Abstract The global energy transition towards a carbon neutral society requires a profound transformation of electricity generation and consumption, as well as of electric power systems. Hydrogen has an important potential to accelerate the process of scaling up clean and renewable energy, however its integration in power systems remains little studied. This paper reviews the current progress and outlook of hydrogen technologies and their application in power systems for hydrogen production, re-electrification and storage. The characteristics of electrolysers and fuel cells are demonstrated with experimental data and the deployments of hydrogen for energy storage, power-to-gas, co- and tri-generation and transportation are investigated using examples from worldwide projects. The current techno-economic status of these technologies and applications is presented, in which cost, efficiency and durability are identified as the main critical aspects. This is also confirmed by the results of a statistical analysis of the literature. Finally, conclusions show that continuous efforts on performance improvements, scale ramp-up, technical prospects and political support are required to enable a cost-competitive hydrogen economy.
The tremendous improvement in performance and cost of lithium-ion batteries (LIBs) have made them the technology of choice for electrical energy storage. While established battery chemistries and cell architectures for Li-ion batteries achieve good power and energy density, LIBs are unlikely to meet all the performance, cost, and scaling targets required for energy storage, in particular, in large-scale applications such as electrified transportation and grids. The demand to further reduce cost and/or increase energy density, as well as the growing concern related to natural resource needs for Li-ion have accelerated the investigation of so-called "beyond Li-ion" technologies. In this review, we will discuss the recent achievements, challenges, and opportunities of four important "beyond Li-ion" technologies: Na-ion batteries, K-ion batteries, all-solid-state batteries, and multivalent batteries. The fundamental science behind the challenges, and potential solutions toward the goals of a low-cost and/or high-energy-density future, are discussed in detail for each technology. While it is unlikely that any given new technology will fully replace Li-ion in the near future, "beyond Li-ion" technologies should be thought of as opportunities for energy storage to grow into mid/large-scale applications.
Traction inverter, as a critical component in electrified transportation, has been the subject of many research projects in terms of topologies, modulation, and control schemes. Recently, some of the well-known electric vehicle manufacturers have utilized higher-voltage batteries to benefit from lower current, higher power density, and faster charging times. With the ongoing trend toward higher DC-link voltage in electric vehicles, some multilevel structures have been investigated as a feasible and efficient option for replacing the two-level inverters. Higher efficiency, higher power density, better waveform quality, and inherent fault-tolerance are the foremost advantages of multilevel inverters which make them an attractive solution for this application. This paper presents an investigation of the advantages and disadvantages of higher DC-link voltage in traction inverters, as well as a review of the recent research on multilevel inverter topologies for electrified transportation applications. A comparison of multilevel inverters with their two-level counterpart is conducted in terms of efficiency, cost, power density, power quality, reliability, and fault tolerance. Additionally, a comprehensive comparison of different topologies of multilevel inverters is conducted based on the most important criteria in transportation electrification. Future trends and possible research areas are also discussed.
Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini
The growing penetration of renewable energy sources is expected to drive higher demand for power reserve ancillary services (AS). One solution is to increase the supply by integrating distributed energy resources (DERs) into the AS market through virtual power plants (VPPs). Several methods have been developed to assess the potential of VPPs to provide services. However, the existing approaches fail to account for AS products' requirements (reliability and technical specifications) and to provide accurate cost estimations. Here, we propose a new method to assess VPPs' potential to deliver power reserve capacity products under forecasting uncertainty. First, the maximum feasible reserve quantity is determined using a novel formulation of subset simulation for efficient uncertainty quantification. Second, the supply curve is characterized by considering explicit and opportunity costs. The method is applied to a VPP based on a representative Swiss low-voltage network with a diversified DER portfolio. We find that VPPs can reliably offer reserve products and that opportunity costs drive product pricing. Additionally, we show that the product's requirements strongly impact the reserve capacity provision capability. This approach aims to support VPP managers in developing market strategies and policymakers in designing DER-focused AS products.
