Hasil untuk "Production of electric energy or power. Powerplants. Central stations"

Sofie Brandtzæg Hårberg, Ola Gjønnes Grendal, Benjamin A D Williamson et al.

Degradation of cross-linked polyethylene (XLPE) insulations by vented water treeing is a phenomenon that can limit the lifetime and reliability of subsea power cables, as well as their voltage rating. Recent studies have shown that inorganic impurities embedded in the bulk of the semi-conductive (SC) screens can be responsible for inception and growth of vented water trees through channel-like nanostructured tracks. Characterization of the entire region of interest, stretching from the contaminant to the vented water tree, has proven challenging with conventional techniques. Here we have developed a qualitative methodology based on synchrotron wide-angle x-ray scattering to spatially locate crystalline impurities in the cable insulation system, enabling detection of very small impurities in a large bulk sample. NaCl was the dominant crystalline impurity and was present in the voids, along the nanostructured tracks in the SC screen and the vented water trees. Trace amounts of NaCl were also detected within a large volume of an unaged cable screen, indicating that impurities are present prior to exposure of the cables to standardized tests including elevated water temperature. These results provide crucial information about the chemical prerequisites for the formation of the nanostructured track degradation causing inception of long vented water trees at the SC screen/XLPE interface.

Chenghao Huang, Jiarong Fan, Weiqing Wang et al.

As power systems advance toward net-zero targets, behind-the-meter renewables are driving rapid growth in distributed energy resources (DERs). Virtual power plants (VPPs) increasingly coordinate these resources to support power distribution network (PDN) operation, with EV charging stations (EVCSs) emerging as a key asset due to their strong impact on local voltages. However, in practice, VPPs must make operational decisions with only partial visibility of PDN states, relying on limited, aggregated information shared by the distribution system operator. This work proposes a safety-enhanced VPP framework for coordinating multiple EVCSs under such realistic information constraints to ensure voltage security while maintaining economic operation. We develop Transformer-assisted Lagrangian Multi-Agent Proximal Policy Optimization (TL-MAPPO), in which EVCS agents learn decentralized charging policies via centralized training with Lagrangian regularization to enforce voltage and demand-satisfaction constraints. A transformer-based embedding layer deployed on each EVCS agent captures temporal correlations among prices, loads, and charging demand to improve decision quality. Experiments on a realistic 33-bus PDN show that the proposed framework reduces voltage violations by approximately 45% and operational costs by approximately 10% compared to representative multi-agent DRL baselines, highlighting its potential for practical VPP deployment.

Matin Farhoumandi, Sheida Bahramirad, Mohammad Shahidehpour et al.

ABSTRACT The proliferation of distributed energy resources (DERs) and the large‐scale electrification of transportation are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers (both consumers and producers) in coordinated power distribution network (PDN) and urban transportation network (UTN). In this new paradigm, peer‐to‐peer (P2P) energy trading is a promising energy management strategy for dynamically balancing the supply and demand in electricity markets. In this paper, we propose the application of Blockchain (BC) to electric vehicle charging station (EVCS) operations to optimally transact energy in a hierarchical P2P framework. In the proposed framework, a decentralised privacy‐preserving clearing mechanism is implemented in the transactive energy market (TEM) in which BC's smart contracts are applied in a coordinated PDN and UTN operation. The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123‐bus PDN and a modified Sioux Falls UTN.

Rachel Stukey, Adithya Melagoda, Sangar Shanthanam et al.

Ubaid Qureshi, Arnob Ghosh, Bijaya Ketan Panigrahi

Junming Li, Rongliang Shi, Zheng Dong et al.

The virtual synchronous generator (VSG) enhances the inertia response of renewable energy system by mimicking traditional synchronous generators (TSG), but inherits the power-frequency oscillation issue of TSG. The grid impedance at the point of common coupling (PCC) leads to the interaction between VSG control loops, which complicates the dynamic performance of the multi-VSG grid-connected system. This paper investigates the modeling, analysis and suppression of the power-frequency oscillation for the multi-VSG grid-connected system. Initially, a mechanical admittance model of the multi-VSG grid-connected system is developed based on the electromechanical analogy principle, and the transfer functions under various disturbances are derived. Subsequently, the frequency-domain response of the transfer function is compared and analyzed with the simulation results to reveal the power-frequency oscillation behavior of the multi-VSG grid-connected system under varying system parameters. Finally, a power-frequency oscillation suppression strategy for the multi-VSG grid-connected system, based on active power feedforward compensation (APFC), is proposed in alignment with its oscillation characteristics, and the design parameters are provided. The efficacy of the proposed oscillation suppression strategy is validated through MATLAB/Simulink simulation and experimental testing.

Jian Zhang, Liangliang Guo, Dekang Zhao et al.

