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Optimal dynamic pricing for smart grid having mixed customers with and without smart meters

Ma, Qian, Meng, Fanlin and Zeng, Xiao-Jun 2018. Optimal dynamic pricing for smart grid having mixed customers with and without smart meters. Journal of Modern Power Systems and Clean Energy 61 (6) , pp. 1244-1254. 10.1007/s40565-018-0389-1

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Abstract

This paper investigates an optimal day-ahead dynamic pricing problem in an electricity market with one electricity retailer and multiple customers. The main objective of this paper is to support the retailer to make the best day-ahead dynamic pricing decision, which maximizes its profit under the realistic assumption that mixed types of customers coexist in the electricity market where some customers have installed smart meters with the embedded home energy management system in their home whereas other customers have not installed smart meters. To this end, we propose a hybrid demand modelling framework which firstly uses an optimal energy management algorithm with bill minimization to model the behavior of customers with smart meters and secondly use a data-driven demand modelling method to model the behavior of customers without smart meters. Such a hybrid demand model can not only schedule usages of home appliances to the interests of customers with smart meters but also be used to understand electricity usage behaviors of customers without smart meters. Based on the established hybrid demand model for all customers, a profit maximization algorithm is developed to achieve optimal prices for the retailer under relevant market constraints. Under the condition of no growth of the revenue (i.e. no increase of total bill from all customers), simulation results indicate our optimization algorithm can improve the profit for around 5% on average.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Springer Verlag
ISSN: 2196-5625
Date of First Compliant Deposit: 17 September 2018
Date of Acceptance: 18 December 2017
Last Modified: 12 Dec 2018 15:10
URI: http://orca.cf.ac.uk/id/eprint/114979

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