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A Model Predictive Control Based Generator Start-Up Optimization Strategy for Restoration With Microgrids as Black-Start Resources

机译:基于模型预测控制的发电机启动优化策略,以微电网作为黑启动资源。

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摘要

Microgrids (MGs) can operate in an islanded mode and serve as black-start resources for power system restoration (PSR). In this work, a model predictive control (MPC) based generator start-up optimization strategy for PSR is proposed utilizing MGs as black-start resources. First, the generator start-up sequence (GSUS) optimization is formulated as a mixed integer linear programming. Then, the uncertainties of MG black-start resources (MBSRs) are modeled by discretizing the probability distribution of the forecast errors, and representative scenarios for MBSRs extracted by formulating the probability mass transportation problem. Third, the generator start-up optimization strategy considering MBSRs is proposed utilizing the MPC technique, in which the optimization objective is to maximize the energy capability of the power systems and minimize the load curtailment of the MGs in each looking-ahead interval. Simulations on the IEEE 118 bus system with MGs and Zhejiang provincial power system in China verify that the proposed strategy for PSR can successfully restore the power system and effectively determine the optimal GSUS.
机译:微电网(MG)可以孤岛模式运行,并充当电力系统恢复(PSR)的黑启动资源。在这项工作中,提出了一种基于模型预测控制(MPC)的PSR发电机启动优化策略,该策略利用MG作为黑启动资源。首先,将发电机启动顺序(GSUS)优化公式化为混合整数线性规划。然后,通过离散化预测误差的概率分布,对MG黑启动资源(MBSR)的不确定性进行建模,并通过制定概率大规模运输问题来提取MBSR的代表性场景。第三,利用MPC技术提出了考虑MBSR的发电机启动优化策略,其优化目标是在每个预见间隔内最大化电力系统的能量容量并最小化MG的负荷削减。在中国的MG 118和浙江省电力系统的IEEE 118总线系统上的仿真验证了所提出的PSR策略可以成功地恢复电力系统并有效地确定最佳的GSUS。

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