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ReLeDP: Reinforcement-Learning-Assisted Dynamic Pricing for Wireless Smart Grid

Zhang, Peiying and Wang, Chao and Aujla, Gagangeet Singh and Batth, Ranbir Singh (2021) 'ReLeDP: Reinforcement-Learning-Assisted Dynamic Pricing for Wireless Smart Grid.', IEEE Wireless Communications, 28 (6). pp. 62-69.


The smart grid must ensure that power providers can obtain substantial benefits by selling energy, while at the same time, they need to consider the cost of consumers. To realize this win-win situation, the smart grid relies on dynamic pricing mechanisms. However, most of the existing dynamic pricing schemes are based on artificial objective rules or conventional models, which cannot ensure the desired effectiveness. Thus, we apply reinforcement learning to model the supply-demand relationship between power providers and consumers in a smart grid. The dynamic pricing problem of the smart grid is modeled as a discrete Markov decision process, and the decision process is solved by Q-learning. Now, the success of any intelligent dynamic pricing scheme relies on timely data transmission. However, the scale and speed of data generation can create several network bottlenecks that can further reduce the performance of any dynamic pricing scheme. Hence, to overcome this challenge, we have proposed an artificial-intelligence-based adaptive network architecture that adopts software-defined networking. In this architecture, we have used a self-organized map-based traffic classification approach followed by a dynamic virtual network embedding mechanism. We demonstrate the effectiveness of the dynamic pricing strategy supported through adaptive network architecture based on various performance indicators. The outcomes suggest that the proposed strategy is of great significance to realize the sustainability of power energy in the future. Lastly, we discuss various implementation challenges and future directions before concluding the article.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:06 May 2022
Date of first online publication:31 December 2021
Date first made open access:06 May 2022

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