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Abstract: Traditional knowledge-based situation awareness (SA) modes struggle to adapt to the escalating complexity of today’s Energy Internet of Things (EIoT), necessitating a pivotal paradigm shift. In response, this work introduces a pioneering data-driven SA framework, termed digital twin-based SA (DT-SA), aiming to bridge existing gaps between data and demands, and further to enhance SA capabilities within the complex EIoT landscape. First, we redefine the concept of digital twin (DT) within the EIoT context, aligning it with data-intensive scientific discovery paradigm (the Fourth Paradigm) so as to waken EIoT’s sleeping data; this contextual redefinition lays the cornerstone of our DT-SA framework for EIoT. Then, the framework is comprehensively explored through its four fundamental steps: digitalization, simulation, informatization, and intellectualization. These steps initiate a virtual ecosystem conducive to a continuously self-adaptive, self-learning, and self-evolving big model (BM), further contributing to the evolution and effectiveness of DT-SA in engineering. Our framework is characterized by the incorporation of system theory and Fourth Paradigm as guiding ideologies, DT as data engine, and BM as intelligence engine. This unique combination forms the backbone of our approach. This work extends beyond engineering, stepping into the domain of data science—DT-SA not only enhances management practices for EIoT users/operators, but also propels advancements in pattern analysis and machine intelligence (PAMI) within the intricate fabric of a complex system. Numerous real-world cases validate our DT-SA framework.
Keywords: Complexity theory, Complex systems, Engines, Digital twins, Decision making, Data models, Energy Internet
Citation: X. He, Y. Tang, S. Ma, Q. Ai, F. Tao and R. Qiu, “Redefinition of Digital Twin and Its Situation Awareness Framework Designing Toward Fourth Paradigm for Energy Internet of Things,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 11, pp. 6873-6888, Nov. 2024.
Abstract: This study presents a formulation of the Superposition Theorem (ST) in the spectrum space, tailored for the analysis of composite events in an active distribution network (ADN). Our formulated ST enables a quantitative analysis on a composite event, uncovering the property of additivity among independent atom events in the spectrum space. This contribution is a significant addition to the existing literature and has profound implications in various application scenarios. To accomplish this, we leverage random matrix theory (RMT), specifically the asymptotic empirical spectral distribution, Stieltjes transform, and R transform. These mathematical tools establish a nonlinear, model-free, and unsupervised addition operation in the spectrum space. Comprehensive details, including a related roadmap, theorems, deductions, and proofs, are provided in this work. Case studies, utilizing field data, validate our newly derived ST formulation by demonstrating a remarkable performance. Our ST formulation is model-free, non-linear, non-supervised, theory-guided, and uncertainty-insensitive, making it a valuable asset in the realm of composite event analysis in ADN.
Keywords: Harmonic analysis, Transforms, Voltage, Mathematical models, Topology, Power system harmonics, Phasor measurement units
Citation: X. He, Q. Ai, Y. Tang, R. C. Qiu and C. Li, “Exploration of Superposition Theorem in Spectrum Space for Composite Event Analysis in an ADN,” in IEEE Transactions on Power Systems, vol. 39, no. 4, pp. 5920-5931, July 2024.
Abstract: Rapid growth of diversity, uncertainty, and coupling effect of units in modern energy systems jointly challenges the traditional model-based situation awareness (SA) in Energy Internet of Things (EIoT). This work explores the digital twin of EIoT (EIoT-DT) and then provides a novel data-driven SA paradigm, named DT-SA, as a promising alternative. Based on the combination of the latest data technologies and machine learning algorithms, DT-SA transfers those stubborn SA challenges to digital space, and then addresses them by building a domain-specific and data-friendly digital twin (DT) model upon massive data. The established model can be quantitatively tested via iterative virtual–real interaction and, thus, be evaluated and updated through closed-loop feedback to improve its performance in the physical world. To this end, some engineering and scientific problems are raised: 1) virtual–real interaction mechanism relevant to resource flow and data flow; 2) unified modeling and analysis of heterogeneous spatial–temporal data; 3) DT configuration and evolution; and 4) domain-specific DT-SA characterization. To solve these problems, cloud-edge-terminal configuration, big data analytics (BDA), DT, and SA indicator systems are studied, respectively. Then, the random matrix theory (RMT) and overarching DT-SA framework are designed as a roadmap. Besides, some potential applications and undergoing projects on the terminal, edge, or cloud are discussed, e.g., condition assessment of equipment, digital monitoring and diagnosis of the power grid network, and EIoT construction in the smart city. Finally, some perspectives and recommendations are proposed in conclusion for future research. This research can be regarded as an efficient handbook for both energy engineering and data science, which may benefit enterprise digitization, smart city, etc.
