Abstract
In this paper we develop and test a distributed algorithm providing Energy Consumption Schedules (ECS) in smart grids for a residential district. The goal is to achieve a given aggregate load profile. The NP-hard constrained optimization problem reduces to a distributed unconstrained formulation by means of Lagrangian Relaxation technique, and a meta-heuristic algorithm based on a Quantum inspired Particle Swarm with Lévy flights. A centralized iterative reputation-reward mechanism is proposed for end-users to cooperate to avoid power peaks and reduce global overload, based on random distributions simulating human behaviors and penalties on the effective ECS differing from the suggested ECS. Numerical results show the protocols effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baharlouei, Z., Hashemi, M., Narimani, H., Mohsenian-Rad, H.: Achieving optimality and fairness in autonomous demand response: benchmarks and billing mechanisms. IEEE Trans. Smart Grid 4(2), 968–975 (2013)
Borean, C., Ricci, A., Merlonghi, G.: Energyhome: a user-centric energy management system. Metering Int. 3, 52 (2011)
Borean, C., Grasso, E.: QPSOL: quantum particle swarm optimization with levys flight. In: ICCGI 2014, Seville (2014)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, UK (2004)
Campos-Nanez, E., Garcia, A., Li, C.: A game-theoretic approach to efficient power management in sensor networks. Operat. Res. 56(3), 552 (2008)
Caron, S., Kesidis, G.: Incentive-based energy consumption scheduling algorithms for the smart grid. In: Proceedings of the 1st IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 391–396 (2010)
GAMA-platform. https://code.google.com/p/gama-platform/
Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870 (1992)
INTrEPID, FP7-ICT project. http://www.fp7-intrepid.eu/
Joo, J.-Y., Ilic, M.D.: Multi-layered optimization of demand resources using lagrange dual decomposition. IEEE Trans. Smart Grid. 4(4), 2081–2088 (2013)
Krause, J., et al.: A survey of swarm algorithms applied to discrete optimization problems. In: Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M. (eds.) Swarm Intelligence and Bio-Inspired Computation. Theory and Applications, pp. 169–191. Elsevier Science & Technology Books, UK (2013)
Liyan J., Lang T.: Day ahead dynamic pricing for demand response in dynamic environments. In: IEEE Conference on Decision and Control (CDC), pp. 5608–5613 (2013)
Pinyol, I., Sabater-Mir, J.: Computational trust and reputation models for open multi-agent systems: a review. Artif. Intell. Rev. 40(1), 1–25 (2013)
Tuyls, K., Nowe, A.: Evolutionary game theory and multi-agent reinforcement learning. Knowl. Eng. Rev. 20(01), 63–90 (2005)
Vinyals, M., et al.: Coalitional energy purchasing in the smart grid In: Proceedings of the IEEE International on Energy Conference and Exhibition, pp. 848–853 (2012)
Acknowledgments
Authours would like to thank Ennio Grasso for the scientific hints on the mathematical modeling and numerical implementation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Borra, D., Iori, M., Borean, C., Fagnani, F. (2015). A Reputation-Based Distributed District Scheduling Algorithm for Smart Grids. In: Giaffreda, R., et al. Internet of Things. User-Centric IoT. IoT360 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-19656-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-19656-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19655-8
Online ISBN: 978-3-319-19656-5
eBook Packages: Computer ScienceComputer Science (R0)