This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

Energy storage systems are a crucial part of microgrids, making up for their autonomous operation. Therefore, much research has focused on increasing their lifetime and operational efficiency. One of the most viable topics is maintaining connected energy storage units (ESUs) at the same state of charge (SoC). Droop control techniques have been developed in centralized, decentralized, and distributed fashions. This paper proposes a novel IoT-based droop control that can achieve SoC balancing not only within the same microgrid but also between multiple microgrids. The proposed architecture increases reliability and flexibility while allowing the system to be expanded, reconfigured, or upgraded, enabling the plug-and-play capability more easily. First, the proposed IoT-based hierarchical control and communication architecture is presented. Then, the proposed SoC balancing technique and its coordination with IoT architecture are introduced. Through 24-hour simulations, the proposed technique’s scalability, adaptiveness, and robustness are validated under different scenarios, including the operation of any microgrids or ESUs with different capacities and the operation in case of a communication failure. Finally, the IoT value chain of the proposed technique is experimentally validated using a physical laboratory microgrid that uses HTTP protocol to send and receive data through the ThingSpeak IoT-cloud service.

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit ($\mu$PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of $\mu$PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive $\mu$PMU on a densely-noded distribution network.

The current electricity grid suffers from numerous challenges due to the lack of an effective energy management strategy that is able to match the generated power to the load demand. This problem becomes more pronounced with microgrids, where the variability of the load is obvious and the generation is mostly coming from renewables, as it depends on the usage of distributed energy sources. Building a smart microgrid would be much more economically feasible than converting the large electricity grid into a smart grid, as it would require huge investments in replacing legacy equipment with smart equipment. In this paper, application of Internet of Things (IoT) technology in different parts of the microgrid is carried out to achieve an effective IoT architecture in addition to proposing the Internet-of-Asset (IoA) concept that will be able to convert any legacy asset into a smart IoT-ready one. This will allow the effective connection of all assets to a cloud-based IoT. The role of which is to perform computations and big data analysis on the collected data from across the smart microgrid to send effective energy management and control commands to different controllers. Then the IoT cloud will send control actions to solve microgrid’s technical issues such as solving energy mismatch problem by setting prediction models, increasing power quality by the effective commitment of DERs and eliminating load shedding by turning off only unnecessary loads so consumers won’t suffer from power outages. The benefits of using IoT on various parts within the microgrid are also addressed.

Due to the high cost of installing large thermal power plants and the resulting emissions from such plants, consumers were encouraged to install rooftop photovoltaic systems. Yet, challenges of integrating solar power into the distribution networks are restraining the amount of allowed generation from rooftop PV systems. Grid operators (GO) started to even limit rooftop PV systems installation capacity toa maximum of their respective load peak value. This paper presents an IoT-based hosting capacity enhancement techniqueby active power curtailment of the power generated from the PV systems at prosumers. Where the IoT establishes a communication channel between the Grid Operator (GO) and all prosumers using the Internet-of-Asset (IoA) architecture. Measurement signals of the Low Voltage (LV) network, solar irradiation, and temperature are necessary for the control technique to generate the reference control voltage for the solar inverter to produce the curtailed PV generation. Theeffect of load and irradiation variation is studied in the presence of the MPPT control, and the GO control, this term isused to describe GO's signal to curtail PV generation. The system has been modeled and simulated via the MATLAB/Simulink® software package. The results validate the effectiveness of this enhancement technique in keeping the overvoltage levels below limits.

Wave energy has a great potential as a renewable energy source. However, the intermittent nature of wave profile causes fluctuations in the output power extracted from wave energy converters. This paper proposes to smooth the output power extracted from wave energy using battery energy storage system and to integrate this power with DC systems. First, oscillating water column used to extract wave energy is described. It is based on reference model 3, which is referred as wave power buoy. Then, the design considerations of wave power buoy with permanent magnet linear generator are discussed and the Egyptian western coastline is taken as a case study. The ocean wave simulation is performed using the WecSim toolbox developed in MATLAB/SIMULINK considering the real irregular wave action. The output power from the linear generator is demonstrated. After that, the wave energy power generation with battery energy storage system is described. The system performance is evaluated through various simulation results. Finally, supercapacitor is used with battery for further enhancement in the performance of the system.