XSPICE models for a generic transmission line, a microstrip line, and coupled microstrips are presented. The developed models extend general-purpose circuit simulation tools using RF circuits design features. The models could be used for circuit simulation in frequency, DC, and time domains for any active or passive RF or microwave schematic (including microwave monolithic integrated circuits—MMICs) involving transmission lines. The presented models could be used with any circuit simulation backend supporting XSPICE extensions and could be integrated without patching the core simulator code. The presented XSPICE models for microstrip lines take into account the frequency dependency of characteristic impedance and dispersion. The models were designed using open-source circuit simulation software. This study provides a practical example of the low-noise RF amplifier (LNA) design with Ngspice simulation backend using the proposed models.
Wireless power transfer (WPT) has been developed as a transformative alternative to traditional plug-in charging for electric vehicles (EVs), offering significant developments in mobile charging. EVs are charged while moving on the roads. This review provides a comprehensive overview of various WPT technologies, including inductive power transfer (IPT), resonant inductive transfer, capacitive power transfer (CPT), microwave power transfer (MWPT) and laser power transfer (LPT), for both near-field and far-field applications. Different WPT topologies, such as series–series (SS), series–parallel (SP), parallel–parallel (PP), parallel–series (PS), LC-S, LC-P, S-SP and LC-LC, are analysed for their specific advantages in EV applications. Additionally, key standards for WPT, including SAE J2954, IEC 61980, ISO 19363, IEEE C95.1-2345 and TA-15, are providing a regulatory framework for safe and efficient implementation. The paper also explores the integration of artificial intelligence (AI) techniques like deep Q-network (DQN) and large language model (LLM) in the WPT system. Further, smart road technologies and cybersecurity measures in WPT systems, with a particular focus on issues such as data protection for cyberattacks, are discussed. The role of the Internet of Things (IoT) and edge computing in monitoring and controlling EVs for optimal charging is discussed. Furthermore, the application of blockchain technology in WPT is discussed. The advancements in coil design are also discussed. Finally, the challenges and limitations of WPT, such as energy transfer efficiency, misalignment of coils, electromagnetic interference (EMI), safety and security, are discussed.
Transportation engineering, Applications of electric power
This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features.
Мета роботи. Метою даної роботи є розробка методології пошуку дефектного приєднання на одній секції шин середньої або низької напруги серед декількох паралельно ввімкнених приєднань.
Методи дослідження. Як відомо, від секції шин головної понижувальної підстанції вищою напругою 35÷154 кВ, тупикової підстанції середньої напруги 10 (6) кВ або трансформаторної підстанції напругою 10(6)/0,4 кВ можуть живитися одночасно від трьох до двох десятків приєднань. Дефект приладу обліку на одному з цих приєднань може бути виявлений тільки за допомогою балансового методу (коли на вводі на секцію шин встановлений прилад обліку (зазвичай, комерційного обліку) та на кожному з приєднань, що відходять з секції шин, теж встановлений лічильник електроенергії).
Пошук дефектного приладу обліку проводиться шляхом перевірки усіх приладів обліку на кожному приєднанні. Серед них і буде виявлений дефектний прилад.
Але ця процедура зазвичай займає багато часу, потребує оформлення організаційно-технічних заходів при роботах в діючих електроустановках, може призвести до аварійних відключень приєднань внаслідок закорочування кіл напруги або розмикання кіл струму при неправильних або помилкових діях персоналу.
Отримані результати. Визначено чотири основні типи дефектів у схемах обліку. Показано, що небаланс між комерційним і технічним обліком може бути використаний для виявлення дефектів на секції шин. Розроблено критерії для швидкої ідентифікації дефектних приєднань на основі статистичних методів, таких як кореляційний аналіз та однофакторний дисперсійний аналіз.
Наукова новизна. На відміну від існуючого підходу запропонована послідовність та розроблені критерії знаходження приєднання з дефектом обліку саме аналітичним методом. В результаті необхідно буде провести заміну приладу або пристрою обліку тільки на одному приєднанні, котре підключено до секції шин, де є дефект в колах обліку. Аналітичний метод ґрунтується на обробці облікових даних та розрахунку декількох статистичних коефіцієнтів.
