Next Article in Journal
Impact of Risk Aversion on the Operation of Hydroelectric Reservoirs in the Presence of Renewable Energy Sources
Next Article in Special Issue
Game Theoretic Spectrum Allocation in Femtocell Networks for Smart Electric Distribution Grids
Previous Article in Journal
Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method
Previous Article in Special Issue
Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks
Open AccessArticle

Microgrids Real-Time Pricing Based on Clustering Techniques

by 1, 2 and 2,*
1
Jiangsu Province Laboratory of Mining Electric and Automation, China University of Mining and Technology, Xuzhou 221000, China
2
Ernst & Young, Brisbane QLD 4000, Australia
*
Author to whom correspondence should be addressed.
Energies 2018, 11(6), 1388; https://doi.org/10.3390/en11061388
Received: 1 May 2018 / Revised: 20 May 2018 / Accepted: 21 May 2018 / Published: 30 May 2018
(This article belongs to the Special Issue Distribution System Operation and Control)
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. View Full-Text
Keywords: clustering technique; improved weighted fuzzy average k-means; microgrids; pattern-based pricing; smart grids clustering technique; improved weighted fuzzy average k-means; microgrids; pattern-based pricing; smart grids
Show Figures

Figure 1

MDPI and ACS Style

Liu, H.; Mahmoudi, N.; Chen, K. Microgrids Real-Time Pricing Based on Clustering Techniques. Energies 2018, 11, 1388. https://doi.org/10.3390/en11061388

AMA Style

Liu H, Mahmoudi N, Chen K. Microgrids Real-Time Pricing Based on Clustering Techniques. Energies. 2018; 11(6):1388. https://doi.org/10.3390/en11061388

Chicago/Turabian Style

Liu, Hao; Mahmoudi, Nadali; Chen, Kui. 2018. "Microgrids Real-Time Pricing Based on Clustering Techniques" Energies 11, no. 6: 1388. https://doi.org/10.3390/en11061388

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop