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An Efficient HEMS for Demand Response Considering TOU Pricing Scheme and Incentives

机译:考虑TOU定价方案和激励措施的需求响应有效休眠

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This paper works on the scheduling of smart household's controllable appliance in Demand Response environment. A smart residential electricity customer requires intelligent Home Energy Management System (HEMS)having the capability to shift and control the controllable appliance's consumption according to their preferences and economy. In this paper., an intelligent HEMS algorithm is proposed for optimal scheduling of household controllable appliances. The optimization problem minimizes the customer energy and reliability cost. Modelling results of two case studies show the effectiveness of proposed HEMS algorithm considering Time of Use (TOU)pricing scheme and incentives. Modelling of electric vehicle (EV)., electric water heater (EWH)and air conditioner (AC)is done in this paper since they are controllable in nature. This HEMS system also considers the installation of PV panel with energy storage system (ESS)., household loads., PV generation forecast and built-in characteristics of controllable appliances. By analyzing the results it can be observed that the daily energy consumption cost reduces in optimization-based approach up to 45.4 % compared with the rule-based approach.
机译:本文涉及智能家庭可控设备在需求响应环境中的调度。智能住宅电力客户需要智能家居能源管理系统(HEMS),其能力根据其偏好和经济来转移和控制可控设备的消费。在本文中,提出了一种智能下摆算法,以获得家用可控设备的最佳调度。优化问题最小化客户能量和可靠性成本。两种案例研究的建模结果表明,考虑使用时间(TOU)定价方案和激励措施的临时算法的有效性。电动车辆(EV)建模。,电热水器(EWH)和空调(AC)在本文中完成,因为它们是自然的控制。该下摆系统还考虑了使用储能系统(ESS)的PV面板的安装。,家庭载荷,PV生成预测和可控设备的内置特性。通过分析结果,可以观察到,与基于规则的方法相比,每日能量消耗成本降低到基于优化的方法,高达45.4%。

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