Search Results (37)

The rapid development of the Internet of Things (IoT) has enabled the implementation of interconnected intelligent systems in extremely dynamic contexts with limited resources. However, traditional paradigms, such as those using ECC-based heuristics and centralised decision-making frameworks, cannot be modernised to ensure resilience, scalability and security while taking quantum threats into account. In this case, we propose a modular architecture that integrates quantum-inspired cryptography (QI), epistemic uncertainty reasoning, the multiscale blockchain MuReQua, and the quantum-inspired decentralised storage engine (DeSSE) with fragmented entropy storage. Each component addresses specific cybersecurity weaknesses of IoT devices: quantum-resistant communication on epistemic agents that facilitate cognitive decision-making under uncertainty, lightweight adaptive consensus provided by MuReQua, and fragmented entropy storage provided by DeSSE. Tested through simulations and use case analyses in industrial, healthcare and automotive networks, the architecture shows exceptional latency, decision accuracy and fault tolerance compared to conventional solutions. Furthermore, its modular nature allows for incremental integration and domain-specific customisation. By adding reasoning, trust and quantum security, it is possible to design intelligent decentralised architectures for resilient IoT ecosystems, thereby strengthening system defences alongside architectures. In turn, this work offers a specific architectural response and a broader perspective on secure decentralised computing, even for the imminent advent of quantum computers. Full article

Effective communication among distributed energy sources (DESs) is essential for optimizing energy allocation across power sources, loads, and storage devices in integrated renewable energy and energy management systems. This paper proposes a novel communication and energy management strategy to address challenges related to communication interference and inefficiencies in energy management. The proposed strategy employs the DC bus as a communication medium, enabling module coordination via pulsed voltage signals. Controller modules regulate the bus voltage based on the energy state of the bus and control the supplementary or absorptive energy flow from slave modules to maintain voltage stability. Simultaneously, communication between the master and slave modules is achieved through pulsed voltage signals of varying pulse widths, and power sharing is realized via droop control. Experimental results demonstrate that the proposed method effectively distributes energy across different voltage levels, enabling the controller modules to precisely regulate the voltage of the slave modules under varying load conditions. Full article

Recent advances in DNA research have increased the necessity for museums to preserve not only morphological specimens but also their DNA, leading us to maintain tissue samples linked to specimens at −80 °C. DNA analysis has become an essential tool for taxonomic research and biodiversity assessment; however, freezer storage for all samples is impractical due to space limitations and operational costs. This creates a pressing need to develop more widely applicable DNA preservation methods. We investigated the comparative effects of traditional preservation methods versus DESS (DMSO/EDTA/saturated NaCl solution) preservation on both morphology and DNA integrity using museum specimens from various taxonomic groups. Our results demonstrated that DESS preservation maintained high-quality DNA fragments exceeding >15 kb at room temperature across all examined species, with nematode samples maintaining DNA integrity even after 10 years of storage. When preserving whole organisms, the optimal preservation solution conditions for maintaining both morphological features and DNA integrity varied among species. Notably, DNA integrity was maintained even after complete evaporation of the DESS solution. These findings suggest that DESS utilization for specimen DNA preservation is effective across many species, not only for long-term storage in environments without freezer facilities but also for temporary preservation until freezing. Full article

This paper presents a novel hierarchical voltage control framework for distribution networks to mitigate voltage violations by coordinating distributed energy storage systems (DESSs). The framework establishes a two-layer architecture that integrates centralized optimization with distributed execution. In the upper layer, a model predictive control (MPC)-based controller computes optimal power dispatch trajectories for critical buses, effectively decoupling slow-timescale optimization from real-time adjustments. In the lower layer, a broadcast-based controller dispatches parameterized power regulation signals, enabling autonomous active power tracking by the DESS units. This hierarchical design explicitly addresses the scalability limitations of conventional centralized control and the cyber vulnerabilities of peer-to-peer distributed strategies. The effectiveness of the proposed control framework is verified on the modified IEEE 34-bus and 123-bus test feeder. The results show that the proposed method can mitigate the average voltage violation by 93.7% and show control robustness even under 60% communication loss condition. Full article

