To clarify the role of fuel and oxidizer particle size in regulating the energy output of CL-20-based thermobaric explosives, formulations with varied particle sizes were prepared and systematically investigated. Explosion calorimetry and confined explosion experiments were conducted to evaluate the effects of aluminum and potassium perchlorate (KClO 4 ) particle size on explosive energy and blast parameters. The results show that KClO 4 particle size has a negligible influence on the total explosion energy. However, reducing the particle size from 255 µm to 10 µm shortens the heat transfer distance and accelerates decomposition, thereby promoting aluminum participation in the anaerobic combustion stage. As a result, the explosion peak overpressure increases by 5.98% and 11.18%, respectively. For the 10 µm KClO 4 formulation, the elevated activation energy limits reactions, leading to a reduced quasi-static pressure. Among formulations containing single-sized aluminum powders, the Al-6 sample exhibits the highest explosion energy and pressure. This behavior is attributed to a dynamic balance between active aluminum content and reaction rate. In contrast, the graded aluminum formulation (Al-G) enables synergistic participation of different particle sizes within the reaction time window. This synergy intensifies and prolongs afterburning, resulting in the best overall explosive performance. These findings provide guidance for optimizing the energy release structure of CL-20-based thermobaric explosives.

Driven by dual-carbon targets, Energy-intensive industrial loads offer significant potential for enhancing power system flexibility via demand response (DR). However, complex production processes and strict material constraints often limit user participation. This paper proposes an optimization method for DR strategy design that accounts for the coupling between energy and material flows across multiple production stages. A unified modeling framework is developed based on discrete and continuous process characteristics to capture load adjustment boundaries under energy–material flow constraints. A price-based incentive strategy is then formulated, and a bilevel Stackelberg game model is established to characterize the interaction between distribution network operators and industrial customers. The model is solved using an iterative algorithm based on the golden section method. Case studies involving steel manufacturing demonstrate that the proposed method effectively motivates industrial customers to participate in DR, reduces system regulation costs, and improves both the availability and economic efficiency of industrial flexible loads.

As the amount of active and reactive resources managed by microgrid operator (MGO) increases, those managed by the distribution system operator (DSO) are gradually decreasing. While the reactive power supply and voltage control of the distribution system are still managed by the DSO, these responsibilities should be assigned to the MGOs to promote the utilization of reactive power resources in microgrids. This paper proposes a pricing and transaction method for active and reactive power in microgrids considering the operational state of the distribution system. First, a bi-level framework and model of active and reactive power joint transaction for MGOs are constructed based on the distribution network needs. Then, using the equilibrium problem with equilibrium constraints (EPEC) approach, the bi-level model is transformed into a single model, which is further linearized to facilitate the solution. Finally, the proposed method is analyzed and verified by the modified 33-node distribution system with four microgrids. The proposed method can make microgrids freely choose to trade active and reactive resources considering the needs of the distribution system, and improve the benefit of microgrids without damaging the benefit of DSO.

The dispatch of demand-side resources is becoming increasingly essential for enhancing the resilience of distribution systems against extreme weather events. Traditional studies rely primarily on direct load curtailment to mitigate power shortages, thereby neglecting the power demand of different customers. To bridge this gap, a novel coordination method of transactive demand response (DR) and rolling outage management of diverse loads is proposed. First, the DR program is designed to in-centivize voluntary load adjustments by coordinating participation of private consumers. Considering the diversity and characteristics of customers during DR, detailed models of diverse loads are established, including an energy-material flow model of industrial loads (ILs), an adjustable load model of commercial loads (CLs), and an outage-sensitive model of residential loads (RLs). When the supply-demand imbalance exceeds the adjustment capacity of DR, rolling outage measures are integrated into the proposed method to reduce losses incurred by load shedding. The coordination of transactive DR and rolling outage management is formulated as a bilevel optimization problem from system operators and responsive load, which is solved by the Stackelberg game-theoretic approach. Finally, the proposed method is tested on the modified IEEE 33-node distribution system. The results show that the proposed method can effectively ensure customer profit and reduce load interruption loss by dispatching demand-side resources during restoration.

