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Zhao, Min, Li, Shunxin, Xiao, Dong, Zhao, Guoliang, Li, Bo, Liu, Li, Chen, Xiangyu, Yang, Min.  2019.  Consumption Ability Estimation of Distribution System Interconnected with Microgrids. 2019 IEEE International Conference on Energy Internet (ICEI). :345–350.
With fast development of distributed generation, storages and control techniques, a growing number of microgrids are interconnected with distribution networks. Microgrid capacity that a local distribution system can afford, is important to distribution network planning and microgrids well-organized integration. Therefore, this paper focuses on estimating consumption ability of distribution system interconnected with microgrids. The method to judge rationality of microgrids access plan is put forward, and an index system covering operation security, power quality and energy management is proposed. Consumption ability estimation procedure based on rationality evaluation and interactions is built up then, and requirements on multi-scenario simulation are presented. Case study on a practical distribution system design with multi-microgrids guarantees the validity and reasonableness of the proposed method and process. The results also indicate construction and reinforcement directions for the distribution network.
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Wan, Mengting, Chen, Xiangyu, Kaplan, Lance, Han, Jiawei, Gao, Jing, Zhao, Bo.  2016.  From Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :1885–1894.

In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.