With the increasing penetration of distributed photovoltaics (PVs) in distribution networks (DNs), issues like voltage violations and fluctuations are becoming more prominent. This paper proposes a spatio-temporal adaptive voltage coordination control strategy involving multiple timescales and multi-device collaboration. Aiming at the heavy workload caused by the continuous sampling of real-time data in the whole domain, an intra-day innovative construction of intra-day minute-level optimization and real-time adaptive control double-layer control mode are introduced. Intra-day minute-level refinement of on-load tap changer (OLTC) and step voltage regulator (SVR) day-ahead scheduling plans to fully utilize OLTC and SVR voltage regulation capabilities and improve voltage quality is discussed. In real-time adaptive control, a regional autonomy mechanism based on the functional area voltage quality risk prognostication coefficient (VQRPC) is innovatively proposed, where each functional area intelligently selects the time period for real-time voltage regulation of distributed battery energy storage systems (DESSs) based on VQRPC value, in order to improve real-time voltage quality while reducing the data sampling workload. Aiming at the state of charge (SOC) management of DESS, a novel functional area DESS available capacity management mechanism is proposed to coordinate DESS output and improve SOC homogenization through dynamically updated power–capacity availability (PCA). And vine model threshold band (VMTB) and deviation optimization management (DOM) strategies based on functional area are innovatively proposed, where DOM optimizes DESS output through the VMTB to achieve voltage fluctuation suppression while optimizing DESS available capacity. Finally, the DESS and electric vehicle (EV) cooperative voltage regulation mechanism is constructed to optimize DESS capacity allocation, and the black-winged kite algorithm (BKA) is used to manage DESS output. The results of a simulation on a modified IEEE 33 system show that the proposed strategy reduces the voltage fluctuation rate of each functional area by an average of 36.49%, reduces the amount of data collection by an average of 68.31%, and increases the available capacity of DESS by 5.8%, under the premise of a 100% voltage qualification rate. Full article

The global energy landscape is witnessing a transformational shift brought about by the adoption of renewable energy technologies along with power system modernisation. Distributed generation (DG), smart grids (SGs), microgrids (MGs), and advanced energy storage systems (AESSs) are key enablers of a sustainable and resilient energy future. This review deepens the analysis of the fulminating change in power systems, detailing the growth of power systems, wind and solar integration, and next-generation high-voltage direct current (HVDC) transmission systems. Moreover, we address important aspects such as power system monitoring, protection, and control, the dynamic modelling of transmission and distribution systems, and advanced metering infrastructure (AMI) development. Emphasis is laid on the involvement of artificial intelligence (AI) techniques in optimised grid operation, voltage control, stability, and the system integration of lifetime energy resources such as islanding and hosting capacities. This paper reviews the key aspects of current advancements in grid technologies and their applications, enabling the identification of opportunities and challenges to be addressed toward achieving a modern, intelligent, and efficient power system infrastructure. It wraps up with a perspective on future research paths as well as a discussion of potential hybrid models that integrate AI and machine learning (ML) with distributed energy systems (DESs) to improve the grid’s resilience and sustainability. Full article

The conversion of industrial waste lignin into sustainable carbon materials is an essential step towards reducing dependency on fossil fuels and mitigating environmental impacts. This review explores various aspects of lignin utilization, with particular focus on the extraction of lignin and the application of lignin-derived carbon materials in energy storge applications. The review explores advanced chemical methods to improve the efficiency of biomass conversion, detailing emerging technologies for lignin extraction from various biomasses using innovative solvents and techniques, such as Ionic Liquids and Deep Eutectic Solvents (DESs). Additionally, it discusses the parameters that impact the hydrothermal carbonization (HTC) process. The produced hydrochar shows potential for use as optimized precursors for energy storage applications. This review also considers the implications of these technologies for environmental sustainability and the circular economy, suggesting future research directions to enhance and scale these processes for global impact. This comprehensive analysis highlights the critical role of advanced biomass conversion technologies in achieving sustainability and outlines pathways for future lignin-based carbon materials innovations. Full article

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data. Full article

Distributed energy storage systems (DESSs), which would become key components in a new power system, can flexibly deliver peak load shaving and demand management. With the popularization of distributed renewable energy generation in a distribution network, the grid impedance varies and DESSs thus have to face stability issues. In order to enhance the system’s stability, a compensation strategy is proposed for the inverter in a DESS. First, a stability analysis model is developed to show the main factors that affect system stability. Then, an improved compensation strategy is proposed for the phase-locked loop (PLL) in a DESS, in which control parameters are adaptively tuned on-line according to real-time conditions to improve the stability of a grid-tied DESS. Simulation and hardware-in-the-loop (HIL) experimental results are given to validate the effectiveness of the proposed strategy. Simulation and experimental results show that the proposed strategy significantly increases the system’s tolerance to grid impedance variations, maintains total harmonic distortion (THD) below 5% during normal operation, and effectively reduces low-order harmonic content caused by impedance fluctuations. Moreover, the strategy is demonstrated to enhance system stability under low state-of-charge (SOC) conditions, showcasing its robustness and adaptability across various operating scenarios. Full article

