Microgrids Real-Time Pricing Based on Clustering Techniques

Hao Liu, Nadali Mahmoudi and Kui Chen
Additional contact information
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

Energies, 2018, vol. 11, issue 6, 1-12

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.

Keywords: clustering technique; improved weighted fuzzy average k-means; microgrids; pattern-based pricing; smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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