An Agent-Based Collaborative Platform for the Optimized Trading of Renewable Energy within a Community

• Autorzy: Tasquier L. , Aversa R.
• Języki publikacji: EN

Abstrakty

EN

Cities are increasingly recognized for their ability to play a catalytic role in addressing climate and energy challenges using technologically innovative approaches. Since energy used in urban areas accounts for about 40% of total EU energy consumption, a change of direction towards renewable energy is necessary in order to alleviate the usage of carbonized electricity and also to save money. A combination of IT and telecommunication technologies is necessary to enable the energy and resources saving. ICT based solutions can be used to enable energy and money saving not only for a single building, but for the whole community of a neighborhood. In this paper a model for the energy cost minimization of a neighborhood together with an agent-based interaction model that reproduces the proposed formal representation is presented. Furthermore the authors present a prototype implementation of this model and first experimental tests.

Słowa kluczowe

EN

collective intelligence; energy cost minimization; multi-agent systems; smart city

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2014
• Tom: nr 4
• Strony: 61--70
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Tasquier L.

• Dipartimento di Ingegneria Industriale e dell’Informazione, Second University of Naples, Aversa, Italy

autor

Aversa R.

• Dipartimento di Ingegneria Industriale e dell’Informazione, Second University of Naples, Aversa, Italy

Bibliografia

• [1] D. Giusto, A. Lera, G. Morabito, and L. Atzori, The Internet of Things. Springer, 2010.
• [2] K. Su, J. Li, and H. Fu, “Smart city and the applications,” in Proc. Int. Conf. Elec. Commun. Contr. ICECC 2011, Ningbo, China, 2011, IEEE, pp. 1028–1031.
• [3] F. Bellifemine, A. Poggi, and G. Rimassa, “JADE–A FIPA-compliant agent framework,” in Proc. Pract. Appl. Intell. Agents Multi-Agents PAAM 1999, London, 1999, vol. 99, no. 97–108.
• [4] “FIPA” [Online]. Available: http://www.fipa.org
• [5] B. Snyder, D. Bosnanac, and R. Davies, ActiveMQ in Action. Shelter Isl., USA: Manning, 2011.
• [6] A. Videla and J. J. Williams, RabbitMQ in Action. Shelter Isl., USA: Manning, 2012.
• [7] A. Rogers, S. D. Ramchurn, and N. R. Jennings, “Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research”, in Proc. 26th AAAI Conf. Artif. Intell. AAAI-12, Toronto, Canada, 2012, pp. 2166–2172.
• [8] I. Prac¸a, C. Ramos, Z. Vale, and M. Cordeiro, “Intelligent agents for negotiation and game-based decision support in electricity markets”, Engin. Intell. Syst. Elec. Engin. Commun., vol. 13, no. 2, p. 147, 2005.
• [9] T. A. Rodden, J. E. Fischer, N. Pantidi, K. Bachour, and S. Moran, “At home with agents: exploring attitudes towards future smart energy infrastructures”, in Proc. SIGCHI Conf. Human Fact. Comput. Syst., Paris, France, 2013, pp. 1173–1182.
• [10] Y. Jia-hai, Y. Shun-kun, and H. Zhao-guang, “A multi-agent trading platform for electricity contract market”, in Proc. 7th Int. Power Engin. Conf. IPEC 2005. Singapore, 2005, pp. 1024–1029.
• [11] D. Whitehead, “The El Farol bar problem revisited: Reinforcement learning in a potential game”, ESE Discussion Papers, no. 186, Edinburgh School of Economics, 2008.
• [12] N. C. Truong, L. Tran-Thanh, E. Costanza, and D. S. Ramchurn, “Activity prediction for agent-based home energy management”, in Proc. 4th Int. Worksh. Agent Technol. for Energy Syst. ATES 2013, Saint Paul, Minnesota, USA, 2013.
• [13] N. Capodieci, E. F. Alsina, and G. Cabri, “A context-aware agent-based approach for deregulated energy market”, in Proc. 21st IEEE Int. Worksh. Enabling Technol. Infrastruc. Collabor. Enterpr. WETICE 2012, Toulouse, France, 2012, pp. 16–21.
• [14] R. Aversa, B. Martino, M. Ficco, and S. Venticinque, “Simulation and support of critical activities by mobile agents in pervasive and ubiquitous scenarios,” in Proc. 10th Int. Symp. Parall. Distrib. Process. Appl. ISPA 2012, Madrid, Spain, 2012, pp. 815–822.
• [15] N. Capodieci, G. Cabri, G. A. Pagani, and M. Aiello, “Adaptive game-based agent negotiation in deregulated energy markets,” in Proc. IEEE Int. Conf. Collabor. Technol. Syst. CTS 2012, Denver, CO, USA, 2012, pp. 300–307.
• [16] A. Amato et al., “Software agents for collaborating smart solar-powered micro-grids”, in Smart Organizations and Smart Artifacts, L. Caporarello, B. Di Martino, and M. Martinez, Eds. Lecture Notes in Information Systems and Organisation, vol. 7, pp. 125–133, Springer, 2014.
• [17] S. Venticinque, L. Tasquier, and B. Di Martino, “A restfull interface for scalable agents based cloud services”, Int. J. of Ad Hoc and Ubiquitous Comput., vol. 16, no. 4, pp. 219–231, 2014.
• [18] L. Tasquier, M. Scialdone, R. Aversa, and S. Venticinque, “Agent based negotiation of decentralized energy production”, in Intelligent Distributed Computing VIII, D. Camacho, L. Braubach, S. Venticinque, and C. Badica, Eds. Studies in Computational Intelligence, vol. 570, pp. 59–67. Springer, 2015.
• [19] S. Venticinque, L. Tasquier, and B. Di Martino, “Agents based cloud computing interface for resource provisioning and management”, in Proc. 6th IEEE Int. Conf.Complex, Intell. Softw. Intens. Syst. CISIS 2012, Palermo, Italy, 2012, pp. 249–256.
• [20] R. Aversa, L. Tasquier, and S. Venticinque, “Cloud agency: A guide through the clouds,” Mondo Digitale, vol. 13, no. 49, 2014.