Reinforcement learning based bilevel real-time pricing strategy for a smart grid with distributed energy resources

Wang, Jingqi; Gao, Yan; Li, Renjie

doi:10.1016/j.asoc.2024.111474

Cited by 2 publications

(1 citation statement)

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“…RL is an intriguing field of machine learning that draws inspiration from our ability to learn by engaging in trial and error [12]. Consider an agent situated within an environment, such as a simulated labyrinth.…”

Section: Reinforcement Learningmentioning

confidence: 99%

A Reinforcement Learning-Based Reverse Auction Enforcing Smart Pricing Policies towards B5G Offloading Strategies

Kaltakis,

Dimos,

Giannoulakis

et al. 2024

Electronics

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In this paper, we present our work on developing a Smart Pricing Policies module specifically designed for individual users and Mobile Network Operators (MNOs). Our framework will operate in a multi-MNO blockchain radio access network (B-RAN) and is tasked with determining prices for resource sharing among users and MNOs. Our sophisticated adaptive pricing system can adjust to situations where User Equipment (UE) shifts out of the coverage area of their MNO by immediately sealing a contract with a different MNO to cover the users’ needs. This way, we aim to provide financial incentives to MNOs while ensuring continuous network optimization for all parties involved. Our system accomplishes that by utilizing deep reinforcement learning (DLR) to implement a reverse auction model. In our reinforcement learning scenario, the MNOs, acting as agents, enter a competition and try to bid the most appealing price based on the user’s request, and based on the reward system, agents that do not win in the current round will adjust their strategies in an attempt to secure a win in subsequent rounds. The findings indicated that combining DRL with reverse auction theory offers a more appropriate method for addressing the pricing and bid challenges, and additionally, administrators can utilize this strategy to gain a notable edge by dynamically selecting and adjusting their methods according to the individual network conditions and requirements.

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Section: Reinforcement Learningmentioning

confidence: 99%