An Explainable AI-Driven Threat Detection and Resilience Optimization Framework for Blockchain-Enabled Decentralized Renewable Energy Grids: A Multi-Layer Security Analysis

Om Prakash Sinha; Priyanshu Kapoorlal Gupta; Abhinaba Chakraborty; Bushra Altaf Momin

doi:10.36948/ijfmr.2026.v08i03.80108

An Explainable AI-Driven Threat Detection and Resilience Optimization Framework for Blockchain-Enabled Decentralized Renewable Energy Grids: A Multi-Layer Security Analysis

Author(s)	Mr. Om Prakash Sinha, Mr. Priyanshu Kapoorlal Gupta, Mr. Abhinaba Chakraborty, Ms. Bushra Altaf Momin
Country	India
Abstract	The fast development of decentralized renewable energy networks, and the adoption of blockchain-based smart grids, has changed the contemporary energy infrastructure by allowing the peer-to-peer trading of energy, providing better transparency, and distributed control mechanisms. Nonetheless, this switch has also created major cyber-physical risks, such as the fake data injection, distributed denial-of-service, and malicious interference with the energy dealings. Conventional artificial intelligence-based threat detection systems though effective in detecting anomalies are usually black-box models which limits interpretation and reduces confidence among stakeholders. In a bid to mitigate these issues, this paper presents an explainable artificial intelligence (XAI)-based multi-layer security architecture that should be used in blockchain-based decentralized renewable energy grids. The framework combines blockchain technology to manage data in a secure and immutable manner, machine learning models to detect threats in real-time and XAI methods, e.g., SHAP and LIME, to improve the transparency and interpretability of model decisions. To achieve the high level of protection and optimization of resilience, a multi-layer security architecture is implemented on physical, network, application, and AI layers. The suggested solution has better detection performance, lower response time, and resiliency of the system with simulated attack conditions. Moreover, explainability mechanisms are associated with the lack of distrust, responsibility, and regulatory adherence in decentralized energy ecosystems. In general, the framework leads to the establishment of secure, transparent, and robust smart grid infrastructures.
Keywords	Explainable Artificial Intelligence (XAI), Blockchain Security, Decentralized Renewable Energy Grids, Threat Detection, Resilience Optimization, Multi-Layer Security Analysis, Cybersecurity in Energy Systems, Sustainable Energy Infrastructure
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-02
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.80108

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An Explainable AI-Driven Threat Detection and Resilience Optimization Framework for Blockchain-Enabled Decentralized Renewable Energy Grids: A Multi-Layer Security Analysis

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