Advertisement

Distributed Storage Management Using Dynamic Pricing in a Self-Organized Energy Community

  • Ebisa Negeri
  • Nico Baken
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7166)

Abstract

We consider a future self-organized energy community that is composed of “prosumer” households that can autonomously generate, store, import and export power, and also selfishly strive to minimize their cost by adjusting their load profiles using the flexibly of their distributed storage. In such scenario, the aggregate load profile of the self-organized community is likely to be volatile due to the flexibility of the uncoordinated selfish households and the intermittence of the distributed generations. Previously, either centralized solutions or cooperation based decentralized solutions were proposed to manage the aggregate load, or the load of an individual selfish household was considered. We study the interplay between selfish households and community behavior by proposing a novel dynamic pricing model that provides an optimal price vector to the households to flatten the overall community load profile. Our dynamic pricing model intelligently adapts to the intermittence of the DGs and the closed-loop feedback that might result from price-responsiveness of the selfish households using its learning mechanism. Our dynamic pricing scheme has distinct import and export tariff components. Based on our dynamic pricing model, we propose a polynomial-time distributed DS scheduling algorithm that runs at each household to solve a cost minimization problem that complies with the selfish nature of the households. Our simulation results reveal that our distributed algorithm achieves up to 72.5% reduction in standard deviation of the overall net demand of the community compared to a distributed scheduling with two-level pricing scheme, and also gives comparable performance with a reference centralized scheduling algorithm.

Keywords

Schedule Algorithm Dynamic Price Iteration Cycle Learning Factor Energy Community 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Melo, H., Heinrich, C.: Energy Balance in a Renewable Energy Community. IEEE EEEIC (May 2011)Google Scholar
  2. 2.
    Mulder, G., Ridder, F., Six, D.: Electricity Storage for Grid-connected Household Dwellings with PV Panels. Solar Energy 84, 1284–1293 (2010)CrossRefGoogle Scholar
  3. 3.
    Cau, T., Kaye, R.: Multiple Distributed Energy Storage Scheduling Using Constructive Evolutionary Programming. In: Proc. of IEEE Power Engineering Society International Conference, vol. 22, pp. 402–407 (August 2002)Google Scholar
  4. 4.
    Vytelingum, P., Voice, T., Ramchurn, S., Rogers, A., Jennings, N.: Agent-based Micro-Storage Management for the Smart Grid. In: Proc. of 9th Int. Conf. on Autonomous Agents and Mutiagent Systems (AAMAS 2010) (May 2010)Google Scholar
  5. 5.
    Roozbehani, M., Dahleh, M., Mitter, S.: On the Stability of Wholesale Electricity Markets Under Real-Time Pricing. In: IEEE Conference on Decision and Control (CDC), pp. 1911–1918 (December 2010)Google Scholar
  6. 6.
    Kok, J., Warmer, C., Kamphuis, I.: PowerMatcher: Multi-Agent Control in Electicity Infrastructure. In: AAMAS 2005 (July 2005)Google Scholar
  7. 7.
    Ipakchi, A., Albuyeh, F.: Grid of The Future. IEEE Power and Energy Magazine, 52–62 (March 2009)Google Scholar
  8. 8.
    Houwing, M., Negenborn, R., Schutter, B.: Demand Response with Micro-CHP systems. Proceedings of the IEEE 99(1), 200–212 (2011)CrossRefGoogle Scholar
  9. 9.
    Ahlert, K., Dinther, C.: Sensitivity Analysis of the Economic Benefits from Electricity Storage at the End Consumer Level. IEEE Transactions on PowerTech, 1–8 (October 2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Ebisa Negeri
    • 1
  • Nico Baken
    • 1
  1. 1.Delft University of TechnologyDelftThe Netherlands

Personalised recommendations