Keywords: Data models, Internet of Things, Uncertainty, Smart cities, Systematics, Digital twins, Complexity theory
Citation: X. He et al., “Situation Awareness of Energy Internet of Things in Smart City Based on Digital Twin: From Digitization to Informatization,” in IEEE Internet of Things Journal, vol. 10, no. 9, pp. 7439-7458, 1 May1, 2023.
Abstract: Transparency is crucial for decision-making within an active distribution network (ADN). To enhance ADN’s transparency, this study develops a novel Blind Decomposition of Composite Events (BDCE) approach rooted in Free Component Analysis (FCA), with a detailed exploration of its related theorems, algorithms, and deductions. Notably, FCA employs non-commutative matrix variables instead of scalar variables, establishing a natural connection to Random Matrix Theory (RMT). By incorporating RMT, FCA-BDCE effectively utilizes spatial–temporal correlation—a matrix-derived spectrum statistic; it allows for the filtration of locally independent noises, such as individual-level measurement error, ubiquitous white noise, while retaining globally influential signals across some specified spatial–temporal span. This capability is particularly valuable when gaining insight into the complex ADN, a landscape with significant diversity and uncertainty. In general, our approach is model-free, theory-guided, and unsupervised, making it particularly suitable for ADN. A comprehensive case study validates the practical effectiveness of our FCA-BDCE approach, demonstrating its superiority over ICA-BDCE.
Keywords: Blind decomposition, Composite event, Free component analysis, Free probability, Random matrix theory
Citation: Xing He, Zhuangyan Zhang, Qian Ai, Zenan Ling, Yuezhong Tang, Robert Qiu, Transparent enhancement of active distribution network through FCA-based blind decomposition, Applied Energy, Volume 379, 2025, 124776, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2024.124776.
摘要：能源互联网现行调控模式主要面向大负荷、大火电机组等能量大户，不适应其分布式能源资源(distributed energy resources,DER)渗透率不断提升的趋势。该文旨在建立多DER主体群智调控框架，通过在虚拟空间系统性地揭示并利用DER的聚合涌现规律，激发其主观能动性，从而开启调度新模式。具体而言，拟以系统论、数据密集型科学发现范式(第四范式)等为指导思想，以虚拟孪生、大数据分析、机器学习与人机混合智能等为内核，以数字孪生、虚拟仿真推演、高维统计、时空数据分析、深度神经网络、人在回路与知识嵌入等为技术手段，设计并逐步完善“虚拟孪生+数据科学+系统论+第四范式”的系统性框架。该框架旨在通过数据贯通、数业融合、虚实交互等手段实现数据赋能提智工程系统，最终形成复杂系统调度新理论。
关键词：调度;虚拟孪生;分布式能源资源;数据密集型科学发现范式;群智;数据赋能；
引用：贺兴,唐跃中,韩烨宸,等.基于数字孪生与元宇宙的能源互联网认知系统论(三)：复杂系统群智调控理论及其框架[J].中国电机工程学报,2024,44(24):9546-9559.
摘要：复杂性是能源互联网的重要特征与演进趋势，复杂性及其所伴随的涌现现象往往使得基于机理模型(白箱子)的仿真、分析、推演方法不再适用于复杂系统。得益于数据科学、仿真技术、孪生技术的共同发展，虚拟孪生技术成为应对复杂系统的利器。面向复杂性，该文对虚拟孪生技术及其应用前景进行探究，并重点讨论虚拟孪生系统的基本特性、支撑理论、技术体系、建设流程及其案例分析等，进一步提出基于虚拟仿真推演技术的涌现现象认知框架。该工作一方面为在能源系统中开展虚拟孪生工作提供参考借鉴，另一方面也有益于虚拟孪生技术的推进。
关键词： 复杂性;认知;涌现;虚拟仿真;能源互联网(EIoT);
引用：贺兴,潘美琪,唐跃中,等.基于数字孪生与元宇宙的能源互联网认知系统论(二)：面向复杂系统涌现现象的虚拟仿真推演框架[J].中国电机工程学报,2024,44(11):4311-4323.
