Active Optimal Dispatch of Microgrid Based on Improved Model Predictive Control

D. Lei, Zhang Xia-wei, Chen Naishi, Sun Qian, Ma Jianwei
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Abstract

In order to cope with the uncertainty of intermittent distributed power supply output in the microgrid, and to solve the problem that the prediction accuracy is declining with the increase of time scale, this paper uses deep learning technology to achieve accurate prediction of new energy output. Based on this, an optimal dispatch of micro-grid based on model predictive control is proposed to fine-tune the coordination and control of wind power, photovoltaic and energy storage equipment in the microgrid so as to maximize the dissipation of the intermittent distributed power supply and track the microgrid operation reference trajectories accurately.
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基于改进模型预测控制的微电网主动优化调度
为了应对微电网中间歇性分布式电源输出的不确定性,解决预测精度随时间尺度增加而下降的问题,本文采用深度学习技术实现对新能源输出的准确预测。在此基础上,提出了一种基于模型预测控制的微网优化调度方法,对微网中风电、光伏和储能设备的协调与控制进行微调,使间歇性分布式电源耗散最大化,准确跟踪微网运行参考轨迹。
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