It is an exciting time for power systems as there are many ground-breaking changes happening simultaneously. There is a global concensus in increasing the share of renewable energy-based generation in the overall mix, transitioning to a more environmental-friendly transportation with electric vehicles as well as liberalizing the electricity markets, much to the distaste of traditional utility companies. All of these changes are against the status quo and introduce new paradigms in the way the power systems operate. The generation penetrates distribution networks, renewables introduce intermittency, and liberalized markets need more competitive operation with the existing assets. All of these challenges require using some sort of storage device to develop viable power system operation solutions. There are different types of storage systems with different costs, operation characteristics, and potential applications. Understanding these is vital for the future design of power systems whether it be for short-term transient operation or long-term generation planning. In this paper, the state-of-the-art storage systems and their characteristics are thoroughly reviewed along with the cutting edge research prototypes. Based on their architectures, capacities, and operation characteristics, the potential application fields are identified. Finally, the research fields that are related to energy storage systems are studied with their impacts on the future of power systems.
Bidirectional DC-DC power converters are increasingly employed in diverse applications whereby power flow in both forward and reverse directions are required. These include but not limited to energy storage systems, uninterruptable power supplies, electric vehicles, and renewable energy systems, to name a few. This paper aims to review these converters from the point of view of topology as well as control schemes. From the point of view of topology, these converters are divided into two main categories, namely non-isolated and isolated configurations. Each category is divided into eight groups along with their respective schematics and a table of summary. Furthermore, the common control schemes and switching strategies for these converters are also reviewed. Some of the control schemes are typically applied to all DC-DC power converters such as PID, sliding mode, fuzzy, model predictive, digital control, etc. In this context, it should be noted that some switching strategies were designed specifically for isolated bidirectional DC-DC converters in order to improve their performance such as single phase shift, dual phase shift, triple phase shift, etc. The features of each topology and control scheme along with their typical applications are discussed, in order to provide a ground of comparison for realizing new configurations or finding the appropriate converter for the specific application.
Lithium-ion batteries (LIBs) are being intensively studied and universally used as power sources for electric vehicle applications. Despite the staggering growth in sales of LIBs worldwide, thermal safety issues still turn out to be the most intolerable pain point, and remain the focus of research for technological improvements. This paper presents a comprehensive overview on thermal safety issues of LIBs, in terms of thermal behavior and thermal runaway modeling and tests for battery cells, and safety management strategies for battery packs. Considering heat generation mechanism and thermal characteristics of LIBs, heat generation, dissipation and accumulation inside a cell are elaborated. The triggering factors leading to thermal runaway are also summarized. Finally, thermal runaway detection and prevention strategies for both cell- and pack-levels are introduced. Different engineering approaches from material refinement and additive adoption to thermal, electrical, and mechanical design are presented for thermal runaway prevention.
Muhammad Iskandar Iskandar, Panca Mudjirahardjo, Bambang Siswojo
The output power quality of conventional inverters often contains high levels of harmonic distortion, which can negatively affect the performance of connected electrical equipment. Therefore, a switching method capable of producing a near-ideal sinusoidal waveform with low total harmonic distortion (THD) is needed. This study presents the design and implementation of a single-phase inverter employing the Unipolar Sinusoidal Pulse Width Modulation (SPWM) technique to generate an output voltage waveform that closely resembles an ideal sine wave. Electrical energy produced by renewable energy requires a control system and an inverter. This inverter can convert direct current (DC) from renewable energy sources into alternating current (AC) with a wave quality that is close to a pure sinusoidal wave. The sinusoidal waves produced by this inverter are very important for maintaining optimal performance of sensitive electronic devices and reducing harmonic distortion which can damage electrical equipment. In this research, the design and manufacture of a single-phase H-Bridge inverter was carried out using the Unipolar Sinusoidal Pulse Width Modulation (USPWM) method. This method was chosen
because it is capable of producing waveforms that are close to pure sinusoidal with lower Total Harmonic Distortion (THD) compared to conventional switching methods. This design includes a simulation stage using Matlab R2024b to analyze
the waveform and inverter performance before hardware implementation. The test results show that the inverter is able
to work well with a THD of 1.14%, an output power of 1,370Watt with a frequency of 50 Hz. These results show that
the Unipolar SPWM method is very effective for controlling single-phase inverters for low to medium power applications.