Практична цінність. В найкоротший термін знайти дефект приладу обліку аналітичним методом. Методика забезпечує швидке та точне виявлення дефектних приєднань на секції шин, що дозволяє уникати аварійних ситуацій та втрат енергії. Це робить її корисною для промислових підприємств, що працюють із великими енергоспоживаннями.
Deep learning, a branch of artificial intelligence, is a data-driven method that uses multiple layers of interconnected units or neurons to learn intricate patterns and representations directly from raw input data. Empowered by this learning capability, it has become a powerful tool for solving complex problems and is the core driver of many groundbreaking technologies and innovations. Building a deep learning model is challenging due to the algorithm's complexity and the dynamic nature of real-world problems. Several studies have reviewed deep learning concepts and applications. However, the studies mostly focused on the types of deep learning models and convolutional neural network architectures, offering limited coverage of the state-of-the-art deep learning models and their applications in solving complex problems across different domains. Therefore, motivated by the limitations, this study aims to comprehensively review the state-of-the-art deep learning models in computer vision, natural language processing, time series analysis and pervasive computing, and robotics. We highlight the key features of the models and their effectiveness in solving the problems within each domain. Furthermore, this study presents the fundamentals of deep learning, various deep learning model types and prominent convolutional neural network architectures. Finally, challenges and future directions in deep learning research are discussed to offer a broader perspective for future researchers.
Integrated energy systems enable the complementary utilization of various forms of energy. With the rapid increase in installed capacity of distributed generations, their intermittency and randomness have posed significant challenges to the operational efficiency and safety of the system. To address the impact of uncertainties in both energy supply and demand on the economic scheduling of building integrated energy systems, this study first models the operational characteristics of each subsystem, including the power grid, natural gas network, and various coupled equipments. Next, a thermal cell model of the building users is constructed based on the quantitative relationship among temperature, thermal radiation, and thermal load. Then, a day-ahead optimization scheduling model for the building integrated energy systems is established with the objective of minimizing the operating cost. A chance-constrained programming approach is employed to convert the non-linear scheduling model into a mixed-integer second-order cone programming problem easy to solve. Finally, simulation analysis is conducted in the Python environment using the CPLEX solver. The results demonstrate that the proposed model and solution method are capable of effectively characterizing and addressing uncertainty risks in the system, facilitating the consumption of renewable energy, and improving the economic efficiency of system operation.
Wenting Wang, Alwaleed Aldhafeeri, Heng Zhou
et al.
Abstract Dissipative Kerr soliton microcombs in microresonators have enabled fundamental advances in chip-scale precision metrology, communication, spectroscopy, and parallel signal processing. Here we demonstrate polarization-diverse soliton transitions and deterministic switching dynamics of a self-stabilized microcomb in a strongly-coupled dispersion-managed microresonator driven with a single pump laser. The switching dynamics are induced by the differential thermorefractivity between coupled transverse-magnetic and transverse-electric supermodes during the forward-backward pump detunings. The achieved large soliton existence range and deterministic transitions benefit from the switching dynamics, leading to the cross-polarized soliton microcomb formation when driven in the transverse-magnetic supermode of the single resonator. Secondly, we demonstrate two distinct polarization-diverse soliton formation routes – arising from chaotic or periodically-modulated waveforms via pump power selection. Thirdly, to observe the cross-polarized supermode transition dynamics, we develop a parametric temporal magnifier with picosecond resolution, MHz frame rate and sub-ns temporal windows. We construct picosecond temporal transition portraits in 100-ns recording length of the strongly-coupled solitons, mapping the transitions from multiple soliton molecular states to singlet solitons. This study underpins polarization-diverse soliton microcombs for chip-scale ultrashort pulse generation, supporting applications in frequency and precision metrology, communications, spectroscopy and information processing.
Muhammad Bin Fayyaz Ahsan, Saad Mekhilef, Tey Kok Soon
et al.
