%0 Journal Article %1 Carvalho201261 %A Carvalho, M.a %A Perez, C.b %A Granados, A.b %D 2012 %J Energy Systems %K Gridlab-D %N 1 %P 61-76 %R 10.1007/s12667-012-0054-0 %T An adaptive multi-agent-based approach to smart grids control and optimization %U http://www.scopus.com/inward/record.url?eid=2-s2.0-84857620276&partnerID=40&md5=ebcd51166afb7db69abf7d71aa9cc888 %V 3 %X In this paper, we describe a reinforcement learning-based approach to power management in smart grids. The scenarios we consider are smart grid settings where renewable power sources (e.g. Photovoltaic panels) have unpredictable variations in power output due, for example, to weather or cloud transient effects. Our approach builds on a multi-agent system (MAS)-based infrastructure for the monitoring and coordination of smart grid environments with renewable power sources and configurable energy storage devices (battery banks). Software agents are responsible for tracking and reporting power flow variations at different points in the grid, and to optimally coordinate the engagement of battery banks (i.e. charge/idle/discharge modes) to maintain energy requirements to end-users. Agents are able to share information and coordinate control actions through a parallel communications infrastructure, and are also capable of learning, from experience, how to improve their response strategies for different operational conditions. In this paper we describe our approach and address some of the challenges associated with the communications infrastructure for distributed coordination. We also present some preliminary results of our first simulations using the GridLAB-D simulation environment, created by the US Department of Energy (DoE) at Pacific Northwest National Laboratory (PNNL). © 2012 Springer-Verlag.

@article{Carvalho201261, abbrev_source_title = {Energy Syst.}, abstract = {In this paper, we describe a reinforcement learning-based approach to power management in smart grids. The scenarios we consider are smart grid settings where renewable power sources (e.g. Photovoltaic panels) have unpredictable variations in power output due, for example, to weather or cloud transient effects. Our approach builds on a multi-agent system (MAS)-based infrastructure for the monitoring and coordination of smart grid environments with renewable power sources and configurable energy storage devices (battery banks). Software agents are responsible for tracking and reporting power flow variations at different points in the grid, and to optimally coordinate the engagement of battery banks (i.e. charge/idle/discharge modes) to maintain energy requirements to end-users. Agents are able to share information and coordinate control actions through a parallel communications infrastructure, and are also capable of learning, from experience, how to improve their response strategies for different operational conditions. In this paper we describe our approach and address some of the challenges associated with the communications infrastructure for distributed coordination. We also present some preliminary results of our first simulations using the GridLAB-D simulation environment, created by the US Department of Energy (DoE) at Pacific Northwest National Laboratory (PNNL). © 2012 Springer-Verlag.}, added-at = {2012-04-22T19:17:31.000+0200}, affiliation = {Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, United States; Institute for Human and Machine Cognition, 15 SE Osceola Ave, Ocala, FL 34471, United States}, author = {Carvalho, M.a and Perez, C.b and Granados, A.b}, author_keywords = {Energy storage devices; Multi-agent systems; Reinforcement learning; Renewable energy management; Smart grids}, biburl = {https://www.bibsonomy.org/bibtex/2946924a5e797eb842a7544f81a550720/adnasiak}, correspondence_address = {Carvalho, M.; Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, United States; email: mcarvalho@fit.edu}, document_type = {Conference Paper}, doi = {10.1007/s12667-012-0054-0}, interhash = {4e618f04b0db2c975e02623bd75ad6ac}, intrahash = {946924a5e797eb842a7544f81a550720}, issn = {18683967}, journal = {Energy Systems}, keywords = {Gridlab-D}, language = {English}, note = {cited By (since 1996) 0}, number = 1, pages = {61-76}, references = {Abbey, C., Robinson, J., Joos, G., Integrating renewable energy sources and storage into isolated diesel generator supplied electric power systems (2008) 13th Power Electronics and Motion Control Conference, 2008. EPE-PEMC 2008, pp. 2178-2183. , 10.1109/EPEPEMC.2008.4635588; Agarwal, Y., Weng, T., Gupta, R., Micro-systems driving smart energy metering in smart grids (2007) DAC'07; Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E., The nonstochastic multiarmed bandit problem (2003) SIAM J. Comput., 32, pp. 48-77. , 1954855 10.1137/S0097539701398375; Caron, S., Kesidis, G., Incentive-based energy consumption scheduling algorithms for the smart grid (2010) First IEEE International Conference on Smart Grid Communications (SmartGridComm) 2010, pp. 391-396. , IEEE New York. 10.1109/SMARTGRID.2010.5622073; Chassin, D., Schneider, K., Gerkensmeyer, C., Gridlab-d: An open-source power systems modeling and simulation environment (2008) Transmission and Distribution Conference and Exposition, 2008. D. IEEE/PES, pp. 1-5. , 10.1109/TDC.2008.4517260, IEEE New York. 10.1109/TDC.2008.4517260; Chassin, D., Schneider, K., Gerkensmeyer, C., Gridlab-d: An open-source power systems modeling and simulation