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An Ising Spin-Based Model to Explore Efficient Flexibility in Distributed Power Systems

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  • Francisco Prieto-Castrillo
  • Amin Shokri Gazafroudi
  • Javier Prieto
  • Juan Manuel Corchado

Abstract

This paper analyses customers’ demand flexibility in a local power trading scenario through an Ising spin-based model. We look at quantitative information on the two-way relationships between power exchanges and spin dynamics. To this end, a modified version of the Metropolis-Hastings algorithm was implemented, including a gradient descent through the constraint surface. This allowed us to analyse the system on a large scale (considering the cumulated benefit of all the actors involved) and also from the perspective of total aggregation. In a maximum flexibility scenario, the total aggregation profit increases with the number of aggregators. We also investigate numerically the effect of aggregator boundaries on the spin dynamics.

Suggested Citation

  • Francisco Prieto-Castrillo & Amin Shokri Gazafroudi & Javier Prieto & Juan Manuel Corchado, 2018. "An Ising Spin-Based Model to Explore Efficient Flexibility in Distributed Power Systems," Complexity, Hindawi, vol. 2018, pages 1-16, May.
    • Handle: RePEc:hin:complx:5905932
    DOI: 10.1155/2018/5905932
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    References listed on IDEAS

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    1. Pawe{l} Sieczka & Janusz A. Ho{l}yst, 2007. "A threshold model of financial markets," Papers 0711.3106, arXiv.org, revised Jun 2008.
    2. Amin Shokri Gazafroudi & Francisco Prieto-Castrillo & Tiago Pinto & Javier Prieto & Juan Manuel Corchado & Javier Bajo, 2017. "Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System," Energies, MDPI, vol. 10(9), pages 1-16, September.
    3. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2015. "Markets, herding and response to external information," Papers 1506.03708, arXiv.org, revised Jun 2015.
    4. Cajueiro, D.O., 2011. "Enforcing social behavior in an Ising model with complex neighborhoods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1695-1703.
    5. Bornholdt, Stefan & Wagner, Friedrich, 2002. "Stability of money: phase transitions in an Ising economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 453-468.
    6. Adrián Carro & Raúl Toral & Maxi San Miguel, 2015. "Markets, Herding and Response to External Information," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-28, July.
    7. Stefan Bornholdt, 2001. "Expectation Bubbles In A Spin Model Of Markets: Intermittency From Frustration Across Scales," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 12(05), pages 667-674.
    8. Zhang, Chunyu & Wang, Qi & Wang, Jianhui & Korpås, Magnus & Khodayar, Mohammad E., 2016. "Strategy-making for a proactive distribution company in the real-time market with demand response," Applied Energy, Elsevier, vol. 181(C), pages 540-548.
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    Cited by:

    1. Wen Jiang & Zeyu Ma & Xinyang Deng, 2019. "An attack-defense game based reliability analysis approach for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    2. Hosna Khajeh & Hannu Laaksonen & Amin Shokri Gazafroudi & Miadreza Shafie-khah, 2019. "Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review," Energies, MDPI, vol. 13(1), pages 1-19, December.
    3. Weiqin Ying & Bin Wu & Yu Wu & Yali Deng & Hainan Huang & Zhenyu Wang, 2019. "Efficient Conical Area Differential Evolution with Biased Decomposition and Dual Populations for Constrained Optimization," Complexity, Hindawi, vol. 2019, pages 1-18, February.
    4. Amin Shokri Gazafroudi & Javier Prieto & Juan Manuel Corchado, 2019. "Virtual Organization Structure for Agent-Based Local Electricity Trading," Energies, MDPI, vol. 12(8), pages 1-11, April.

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