Future agriculture is poised to shift towards smarter, more sustainable production modes. This innovation are performed as the integration of greenhouse with photovoltaic energy storage systems (PESS). Agricultural park operators (APOs) may efficiently leverage solar energy to enhance both crop growth and overall energy management. Thus, APOs transform into prosumers via the deployment and management of PESS. Beyond benefits known to all, this transition presents a trade-off for APOs: 1) Using energy storage to save more solar energy, thereby extending growth time per day for crops utilize stored power. 2) Lease the energy storage to utilities for additional revenue or offset part of the electricity bill. In response to this future practical and meaningful challenge, this paper develops a bi-level optimization model of strategic decision-making and designs energy management for operators. The upper level highlighted maximizing profits of efficient and daily management for agricultural park. The upper level comprises two parts: (i) Maximizing profits in the ancillary services market and (ii) Minimizing the cost of electricity procurement. The bi-level model is reformulated as a mathematical program with equilibrium constraints (MPEC) problem via the Karush-Kuhn-Tucker (KKT) method. Simulations indicate that deploying photovoltaic and battery systems may reduce costs of electricity procurement and crop growth cycles, increase net profit up to 33 %. Additionally, crop prices and ancillary service prices significantly influence strategy options. (c) 2025 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

The uncertainty of distributed PV output in the high-energy-consuming steel industrial microgrid (SIMG) can have an impact on the energy management strategy of the SIMG and even increase the operational risk in the distribution market. Considering the uncertainty of distributed PV in SIMG, this paper proposes an energy management method for SIMG under distribution market based on the distributionally robust chance-constraint (DRCC), to optimal the processes of steel industrial production. Firstly, according to the form of energy flow and information flow of SIMG, the transactional mode of participating in distribution market-clearing is proposed. A time series model of the steel production process is included in the energy management for SIMG, and the bi-level energy optimization management model under the environment of distribution market is further constructed. Then, DRCC method is applied to deal with the uncertainty of distributed PV output and the distributionally robust optimization model of energy management for SIMG based on moment information is constructed. Conditional value-at-risk (CVaR) theory and duality theory are introduced to transform the distributionally robust optimization model into a second-order cone (SOC) programming form. Finally, the primal-dual counterpart condition and the linearization method are introduced to transform the bi-level model into a mixed integer SOC programming (MISOCP) problem. The results show that the proposed method can take into account the risk and economy of energy management.

Introduction:Mitigating three-phase unbalance in rural distribution networks is a significant challenge, especially with the integration of photovoltaic and energy storage systems (PESS). While Distributed Static Transfer Switches (STS) offer a promising solution by regulating load phase sequences, conventional approaches are costly and inefficient, limiting large-scale implementation.Methods:To address these limitations, we propose a bi-level optimization model for the siting and capacity optimization of STS in rural networks. The upper-layer model focuses on minimizing investment and maintenance costs for STS, while considering branch loss reduction and three-phase unbalance mitigation. The lower-layer model aims to minimize three-phase unbalance in daily operations, with the integration of PESS. We use the hyperparameter alternating iteration (HAI) method to iteratively refine the bi-level model and obtain optimal planning and operational solutions.Results:We applied the proposed model to an IEEE-13 benchmark case study. The results demonstrated that the bi-level optimization approach effectively reduced three-phase unbalance in the rural distribution system while minimizing STS planning costs.Discussion:This innovative approach provides a cost-effective and efficient solution for mitigating three-phase unbalance in rural distribution networks, enhancing the feasibility of large-scale STS deployment. The integration of PESS further contributes to system stability, making this model a robust tool for future network planning.

The urban distribution network contains a significant number of multi- or three-terminal connections. Fault location in such networks is severely constrained by the lack of necessary conditions for deploying measuring instruments at intermediate connection points and the presence of erroneous or missing distribution line characteristics. This study designs a fault determination time index by analyzing the changes in Micro-phasor measurement unit (mu PMU) measurement data at end nodes when an unbalanced fault occurs in the distribution line. A fault localization model incorporating frequency parameters is proposed, considering the influence of source load variations and the regular fluctuations of the fundamental frequency, which affect the characteristics of the distribution lines. To enhance the model's accuracy and efficiency, a solution approach combining Simulated Annealing Algorithm and trust region methods is suggested, addressing the impact of the initial values on the fault localization model calculation process. A three-terminal distribution line model is constructed in Matlab, and various failure scenarios are simulated. Using these frequency values significantly improved the accuracy of the fault location model's estimation results, achieving accuracy more than three times higher than models that do not consider frequency. The fault distance estimation error is reduced to less than 50 m. And, the model's applicable fault scenario is increased to 2000 ohm. The results demonstrate that the proposed technique significantly improves the calculation efficiency and accuracy of the fault location model, providing a robust solution for fault location and line parameter estimation in distribution networks.

A regional electricity-heating integrated energy system (REH-IES) can make extensive use of renewable energy sources, realize complementary and coordinated operation of multiple energy sources, reduce carbon emissions and promote the development of zero/low carbon systems. This paper proposes a risk-averse stochastic optimal scheduling model for REH-IES. An energy flow framework for the REH-IES is proposed considering energy interaction between the electric-heating microgrids (EHMs) and electricity distribution network and the heating network. Then, considering the uncertainties of power output of renewable energy sources, dynamic characteristics of pipelines in the heating network, and thermal inertia of smart buildings, a stochastic optimal scheduling model for the REH-IES is established. Uncertainties of renewable energy sources bring financial risks to optimal scheduling of the REH-IES. Therefore, conditional value-at-risk (CVaR) theory is adopted to measure the risk and to limit the risk within an acceptable range, to achieve minimum expected scheduling cost of the REH-IES. The stochastic programming-based problem is transformed into a second-order cone programming (SOCP) model through second-order cone relaxation method. Case studies verify the stochastic optimal scheduling model can reduce expected scheduling cost of the REH-IES, promote consumption of renewable energy sources and reduce carbon emissions.

