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Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties

机译：基于两阶段随机编程的微电网能源管理模型预测控制策略

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摘要

Microgrids (MGs) are presented as a cornerstone of smart grid, which can integrate intermittent renewable energy sources (RES), storage system, and local loads environmentally and reliably. Due to the randomness in RES and load, a great challenge lies in the optimal operation of MGs. Two-stage stochastic programming (SP) can involve the forecast uncertainties of load demand, photovoltaic (PV) and wind production in the optimization model. Thus, through two-stage SP, a more robust scheduling plan is derived, which minimizes the risk from the impact of uncertainties. The model predictive control (MPC) can effectively avoid short sighting and further compensate the uncertainty within the MG through a feedback mechanism. In this paper, a two-stage SP based MPC stratey is proposed for microgrid energy management under uncertainties, which combines the advantages of both two-stage SP and MPC. The results of numerical experiments explicitly demonstrate the benefits of the proposed strategy.
机译：MicroGrids（MGS）作为智能电网的基石表示，可集成间歇可再生能源（RES），存储系统和局部负载环境和可靠性。由于RES和LOAD中的随机性，巨大的挑战在于MGS的最佳操作。两阶段随机编程（SP）可以涉及在优化模型中涉及负载需求，光伏（PV）和风力产生的预测不确定性。因此，通过两级SP，导出更强大的调度计划，从而最大限度地减少了不确定性的影响的风险。模型预测控制（MPC）可以有效地避免短视镜头，并通过反馈机制进一步补偿MG内的不确定性。在本文中，提出了一种用于不确定性的微电网能量管理的两级SP基MPCStratey，其结合了两级SP和MPC的优点。数值实验的结果明确证明了拟议策略的益处。

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