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arXiv 2104.06346math.OCcs.SYeess.SY
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一种微网随机最优控制的分布式混合调度框架

A Distributed Mixed-Integer Framework to Stochastic Optimal Microgrid Control

Andrea Camisa, Giuseppe Notarstefano

摘要:

研究了由储能、发电机、可再生能源、关键和可控负荷组成的微网的分布式控制问题。我们考虑一个随机制定的最优控制问题的微电网,适当地考虑到可再生能源产生的电力的不可预测性。所得到的问题是一个混合线性规划,是NP-难的和非凸的。此外,所考虑的框架的特点是,没有中央单元可以用来执行优化,而是单位必须相互合作,通过相邻的通信。为了解决这个问题,我们诉诸于一个分布式的方法的基础上的原始分解方法。由此产生的算法是能够计算高质量的可行解的两阶段随机最优化问题,我们还提供了一个理论上的约束违反的上界。最后,一个具有大量设备的情况下的Monte Carlo数值计算显示了所提出的分布式控制方法的有效性。数值实验是在生成对抗网络训练的欧盟开源历史数据集上获得的现实场景上进行的。

英文摘要:

This paper deals with distributed control of microgrids composed of storages, generators, renewable energy sources, critical and controllable loads. We consider a stochastic formulation of the optimal control problem associated to the microgrid that appropriately takes into account the unpredictable nature of the power generated by renewables. The resulting problem is a Mixed-Integer Linear Program and is NP-hard and nonconvex. Moreover, the peculiarity of the considered framework is that no central unit can be used to perform the optimization, but rather the units must cooperate with each other by means of neighboring communication. To solve the problem, we resort to a distributed methodology based on a primal decomposition approach. The resulting algorithm is able to compute high-quality feasible solutions to a two-stage stochastic optimization problem, for which we also provide a theoretical upper bound on the constraint violation. Finally, a Monte Carlo numerical computation on a scenario with a large number of devices shows the efficacy of the proposed distributed control approach. The numerical experiments are performed on realistic scenarios obtained from Generative Adversarial Networks trained an open-source historical dataset of the EU.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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