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Hierarchical smart energy management strategy based on cooperative distributed economic model predictive control for multi-microgrids systems

机译:基于合作分布式经济模型预测控制的分层智能能量管理战略,用于多微电网系统的预测控制

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With the new edge of smart grids technology, the concept of efficient energy management among cooperative microgrids (MGs) distribution networks for the next generation of energy trading applications has become more and more challenging. This article presents smart energy dispatch for multi-MGs (MMGs) systems based on the cooperative distributed economic model predictive control (DEMPC) within a hierarchical structure. The optimization framework consists of two levels. First, a DEMPC coordinator computes the optimal energy dispatch solution, taking into account the energy surplus with a power-sharing policy, the retail electricity price based on the time of use (TOU), and the distributed generators units. For the second level, each local controller executes the energy scheduling reference from the first level. Thus, the MMGs system achieves a global supply-demand balance. Moreover, the proposed approach improves the utilization of renewable power and demonstrates optimal energy dispatch while reducing operating costs. Finally, to validate the efficiency and resiliency of our proposed approach, two cases of study are presented based on three interconnected MGs system under a cooperative and non-cooperative optimization. Simulation results show the advantage of the cooperative strategy with 28% for MG 1, and 20% for MG 2 in terms of daily operating costs savings.
机译:随着智能电网技术的新边缘,合作微电网(MGS)分销网络中的高效能量管理的概念越来越具有挑战性。本文介绍了基于分层结构中的协同分布式经济模型预测控制(DEMPC)的基于协同分布式经济模型预测控制(DEMPC)的多MGS(MMGS)系统的智能能量调度。优化框架由两个级别组成。首先,DEMPC协调员计算最佳能量调度解决方案,考虑到具有权力共享政策的能量盈余,基于使用时间(tou)和分布式发电机单元的零售电价。对于第二级,每个本地控制器从第一级执行能量调度参考。因此,MMGS系统实现了全球供需平衡。此外,所提出的方法改善了可再生能力的利用,并在降低运营成本的同时,展示了最佳能源调度。最后,为了验证我们所提出的方法的效率和弹性,在合作和非合作优化下基于三个互联的MGS系统提出了两种研究。仿真结果表明,在日常运营成本节省的情况下,MG 1的合作策略具有28%的合作策略的优势,以及MG 2的20%。

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