An adaptive agent-based system for deregulated smart grids | Service Oriented Computing and Applications Skip to main content
Log in

An adaptive agent-based system for deregulated smart grids

  • Original Research Paper
  • Published:
Service Oriented Computing and Applications Aims and scope Submit manuscript

Abstract

The power grid is undergoing a major change due mainly to the increased penetration of renewables and novel digital instruments in the hands of the end users that help to monitor and shift their loads. Such transformation is only possible with the coupling of an information and communication technology infrastructure to the existing power distribution grid. Given the scale and the interoperability requirements of such future system, service-oriented architectures (SOAs) are seen as one of the reference models and are considered already in many of the proposed standards for the smart grid (e.g., IEC-62325 and OASIS eMIX). Beyond the technical issues of what the service-oriented architectures of the smart grid will look like, there is a pressing question about what the added value for the end user could be. Clearly, the operators need to guarantee availability and security of supply, but why should the end users care? In this paper, we explore a scenario in which the end users can both consume and produce small quantities of energy and can trade these quantities in an open and deregulated market. For the trading, they delegate software agents that can fully interoperate and interact with one another thus taking advantage of the SOA. In particular, the agents have strategies, inspired from game theory, to take advantage of a service-oriented smart grid market and give profit to their delegators, while implicitly helping balancing the power grid. The proposal is implemented with simulated agents and interaction with existing Web services. To show the advantage of the agent with strategies, we compare our approach with the “base” agent one by means of simulations, highlighting the advantages of the proposal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://www.abb.com/industries/.

  2. www.wunderground.com/history/.

  3. www.soda-is.com.

  4. www.navlost.eu.

References

  1. ABB (2011) Wind power plants, technical application papers. Tech. rep. 13, ABB

  2. Asad O, Erol-Kantarci M, Mouftah H (2011) Sensor network web services for demand-side energy management applications in the smart grid. In: Consumer communications and networking conference (CCNC), 2011 IEEE, pp 1176–1180. doi:10.1109/CCNC.2011.5766363

  3. Bellifemmine F, Poggi A, Rimassa G (1999) Jade—a fipa-compliant agent framework. In: Proceedings of the 4th international conference on the practical applications of agents and multiagent systems PAAM99, pp 97–108

  4. Brazier F, Cornelissena F, Gustavsson R, Jonker C, Lindeberg O, Polaka B, Treur J (2002) A multi-agent system performing one-to-many negotiation for load balancing of electricity use. Electron Commer Res Appl 1(2):208–224

    Article  Google Scholar 

  5. Brinkman, Denholm, Drury, Margolis, Mowers (2011) Toward a solar-powered grid. IEEE Power Energy Mag 9(3):24–32

    Article  Google Scholar 

  6. Cancelo JR, Espasa A (1995) Modelización del efecto temperatura en el consumo de electricidad: un ejercicio de búsqueda de especificación en relaciones dinámicas no lineales. Estadıstica Española 37(139):183–200

    Google Scholar 

  7. Capodieci N (2011) P2P energy exchange agent platform featuring a game theory related learning negotiation algorithm. Master’s thesis, University of Modena and Reggio Emilia

  8. Capodieci N, Alsina EF, Cabri G (2012) A context-aware agent-based approach for deregulated energy market. In: Proceedings of the 21st IEEE international WETICE conference ACEC track at WETICE 2012 conference, Tolouse, France, pp 16–21

  9. Capodieci N, Cabri G, Pagani G, Aiello M (2012) An agent-based application to enable deregulated energy markets. In: Computer software and applications conference (COMPSAC), 2012 IEEE 36th annual, pp 638–647. doi:10.1109/COMPSAC.2012.90

  10. Capodieci N, Pagani A, Cabri G, Aiello M (2011) Smart meter aware domestic energy trading agents. In: Proceedings of the first international E-energy market challenge (IEEMC 2011) at the 8th international conference on autonomic computing Karlsruhe, Germany, June 2011

  11. Capodieci N, Pagani GA, Cabri G, Aiello M (2011) Smart meter aware domestic energy trading agents. In: Proceedings of the 2011 workshop on E-energy market challenge, IEEMC ’11, pp 1–10. ACM

  12. Chakrabarti AS, Chakrabarti BK, Chatterjee A, Mitra M (2009) The Kolkata Paise Restaurant problem and resource utilization. Phys A Stat Mech Appl 388(12):2420–2426

