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An optimal real-time pricing for demand-side management: A Stackelberg game and genetic algorithm approach

Meng, Fan-Lin and Zeng, Xiao-Jun 2014. An optimal real-time pricing for demand-side management: A Stackelberg game and genetic algorithm approach. Presented at: 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6-11 July 2014. 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1703-1710. 10.1109/IJCNN.2014.6889608

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Abstract

This paper proposes a real-time pricing scheme for demand response management in the context of smart grids. The electricity retailer determines the retail price first and announces the price information to the customers through the smart meter systems. According to the announced price, the customers automatically manage the energy use of appliances in the households by the proposed energy management system with the aim to maximize their own benefits. We model the interactions between the electricity retailer and its customers as a 1-leader, N-follower Stackelberg game. By taking advantage of the two-way communication infrastructure, the sequential equilibrium can be obtained through backward induction. At the followers' side, given the electricity price information, we develop efficient algorithms to maximize customers' satisfaction. At the leader's side, we develop a genetic algorithms based real-time pricing scheme by considering the expected customers' reactions to maximize retailer's profit. Experimental results indicate that the proposed scheme can not only benefit the retailers but also the customers.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: IEEE
ISBN: 9781479914845
Last Modified: 04 Oct 2018 14:46
URI: http://orca.cf.ac.uk/id/eprint/115216

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