Technoeconomic Assessment Based on Active Context-Knowledge Orchestration for Power Internet of Things
Technoeconomic Assessment Based on Active Context-Knowledge Orchestration for Power Internet of Things
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DOI：
10.1155/2021/5499653
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发表时间：
2021-10
影响因子：
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通讯作者：
Yuanshuo Zheng;Shujuan Sun;Chenyang Li;Jingtang Luo;Jiuling Dong;Yudong Wang;Xiaolong Yang
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文献类型：
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作者：
Yuanshuo Zheng;Shujuan Sun;Chenyang Li;Jingtang Luo;Jiuling Dong;Yudong Wang;Xiaolong Yang
电力物联网（Power Internet of Things，简称PIoT）是为智能电网（smart grid，简称SG）提供泛在感知能力的信息基础设施。为了更好地部署和利用PIoT，必须从技术性能和经济效益两个方面对其感知能力进行综合评估。然而，由于SG情景的高度多样性和异质性，目前还没有PIoT的评估框架。此外，在评估框架中的量度之间存在信息重叠。为了提高评估的有效性和及时性，迫切需要能够消除指标间冗余信息、简化评估框架的评估模型。因此，首先，针对电力系统复杂多样的需求，提出了PIoT技术能力和经济能力评估的通用评估框架。其次，利用主动上下文知识编排技术对配电场景（简称PDS）的需求特征进行了精确分析。对通用评估框架进行实例化，构建PDS中的实例化评估方案。在此基础上，基于实例化评价方案建立了北京市PIoT效率评价模型。最后，利用机器学习技术对评估模型进行进一步细化，提高评估效率。该改进模型实现了从23维指标中提取4维指标进行评估，最终将评估效率提高了82.6%。
Power Internet of Things (abbreviated as PIoT) is the information infrastructure to provide ubiquitous perception ability for smart grid (abbreviated as SG). To better deploy and utilize PIoT, its perception ability must be comprehensively assessed in terms of technical performance and economic benefits. However, at present, there is no assessment framework for PIoT due to the high diversity and heterogeneousness of SG scenarios. Additionally, there is information overlap between metrics in the assessment framework. The assessment model which could remove redundant information between metrics and simplify the assessment framework is an urgent demand to improve the effectiveness and timeliness of assessment. Consequently, first, aiming at the power system requirements of complex and diverse, a general assessment framework is put forward to assess the ability of PIoT in terms of technology and economy. Next, the requirement characteristics of power distribution scenario (abbreviated as PDS) are precisely analyzed with active context-knowledge orchestration technology. The general assessment framework is instantiated to build an instantiation assessment scheme in PDS. Moreover, an assessment model is established based on the instantiation assessment scheme to assess the efficiency of PIoT in Beijing. Finally, the assessment model is further refined with the machine learning technology to improve the efficiency of assessment. This refinement model achieves the extraction of 4-dimensional metrics from 23-dimensional metrics for assessment and finally improves assessment efficiency by 82.6%.