The advent of smart grid technologies provides new tools and services to optimally manage the electricity grids. One of the most interesting services that emerged with the development of Information and Communication Technologies (ICTs) is energy demand management. This service permits us to face the issues caused by the ever-increasing energy demand such as grid congestion during peak hours, increasing energy generation costs, and even blackouts. In this paper, we investigate the problem of consumer-side optimization of residential energy demand. Our main aim is to better distribute the energy consumption over a day to avoid or reduce the demand during peak hours. Hence, we propose a fog computing-based model for energy demand scheduling using energy consumption cost as an incentive. In this model, the fog nodes schedule the appliances’ operations in order to reduce the individual and global energy bills whilst respecting consumers’ preferences. The proposed approach performs a multi-agent system-based cooperative scheduling game with minimal interactions between the nodes. Moreover, we present a fog nodes’ assignment scheme to decide which node will handle which appliances’ schedules. The nodes’ assignment strategy aims to optimize the use of fog nodes’ resources whilst reducing the scheduling process latency. The performance evaluation shows that the use of fog computing can achieve interesting results in terms of the reduction of energy consumption cost. For instance, the energy consumption during peak hour decreases by more than 25% from 670 kWh to 500 kWh when the scheduling game is performed. As a consequence, the energy consumption cost decreases by 7% from 806 € to 750 € .

中文翻译：

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能源消耗调度作为智能电网中的雾计算服务


智能电网技术的出现提供了新的工具和服务来优化管理电网。随着信息和通信技术（ICT）的发展而出现的最有趣的服务之一是能源需求管理。这项服务使我们能够应对不断增长的能源需求带来的问题，例如高峰时段电网拥堵、能源发电成本增加，甚至停电。在本文中，我们研究了消费者侧住宅能源需求优化问题。我们的主要目标是更好地分配一天的能源消耗，以避免或减少高峰时段的需求。因此，我们提出了一种基于雾计算的能源需求调度模型，以能源消耗成本作为激励。在此模型中，雾节点安排设备的运行，以减少个人和全球能源费用，同时尊重消费者的偏好。所提出的方法执行基于多代理系统的协作调度游戏，节点之间的交互最少。此外，我们提出了一个雾节点的分配方案来决定哪个节点将处理哪些设备的调度。节点分配策略旨在优化雾节点资源的使用，同时减少调度过程延迟。性能评估表明，使用雾计算在降低能耗成本方面可以取得有趣的结果。例如，进行调度游戏后，高峰时段的能耗从 670 kWh 减少到 500 kWh，降低了 25% 以上。因此，能源消耗成本降低了 7%，从 806 欧元降至 750 欧元。