Wind power systems provide significant benefits, including ease of installation, high efficiency, and scalability. However, one of the major challenges in these systems is the occurrence of sub-synchronous oscillations (SSOs), which can severely compromise power grid stability. This study examines recent advancements in SSO mitigation strategies for Doubly-Fed Induction Generator (DFIG)-based wind energy systems, the most widely adopted technology in modern wind turbines. Various control approaches, such as intelligent controllers, adaptive control mechanisms, and predictive algorithms, are reviewed. Simulation experiments carried out using MATLAB/Simulink software confirm the effectiveness of the proposed Direct Current Vector (DCV) control method in attenuating SSOs and improving overall system performance. In addition to identifying current challenges and research gaps, this work emphasizes the critical importance of ongoing research to develop robust SSO mitigation techniques for grid-connected wind power systems. The results demonstrate that incorporating advanced technologies and sophisticated control strategies plays a vital role in reducing sub-synchronous oscillations and enhancing the operational performance of wind energy systems.
Applications of electric power, Distribution or transmission of electric power
To achieve net-zero emissions by 2050, all-electric transportation is a promising option. In the U.S., the transportation sector contributes the largest share (29 percent) of greenhouse gas emissions. While electric vehicles are approaching maturity, aviation is only beginning to develop electrified aircraft for commercial flights. More than 75 percent of aviation emissions come from large aircraft, and this impact will worsen with 4-5 percent annual air travel growth. Aircraft electrification has led to two types: more electric aircraft (MEA) and all-electric aircraft (AEA). A MEA replaces subsystems such as hydraulics with electric alternatives, whereas an AEA uses electrically driven subsystems and provides thrust fully from electrochemical energy units (EEUs). For wide-body AEA, thrust demand is about 25 MW plus 1 MW for non-thrust loads, creating major challenges for electric power system (EPS) design. Achieving maximum power density requires minimizing mass and volume. Increasing voltage into the kilovolt range using medium-voltage direct current (MVDC) is a feasible option to enhance power transfer. Consequently, designing an MVDC EPS for wide-body AEA is critical. Because EPS failures could jeopardize passenger safety, reliability and resilience are essential. This chapter presents a load-flow model for DC systems to determine power flows in both normal and single-contingency conditions, followed by analysis of optimal MVDC EPS architectures. A complete EPS for wide-body AEA is introduced, with EEUs and non-propulsion loads located, distances estimated, and flow studies performed. Multiple architectures are evaluated for reliability, power density, power loss, and cost to identify optimal solutions.
Power modules are core components of inverters in electric vehicles and their packaging technology has a critical impact on system performance and reliability. Conventional single sided cooled power modules have been one of the most common package structures for automotive applications. However, this design limits the performance of IGBT and future SiC power module due to parasitic inductance and heat dissipation issues. Power module packaging technologies have been experiencing extensive changes as the performance expectations of the power semiconductor has increased. Over the past few decades, methods of double-sided cooling have attracted increased interest to enhance the power density of the inverter and effectively reduce their cost. This paper presents a comprehensive review of double-sided cooled packaging technology for automotive power modules. Technical details and innovative features of state-of-the-art automotive power modules from research institutes and major industry manufacturers are reviewed and their path into commercial vehicles is evaluated.
K. Krishnamoorthy, Parthiban Pazhamalai, V. Mariappan
et al.
The development of high‐performance electrodes that increase the energy density of supercapacitors (SCs) (without compromising their power density) and have a wide temperature tolerance is crucial for the application of SCs in electric vehicles. Recent research has focused on the preparation of multicomponent materials to form electrodes with enhanced electrochemical properties. Herein, a siloxene–graphene (rGO) heterostructure electrode‐based symmetric SC (SSC) is designed that delivers a high energy density (55.79 Wh kg−1) and maximum power density of 15 000 W kg−1. The fabricated siloxene–rGO SSC can operate over a wide temperature range from –15 to 80 °C, which makes them suitable for applications in automobiles. This study shows the practical applicability of siloxene–rGO SSC to drive an electric car as well as to capture the braking energy in a regenerative brake‐electric vehicle prototype. This work opens new directions for evaluating the use of siloxene–rGO SSC as suitable energy devices in electric vehicles.
Abstract To improve the position tracking accuracy of permanent magnet linear synchronous motors (PMLSM), this paper introduces a virtual position predictive control (VPPC) with a system delay observer (SDO). Un‐like conventional position predictive control (CPPC), which ignores the complexity of prediction models and velocity adjustments, the proposed VPPC combines a simplified position model with an active variable speed control. This design accelerates mover response and increases maximum reference speeds through active adjustments to better estimate the predicted output. Since the prediction period in CPPC often misaligns with the system delay, resulting in additional prediction errors, this paper further explores the relationship between predictive periods and system delay. Based on this analysis and a unified modelling concept, an SDO is included to observe and compensate for system delay, correcting the prediction period and optimising the control model. Experimental results on a PMLSM platform confirm the superior position tracking performance of the VPPC with SDO compared to conventional controllers.
