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Optimal Day-Ahead Scheduling of the Renewable Based Energy Hubs Considering Demand Side Energy Management

机译:考虑需求侧能量管理的可再生基于能源中心的最佳日落调度

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In recent decades, the rising penetration of various types of distributed energy resources has made interactions between all types of energy inevitable. In this respect, energy hubs are created with the aim of considering the interactions between multi-carrier energy systems throughout the smart grids. In this research, optimal scheduling of the multi-energy hubs is considered in the day-ahead market with the aim of minimizing the energy hub's cost. Because of the high usage of the clean energy production potential by employing the wind turbines and PV panels at each energy hub, the proposed model will mitigate the greenhouse gas emissions through reducing the operation of the gas-fired systems over the scheduling horizon. The combined cooling/heating and power system is also used as a backup unit for the stochastic producers to ensure energy supply with minimum load shedding. Moreover, electrical and thermal energy storage devices are also employed for storing energy during time intervals when there is a large amount of clean and free energy production. The Monte-Carlo simulation approach is used for modeling the uncertain behaviors of the stochastic producers and fast forward selection method is also used for the scenario reduction process. The flexibility of the energy demand is also investigated using demand response programs. In order to validate the effectiveness of the proposed model, IEEE 10-bus standard test system integrated with distributed energy resources is used. Simulation results demonstrate the applicability and usefulness of the proposed model in the energy management of multi energy hubs.
机译:近几十年来,各种类型的分布能源资源的渗透性上升已经在各种能源之间进行了相互作用。在这方面,以旨在考虑整个智能电网之间的多载波能量系统之间的相互作用来创建能量集线器。在这项研究中,在现代市场中考虑了多能集线器的最佳调度,目的是最大限度地减少能量中心的成本。由于通过在每个能量枢纽采用风力涡轮机和PV面板对清洁能量产生电位的高度,所提出的模型将通过减少调度地平线上的燃气系统的运行来减轻温室气体排放。组合的冷却/加热和电力系统也用作随机生产商的备用单元,以确保能量供应具有最小载荷脱落。此外,当存在大量清洁和自由能量产生时,电气和热能存储装置也用于在时间间隔期间存储能量。 Monte-Carlo仿真方法用于对随机生产者的不确定行为建模,快进选择方法也用于场景还原过程。还使用需求响应计划研究了能量需求的灵活性。为了验证所提出的模型的有效性,使用了与分布式能源集成的IEEE 10-Bus标准测试系统。仿真结果表明,拟议模型在多能量枢纽的能量管理中的适用性和有用性。

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