A Self-configurable Agent-Based System for Intelligent Storage in Smart Grid

  • Juan M. Alberola
  • Vicente Julián
  • Ana García-Fornes
Part of the Communications in Computer and Information Science book series (CCIS, volume 365)


Next generation of smart grid technologies demand intelligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multiagent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this paper we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-configurable mechanism which allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which electricity is purchased from the market.


smart grid multiagent systems storage 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Momoh, J.A.: Smart grid design for efficient and flexible power networks operation and control. In: IEEE PES Power Systems Conference and Exposition, pp. 15–18 (2009)Google Scholar
  2. 2.
    Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: Design and implementation. In: IEEE/PES Power Systems Conference and Exposition, pp. 1–8 (2009)Google Scholar
  3. 3.
    Vytelingum, P., Voice, T.D., Ramchurn, S., Rogers, A., Jennings, N.R.: Agent-based micro-storage management for the Smart Grid. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 39–46 (2010)Google Scholar
  4. 4.
    Vytelingum, P., Voice, T.D., Ramchurn, S., Rogers, A., Jennings, N.R.: Intelligent agents for the smart grid. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 1649–1650 (2010)Google Scholar
  5. 5.
    Van Dam, K.H., Houwing, M., Bouwmans, I.: Agent-based control of distributed electricity generation with microcombined heat and power-cross-sectoral learning for process and infrastructure engineers. Computers & Chemical Engineering 32, 205–217 (2008)CrossRefGoogle Scholar
  6. 6.
    Oyarzabal, J., Jimeno, J., Ruela, J., Engler, A., Hardt, C.: Agent based Micro Grid Management System. In: International Conference on Future Power Systems, vol. 18(8) (2005)Google Scholar
  7. 7.
    Reddy, P.P., Veloso, M.M.: Factored Models for Multiscale Decision Making in Smart Grid Customers. In: Proceedings of AAAI 2012, the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)Google Scholar
  8. 8.
    Mihailescu, R.C., Vasirani, M., Ossowski, S.: Dynamic coalition formation and adaptation for virtual power stations in smart grids. In: Proc. of the 2nd Int. Workshop on Agent Technologies for Energy Systems, pp. 85–88 (2011)Google Scholar
  9. 9.
    Nourai, A.: Installation of the First Distributed Energy Storage System (DESS) at American Electric Power (AEP). Technical report, Sandia National Laboratories (2007)Google Scholar
  10. 10.
    Eyer, J., Corey, G.: Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide. Technical report, Sandia National Laboratories (2010)Google Scholar
  11. 11.
    Mohd, A., Ortjohann, E., Schmelter, A., Hamsic, N., Morton, D.: Challenges in integrating distributed Energy storage systems into future smart grid. In: IEEE International Symposium on Industrial Electronics, pp. 1627–1632 (2008)Google Scholar
  12. 12.
    Costa, L., Bourry, F., Juban, J., Kariniotakis, G.: Management of energy storage coordinated with wind power under electricity market conditions. In: 10th International Conference on Probabilistic Methods Applied to Power Systems, pp. 259–266 (2008)Google Scholar
  13. 13.
    Pinson, P., Chevallier, C., Kariniotakis, G.N.: Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power. IEEE Transactions on Power Systems 22(3), 1148–1156 (2007)CrossRefGoogle Scholar
  14. 14.
    Maly, D.K., Kwan, K.S.: Optimal battery energy storage system (BESS) charge scheduling with dynamic programming. IEE Proceedings-Science, Measurement and Technology 142(6), 453–458 (1995)CrossRefGoogle Scholar
  15. 15.
    Alberola, J.M., Julian, V., Garcia-Fornes, A.: Multi-Dimensional Adaptation in MAS Organizations. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics (in press, 2013)Google Scholar
  16. 16.
    Alberola, J.M., Julian, V., Garcia-Fornes, A.: Multi-dimensional Transition Deliberation for Organization Adaptation in Multiagent Systems. In: Proc. 11th Int. Conf. on Aut. Agents and MAS, AAMAS 2012, pp. 1379–1380 (2012)Google Scholar
  17. 17.
    Conejo, A.J., Plazas, M.A., Espinola, R., Molina, A.B.: Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. IEEE Transactions on Power Systems 20(2), 1035–1042 (2005)CrossRefGoogle Scholar
  18. 18.
    Mohsenian, A.H., Leon-Garcia, A.: Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. IEEE Trans. Smart Grid 1(2), 120–133 (2010)CrossRefGoogle Scholar
  19. 19.
    Szkuta, B., Sanabria, L., Dillon, T.: Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems 14(3), 851–857 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juan M. Alberola
    • 1
  • Vicente Julián
    • 1
  • Ana García-Fornes
    • 1
  1. 1.Departament de Sistemes Informàtics i ComputacióUniversitat Politècnica de ValènciaValènciaSpain

Personalised recommendations