Perovskite energetic materials (PEMs) are emerging combinations of oxidants and reductives, which are promising in explosives owing to the advantages of high energy, simple synthesis and low cost. However, the friction sensitivity of the currently reported PEMs is so high that it limits the further application of PEMs. In this work, a tetrahedral nitrogen-atom-arrangement structure, urotropine, is introduced as A-site cation of PEMs, then four urotropine-based PEMs ([C6H14N4][M(ClO4)3], named TAPs) are successfully constructed experimentally for the first time. The crystal structure, reaction progress, thermal decomposition, sensitivity, and detonation performance of TAPs are characterized. The results indicate that, different from the existing cubic PEMs, the crystal structure of TAPs experiences compression along the c-axis, despite the c-axis length being twice that of the a or b-axes. As expected, the friction sensitivity is remarkably reduced and the detonation performance is significantly improved. Moreover, the hardness of A-site cations is proposed as a key factor affecting the impact sensitivity of PEMs, while C─H···O hydrogen bonds play an important role in regulating friction sensitivity. The emergence of TAPs provides a design concept of high-energy insensitive PEMs and a unique perspective for understanding the mechanical sensitivity of energetic materials.

To move from one-off restricted utilization of custom technologies upgrade to joint implementations at scale, demanding a recognized standard test benchmark for plausibility inspection. Therefore, the test benchmark of integrated energy systems (IESs) needs to exhibit generality and universality to adapt to and match the state-of-the-art research. The publicly available test benchmark developed in this paper consists of two levels: i) Local integrated energy system (LIES), which encompasses an electricity distribution grid and a primary heating network; ii) Community integrated energy system (CIES), which incorporates a microgrid and a secondary heating network in a campus. The database of the test benchmark stems from investigations and mirrors the physical assets of a real-urban energy system and takes into account the virtual deployment of future planning for wind power and hydrogen systems. LIES showcases key features of the IES located in a densely populated area, and CIES is a critical load encapsulated in LIES. The parameters and details of the database shared in the appendix involve cable impedance, pipeline parameters, electrical and thermal loads, etc., supporting independent access and the customized reconfiguration for the simulation and computation of emerging technologies. The practicality and validity are examined through the multiple instantiated basic energy flow calculations and myriad derivative calculations.

The gradual integration of distributed wind power into distribution networks presents significant challenges for identifying faults, as it requires accurate and timely fault identification based on large-scale data. This paper proposes a novel fault situation identification framework driven by digital twins to overcome the existing bottleneck of asynchronous online fault identification and offline post-event analysis. The framework employs a high-precision digital twin avatar with parallel strategies of fully electromagnetic transient calculation to obtain the real-time operation status of distribution networks connected with distributed wind power. Furthermore, the framework employs skip-connected dilated causal convolution to mine meaningful multi-time-scale features from massive data, while using an automatic hyper-parameter tuning strategy. A case study based on IEEE 33-node standard distribution network and a real-world distribution network demonstrates the effectiveness of this framework, achieving ultra-real-time and high-precision simulation and effective fault identification even under noise or missing data, accelerating traditional post-fault analysis to microsecond-level situation identification.

Prudent and sensible deployment of local storage may unlock the potential of mitigating the adverse impact, especially addressing the challenge of uncertainty from independent renewable energy deployment. This paper designs a photovoltaic battery system (PVBS) and a gas turbine hydrogen tank system (GTTS) as the local energy communities to mitigate the uncertainty in the integrated electric-gas systems (IEGS). Firstly, this paper quantified the contribution of the co-located energy storage and remodeled the output of PVBS and GTTS as approximate beta distributions, assessing the local effects of uncertainty mitigation. Subsequently, the steadystate energy flow models incorporated with uncertainty variables of IEGS are reformulated into the chaotic mathematical counterpart via the generalized polynomial chaos method (GPC), highlighting the global performance of novel designs. In the subsequent step, the independent random variables are factorized from chaotic equations leveraged by the improved Galerkin method and remodeled into a high-dimensional deterministic equation. Based on this, the probabilistic distributions of state variables with higher order uncertainty parameters for IEGS, such as bus voltage, natural gas node pressure, etc., are sequentially computed via the Newton method. Furthermore, this paper performed a comparative simulation in the IEGS 4-12 system and IEGS 118-96 system and empirically tested the feasibility and validity of the novel design in uncertainty mitigation. Through the quantitative instantiation, enhancing the co-located energy storage capacity, increasing the outlet pressure of compressors, and decreasing the coupling level between subsystems may mitigate the uncertainty within IEGS. Derived GPC algorithms improve the computational speed and accuracy than other methods for the computation of multiple uncertainty variables in IEGS. This exploration delves into the local energy communities in various operating conditions and offers valuable suggestions for sizing and control strategies for the novel design in IEGS.

