Research on Regional Flexible Load Low Carbon Dispatching Based on Cloud Model

Yan Dong, Haoyang Wang, Yongsheng Zhu, Caijing Nie, Dongya Wu
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Abstract

With the widespread deployment of renewable energy generation devices and the continuous access of demand side resources in the new microgrid system, power system operations are gradually moving away from the traditional top-down hierarchy. The uncertainty it brings to the system is becoming increasingly unconditional, making it difficult for optimal power system dispatching to operate effectively. Based on the above problems, this paper proposes a novel dispatching scheme to achieve the optimal operation of the system using an iterative coordination method of the supply demand interaction. On the supply side, the peak-flat-valley time division rules are proposed to develop a dynamic electricity price based on fuzzy C-means clustering. Furthermore, the system carbon and economic costs are integrated and modeled as minimization objectives. On the demand side, the power consumption behavior of flexible loads is evaluated considering demand response uncertainty based on the cloud model theory. Case studies demonstrate the validity of proposed model and adopted method including reducing the peak-to-valley difference and CO2 emission. Moreover, cloud model has a significant effect in dealing with the uncertainty of load demand response.
基于云模型的区域柔性负荷低碳调度研究
随着可再生能源发电设备的广泛部署和新型微电网中需求侧资源的不断接入,电力系统运行正逐渐摆脱传统自上而下的层级结构。它给系统带来的不确定性变得越来越无条件,使得电力系统优化调度难以有效运行。针对上述问题,本文提出了一种新的调度方案,利用供需交互的迭代协调方法实现系统的最优运行。在供电侧,提出了峰平谷分时规则,建立了基于模糊c均值聚类的动态电价。此外,系统碳和经济成本被整合并建模为最小化目标。在需求侧,基于云模型理论,考虑需求响应的不确定性,对柔性负荷的用电行为进行了评价。通过实例分析,验证了该模型的有效性和所采用的方法在减少峰谷差和CO2排放方面的有效性。此外,云模型在处理负荷需求响应的不确定性方面效果显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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