Search Results (169)

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

Abnormal operations, such as faults occurring in an electrical power system (EPS), disrupt its balanced operation, posing potential hazards to human lives and the system’s equipment. Effective monitoring, control, protection, and coordination are essential to mitigate these risks. The complexity of these processes is further compounded by the presence of intermittent distributed energy resources (DERs) in active distribution networks (ADNs) with bidirectional power flow, which introduces a fast-changing dynamic aspect to the system. The deployment of phasor measurement units (PMUs) within the EPS as highly responsive equipment can play a pivotal role in addressing these challenges, enhancing the system’s resilience and reliability. However, synchrophasor measurement-based studies and analyses of power system phenomena may be hindered by the absence of PMU blocks in certain simulation tools, such as PSCAD, or by the existing PMU block in Matlab/Simulink R2021b, which exhibit technical limitations. These limitations include providing only the positive sequence component of the measurements and lacking information about individual phases, rendering them unsuitable for certain measurements, including unbalanced and non-symmetrical fault operations. This study proposes a new reliable PMU model in Matlab and tests it under normal and abnormal conditions, applying real-time simulation and controller-hardware-in-the-loop (CHIL) techniques. Full article

This paper proposes a Distributed Cooperative Algorithm (DCA) that solves the windup problem caused by the saturation of the Distributed Energy Resource (DER) PI-based control unit. If the reference reactive current output by the PI exceeds the maximum reactive power capacity of the DER, the control unit saturates, preventing the optimal voltage regulation at the connection node of the Active Distribution Network (ADN). Instead of relying on a centralized solution, we proposed a cooperative approach in which each DER’s control unit takes part in the DCA. If a control unit saturates, the voltage regulation error is not null, and the algorithm is activated to assign a share of this error to all DERs’ control units according to a weighted average principle. Subsequently, the algorithm determines the control unit’s new value of the voltage setpoint, desaturating the DER and enhancing the voltage profile. The proposed DCA is independent of the design of the control unit, does not require parameter tuning, exchanges only the regulation error at a low sampling rate, handles multiple saturations, and has limited communication requirements. The effectiveness of the proposed DCA is validated through numerical simulations of an ADN composed of two IEEE 13-bus Test Feeders. Full article

Laser propulsion is a new conceptual technology that drives spacecraft and possesses advantages such as high specific impulse, large payload ratio, and low launch cost. It has potential applications in diverse areas, such as space debris mitigation and removal, microsatellite attitude control, and orbital maneuvering. Liquid pulse laser propulsion has notable advantages among the various laser propulsion systems. We review the concept and the theory of liquid laser propulsion. Then, we categorize the current state of research based on three types of propellants—non-energetic liquids, energetic liquids, and liquid metals—and provide an analysis of the propulsion characteristics arising from the laser ablation of liquids such as water, glycidyl azide polymer (GAP), hydroxylammonium nitrate (HAN), and ammonium dinitramide (ADN). We also discuss future research directions and challenges of pulsed liquid laser propulsion. Although experiments have yielded encouraging outcomes due to the distinctive properties of liquid propellants, continued investigation is essential to ensure that this technology performs reliably in actual aerospace applications. Consistent results under both spatial and ground conditions remain a key research content for fully realizing its potential. Full article

The integration of battery energy storage systems (BESSs) within active distribution networks (ADNs) entails optimized day-ahead charge/discharge scheduling to achieve effective peak shaving.The primary objective is to reduce peak demand and mitigate power deviations caused by intermittent photovoltaic (PV) output. Quasi-static time-series (QSTS) co-simulations for determining optimal heuristic solutions at each time interval are computationally intensive, particularly for large-scale systems. To address this, a two-stage intelligent BESS scheduling approach implemented in a MATLAB–OpenDSS environment with parallel processing is proposed in this paper. In the first stage, a rule-based decision tree generates initial charge/discharge setpoints for community BESS units. These setpoints are refined in the second stage using an optimization algorithm aimed at minimizing community net load power deviations and reducing peak demand. By assigning each ADN community to a dedicated CPU core, the proposed approach utilizes parallel processing to significantly reduce the execution time. Performance evaluations on an IEEE 8500-node test feeder demonstrate that the approach enhances peak shaving while reducing QSTS co-simulation execution time, utility peak demand, distribution network losses, and point of interconnection (POI) nodal voltage deviations. In addition, the use of smart inverter functions improves BESS operations by mitigating voltage violations and active power curtailment, thereby increasing the amount of energy shaved during peak demand periods. Full article

