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QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

机译:考虑可再生能源和电动车辆的QoE感知智能家居能源管理

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摘要

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.
机译:为了减少峰值负荷和电费,同时保持用户的舒适性,具有可再生能源(RES)和电动车辆(EV)的智能家居能源管理系统(Shems)的经验质量(QoE)-aware智能家电控制算法提出。所提出的算法通过延迟延迟耐受电器的启动时间从峰值到非高峰时段,控制加热,通风和空调(HVAC)的温度设定,并适当地调度放电和充电,从而降低峰值负荷和电费。 EV的时期。在本文中,通过QoE功能评估用户舒适性。为了保留用户的QoE,通过QoE阈值限制Hove Appliance的起始时间和HVAC的温度设定的延迟。此外,为了解决峰值负荷/电费减少和用户QoE之间的权衡问题,还设计了一种模糊逻辑控制器,用于动态调整QoE阈值以优化用户的QoE。仿真结果表明,具有模糊控制的QoE阈值的提议的智能设备控制算法显着降低了峰值负荷和电费,同时最佳地保持用户的QoE。与基线案例相比,该方案根据res和EV的情况将电费减少65%。另外,与文献中设备的最佳调度方法相比,所提出的方案实现了更好的峰值负荷减少性能和用户的QoE。

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