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

机译:考虑需求侧能源管理的可再生能源枢纽的日前最优调度

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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:在最近的几十年中,各种类型的分布式能源的日益普及,不可避免地导致了所有类型能源之间的相互作用。在这方面,创建能源枢纽的目的是考虑整个智能电网中多载波能源系统之间的相互作用。在这项研究中,在日后市场中考虑了多能源枢纽的最佳调度,目的是将能源枢纽的成本降至最低。由于在每个能源枢纽都采用了风力涡轮机和光伏板,清洁能源生产潜力得到了充分利用,因此所提出的模型将通过在计划范围内减少燃气系统的运行来减轻温室气体的排放。冷却/加热和动力系统的组合也被用作随机生产者的备用设备,以确保以最小的负荷减少来提供能源。此外,还存在电能和热能存储设备,用于在大量清洁和自由能产生的时间间隔内存储能量。蒙特卡洛模拟方法用于对随机生产者的不确定行为进行建模,而快速前向选择方法也用于情景减少过程。能源需求的灵活性也可以通过需求响应程序进行研究。为了验证所提出模型的有效性,使用了集成了分布式能源的IEEE 10总线标准测试系统。仿真结果证明了该模型在多能源枢纽的能源管理中的适用性和实用性。

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