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Intelligent Policy-Maker for Electricity Market Flexibility: Automatic Rail Switching in Smart Grid Payment Systems Under Infrastructure Outages
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Shohreh Ajoudanian *1   |
| 1- Department of Computer Engineering, Na.C., Islamic Azad University, Najafabad, Iran & Big Data Research Center, Na.C., Islamic Azad University, Najafabad, Iran , shajoudanian@iau.ac.ir |
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Abstract: (30 Views) |
This paper presents an intelligent policy-maker for enhancing the flexibility and resilience of smart electricity grids in electricity markets. This system optimizes the performance of smart grid payment systems by enabling automated switching between different payment rails (card, QR code, and digital wallet) during infrastructure outages. The proposed framework employs a multi-objective decision-making model (risk, cost, and latency) to simultaneously optimize these factors while respecting operational constraints, selecting the optimal switching path. The policy engine combines signals related to application, transfer, and network to provide a view of the health of each rail. By estimating risk, cost, and latency, it identifies the optimal path. This approach is increasingly important in smart grid-based electricity markets, which are facing growing transaction volumes and dependence on digital infrastructure. To address uncertainty, conservative exploration methods and a dynamic risk budget are used. The system architecture includes signal collection, estimators, a decision-making judge, a switching agent, and an immutable log. Policies are stored in a versioned manner to enable replay and auditing. Evaluation results show that this approach increases success rates, reduces latency and cost, and improves the stability and responsiveness of the electricity market during critical situations. This research represents an important step towards the development of robust and efficient smart grid payment systems. |
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| Keywords: Electricity Market, Smart Grid, Intelligent Policy-Maker, Switching, Payment System, Latency, Risk. |
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Full-Text [PDF 741 kb]
(34 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2025/07/6 | Accepted: 2025/10/6 | Published: 2025/12/1
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