Background/Objectives: Alterations in long non-protein-coding RNAs (lncRNAs) are known to influence cellular proliferation, apoptosis, and metastasis in human cancers, including renal cell carcinoma (RCC). Methods: Using pyrosequencing, we analyzed DNA methylation (DNAm) at 23 loci within the LINC00404 CpG island across 28 human cancer cell line models, 181 RCC tumor tissues, 154 paired tumor-adjacent normal tissues (adNs), and 194 metastatic tissue samples. Results: Our analysis revealed that all CpG sites exhibited tumor-specific hypermethylation (all p ≤ 1.4 × 10⁻⁵). Moreover, primary RCC tissues with distant metastases (M1) and metastatic tissue samples (Mtx) showed significant hypermethylation compared to RCC without distant metastases (M0). Notably, DNAm in Mtx displayed a significant increase in 22 CpG sites, compared to 12 CpG sites in the M1/M0 comparison, suggesting that DNAm in Mtx differs both qualitatively and quantitatively. Conclusions: Given that elevated levels of DNAm were also observed in the majority of cell line models, our findings suggest that LINC00404 may play a pivotal role in the malignant development and progression of RCC metastasis, as well as in other human cancers. Full article

In distribution networks, energy storage serves as a crucial means to mitigate power fluctuations from renewable energy sources. However, due to its high cost, energy storage remains a resource whose large-scale adoption in power systems faces significant challenges. In recent years, the emergence of shared energy storage business models has provided new opportunities for the efficient operation of multi-distribution networks. Nevertheless, distribution network operators and shared energy storage operators belong to different stakeholders, and traditional centralized scheduling strategies suffer from issues such as privacy leakage and overly conservative decision-making. To address these challenges, this paper proposes a Nash bargaining game-based optimal energy management and trading strategy for multi-distribution networks with shared energy storage. First, we establish optimal scheduling models for active distribution networks (ADNs) and shared energy storage operators, respectively, and then develop a cooperative scheduling model aimed at maximizing collaborative benefits. The interactive variables—power exchange and electricity prices between distribution networks and shared energy storage operators—are iteratively solved using the Alternating Direction Method of Multipliers (ADMM). Finally, case studies based on modified IEEE-33 test systems validate the effectiveness and feasibility of the proposed method. The results demonstrate that the presented approach significantly outperforms conventional centralized optimization and distributed robust techniques, achieving a maximum improvement of 3.6% in renewable energy utilization efficiency and an 11.2% reduction in operational expenses. While maintaining computational performance on par with centralized methods, it effectively addresses data privacy concerns. Furthermore, the proposed strategy enables a substantial decrease in load curtailment, with reductions reaching as high as 63.7%. Full article

The increasing integration of distributed photovoltaics (PVs) has intensified voltage violations in active distribution networks (ADNs). Traditional centralized voltage regulation approaches face substantial challenges in terms of communication and computation. Distributed control methods can help mitigate these issues through distributed algorithms but struggle to track real-time fluctuations in PV generation. Local control offers fast voltage adjustments but lacks coordination among different PV units. This paper presents a hierarchical distributed and local voltage control strategy for PV clusters. First, the alternating direction method of multipliers (ADMM) algorithm is adopted to coordinate the reactive power outputs of PV inverters across clusters, providing reference values for local control. Then, in the local control phase, a Q-P control strategy is utilized to address real-time PV fluctuations. The flexibility of the local control strategy is enhanced using the lifted linear decision rule, enabling a rapid response to PV power fluctuations. Finally, the proposed strategy is tested on both the modified IEEE 33-node distribution system and a practical 53-node distribution system to evaluate its performance. The results demonstrate that the proposed method effectively mitigates voltage issues, reducing the average voltage deviation by 53.93% while improving flexibility and adaptability to real-time changes in PV output. Full article

