This paper describes high-level findings from an innovative network of high-precision phasor measurement units (PMUs), or micro-PMUs ( ${\mu }$ PMUs), designed to provide an unprecedented level of visibility for power distribution systems. We present capabilities of the technology developed in the course of a three-year ARPA-E funded project, along with challenges and lessons learned through field deployments in collaboration with multiple electric utilities. Beyond specific applications and use cases for ${\mu }$ PMU data studied in the context of this project, this paper discusses a broader range of diagnostic applications that appear promising for future work, especially in the presence of high penetrations of variable distributed energy resources.
Stefania Conti, Giovanni Aiello, Salvatore Coco
et al.
The growing penetration of Electric Vehicles (EVs) in power distribution networks presents both challenges and opportunities for grid operators and planners. This paper provides a comprehensive review of recent advances in optimization techniques and machine learning (ML) approaches for the efficient management of EV charging and integration in low- and medium-voltage distribution systems. Optimization methods are analyzed with reference to their objectives—such as load flattening, voltage regulation, loss minimization, and infrastructure cost reduction—and their capability to handle multi-objective, stochastic, and real-time constraints. Concurrently, the role of ML is explored in load forecasting, user behavior modeling, anomaly detection, and adaptive control strategies. Particular attention is given to hybrid approaches that combine optimization algorithms (e.g., MILP, heuristic methods) with data-driven models (e.g., neural networks, reinforcement learning), highlighting their effectiveness in enhancing grid flexibility and resilience. This review adopts a unified system-level perspective that links EV management objectives, optimization techniques, and machine learning-based solutions within distribution networks. In addition, particular attention is devoted to data availability, reproducibility, and practical deployment aspects, with the aim of identifying current limitations and providing actionable insights for future research and real-world applications. This study aims to support the development of intelligent energy management strategies for EVs, fostering a sustainable and reliable evolution of distribution networks.
Abstract Vehicle-to-Grid (V2G) is a promising technology that allows the batteries of idle or parked electric vehicles (EVs) to operate as distributed resources, which can store or release energy at appropriate times, resulting in a bidirectional exchange of power between the ac grid and the dc EV batteries. This bidirectional exchange of power is realized using bidirectional power electronic converters that connect the grid with the EV battery. Most research on bidirectional converters for V2G applications focuses on using two dedicated power conversion stages – a bidirectional ac-dc conversion stage that helps in power factor correction, followed by a bidirectional dc-dc conversion stage that provides voltage matching. However, a single bidirectional ac-dc conversion stage can also facilitate V2G and grid-to vehicle (G2V) active power transfers. This paper reviews and compares the various bidirectional ac-dc and dc-dc converter topologies that facilitate V2G and G2V active power flows. Moreover, the paper discusses the various classes of charger/discharger systems reported for V2G applications, like on-board/off-board, integrated/non-integrated and conductive/inductive, and a comparative statement is made based on certain proposed criteria. Further, the current trends in the application of wide band-gap devices in high power-dense V2G capable converters and integration of renewable energy sources into EV charging/discharging infrastructures have also been discussed.
Benabdallah Naima, Belabbas Belkacem, Tahri Ahmed
et al.
Abstract Recently, there has been a focus on renewable energy sources such as solar power. These grid-connected systems play a significant role in meeting energy demand and mitigating global warming. Despite these numerous benefits, the stability and quality of power are the main drawbacks facing these energy systems. Boost converters are used to maximize the power output of the photovoltaic system and power the load. The maximum power point tracking (MPPT) strategy based on the perturb and observe (P&O) algorithm is often used to control the inverter. This strategy yields unsatisfactory results in terms of operational performance and durability. This paper presents a new MPPT strategy for a photovoltaic inverter to improve power quality, stability, and dynamic performance. This new strategy is based on combining modified finite control set model predictive control (MFCS-MPC) with an adaptive P&O algorithm. This proposed strategy differs from the conventional strategy and some existing strategies in terms of robustness, operational performance, and dynamic response. MATLAB/Simulink was used to build and validate a comprehensive mathematical model of the studied system. The effectiveness, efficiency, and performance were studied under different operating conditions. The results show that using the designed MPPT approach achieves a total harmonic distortion of 1.22%, significantly outperforming the conventional P&O algorithm (6%) and complying with IEEE-519 standards. Also, using this designed MPPT approach improves tracking response time by an estimated 35% and reduces overshoot by 28%, ensuring stability under rapid changes in radiation. Furthermore, the new weighted cost function in MFCS-MPC reduces switching losses by 15% while maintaining harmonic suppression. These results highlight the effectiveness of the proposed MPPT approach and its ability to improve system properties compared to the conventional MPPT approach, making it a reliable solution for other energy applications such as wind energy and electric vehicles.
