MicroGrid Resilience-Oriented Scheduling: A Robust MISOCP Model
MicroGrid Resilience-Oriented Scheduling: A Robust MISOCP Model
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DOI：
10.1109/tsg.2020.3039713
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发表时间：
2021-05
影响因子：
9.6
通讯作者：
Natalia-Maria Zografou-Barredo;C. Patsios;Ilias Sarantakos;P. Davison;S. Walker;P. Taylor
中科院分区：
文献类型：
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作者：
Natalia-Maria Zografou-Barredo;C. Patsios;Ilias Sarantakos;P. Davison;S. Walker;P. Taylor
提出了一种鲁棒混合二阶锥规划（R-MISOCP）模型，用于微电网的分布式优化调度。这是为由于主电网的预定中断而孤岛化的MG开发的，其中最小化运营成本和甩负荷是至关重要的。所介绍的模式有两个主要好处。首先，使用精确的二阶锥潮流模型（SOC-PF），保证全局最优性。通过与修改后的IEEE 33节点网络上的分段线性潮流模型的比较，它表明，未能准确建模潮流方程，可能会导致显着低估的运营成本的近12%。其次，不确定性建模使用一个强大的方法，允许MG运营商愿意容忍的不确定性之间的权衡，和性能。在这篇文章中，考虑的性能标准是运营成本和甩负荷。市场价格、需求、可再生能源发电量和孤岛持续时间被视为不确定变量。结果表明，通过控制预算的不确定性，MG运营商可以实现近20%的运营成本降低，相比，一个完全鲁棒的时间表，同时实现0%的可能性，脱落比预期更多的需求。
This article introduces a Robust Mixed-Integer Second Order Cone Programming (R-MISOCP) model for the resilience-oriented optimal scheduling of microgrids (MGs). This is developed for MGs that are islanded due to a scheduled interruption from the main grid, where minimizing both operational costs and load shedding is critical. The model introduced presents two main benefits. Firstly, an accurate second order cone power flow model (SOC-PF) is used, which ensures global optimality. Through a comparison with a piecewise linear power flow model on a modified IEEE 33 bus network, it is demonstrated that failure to accurately model power flow equations, can result in a significant underestimation of the operational cost of almost 12%. Secondly, uncertainty is modelled using a robust approach which allows trade-offs between the uncertainty that a MG operator is willing to tolerate, and performance. In this article, performance criteria considered are operational cost and load shedding. Market price, demand, renewable generation and islanding duration are considered as uncertain variables. Results show that by controlling the budget of uncertainty, the MG operator can achieve an almost 20% reduction in the operating cost, compared to a fully robust schedule, while achieving 0% probability of shedding more demand than expected.