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使用人工智能集成区块链框架优化智能电动汽车充电网络中的需求响应和负载平衡。

Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework.

作者信息

Singh Arvind R, Kumar R Seshu, Madhavi K Reddy, Alsaif Faisal, Bajaj Mohit, Zaitsev Ievgen

机构信息

School of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan, P. R. China.

Department of EEE, Vignan's Foundation for Science Technology and Research (Deemed to be University), Guntur, Andhra Pradesh, 522213, India.

出版信息

Sci Rep. 2024 Dec 30;14(1):31768. doi: 10.1038/s41598-024-82257-2.

Abstract

The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.

摘要

电动汽车(EV)接入电网带来了若干关键挑战,例如可扩展性有限、实时需求管理效率低下以及集中式架构中存在重大数据隐私和安全漏洞。此外,对分散式系统的需求不断增加,这就需要强大的解决方案来处理不断增长的电动汽车数量,同时确保电网稳定性并优化能源利用。为应对这些挑战,本文提出了利用人工智能实现需求响应和负载平衡(DR-LB-AI)框架。所提出的框架利用人工智能进行预测性需求预测和动态负载分配,实现电动汽车充电基础设施的实时优化。此外,采用区块链技术促进分散式安全通信,确保能源交易防篡改,同时提高利益相关者之间的透明度和信任度。DR-LB-AI框架显著提高了能源分配效率,在高峰期将电网过载减少了20%。通过先进的需求预测和自主负载调整,该系统提高了电网稳定性并优化了整体能源利用。区块链集成进一步增强了安全性和隐私性,通过其分散式框架实现了97.71%的数据保护提升。此外,该系统实现了98.43%的可扩展性提升,有效管理了不断增长的电动汽车数量,并通过使用不可变交易记录将透明度和信任度提高了96.24%。总体而言,研究结果表明,DR-LB-AI不仅减轻了高峰需求压力,还加快了负载平衡的响应时间,有助于建立更具弹性、可扩展性和可持续性的电动汽车充电基础设施。这些进展对于智能电网的长期可行性和电动汽车的持续扩展至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36a6/11685539/c5df24d2a892/41598_2024_82257_Fig1_HTML.jpg

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