With the proposal of carbon peaking and carbon neutrality goals, the proportion of distributed renewable energy generation in active distribution networks (ADNs) has been continuously increasing. While this has effectively reduced greenhouse gas emissions, it has also given rise to power quality issues such as excessive or insufficient voltage amplitudes. To effectively address this problem, this paper proposes a multi-resource coordinated dynamic reactive power–voltage coordination optimization method. Firstly, an improved Generative Convolutional Adversarial Network (GCAN) is used to generate typical wind and solar power output scenarios. Based on these generated typical scenarios, a voltage control model for ADNs is established with the objective of minimizing voltage fluctuations, fully exploiting the dynamic reactive power regulation resources within the ADN. In view of the non-convex and nonlinear characteristics of the model, an improved Gray Wolf Optimizer (GWO) algorithm is employed for model optimization and solution seeking. Finally, the effectiveness and feasibility of the proposed method are demonstrated through simulations using modified IEEE-33-bus and IEEE-69-bus test systems. Full article

SE is critical in ADNs integrating renewable DGs. Traditional SE methods face the challenges of increasing SE errors and decreased robustness due to the adverse impact of bad data in measurements and topology parameters. To address these issues, this paper proposes a robust SE method that considers bad data in measurements and topology parameters. First, a bad measurement data processing model is proposed to improve measurement and SE accuracy by generating high-precision pseudo-measurements through adaptive learning from historical data sequences to replace the bad measurement data in measurements. Second, a robust SE model combining network estimation and linear estimation is proposed, which enhances SE accuracy and robustness under bad data generated in measurements and topology parameters in ADNs. In a simulation, the proposed method’s effectiveness is verified on the modified IEEE 33-node system. Full article

In the context of rapid advancement of smart cities, a distribution network (DN) serving as the backbone of urban operations is a way to confront multifaceted challenges that demand innovative solutions. Central among these, it is imperative to optimize resource allocation and enhance the efficient utilization of diverse energy sources, with particular emphasis on seamless integration of renewable energy systems into existing infrastructure. At the same time, considering that the traditional power system’s “rigid”, instantaneous, dynamic, and balanced law of electricity, “source-load”, is difficult to adapt to the grid-connection of a high proportion of distributed generations (DGs), the collaborative interaction of multiple flexible controllable resources, like flexible loads, are able to supplement the power system with sufficient “flexibility” to effectively alleviate the uncertainty caused by intermittent fluctuations in new energy. Therefore, an active distribution network (ADN) intraday, reactive, power optimization-scheduling model is designed. The dynamic reactive power collaborative interaction model, considering the integration of DG, energy storage (ES), flexible loads, as well as reactive power compensators into the IEEE 33-node system, is constructed with the goals of reducing intraday network losses, keeping voltage deviations to a minimum throughout the day, and optimizing static voltage stability in an active distribution network. Simulation outcomes for an enhanced IEEE 33-node system show that coordinated operation of source–network–load–storage effectively reduces intraday active power loss, improves voltage regulation capability, and achieves secure and reliable operation under ADN. Therefore, it will contribute to the construction of future smart city power systems to a certain extent. Full article

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN “key” nodes, a “key” nodes selection approach that used improved K-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users’ interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver. Full article

