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Microgrids Real-Time Pricing Based on Clustering Techniques

Author

Listed:
  • Hao Liu

    (Jiangsu Province Laboratory of Mining Electric and Automation, China University of Mining and Technology, Xuzhou 221000, China)

  • Nadali Mahmoudi

    (Ernst & Young, Brisbane QLD 4000, Australia)

  • Kui Chen

    (Ernst & Young, Brisbane QLD 4000, Australia)

Abstract

Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.

Suggested Citation

  • Hao Liu & Nadali Mahmoudi & Kui Chen, 2018. "Microgrids Real-Time Pricing Based on Clustering Techniques," Energies, MDPI, vol. 11(6), pages 1-12, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1388-:d:149632
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    References listed on IDEAS

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    1. Lo Prete, Chiara & Hobbs, Benjamin F. & Norman, Catherine S. & Cano-Andrade, Sergio & Fuentes, Alejandro & von Spakovsky, Michael R. & Mili, Lamine, 2012. "Sustainability and reliability assessment of microgrids in a regional electricity market," Energy, Elsevier, vol. 41(1), pages 192-202.
    2. Hong, Ying-Yi & Chang, Wen-Chun & Chang, Yung-Ruei & Lee, Yih-Der & Ouyang, Der-Chuan, 2017. "Optimal sizing of renewable energy generations in a community microgrid using Markov model," Energy, Elsevier, vol. 135(C), pages 68-74.
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    Cited by:

    1. Sandipan Patra & Sreedhar Madichetty & Malabika Basu, 2021. "Development of a Smart Energy Community by Coupling Neighbouring Community Microgrids for Enhanced Power Sharing Using Customised Droop Control," Energies, MDPI, vol. 14(17), pages 1-17, August.
    2. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    3. Bandeiras, F. & Pinheiro, E. & Gomes, M. & Coelho, P. & Fernandes, J., 2020. "Review of the cooperation and operation of microgrid clusters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    4. Liu, Jin-peng & Zhang, Teng-xi & Zhu, Jiang & Ma, Tian-nan, 2018. "Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration," Energy, Elsevier, vol. 164(C), pages 560-574.
    5. Nakyoung Kim & Sangdon Park & Joohyung Lee & Jun Kyun Choi, 2018. "Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance," Energies, MDPI, vol. 11(9), pages 1-20, September.
    6. Villanueva-Rosario, Junior Alexis & Santos-García, Félix & Aybar-Mejía, Miguel Euclides & Mendoza-Araya, Patricio & Molina-García, Angel, 2022. "Coordinated ancillary services, market participation and communication of multi-microgrids: A review," Applied Energy, Elsevier, vol. 308(C).

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