Abstract This paper proposes a model predictive control strategy for induction motors driven by three‐level inverters, enabling effective switching frequency adjustment. First, a three‐dimensional satisfaction space optimisation strategy is proposed, eliminating the need for weight coefficient adjustments through self‐constraints and mutual constraints of the optimisation variables. The optimal switching state is selected by comparing the maximum average dwell time within the satisfaction space, thus reducing the inverter switching frequency. Second, switching frequency is treated as an auxiliary optimisation variable, and a dynamic sliding window method is designed to efficiently track switching frequency in variable‐speed systems. The boundaries of the three‐dimensional satisfaction space are adjusted to regulate the switching frequency. Finally, experimental results demonstrate that the proposed strategy maintains the switching frequency of 300 Hz across the entire speed range, achieving excellent dynamic and steady‐state performance at this low switching frequency.
Sa’id A’inul Yaqin, Mochammad Rusli, Bambang Siswojo
et al.
The synthesis of nitroglycerin requires extremely precise control of reactant volumes due to its highly sensitive and hazardous nature. This study evaluates the performance of a capacitive level sensor integrated with a Fuzzy-PID controller capable of adapting to error dynamics while utilizing an ESP32 microcontroller and a peristaltic pump. The sensor was tested for accuracy, precision, linearity, resolution, response time, hysteresis, stability, and sensitivity. The results demonstrated an accuracy of 99.4%, an R² value of 0.997, a resolution of 0.1 mL, and a response time of 4.98 seconds. The system exhibited high precision and stable performance, offering a potential solution for enhancing the reliability and safety of nitroglycerin production.
We study the first power correction to the heavy electron form factor in QED and show that it factorizes as a derivative operator. We discuss the result in QED with no light fermions, where the first power correction can be written explicitly in terms of one-loop integrals and the anomalous magnetic moment. In the presence of light fermions, the heavy electron form factor admits a representation as a sum over matrix elements, each of which receives corrections from higher orders in perturbation theory. From this analysis, we are able to extract the next-to-leading power soft photon theorem in the limit of heavy fermion-initiated dijet events. This is a first step towards studying the heavy quark form factor in the non-abelian theory.
Chinmoy Biswas, Nafis Faisal, Vivek Chowdhury
et al.
Sparsity, defined as the presence of missing or zero values in a dataset, often poses a major challenge while operating on real-life datasets. Sparsity in features or target data of the training dataset can be handled using various interpolation methods, such as linear or polynomial interpolation, spline, moving average, or can be simply imputed. Interpolation methods usually perform well with Strict Sense Stationary (SSS) data. In this study, we show that an approximately 62\% sparse dataset with hourly load data of a power plant can be utilized for load forecasting assuming the data is Wide Sense Stationary (WSS), if augmented with Gaussian interpolation. More specifically, we perform statistical analysis on the data, and train multiple machine learning and deep learning models on the dataset. By comparing the performance of these models, we empirically demonstrate that Gaussian interpolation is a suitable option for dealing with load forecasting problems. Additionally, we demonstrate that Long Short-term Memory (LSTM)-based neural network model offers the best performance among a diverse set of classical and neural network-based models.
Abstract To the challenges posed by the complex structure and low reliability of existing hydraulic actuators used for more electric aircraft landing gear, this paper proposes an optimised design method for an electric actuator based on the principles of tubular permanent magnet linear motor employing a Halbach array. For this electric actuator, a subdomain model considering the longitudinal end effect is established by setting virtual permanent magnet regions at both ends of the Halbach array. The effectiveness of the proposed method is verified through a comparison with the finite element method. Subsequently, a surrogate model is developed using the proposed subdomain model, and an uncertain optimisation model considering machining and assembly errors is developed based on the interval possibility and the interval order theory. Compared to its initial design, the optimised structure increased the average thrust by 13.4% and reduced the thrust ripple rate by 87.2%, significantly improving the overall performance of the electric actuator. Finally, prototype experiments are carried out to verify the effectiveness of the proposed subdomain method and optimised approach.