environment (2008) Transmission and Distribution Conference and Exposition, pp. 1-5. , D. IEEE/PES IEEE, New York (2008). doi: 10.1109/TDC.2008.4517260; Chinchuluun, A., Pardalos, P., Enkhbat, R., (2010) Optimization and Optimal Control: Theory and Applications, 39. , Springer Berlin 1196.90011 10.1007/978-0-387-89496-6; Cupp, J., Beehler, M., (2008) Implementing Smart Grid Communications, , Burns & McDonnell Marketing Communications; Dagdougui, H., Minciardi, R., Ouammi, A., Sacile, R., Optimal control of a regional power microgrid network driven by wind and solar energy (2011) 2011 IEEE International Systems Conference (SysCon), pp. 86-90. , 10.1109/SYSCON.2011.5929056; Dam, Q., Mohagheghi, S., Stoupis, J., Intelligent demand response scheme for customer side load management (2008) Energy 2030 Conference, 2008. ENERGY 2008, pp. 1-7. , IEEE New York. 10.1109/ENERGY.2008.4781013; Dave Cliff, J.B., (1997) Minimal Intelligence Agents for Bargaining Behaviors in Market-Based Environments; Divya, K., Γstergaard, J., Battery energy storage technology for power systems: An overview (2009) Electr. Power Syst. Res., 79 (4), pp. 511-520. , 10.1016/j.epsr.2008.09.017; Hatami, S., Pedram, M., Minimizing the electricity bill of cooperative users under a quasi-dynamic pricing model (2010) 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 421-426. , IEEE New York. 10.1109/SMARTGRID.2010.5622080; Kaldellis, J.K., Zafirakis, D., Optimum energy storage techniques for the improvement of renewable energy sources-based electricity generation economic efficiency (2007) Energy, 32 (12), pp. 2295-2305. , DOI 10.1016/j.energy.2007.07.009, PII S0360544207001302; Kersting, W., Radial distribution test feeders (2001) Power Engineering Society Winter Meeting, 2001 IEEE, 2, pp. 908-912. , IEEE New York; Korpaas, M., Holen, A.T., Hildrum, R., Operation and sizing of energy storage for wind power plants in a market system (2003) Int. J. Electr. Power Energy Syst., 25 (8), pp. 599-606. , 10.1016/S0142-0615(03)00016-4; Lagorse, J., Paire, D., Miraoui, A., A multi-agent system for energy management of distributed power sources (2010) Renew. Energy, 35 (1), pp. 174-182. , 10.1016/j.renene.2009.02.029; Lφhndorf, N., Minner, S., Optimal day-ahead trading and storage of renewable energies-an approximate dynamic programming approach (2010) Energy Syst., 1, pp. 61-77. , 10.1007/s12667-009-0007-4; O'Neill, D., Levorato, M., Goldsmith, A., Mitra, U., Residential demand response using reinforcement learning (2010) 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 409-414. , IEEE New York. 10.1109/SMARTGRID.2010.5622078; Ramchurn, S., Vytelingum, P., Rogers, A., Jennings, N., Agent-based homeostatic control for green energy in the smart grid (2011) ACM Trans. Intel. Syst. Technol., 2 (4); Roche, R., Blunier, B., Miraoui, A., Hilaire, V., Koukam, A., Multi-agent systems for grid energy management: A short review (2010) IECon 2010-36th Annual Conference on IEEE Industrial Electronics Society, pp. 3341-3346. , 10.1109/IECON.2010.5675295; Samadi, P., Mohsenian-Rad, A., Schober, R., Wong, V., Jatskevich, J., Optimal real-time pricing algorithm based on utility maximization for smart grid (2010) 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 415-420. , IEEE New York. 10.1109/SMARTGRID.2010.5622077; Sutton, R.S., Barto, A.G., (1998) Reinforcement Learning: An Introduction, , MIT Press Cambridge; Suvire, G., Mercado, P., Ontiveros, L., Comparative analysis of energy storage technologies to compensate wind power short-term fluctuations (2010) Transmission and Distribution Conference and Exposition: Latin America (T D-LA), 2010 IEEE/PES, pp. 522-528. , 10.1109/TDC-LA.2010.5762932; Teleke, S., Baran, M., Bhattacharya, S., Huang, A., Rule-based control of battery energy storage for dispatching intermittent renewable sources (2010) IEEE Trans. Sustain. Energy, 1 (3), pp. 117-124. , 10.1109/TSTE.2010.2061880; Vandael, S., De Craemer, K., Bouckι, N., Holvoet, T., Deconinck, G., Decentralized coordination of plug-in hybrid vehicles for imbalance reduction in a Smart Grid (2011) Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 803-810. , K. Tumer P. Yolum E. Sonenberg P. Stone (eds). ifaamas; Venayagamoorthy, G., Potentials and promises of computational intelligence for smart grids (2009) Power & Energy Society General Meeting, 2009. PES'09 IEEE, pp. 1-6. , IEEE New York. 10.1109/PES.2009.5275179; Vytelingum, P., Voice, T., Ramchurn, S., Rogers, A., Jennings, N., Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid (2011) J. Art. Intel. Res., 42, pp. 765-813. , 05996842; Vytelingum, P., Voice, T.D., Ramchurn, S.D., Rogers, A., Jennings, N.R., Agent-based micro-storage management for the smart grid (2010) Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Vol. 1 AAMAS '10, pp. 39-46. , International Foundation for Autonomous Agents and Multiagent Systems Richland; Wei, C., Hu, H., Chen, Q., Yang, G., Learning agents for storage devices management in the smart grid (2010) 2010 International Conference on Computational Intelligence and Software Engineering (CiSE), pp. 1-4. , IEEE New York. 10.1109/CISE.2010.5676772}, source = {Scopus}, timestamp = {2012-04-23T09:59:24.000+0200}, title = {An adaptive multi-agent-based approach to smart grids control and optimization}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84857620276&partnerID=40&md5=ebcd51166afb7db69abf7d71aa9cc888}, volume = 3, year = 2012 }