This paper presents a single-objective function optimization method for the optimal sizing and cost of a hybrid energy storage system (HESS) that integrates lithium-ion batteries (LIB) and supercapacitors (SC) for electric vehicle (EV) applications. The study introduces a comprehensive framework for EV modeling, incorporating simulated EV data to enhance accuracy. A key achievement involves adapting the modified–WLTC driving cycle, iteratively employed in EV simulations to accurately capture the spectrum of power and energy profiles within the designated range, ensuring adherence to BMW-i3's top speed requisites. The proposed method's validity is established by comparing optimization results using Particle Swarm Optimization (PSO) and Firefly Algorithm (FA), indicating comparable HESS sizing and cost outcomes. Notably, the PSO algorithm demonstrates superior accuracy and computational efficiency. Through PSO, the optimal LIB-SC HESS weight is determined at 160 kg with an optimal cost of $27,660, while FA yields 161 kg and $28,270, surpassing LIB-Only EV models by approximately 21 % in improved sizing. This research significantly contributes by establishing a robust EV modeling paradigm, employing simulated data for optimization, and successfully implementing PSO to determine optimal HESS parameters, advancing the field of efficient EV energy storage and promoting cost-effective, sustainable electric transportation.
We explore the existence of a continuous marginal law with respect to the Lebesgue measure for each component $(X,Y,Z)$ of the solution to coupled quadratic forward-backward stochastic differential equations (QFBSDEs) {for which the drift coefficient of the forward component is either bounded and measurable or Hölder continuous. Our approach relies on a combination of the existence of a weak {\it decoupling field} (see \cite{Delarue2}), the integration with respect to space time local time (see \cite{Ein2006}), the analysis of the backward Kolmogorov equation associated to the forward component along with an Itô-Tanaka trick (see \cite{FlanGubiPrio10})}. The framework of this paper is beyond all existing papers on density analysis for Markovian BSDEs and constitutes a major refinement of the existing results. We also derive a comonotonicity theorem for the control variable in this frame and thus extending the works \cite{ChenKulWei05}, \cite{DosDos13}. As applications of our results, we first analyse the regularity of densities of solution to coupled FBSDEs. In the second example, we consider a regime switching term structure interest rate models (see for e.g., \cite{ChenMaYin17}) for which the corresponding FBSDE has discontinuous drift. Our results enables us to: firstly study classical and Malliavin differentiability of the solutions for such models, secondly the existence of density of such solutions. Lastly we consider a pricing and hedging problem of contingent claims on non-tradable underlying, when the dynamic of the latter is given by a regime switching SDE (i.e., the drift coefficient is allowed to be discontinuous). We obtain a representation of the derivative hedge as the weak derivative of the indifference price function, thus extending the result in \cite{ArImDR10}.
Topology diagrams are widely seen in power system applications, but their automatic generation is often easier said than done. When facing power transmission systems with strongly-meshed structures, existing approaches can hardly produce topology diagrams catering to the aesthetics of readers. This paper proposes an integrated framework for generating aesthetically-pleasing topology diagrams for power transmission systems. Input with a rough layout, the framework first conducts visibility region analysis to reduce line crossings and then solves a mixed-integer linear programming problem to optimize the arrangement of nodes. Given that the complexity of both modules is pretty high, simplification heuristics are also proposed to enhance the efficiency of the framework. Case studies on several power transmission systems containing up to 2,046 nodes demonstrate the capability of the proposed framework in generating topology diagrams conforming to aesthetic criteria in the power system community. Compared with the widespread force-directed algorithm, the proposed framework can preserve the relative positions of nodes in the original layout to a great extent, which significantly contributes to the identification of electrical elements on the diagrams. Meanwhile, the time consumption is acceptable for practical applications.
Digital filtering is a fundamental technique in digital signal processing, which operates on a digital sequence without any information on how the sequence was generated. This paper proposes a methodology for designing the equivalent of digital filtering for neuromorphic samples, which are a low-power alternative to conventional digital samples. In the literature, filtering using neuromorphic samples is performed by filtering the reconstructed analog signal, which is required to belong to a predefined input space. We show that this requirement is not necessary, and introduce a new method for computing the neuromorphic samples of the filter output directly from the input samples, backed by theoretical guarantees. We show numerically that we can achieve a similar accuracy compared to that of the conventional method. However, given that we bypass the analog signal reconstruction step, our results show significantly reduced computation time for the proposed method and good performance even when signal recovery is not possible.