【Author】
Wang, Zhishang; Ben Abdallah, Abderazek
【Abstract】A Virtual Power Plant (VPP) balances the load on a power grid by allocating power generated by various interconnected units during periods of peak demand. In addition, demand-side energy devices such as Electric Vehicles (EVs) and mobile robots can also balance energy supply and demand when effectively deployed. However, the fluctuation of energy generated by renewable resources makes balancing energy supply a challenging goal. This paper proposes a semi-decentralized robust network of electric vehicles (NoEV) integration system for power management in a smart grid platform. The proposed approach integrates an aggregator with EV fleets into a blockchain framework. The EVs execute a multi-stage algorithm to predict the power consumption based on a novel federated learning algorithm named Federated Learning for Qualified Local Model Selection (FL-QLMS). From the evaluation results, the proposed system requires 35% fewer transactions in short intervals and propagation delays than the previous approaches and achieves better network efficiency while maintaining a high level of security. Moreover, NoEV achieves a 5.7% lower root mean square error (RMSE) than the conventional approach for power consumption prediction, which is a significant improvement. In addition, the FL-QLMS approach outperforms state-of-the-art methods in terms of robustness to client-side attacks. The evaluation results also show that the performance of FL-QLMS is not affected when 10% to 40% percent of the models are manipulated.
【Keywords】Blockchains; Collaborative work; Power demand; Electric vehicles; Computational modeling; Vehicle-to-grid; Renewable energy sources; AI-enabled; blockchain-based; robust; power-management; EVs; smart grid
【标题】一种半分散式电动汽车网络中的鲁棒多级功耗预测方法
【摘要】虚拟发电厂 (VPP) 通过分配各种互连单元在高峰需求期间产生的电力来平衡电网的负载。此外，电动汽车（EV）和移动机器人等需求侧能源设备在有效部署时也可以平衡能源供需。然而，可再生资源产生的能量波动使得平衡能源供应成为一个具有挑战性的目标。本文提出了一种用于智能电网平台中电源管理的半分散式稳健电动汽车网络 (NoEV) 集成系统。所提出的方法将聚合器与 EV 车队集成到区块链框架中。电动汽车执行多阶段算法来预测功耗，该算法基于一种名为“用于合格本地模型选择的联邦学习”(FL-QLMS) 的新型联邦学习算法。从评估结果来看，与以前的方法相比，所提出的系统在短时间间隔和传播延迟中所需的事务减少了 35%，并在保持高安全性的同时实现了更好的网络效率。此外，与传统的功耗预测方法相比，NoEV 的均方根误差 (RMSE) 降低了 5.7%，这是一个显着的改进。此外，FL-QLMS 方法在对客户端攻击的鲁棒性方面优于最先进的方法。评估结果还表明，当 10% 到 40% 的模型被操纵时，FL-QLMS 的性能不受影响。
【关键词】区块链；协作工作；电力需求；电动汽车；计算建模；车辆到电网；可再生能源;启用人工智能；基于区块链；强大的;能源管理;电动汽车；智能电网
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