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基于鲁棒随机模型预测控制的园区综合能源系统两阶段优化
刘春明, 李瑞月, 尹钰君, 刘念
华北电力大学 电气与电子工程学院,北京 102206
摘要:
可再生能源发电与负荷的不确定性给园区综合能源系统的安全准确调度带来了巨大挑战,为了提高系统调度的准确度和运行经济性,提出了一种基于鲁棒随机模型预测控制的两阶段优化调度策略。该模型考虑到园区综合能源系统中负荷预测误差分布的规律性和可再生能源出力波动的概率分布难以准确刻画的特点,分别利用随机优化和鲁棒优化处理负荷侧和发电侧的不确定性;在第二阶段的优化目标中加入协调策略以使实时优化控制变量值尽量接近日前全局优化值,从而在修正第一阶段预测误差的同时克服实时优化过程中的短视效应;通过算例对比分析证明了所提策略可以有效平衡不同优化时间尺度上的预测误差与系统短视效应所带来的经济性影响,有利于兼顾系统的经济性与鲁棒性。
关键词:  园区综合能源系统  鲁棒随机模型预测控制  两阶段优化  可再生能源
DOI:10.16081/j.epae.202201014
分类号:TM73;TK01
基金项目:
Two-stage optimization for community integrated energy system based on robust stochastic model predictive control
LIU Chunming, LI Ruiyue, YIN Yujun, LIU Nian
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Abstract:
The uncertainties of renewable energy output and load bring great challenges to the safe and accurate scheduling of community integrated energy system. The two-stage optimal scheduling strategy based on robust stochastic model predictive control is proposed to improve the accuracy and operating economy of system scheduling. Considering the regularity of load prediction error distribution and the difficulty in describing the probability distribution of renewable energy output fluctuation in community integrated energy system, the stochastic optimization and robust optimization are applied in the model to deal with the uncertainty of the source and load side respectively. In addition, a coordination strategy is added to the optimization objective in the second stage to make the real-time optimal control variable values as close as possible to the day-ahead global optimal values, thus correcting the prediction error in the first stage and overcoming the short-sighted effect in the process of real-time optimization at the same time. Case study comparison is employed to verify that the proposed strategy can effectively balance the prediction error in different optimization time scales and the economic influence caused by the short-sighted effect of system, which is beneficial to both the economy and robustness of the system.
Key words:  community integrated energy system  robust stochastic model predictive control  two-stage optimization  renewable energy

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