摘要：态势感知是能源互联网的核心任务，为运管调控各项决策提供重要辅助信息。能源互联网的复杂定性对其态势感知任务提出严峻挑战——受其所辖单元日益增长的规模、种类、主动性、耦合性以及外界环境等不确定因素的影响，能源互联网其复杂性与日俱增。系统复杂性所衍生的一些问题超出经典简化论的讨论范畴，亟需一种新的认知手段。该文通过关联数据、数据科学、物理系统与具体应用，提出能源互联网数字孪生系统；进一步，延伸出一种新型数据驱动的态势感知方法论，即数字孪生态势感知(digitaltwinsituation awareness,DT-SA)。其核心思想是将真实世界的固有难题转化到虚拟空间，继而借助复杂系统理论和大数据分析等工具予以解决，具体涉及孪生体建模、分析和认知等核心环节。此外，该文也讨论元宇宙技术对孪生体互联的增强作用，以及相关的科学问题。该系列研究有益于推进能源系统领域和数据科学领域的交叉融合，对于企业数字化转型、数字城市建设等具备参考价值。
关键词：态势感知;不确定性;能源互联网;复杂系统;数字孪生;大数据分析;元宇宙;
引用：贺兴,陈旻昱,唐跃中,等.基于数字孪生与元宇宙技术的能源互联网态势感知系统论方法研究(一)：概念、挑战与研究框架[J].中国电机工程学报,2024,44(02):547-561.
摘要：超大型城市能源系统汇集了多种分布式能源资源(distributed energy resources,DER)，包括具备不确定性的新能源发电单元(如光伏、风机)，具备可调性的柔性用能单元(如空调、蓄冷)以及兼具充放功能的能源产消者(如电动汽车、储能)，虚拟电厂(virtual power plant,VPP)已成为重塑上述DER生态关系并对其进行协同管控的有效途径。该文梳理了当前城市级VPP建设的目标与现状，明确了超大型城市VPP工程所面临的挑战，即对于由海量DER所诱发的多主体多目标高不确定性场景缺乏系统性的建模、仿真、推演、分析、决策与校核手段。为应对上述挑战，该文结合城市电网数字化整体架构与建设进程，提出了超大型城市VPP数字孪生技术框架，继而得以对城市能源系统的态势轨迹进行系统性地推演与分析；进一步，聚焦智能决策目标，探究了框架下的技术路径、关键技术、理论工具等。该框架已经助力临港新片区VPP工程示范落地，为城市级VPP建设提供参考。最后，展望了超大型城市下数字孪生VPP的研究方向与应用前景。
关键词：虚拟电厂;分布式能源资源;数字孪生;态势感知;智能决策;
引用：[1]周翔,贺兴,陈赟,等.超大型城市虚拟电厂的数字孪生框架设计及实践[J].电网技术,2024,48(07):2681-2690.
Abstract: Transparency is crucial for decision-making within an active distribution network (ADN). To enhance ADN’s transparency, this study develops a novel Blind Decomposition of Composite Events (BDCE) approach rooted in Free Component Analysis (FCA), with a detailed exploration of its related theorems, algorithms, and deductions. Notably, FCA employs non-commutative matrix variables instead of scalar variables, establishing a natural connection to Random Matrix Theory (RMT). By incorporating RMT, FCA-BDCE effectively utilizes spatial–temporal correlation—a matrix-derived spectrum statistic; it allows for the filtration of locally independent noises, such as individual-level measurement error, ubiquitous white noise, while retaining globally influential signals across some specified spatial–temporal span. This capability is particularly valuable when gaining insight into the complex ADN, a landscape with significant diversity and uncertainty. In general, our approach is model-free, theory-guided, and unsupervised, making it particularly suitable for ADN. A comprehensive case study validates the practical effectiveness of our FCA-BDCE approach, demonstrating its superiority over ICA-BDCE.
Keywords: Architecture, Big data, Group-work mode, High-dimension, Large-scale distributed system, Mean spectral radius (MSR), Random matrix, Smart grid
Citation: X. He, Q. Ai, R. C. Qiu, W. Huang, L. Piao and H. Liu, “A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory,” in IEEE Transactions on Smart Grid, vol. 8, no. 2, pp. 674-686, March 2017.