Agent-Based Appliance Scheduling for Energy Management in Industry 4.0
With the growing concerns regarding energy consumption, companies and industries worldwide are looking for ways to reduce their costs and carbon footprint linked to energy usage. The rising cost of energy makes energy saving and optimisation a real stake for businesses which have started to implement more intelligent energy management techniques to achieve a reduction of costs. As industries migrate towards more renewable energy sources and more sustainable consumption models, decentralised energy infrastructure is required where actors can manage and monetise energy capabilities.
In fish processing industries, energy is utilised to operate a range of cold rooms and freer units to store and process fish. Modelling thermal loads, appliance scheduling and integration of renewable energy represent key aspects in such industries. To enable the transition towards Industry 4.0 and to efficiently optimise energy in fish industries, multi-agent systems can provide the mechanisms for managing energy consumption and production with standalone entities that can interact and exchange energy with a view of achieving more flexible and informed energy use.
In this paper, we propose a multi-agent coordination framework for managing energy in the fish processing industry. We demonstrate how agents can be devised to model appliances and buildings and to support the formation of smart energy clusters. We validate our research based on a real use-case scenario in Milford Haven port in South Wales by showing how multi-agent systems can be implemented and tested for a real fish industrial site.
KeywordsMulti-agent systems Appliance scheduling Energy management Cost Smart industries
This work is part of the EU INTERREG piSCES project: “Smart Cluster Energy System for the Fish Processing Industry”, grant number 504460.
- 3.El Nabouch, D., Matta, N., Rahim-Amoud, R., Merghem-Boulahia, L.: An agent-based approach for efficient energy management in the context of smart houses. In: Corchado, J.M., et al. (eds.) PAAMS 2013. CCIS, vol. 365, pp. 375–386. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38061-7_35CrossRefGoogle Scholar
- 6.Nieße, A., et al.: Market-based self-organized provision of active power and ancillary services: an agent-based approach for smart distribution grids. In: Proceedings of 2012 Complexity in Engineering (COMPENG). IEEE (2012)Google Scholar
- 7.Shrouf, F., Ordieres, J., Miragliotta, G.: Smart factories in Industry 4.0: a review of the concept and of energy management approached in production based on the Internet of Things paradigm. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE (2014)Google Scholar
- 8.Abras, S., Ploix, S., Pesty, S., Jacomino, M.: A multi-agent home automation system for power management. In: Cetto, J.A., Ferrier, J.L., Costa dias Pereira, J., Filipe, J. (eds.) Informatics in Control Automation and Robotics. LNEE, vol. 15, pp. 59–68. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79142-3_6CrossRefzbMATHGoogle Scholar
- 9.Brazier, F., Cornelissen, F., Gustavsson, R., Jonker, C.M., Lindeberg, O., Polak, B.: Agents negotiating for load balancing of electricity use, Amsterdam, Netherlands, pp. 622–629 (1998)Google Scholar
- 10.Ygge, F., Akkermans, J.M.: Power load management as a computational market. Högskolan i Karlskrona/Ronneby...Kyoto, Japan, pp. 393–400 (1996)Google Scholar
- 12.Tolbert, L.M., Qi, H., Peng, F.Z.: Scalable multi-agent system for real-time electric power management, vol. 3, Vancouver, BC, Canada, pp. 1676–1679 (2001)Google Scholar
- 13.Lum, R., Kotak, D.B., Gruver, W.A.: Multi-agent coordination of distributed energy systems, vol. 3, Waikoloa, HI, United States, pp. 2584–2589 (2005)Google Scholar
- 14.Zhenhua, J.: Agent-based control framework for distributed energy resources microgrids, Hong Kong, China, pp. 646–52 (2007)Google Scholar
- 15.Dimeas, A.L., Hatziargyriou, N.D.: A MAS architecture for microgrids control, Arlington, VA, United States, vol. 2005, pp. 402–406 (2005)Google Scholar