To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network. Full article

The recent advancements of ionic liquids (ILs) and deep eutectic solvents (DESs) in the synthesis of cobalt-based catalysts for water splitting is reviewed. ILs and DESs possess unique physical and chemical properties, serving as solvents, templates, and reagents. Combined with calcination techniques, their advantages can be fully leveraged, enhancing the stability and activity of resulted catalysts. In these solvents, not only are they suitable for simple one-step calcination, but also applicable to more complex multi-step calcination, suitable for more complex reaction conditions. The designability of ILs and DESs allows them to participate in the reaction as reactants, providing metal and heteroatoms, simplifying the preparation system of cobalt phosphide, sulfide, and nitride. This work offers insights into design principles for electrocatalysts and practical guidance for the development of efficient and high-performance materials for hydrogen production and energy storage systems. Full article

Deep eutectic solvents (DESs) have received extensive attention in green chemistry because of their ease of preparation, cost-effectiveness, and low toxicity. Pickering emulsions offer advantages such as long-term stability, low toxicity, and environmental friendliness. The oil phase in some Pickering emulsions is composed of solvents, and DESs can serve as a more effective alternative to these solvents. The combination of DESs and Pickering emulsions can improve the applications of green chemistry by reducing the use of harmful chemicals and enhancing sustainability. In this study, a Pickering emulsion consisting of a DES (menthol:octanoic acid = 1:1) in water was prepared and stabilized using starch nanoparticles (SNPs). The emulsion was thoroughly characterized using various techniques, including optical microscopy, transmission microscopy, laser particle size analysis, and rheological measurements. The results demonstrated that the DES-in-water Pickering emulsion stabilized by the SNPs had excellent stability and retained its structural integrity for more than 200 days at room temperature (20 °C). This prolonged stability has significant implications for many applications, particularly in the field of storage and transportation. This Pickering emulsion based on DESs and SNPs is sustainable and stable, and it has great potential to improve green chemistry practices in various fields. Full article

In low-voltage distribution networks, distributed energy storage systems (DESSs) are widely used to manage load uncertainty and voltage stability. Accurate modeling and estimation of voltage fluctuations are crucial to informed DESS dispatch decisions. However, existing parametric probabilistic approaches have limitations in handling complex uncertainties, since they always rely on predefined distributions and complex inference processes. To address this, we integrate the patch time series Transformer model with the non-parametric Huberized composite quantile regression method to reliably predict voltage fluctuation without distribution assumptions. Comparative simulations on the IEEE 33-bus distribution network show that the proposed model reduces the DESS dispatch cost by 6.23% compared to state-of-the-art parametric models. Full article

In recent years, the penetration rate of intermittent distributed generation (DG) in active distribution networks has been steadily increasing, leading to prominent issues such as voltage violations and network congestion. Coordinated operation of soft open points (SOPs) and distributed energy storage systems (DESSs) enable flexible resource management in both spatial and temporal dimensions, allowing real-time voltage regulation and flow control. This enhances the controllability, sustainability, and economic efficiency of the distribution network, ultimately improving user satisfaction. The optimization of this coordinated configuration has become a central challenge in research. Taking the different characteristics of DESSs and SOPs into account, this paper proposes a coordinated configuration method by introducing local marginal price (LMP) under the configuration scheme. The framework is modeled as a three-level problem, including planning and operation levels. Initially, typical scenarios are generated to address the uncertainty of distributed generation operation. At the upper level, the DESS configuration is optimized to minimize annual operational costs, while at the middle level, SOPs are planned based on the annual comprehensive operational cost of the distribution network. At the lower level, the objective is set as social welfare maximization to reflect user satisfaction by incorporating LMP as a planning indicator. It is then transformed and solved as a mixed-integer second-order programming model with a hybrid optimization algorithm. The model is established with the modified IEEE 33-node distribution system as a case study to validate the feasibility of the proposed configuration method. The case study results demonstrate the effectiveness of the proposed approach in optimally configuring SOPs with DESSs to reduce overall annual operating costs and enhance the economic efficiency of the system. Full article

Deep eutectic solvents (DESs) have recently gained increased attention for their potential in biotechnological applications. DESs are binary mixtures often consisting of a hydrogen bond acceptor and a hydrogen bond donor, which allows for tailoring their properties for particular applications. If produced from sustainable resources, they can provide a greener alternative to many traditional organic solvents for usage in various applications (e.g., as reaction environment, crystallization agent, or storage medium). To navigate this large design space, it is crucial to comprehend the behavior of biomolecules (e.g., enzymes, proteins, cofactors, and DNA) in DESs and the impact of their individual components. Molecular dynamics (MD) simulations offer a powerful tool for understanding thermodynamic and transport processes at the atomic level and offer insights into their fundamental phenomena, which may not be accessible through experiments. While the experimental investigation of DESs for various biotechnological applications is well progressed, a thorough investigation of biomolecules in DESs via MD simulations has only gained popularity in recent years. Within this work, we aim to provide an overview of the current state of modeling biomolecules with MD simulations in DESs and discuss future directions with a focus for optimizing the molecular simulations and increasing our fundamental knowledge. Full article