    Article  Google Scholar 

  13. Cox WT, Considine T (2009) Architecturally significant interfaces for the smart grid. In: Grid-interop—the road to an interoperable grid, Denver, Colorado, USA

  14. Dobson I, Chen J, Thorp J, Carreras B, Newman D (2002) Examining criticality of blackouts in power system models with cascading events. In: Proceedings of the 35th annual Hawaii international conference on system sciences, 2002, HICSS, p 10. doi:10.1109/HICSS.2002.993975

  15. Fadlullah ZM, Nozaki Y, Takeuchi A, Kato N (2011) A survey of game theoretic approaches in smart grid. In: International conference on wireless communications and signal processing (WCSP), 2011, pp 1–4. IEEE

  16. Fan S, Chen L (2005) Short-term load forecasting based on an adaptive hybrid method. Osaka Sangyo University

  17. Fan S, Chen L (2006) Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 21(1):392–401

    Article  Google Scholar 

  18. Farago J, Greenwald A, Hall K (2002) Fair and efficient solutions to the Santa Fe Bar problem. In: Proceedings of Grace Hopper: celebration of women in computing

  19. Faruqui A, Hledik R, Tsoukalis J (2009) The power of dynamic pricing. Electr J 22(3):42–56

    Article  Google Scholar 

  20. Ferber J, Müller JP (1996) Influences and reaction: a model of situated multiagent systems. In: Proceedings of second international conference on multi-agent systems (ICMAS-96), pp 72–79

  21. Groppi F, Zuccaro C (2007) Impianti solari fotovoltaici. Attualitá elettronica 9:36–39

    Google Scholar 

  22. Ibars C, Navarro M, Giupponi L (2010) Distributed demand management in smart grid with a congestion game. In: First IEEE international conference on smart grid communications (SmartGridComm), 2010, pp 495–500. IEEE

  23. Karnouskos S (2010) The cooperative internet of things enabled smart grid. In: Proceedings of the 14th IEEE international symposium on consumer electronics (ISCE2010), Braunschweig, Germany, June 07–10

  24. Karnouskos S (2011) Future smart grid prosumer services. In: 2nd IEEE PES international conference and exhibition on innovative smart grid technologies (ISGT Europe), 2011, pp 1–2. doi:10.1109/ISGTEurope.2011.6162832

  25. Khan A, Mouftah H (2011) Web services for indoor energy management in a smart grid environment. In: IEEE 22nd international symposium on personal indoor and mobile radio communications (PIMRC), 2011, pp 1036–1040

  26. Koster M (2011) Reliable multi-agent system for a large scale distributed energy trading network. Master’s thesis, University of Groningen

  27. Leeds D (2009) The smart grid in 2010: market segments, applications and industry players. Tech. rep., DTM research

  28. Li H, Sun J, Tesfatsion L (2008) Dynamic imp response under alternative price-cap and price-sensitive demand scenarios. In: Power and energy society general meeting—conversion and delivery of electrical energy in the 21st century, 2008 IEEE, pp 1–8. doi:10.1109/PES.2008.4596069

  29. Mohsenian-Rad AH, Wong VW, Jatskevich J, Schober R, Leon-Garcia A (2010) Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1(3):320–331

    Article  Google Scholar 

  30. Moral-Carcedo J, Vicens-Otero J (2005) Modelling the non-linear response of Spanish electricity demand to temperature variations. Energy Econ 27(3):477–494

    Article  Google Scholar 

  31. Morgan MG, Apt J, Lave LB, Ilic MD, Sirbu M, Peha JM (2009) The many meanings of “smart grid”. Tech. rep., Carnegie Mellon University

  32. National Energy Technology Laboratory (2007) A system view of the modern grid. Tech. rep., U.S. Department of Energy—Office of Electricity Delivery and Energy Reliability

  33. National Energy Technology Laboratory (2007) A vision for the modern grid. Tech. rep., U.S. Department of Energy—Office of Electricity Delivery and Energy Reliability

  34. Office of the National Coordinator for Smart Grid Interoperability (2010) Nist framework and roadmap for smart grid interoperability standards, release 1.0. Tech. rep. NIST special publication 1108, National Institute of Standards and Technology

  35. Pagani GA, Aiello M (2012) Service-orientation and the smart grid state and trend. Serv Oriented Comput Appl 6(3):267–282

    Article  Google Scholar 

  36. Pagani GA, Aiello M (2014) Generating realistic dynamic prices and services for the smart grid. IEEE Syst J 9(1):191–198

