractions for IoT-oriented software engineering. IEEE Softw. 34(1), 38–45 (2017)
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Akasiadis, C., Iatrakis, G., Spanoudakis, N., Chalkiadakis, G. (2022). An Open MAS/IoT-Based Architecture for Large-Scale V2G/G2V. In: Dignum, F., Mathieu, P., Corchado, J.M., De La Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Lecture Notes in Computer Science(), vol 13616. Springer, Cham. https://doi.org/10.1007/978-3-031-18192-4_1
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