With the expansion of power grid scale and the increase of power components, the maintenance methods of power system become more and more complex. It is difficult to evaluate the low-frequency oscillation risk of power grid under massive maintenance only by traditional methods. To solve this problem, a risk prediction method of low-frequency oscillation in maintenance power network based on long short term memory (LSTM) neural network was proposed. Firstly, the unified coding method of power system maintenance mode was proposed, so that the computer can quickly and accurately identify the operation state of power grid under various maintenance modes. Then, based on the historical data measured in real time by phasor measurement unit (PMU), the number of low-frequency oscillation of power grid under different maintenance modes was predicted by using LSTM neural network, so as to evaluate the risk of low-frequency oscillation of power grid under maintenance. Finally, a regional power grid in central China was taken as an example to verify the accuracy and rapidity of the proposed method.
Applications of electric power, Production of electric energy or power. Powerplants. Central stations
Abstract Large‐scale design optimisation techniques enable the design of high‐performance electric machines. Electromagnetic 3D finite element analysis (FEA) is typically employed in optimisation studies for accurate analysis of axial flux permanent magnet (AFPM) machines, which require extensive computational resources. To reduce the computational burden, a FEA‐based mathematical method relying on the geometric and magnetic symmetry of coreless AFPM machines is proposed to estimate the machine performance indicators using the least number of FEA solutions, thereby significantly lowering the running time. This method is generally applicable to AFPM machines with low saturation effects and cogging torque as exemplified for a printed circuit board (PCB) stator coreless AFPM machine. To further reduce the computation time, a systematically simplified equivalent 3D FEA model for planar PCB coils integrated with this machine is also proposed. The practical implementation of the introduced method is elaborated based on an example optimisation study, and an analytical method for fast design scaling is also discussed. The results of the proposed approach are compared with detailed transient FEA results, and a prototype 26‐pole PCB stator coreless AFPM machine was also used to validate the results experimentally.
Array-designed triboelectric nanogenerators (AD-TENGs) have firmly established themselves as state-of-the-art technologies for adeptly converting mechanical interactions into electrical signals. Central to the AD-TENG’s prowess is its inherent modularity and the multifaceted, grid-like design that pave the way to robust and adaptable detection platforms for wearables and real-time health monitoring systems. In this review, we aim to elucidate the quintessential role of array design in AD-TENGs for healthcare detection, emphasizing its ability to heighten sensitivity, spatial resolution, and dynamic monitoring while ensuring redundancy and simultaneous multi-detection. We begin from the fundamental aspects, such as working principles and design basis, then venture into methodologies for optimizing AD-TENGs that ensure the capture of intricate physiological changes, from nuanced muscle movements to sensitive electronic skin. After this, our exploration extends to the possible cutting-edge electronic systems that are built with specific advantages in filtering noise, magnifying signal-to-noise ratios, and interpreting complex real-time datasets on the basis of AD-TENGs. Culminating our discourse, we highlight the challenges and prospective pathways in the evolution of array-designed AD-TENGs, stressing the necessity to refine their sensitivity, adaptability, and reliability to perfectly align with the exacting demands of contemporary healthcare diagnostics.
The rising integration of variable renewable energy sources (RES), like solar and wind power, introduces considerable uncertainty in grid operations and energy management. Effective forecasting models are essential for grid operators to anticipate the net load - the difference between consumer electrical demand and renewable power generation. This paper proposes a deep learning (DL) model based on long short-term memory (LSTM) networks for net load forecasting in renewable-based microgrids, considering both solar and wind power. The model's architecture is detailed, and its performance is evaluated using a residential microgrid test case based on a typical meteorological year (TMY) dataset. The results demonstrate the effectiveness of the proposed LSTM-based DL model in predicting the net load, showcasing its potential for enhancing energy management in renewable-based microgrids.
High-wattage demand-side appliances such as Plug-in Electric Vehicles (PEVs) are proliferating. As a result, information on the charging patterns of PEVs is becoming accessible via smartphone applications, which aggregate real-time availability and historical usage of public PEV charging stations. Moreover, information on the power grid infrastructure and operations has become increasingly available in technical documents and real-time dashboards of the utilities, affiliates, and the power grid operators. The research question that this study explores is: Can one combine high-wattage demand-side appliances with public information to launch cyberattacks on the power grid? To answer this question and report a proof of concept demonstration, the study scrapes data from public sources for Manhattan, NY, USA using the electric vehicle charging station smartphone application and the power grid data circulated by the U.S. Energy Information Administration, New York Independent System Operator, and the local utility in New York. It then designs a novel data-driven cyberattack strategy using state-feedback based partial eigenvalue relocation, which targets frequency stability of the power grid. The study establishes that while such an attack is not possible at the current penetration level of PEVs, it will be practical once the number of PEVs increases.