The capacity of distributed photovoltaic impacts the safe and reliable operation of the distribution network. Energy storage in distribution feeders has emerged as a pivotal solution for consuming distributed photovoltaic. However, it is challenged to balance the economic viability of energy storage investing and grid reliability. In response, a method of optimizing the configuration of energy storage in distribution feeders based on the new model of counter configuration of the renewables from energy storage is proposed, which takes into account both the economy and reliability aspects. Firstly, an economic model for optimizing energy storage in distribution feeders is established. Secondly, a grid reliability simulation is performed based on the results of the economic optimization of energy storage in distribution feeders. Finally, considering the comprehensive indexes of economy and reliability, the different nodes are evaluated for site selection using technique for order preference by similarity to ideal solution (TOPSIS), and the energy storage configuration scheme is determined by the cumulative distribution curve. The simulation results show that the consuming capacity of distributed photovoltaic in the distribution feeders is effectively improved, and better balance the economic viability of energy storage investing with grid reliability, which can be used as a reference for the reasonable determination of ratios of the energy storage for renewables.
Мета роботи. Метою даної роботи є описання критеріїв оптимальності для визначення напрямку вирішення задачі оптимізації транспортно-технологічних маршрутів зернової продукції на елеваторному комплексі.
Методи дослідження. Був застосований аналітичний метод дослідження, для визначення технологічних харакетиристик лінії елеваторного комплексу та залежності якості транспортуючої продукції від побудованого маршруту транспортування. Застосовано математичний метод для опису складових елементів критеріїв оптимізації.
Отримані результати. Був визначений один із напрямків підвищення й покращення технологічного процесу на елеваторі, шляхом удосконалення алгоритмів керування в напрямку оптимізації технологічних процесів елеватора за багатьма критеріями. За критерії оптимізації, для забезпечення оптимальних умов транспортування повинні використовуватися мінімум втрат, мінімум часу на транспортування, мінімум енергозатрат та мінімальна довжина прокладеного шляху транспортування, тому був виконаний опис основних критеріїв які в першу чергу можуть вплинути на ефективність роботи елеваторного комплексу.
Наукова новина. Визначений напрямок вирішення задачі оптимізації транспортно-технологічних маршрутів. Аналітичне виведення критерію мінімум енергозатрат та критерію мінімум бою зерна.
Практична цінність. Оптимізація елеваторного комплексу для забезпечення варіативного транспортування зернових мас, що вплине на підвищення продуктивності і якості технологічного процесу елеваторного комплексу.
Muhammad Omer Shah, Manfredi Caruso, Salvatore Pennisi
This study describes a high-performance second-generation Current Conveyor (CCII) operating at 0.35 V and achieving rail-to-rail operation at the Y terminal and class AB current drive at the X and Z terminals. The solution utilizes a low-voltage subthreshold bulk-driven CMOS OTA that was experimentally developed earlier, making systematic use of body terminals to improve small-signal and large-signal performance. The circuit has a high open-loop voltage gain and uses cascoded current mirror topologies, resulting in precise voltage and current transfer with bandwidths of 1.33 MHz and 2.13 MHz, respectively. The CCII offers a linear current drive up to 2.5 µA while consuming a total quiescent current of 2.86 µA (758 nA in the output branches), displaying one the highest figures of merit in terms of current utilization for sub 1 V solutions.
Marwa M. Ahmed, Mohamed A. Enany, Ahmed A. Shaier
et al.