Artificial membraneless organelle with molecularly crowded and confined microenvironment shows great potential for improving functional RNA properties and developing biosensing. However, the performance of current artificial membraneless organelles is impaired with polycationic components. In this work, we demonstrate the construction of a purely fluorescent RNA aptamer based artificial membraneless organelle (FRAME) for improving RNA performance and biosensing. The fluorescent RNA aptamer in FRAME exhibited higher enzymatic resistance, thermostability, photostability and binding affinity than that dispersed in solution. The FRAME can be easily designed as a biosensor for tetracycline detection in lake water and tap water, which eliminated the adverse effects of polycationic components and demonstrated about 42.8-fold sensitivity improvement than the biosensor prepared with Poly-L-Lysine and RNA. By introducing epithelial cell adhesion molecule (EpCAM) aptamer to specific recognition of tumor cells, the FRAME is easily engineered as an ultra-sensitive probe to precisely identify cancer cell phenotypes. We believe that the proposed FRAME system would provide a universal platform for biosensing.

The primary sources of harmonic distortion in the new power system are a high percentage of access to power electronic equipment and a high percentage of renewable energy grid connections. These factors pose a major risk to the UHV converter transformer oil-paper insulation performance. However, the influence law of harmonic voltage on the electric field distribution characteristics (EFDC) of oil-paper insulation is still unclear; thus, the law of variation of spatial maximum electric field strength (SMEFS) under the action of harmonic voltage and the EFDC on the surface of oil-immersed insulating paper are investigated in this paper by building a two-dimensional simulation model of the electric field distribution of oil-paper insulation under ball-plate and cylinder-plate electrode structure. The results indicate that the two electrode structures' SMEFS are situated in the narrow oil gap between the oil-impregnated insulating paper and the high-voltage electrode; under the influence of the same harmonic components, the insulating paper's surface exhibits an inverse power function decay in the electric field distribution, with the cylinder-plate electrode decaying more quickly than the ball-plate electrode. Meanwhile, for both electrode structures, with the increase of the 5th and 13th harmonics, the SMEFS and the insulating paper surface field strength both increase linearly and are higher than that of the power frequency field strength, while for the 7th and 11th harmonics, the ball-plate electrode weakens the electric field and then increases it with the increase of the harmonic content, whereas the cylinder-plate electrode only increases the electric field.

When the holophically embedded power flow calculation method（HELM） is used to solve the power flow of the power system, if the initial point of the voltage power series of "flat start" is far away from the actual value of the power flow solution of the system, the calculation is easy to increase. Moreover, in the process of solving the voltage of each node, th e calculation based on the traditional CPU serial computing architecture takes a long time. This paper presents a holomorphic e mbedded power flow parallel computing method based on the initial points of adaptive power series. The initial points of holomorphic embedded power series of the system are adjusted adaptively according to the admittance matrix of the system node and the v oltage of the equilibrium node. The calculation model of holomorphic embedded power series based on the initial points of the adaptive power series is constructed to accelerate the convergence of the voltage power series. Based on the multi-core CPU computing platform, the parallelization of voltage Pade approximation at each node is realized to improve the solving efficiency of the system power flow equation. The analysis results of different scale test systems show that the proposed method can solve the h olomorphic embedded power flow efficiently and accurately.

With the increasing penetration of distributed energy resources (DERs) into distribution systems, microgrids can participate in the market clearing of distribution-level electricity market. This paper proposes a leader-followers -type bi-level model for energy management and market clearing of distribution system with microgrids based on Stackelberg game. Distribution system operator (DSO) is considered as the leader, and microgrid operators (MOs) as followers. In the proposed model, market clearing problem of distribution system is modeled in the upper level, while the energy management problem of microgrids is modeled in the lower level. The distribution locational marginal price (DLMP) is obtained in the upper level model and then transmitted to the lower level model as price signals for microgrids. The model is then reformulated as a Mathematical Program with Equi-librium Constraints (MPEC) problem and the stochastic MPEC model is constructed with the uncertainty of PVs in microgrids. Existence of equilibrium of the proposed single leader multi-follower game is analyzed. Numerical results demonstrate the effectiveness of the proposed model.