  37. Papazoglou M, Aiello M, Giorgini P (2004) Service-oriented computing and software agents. In: Cavedon L, Maamar Z, Martin D, Benatallah B (eds) Extending web services technologies. Kluwer, Boston, pp 29–52

    Chapter  Google Scholar 

  38. Pollitt M (2008) The arguments for and against ownership unbundling of energy transmission networks. Energy Policy 36(2):704–713. doi:10.1016/j.enpol.2007.10.011. http://www.sciencedirect.com/science/article/pii/S0301421507004478

  39. Ramchurn S, Vytelingum P, Rogers A, Jennings N (2011) Agent-based control for decentralised demand side management in the smart grid. In: 10th international conference on autonomous agents and multiagent systems (AAMAS 2011), pp 5–12

  40. Ramchurn S, Vytelingum P, Rogers A, Jennings NR (2012) Putting the “smarts” into the smart grid: a grand challenge for artificial intelligence. Communications of the ACM 55(4):86–97. http://eprints.soton.ac.uk/272606/

  41. Rapoport A, Chammah AM (1965) Prisoner’s dilemma. University of Michigan Press, Ann Arbor

    Google Scholar 

  42. Robu V, Poutré HL (2007) Designing bidding strategies in sequential auctions for risk averse agents. In: Proceedings of AMEC07

  43. Roe C, Meliopoulos S, Entriken R, Chhaya S (2011) Simulated demand response of a residential energy management system. In: Energytech, 2011 IEEE, pp 1–6. doi:10.1109/EnergyTech.2011.5948530

  44. Rosenthal RW (1973) A class of games possessing pure-strategy Nash equilibria. Int J Game Theory 2(1):65–67

    Article  MathSciNet  MATH  Google Scholar 

  45. Schuelke A, Erickson K (2011) Serving solar variations with consumption control of smart appliances and electric vehicles. In: 2nd IEEE PES international conference and exhibition on innovative smart grid technologies (ISGT Europe), 2011, pp 1–8. doi:10.1109/ISGTEurope.2011.6162788

  46. Takamori H, Nagasaka K (2007) Toward designing value supportive infrastructure for electricity trading. In: The 9th IEEE international conference on E-commerce technology and the 4th IEEE international conference on enterprise computing, E-commerce, and E-services, 2007. CEC/EEE 2007

  47. Vaitheeswaran V (2005) Power to the people. Earthscan, London

    Google Scholar 

  48. Verschueren T, Haerick W, Mets K, Develder C, De Turck F, Pollet T (2010) Architectures for smart end-user services in the power grid. In: Network operations and management symposium workshops (NOMS Wksps), 2010 IEEE/IFIP, pp 316–322. doi:10.1109/NOMSW.2010.5486557

  49. Vytelingum P, Voice T, Ramchurn S, Rogers A, Jennings N (2010) Agent-based micro-storage management for the smart grid. In: 9th International conference on autonomous agents and multiagent systems (AAMAS 2010)

  50. Wang D, de Wit B, Parkinson S, Fuller J, Chassin D, Crawford C, Djilali N (2012) A test bed for self-regulating distribution systems: modeling integrated renewable energy and demand response in the GridLAB-d/MATLAB environment. In: Innovative smart grid technologies (ISGT), 2012 IEEE PES, pp 1–7. doi:10.1109/ISGT.2012.6175809

  51. Weyns D, Georgeff M (2009) Self-adaptation using multiagent systems. IEEE Softw 27(1):86–91

    Article  Google Scholar 

  52. Whitehead D (2008) The El Farol bar problem revisited: reinforcement learning in a potential game. Tech. rep., ESE discussion papers 186 Edinburgh School of Economics, University of Edinburgh

  53. Yilmaz C, Albayrak S, Lützenberger M (2014) Smart grid architectures and the multi-agent system paradigm. In: ENERGY 2014, the fourth international conference on smart grids, green communications and IT energy-aware technologies, pp 90–95

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicola Capodieci.

Additional information

The work is supported by ASCENS (EU FP7-FET, Contract No. 257414) and Energy Smart Offices (NWO Smart Energy Systems programme, Contract No. 647.000.004). We thank Marcel Koster for the contribution to the distributed platform experimentation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Capodieci, N., Pagani, G.A., Cabri, G. et al. An adaptive agent-based system for deregulated smart grids. SOCA 10, 185–205 (2016). https://doi.org/10.1007/s11761-015-0180-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11761-015-0180-3

Keywords

Navigation