Propellants play a crucial role in the propulsion systems of aerospace vehicles, and their combustion characteristics are susceptible to external environmental conditions. This study systematically investigated the impact of various initial conditions on the combustion process of ADN-based propellant, including combustion products, equilibrium pressure, adiabatic temperature, and ignition delay time. The results indicate that the primary combustion products of ADN-based propellant include N₂O, N₂, CO₂, OH, and others. ADN-based propellant exhibits a distinct two-stage combustion process under low pressure and temperature conditions (P₀ = 2 atm, T₀ = 586 K). Conversely, under high pressure and temperature conditions (P₀ = 10 atm, T₀ = 2930 K), the two stages of combustion occur almost simultaneously, making them difficult to distinguish. Furthermore, as the initial temperature increases, the ignition delay time decreases significantly, and the combustion rate accelerates. When the initial temperature rises from 400 K to 2800 K at a pressure of P₀ =10 atm, the ignition delay time decreases from 3.5 ms to 0.6 μs. Interestingly, changes in initial pressure have a relatively minor impact on the ignition delay time compared to changes in temperature. Therefore, temperature has a more crucial influence on the combustion characteristics of ADN-based propellant than pressure. This study holds promise for providing new combustion optimization strategies for the aerospace industry and promoting the development of aircraft designs towards higher performance and sustainability. Full article

Dairy-borne Pseudomonas spp., known for causing spoilage, may also exhibit antibiotic resistance and form biofilms, enhancing their persistence in dairy environments and contaminating final products. This study examined biofilm formation and antibiotic resistance in 106 Pseudomonas spp. strains isolated from milk, whey, and spoiled dairy products. Phylogenetic analysis (based on partial ileS sequences) grouped most strains within the P. fluorescens group, clustering into the P. fluorescens, P. gessardii, P. koorensis, and P. fragi subgroups. Biofilm formation in polystyrene microplates was assessed at 6 °C and 25 °C by crystal violet staining. After 48 h, 72% and 65% of Pseudomonas strains formed biofilms at 6 °C and 25 °C, respectively, with higher biomass production at 6 °C. High biofilm producers included most P. fluorescens, P. shahriarae, P. salmasensis, P. atacamensis, P. gessardii, P. koreensis, and P. lundensis strains. The adnA gene, associated with biofilm formation, was detected in 60% of the biofilm producers, but was absent in P. fragi, P. lundensis, P. weihenstephanensis, and P. putida. Antibiotic susceptibility was tested using the disk diffusion method. All strains were susceptible to amikacin and tobramycin; however, 73% of the strains were resistant to aztreonam, 28% to imipenem and doripenem, 19% to ceftazidime, 13% to meropenem, and 7% to cefepime. A multiple antibiotic resistance index (MARI) > 0.2 was found in 30% of the strains, including multidrug-resistant (n = 15) and extensively drug-resistant (n = 3) strains. These findings highlight Pseudomonas spp. as persistent contaminants and antibiotic resistance reservoirs in dairy environments and products, posing public health risks and economic implications for the dairy industry. Full article

The vigorous development of micro–nano satellites urgently requires satellite-borne propulsion systems as support. Pulsed laser ablation micro-propulsion can meet these high demands. Ammonium dinitramide (ADN), as a green monopropellant, can serve as the working substance for laser ablation. This work investigated the micro-propulsion performance of liquid propellants composed of ADN and water with different ADN mass fractions, aiming to clarify the enhancement effect of chemical energy. Through the single-pulse impulse measurement, the results show that the 70 wt.% ADN had a maximum specific impulse of 167.55 s, a 19% increase compared to H₂O. The established semi-empirical model of the micro-propulsion performance fits well with the experimental data and can effectively explain the variations in the patterns of the propulsion’s parameters. The chemical energy’s actual rate of contribution to the increase in the kinetic energy was positively correlated with the ADN’s mass fraction and negatively correlated with the laser energy, with an actual contribution rate of 36% for 70 wt.% ADN at a laser energy of 60 mJ. Furthermore, based on the relationship between the ablation efficiency, chemical-specific energy, and laser specific energy, it was found that the ablation efficiency can be improved by increasing the chemical specific energy and reducing the laser specific energy while ensuring the breakdown. This work provides a scientific approach to quantitatively analyze the enhancement in the propulsion’s performance by chemical energy in laser micro-ablation, which is expected to be extended to other energetic liquid propellants. Full article