The wireless power transfer (WPT) system holds potential as a viable solution for charging electric vehicles (EVs) owing to its benefits including safety, automated operation, efficiency, and simplicity. Among the WPT technologies, inductive power transfer (IPT) stands out as particularly well-suited for charging EV batteries. This is primarily due to its capability to transmit high power across considerable air gap distances, accommodating the ground clearance requirements of most EVs, operating automatically without driver involvement, ensuring safety and convenience even in challenging conditions such as snow, rain, and dust, and offering maintenance-free operation by eliminating the need for plug-in connections. This manuscript provides a comprehensive exploration and analysis of the progress made in IPT technology. The manuscript introduces the operational principle of the IPT system and highlights the benefits of its components. Additionally, it discusses the transmitter and receiver architectures, outlines the characteristics of various charging pads, in case of both stationary and in-motion charging scenarios. Furthermore, it delves into different compensation circuit topologies and various WPT designs based on compensating structures associated with the IPT system. It also categorizes the converter topologies utilized in the system and presents the operating technique for each one. In addition, the ongoing research and development (R&D) endeavors pertaining to each technology are discussed, addressing challenges, existing gaps, and offering recommendations for further advancements in both stationary and in-motion charging applications.
Wireless charging has become increasingly popular in low and medium-power applications, such as motor-driven carriers, unmanned aerial vehicles, electric bicycles, and inspection robots. Nevertheless, pad misalignment can significantly affect the system performance, leading to higher losses and a reduction in the power output. Particularly in the all directions of anti-misalignment strategies, a greater number of inverters and compensations are necessitated, leading to increased system dimensions and heightened complexity. This article proposes a new hybrid coupler that only uses a bipolar pad as the secondary pad and a solenoid (S coil) with an added quadrature coil (Q coil) as the primary pad. We develop and test a 50W prototype in the laboratory. The size of the coupler is 355 mm <inline-formula> <tex-math notation="LaTeX">$ \times 290$ </tex-math></inline-formula> mm. The experimental and simulation results demonstrate that when the distance between the transmitter and receiver is 60 mm, the transmission efficiency remains within a range of 72% to 90% for all directions of anti-misalignment, while the output voltage exhibits fluctuations within a margin of 10%. The results indicate that the coupler has a great potential for use in low and medium power applications, and can even be used in high-power equipment.
With the development of the electric power system, the smart grid has become an important part of the smart city. The rational transmission of electric energy and the guarantee of power supply of the smart grid are very important to smart cities, smart cities can provide better services through smart grids. Among them, predicting and judging city electric power consumption is closely related to electricity supply and regulation, the location of power plants, and the control of electricity transmission losses. Based on big data, this paper establishes a neural network and considers the influence of various nonlinear factors on city electric power consumption. A model is established to realize the prediction of power consumption. Based on the permutation importance test, an evaluation model of the influencing factors of city electric power consumption is constructed to obtain the core characteristic values of city electric power consumption prediction, which can provide an important reference for electric power related industry.
Odin Foldvik Eikeland, Colin C. Kelsall, Kyle Buznitsky
et al.
As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand. In this study, we modeled an existing thermal energy storage unit with estimated capital costs that are sufficiently low to enable large-scale deployment in the electric power system. Our analysis emphasizes the value of using such units to cost-effectively improve renewable energy dispatchability. This study modeled an existing real-world grid rather than simulating hypothetical future electric power systems. The storage unit coupled with a photovoltaic (PV) system was modeled with different storage capacities, whereas each storage unit size had various discharge capacities. The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO$_2$ emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO$_2$ reduction scenarios, as the storage unit is utilized differently. When CO$_2$ emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit. The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems.
Germán Morales-España, Ricardo Hernández-Serna, Diego A. Tejada-Arango
et al.
In this paper, we aim to analyse the impact of hydrogen production decarbonisation and electrification scenarios on the infrastructure development, generation mix, $CO_{2}$ emissions, and system costs of the European power system, considering the retrofit of the natural gas infrastructure. We define a reference scenario for the European power system in 2050 and use scenario variants to obtain additional insights by breaking down the effects of different assumptions. The scenarios were analysed using the European electricity market model COMPETES, including a proposed formulation to consider retrofitting existing natural gas networks to transport hydrogen instead of methane. According to the results, 60% of the EU's hydrogen demand is electrified, and approximately 30% of the total electricity demand will be to cover that hydrogen demand. The primary source of this electricity would be non-polluting technologies. Moreover, hydrogen flexibility significantly increases variable renewable energy investment and production, and reduces $CO_{2}$ emissions. In contrast, relying on only electricity transmission increases costs and $CO_{2}$ emissions, emphasising the importance of investing in an $H_{2}$ network through retrofitting or new pipelines. In conclusion, this paper shows that electrifying hydrogen is necessary and cost-effective to achieve the EU's objective of reducing long-term emissions.