With recent advances in information and communication technology (ICT), the bleeding edge concept of digital twin (DT) has enticed the attention of many researchers to revolutionize the entire modern industries. DT concept refers to a digital representation of a physical entity that is able to reflect its physical behavior by applying platforms and bidirectional interaction of data in real-time. The remarkable deployment of the internet of things in the power grid has led to reliable access to information that improves its performance and equips it with a powerful tool for real-time data management and analysis. This paper aims to trace the continuous investigation and propose practical ideas in originating and developing DT technology, according to various application domains of power systems, and also describes the proposed solutions to deal with the challenges associated with DT. Indeed, with the development of modern cities, different energy layers such as transportation systems, smart grids, and microgrids have emerged facing various issues that challenge the multi-dimensional energy management system. For example, in transportation systems, traffic is a major problem that requires real-time management, planning, and analysis. In power grids, remote data transfer within the grid and also various analyzes needing real data are just some of the current challenges in the field. These problems can be cracked by providing and analyzing a real twin framework in each section. All in all, this paper aims to survey different applications of DT in the development of the various aspects of energy management within a city including transportation systems, power grids, and microgrids. Besides, the security of DT technology based on ML is discussed. It also provides a complete view for the readers to be able to develop and deploy a DT technology for various power system applications.

The increase in power outages caused by high-impact low-probability events, such as extreme weather-related climate variation events, is the main reason behind studying power system resilience. However, the concepts of resilience as well as the proactive procedures that can be carried out to address these events are such have not so far been satisfactorily investigated notwithstanding their growing benefits. This paper exhibits a review of the current research on power system resilience; which predominantly focuses on proactive resilience against natural disasters. Firstly, it gives a theoretical framework to acquire insights into power system resilience and its key features. Secondly, it presents frameworks for proactive resilience of power systems with a spotlight on the extreme weather events and their effect. Finally, various strategies for preparing, hardening and enhancing proactive resilience with a focus on microgrids for improving power system resilience are reviewed.

With the development of the Internet of Things (IoT) technologies, implementing intelligent controls in buildings to reduce energy consumption is becoming increasingly popular. Building climate control is of strong research interest due to high energy savings potentials associated with heating, ventilation and air-conditioning (HVAC) controls. Because the operation of HVAC systems directly influences occupant thermal comfort, building-specific thermal models are needed for proper control. Such models describe how indoor temperature changes under different environmental conditions and HVAC status. Previous studies mainly developed thermal models for accurate building thermal simulation which are not practical for use in real-world applications as they require domain knowledge and lengthy building-by-building configuration. In this paper, taking advantage of IoT technologies, a plug & play learning framework is proposed to automatically identify the thermal model of each thermal zone in a building without manual configuration. In contrast to the existing methods, a thermal model is learnt using low-resolution temperature readings from IoT-based smart thermostats. The learning framework has been validated using data collected from an IoT platform installed in a building over a summer. The performance of the learnt model and the error of indoor temperature prediction based on this model have been evaluated and quantified. Findings demonstrate the learning process can be automated with either edge-computing or cloud-computing. This study shows the validity of the proposed IoT-based thermal model learning framework, and offers a pragmatic solution for providing reliable thermal models to future smart building climate controls.

The increasing of grid-connected variable renewable energy (VRE) on the demand side causes balance problems in the power system. Thus, dealing with the uncertainty, variability, and consequently, flexibility requirement is becoming an urgent challenge to the power system operators. Virtual power plant (VPP), which bundles different types of small-scale distributed energy resources (DERs) into a single unit for optimization will effectively mitigate those uncertainties. An optimal VPP energy management method is proposed in this article for optimal energy and operating reserve (OR) scheduling. The studied VPP is a cluster of dispersed generating units (including dispatchable and stochastic power sources), flexible loads, as well as storage units. VPP operator has to make decisions based on the uncertainty coming from the stochastic VRE, load demand, as well as market electricity price. Thus, a dynamic risk reserve quantification method is proposed to cover both VRE power and load forecast uncertainties, while information gap decision theory is applied in the unit commitment procedures to study the impact of price uncertainty on the decision-making of VPP operators. Finally, the proposed method is implemented and verified with a case study, and optimal decisions are discussed.

The power consumption of inverter air conditioners (IACs) can be regulated flexibly by adjusting the compressor's operating frequency, which have been proven suitable for providing regulation capacities to power systems. Considering the rapid phasing out of traditional generating units, massive IACs create huge alternative regulation potential. However, the impact of IACs on the power system's stability is rarely studied. To address this issue, this paper proposes the modelling and control methods of IACs to provide regulation capacities to power systems. On this basis, a novel power system model with the control loop of large-scale IACs is developed, where the communication latency during the control signal transfer process is also considered. Then, the dynamic performance and steady-state errors of the novel power system are evaluated, showing that IACs can quickly participate in and smoothly withdraw from the regulation process. Furthermore, the stabilities and sensitivities of power systems with and without IACs are compared, in order to illustrate that both the stability margin and robustness of the power system can be increased via the control loop of IACs. Finally, the effectiveness of the proposed models and methods are verified by numerical studies.

In this paper, the optimal demand response strategy of a commercial building‐based virtual power plant with real‐world implementation in heavily urbanised area is studied. Instead of modelling the decision‐making process as an optimisation problem, a reinforcement learning method is used to seek the optimal strategy, which could update its performance with minimal manpower manipulation. Specifically, the data collection from several commercial buildings, including hotel, shopping mall and office, in Huangpu district, Shanghai city is analysed to deploy the demand response program. Compared with the conventional demand response strategy based on optimisation, the learnt strategy does not rely on the forecasting information as input and could adapt to the changing demand response incentive automatically. It may not produce the best result every time, but can guarantee the benefit in a non‐deterministic way in long‐term operation. The real‐world deployment of the Huangpu virtual power plant involving hardware and software platform is also introduced, as well as its future development projection.