Skip to main content

Distributed Optimization of Finite Resource Planning for Asincronous and Non-linear Systems: Application to Power Management

  • Conference paper
Advances on Practical Applications of Agents and Multi-Agent Systems

Abstract

This paper introduces a Multiagent System (MAS) for optimal resource planning in non-linear systems. The distributed planning strategy, which is based on interaction of agents, is composed by two phases. The first one uses an iterative double auction based protocol and allows requesting for getting back the previously allocated resources in order to establish a better planning. The second phase takes the previous resource allocation for finding better alternative paths. The proposal has been applied into our house-lab power system for optimize the use of renewable power supplies. Power production and requirements are simulated using the average of power consumption measures of each component.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Fangwen, F., van der Schaar, M.: Learning to Compete for Resources in Wireless Stochastic Games. IEEE Trans. on Vehicular Technology 58(4) (2009)

    Google Scholar 

  2. Izakian, H., Abraham, A., Ladani, B.T.: An auction method for resource allocation in computational grids. Future Generation Computer Systems 26(2) (2009)

    Google Scholar 

  3. Iosifidis, G., Koutsopoulos, I.: Double auction mechanisms for resource allocation in autonomous networks. IEEE Journal on Selected Areas in Communications 28(1), 95–102 (2010)

    Article  Google Scholar 

  4. Akay, B., Karaboga, D.: A modified Artificial Bee Colony algorithm for real-parameter optimization. Information Sciences (2010)

    Google Scholar 

  5. Goel, T., Stander, N., Lin, Y.-Y.: Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations. Structural and Multidisciplinary Optimization 41(3), 421–432 (2010)

    Article  MathSciNet  Google Scholar 

  6. Nedic, A., Ozdaglar, A.: Distributed Subgradient Methods for Multi-Agent Optimization. IEEE Transactions on Automatic Control 54(1), 48–61 (2009)

    Article  MathSciNet  Google Scholar 

  7. Bellifemine, F., Poggi, A., Rimassa, G.: Developing multi-agent systems with a FIPA-compliant agent framework. Software: Practice and Experience 31, 103–128 (2001)

    Article  MATH  Google Scholar 

  8. Faisal, A.M.: Microgrid modelling and online management. Helsinki University of Technology, Espoo (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafael J. Valdivieso-Sarabia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Valdivieso-Sarabia, R.J., Ferrandez-Pastor, F.J., Garcia-Chamizo, J.M. (2012). Distributed Optimization of Finite Resource Planning for Asincronous and Non-linear Systems: Application to Power Management. In: Demazeau, Y., Müller, J., Rodríguez, J., Pérez, J. (eds) Advances on Practical Applications of Agents and Multi-Agent Systems. Advances in Intelligent and Soft Computing, vol 155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28786-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28786-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28785-5

  • Online ISBN: 978-3-642-28786-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics