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Optimal Operation of Residential Energy Hubs in Smart Grids

机译:智能电网中居民能源枢纽的优化运行

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This paper presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in smart grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort level. Novel mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in Mixed Integer Linear Programming (MILP) optimization problems with the objective functions of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. Several realistic case studies are carried out to examine the performance of the mathematical model, and experimental tests are carried out to find practical procedures to determine the parameters of the model. The application of the proposed model to a real household in Ontario, Canada is presented for various objective functions. The simulation results show that savings of up to 20% on energy costs and 50% on peak demand can be achieved, while maintaining the household owner's desired comfort levels.
机译:本文提出了住宅能源枢纽的数学优化模型,可以将其轻松地集成到智能电网的自动化决策技术中,并且可以在实时框架内有效解决,从而在适当的情况下最佳地控制所有主要住宅能源负荷,存储和生产组件考虑客户的喜好和舒适度。制定了满足主要家庭需求的新颖数学模型,即冰箱,冰柜,洗碗机,洗衣机和烘干机,炉灶,热水器,热水浴缸​​和泳池泵。此外,还开发了住宅能源系统其他组件(包括照明,供暖和空调)的数学模型,并提出了用于太阳能光伏板和储能/发电设备的通用模型。所开发的数学模型导致混合整数线性规划(MILP)优化问题,其目标功能是在考虑最终用户的同时将能耗,电力和天然气的总成本,排放,峰值负荷和/或这些目标的任意组合减到最少偏好。进行了一些实际的案例研究以检查数学模型的性能,并进行了实验测试以找到确定模型参数的实用程序。提出了该模型在加拿大安大略省的一个真实家庭中的应用,以实现各种目标函数。仿真结果表明,在保持住户业主所需的舒适水平的同时,可以节省多达20%的能源成本和50%的高峰需求。

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