The rock mass damage and failure induced by underground coal resource exploitation are strongly influenced by the confining pressure unloading (CPU) rate. However, the impact of CPU rate as a sole variable remains inadequately understood. This study utilizes discrete element numerical tests to explore the influence of CPU rate on the mesoscopic fracturing mechanism of sandstone. Homogeneous three-dimensional models with consistent mesoscopic parameters and constant axial pressure are subjected to varying CPU rates. By isolating the CPU rate as the sole variable, macroscopic failure patterns, mesoscopic damage evolution, and energy density distributions are investigated. The numerical results are validated against existing physical experimental results, confirming the rationality of the discrete element model parameters. The results show that lower CPU rates induce multistage, sudden, and progressive failure, characterized by stepwise increases in energy density, more abrupt fractures, and enhanced mobilization of local load-bearing capacity. The defined medium CPU rate results in distinct physical responses, attributed to particle rearrangement driven by unloading rate. Particle displacement is identified as a quantitative indicator of rock damage. The results underscore the importance of isolating the CPU rate effect to improve the understanding of rock fracturing mechanisms and associated physical properties.

Zhihao Lu, Zhong Wu

Speed control accuracy of gimbal servo systems for control moment gyroscopes (CMGs) is crucial for spacecraft attitude control. However, multiple disturbances from internal and external factors severely degrade the speed control accuracy of gimbal servo systems. To suppress the effects of these complex disturbances on speed control accuracy, a control method based on a proportional–integral–resonant (PIR) controller and a noise reduction extended disturbance observer (NREDO) is proposed in this paper. First, the disturbance dynamic model of an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mi>n</mi><mo>+</mo><mn>1</mn><mo>)</mo></mrow></semantics></math></inline-formula>th-order NREDO is derived. The integral of the virtual measurement of the lumped disturbance is an augmented state in the model. NREDO states are updated by using the estimation error of the augmented state. The NREDO significantly enhances the measurement noise suppression performance compared with an EDO. Second, a resonant controller is introduced to suppress the high-frequency rotor dynamic imbalance torque. The PIR controller is composed of a resonant controller in parallel with a PI controller. Numerical simulation and experimental results demonstrate the effectiveness of the proposed method.

Walid Abdelfattah, Munthar Kadhim Abosaoda, Krunal Vaghela et al.

Accurate prediction of biochar yield from biomass pyrolysis is essential for optimizing production in sustainable agriculture, yet remains technically challenging due to multiple interacting factors. This study developed a predictive framework using a curated dataset of 211 samples, each including 14 normalized input features (chemical, physical, operational) and one output variable (biochar yield, wt%). Machine learning modeling utilized Gradient Boosted Decision Trees (GBDT), with hyperparameters exhaustively tuned via Gaussian Processes Optimization (GPO), Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), and Batch Bayesian Optimization (BBO). Models were evaluated on a train-test split (90% training, 10% testing) and the best performance was achieved by the GBDT–BPI model: total R² = 0.982, mean squared error (MSE) = 1.65, average absolute relative error percentage (AARE%) = 1.35; on the test set, R² = 0.693, MSE = 15.2, AARE% = 9.54. Comparative analysis showed GBDT–BPI outperformed GBDT–GPO (total R² = 0.978; MSE = 2.01; AARE% = 1.72), GBDT–ES (total R² = 0.976; MSE =  2.13; AARE% = 3.81), and GBDT–BBO (total R² = 0.980; MSE = 1.81; AARE% = 2.58). Sensitivity study presented reside duration, temperature, and fixed carbon as the top parameters of yield. Time efficiency was comparable for all optimizers, with BBO taking the longest (313 s/500 iterations). Diagnostic leverage analysis demonstrated high data quality, with less than 1% flagged as influential outliers. This integrated approach delivered high-accuracy, interpretable prediction, and revealed critical parameters for process optimization in biomass pyrolysis workflows.

Xiaojie Tao, Yaoyu Fan, Zhaoyi Ye et al.

The integration of heavy-duty electric vehicles (EVs) with Vehicle-to-Grid (V2G) capability can enhance primary frequency response and improve stability in power systems with high renewable penetration. This study evaluates the technical potential of heavy-duty EV fleets to support the California power grid under three practical charging strategies: immediate charging, delayed charging, and constant-minimum-power charging. We develop a simulation framework that couples aggregated frequency dynamics with battery and charger constraints, state-of-charge management, and fleet-availability profiles. Performance is assessed using standard frequency security metrics, including nadir, rate-of-change-of-frequency, overshoot, and settling time, across credible contingency scenarios and renewable generation conditions. Results indicate that both non-V2G modes and V2G-enabled operation can contribute meaningful primary response, with V2G providing the strongest and fastest support while respecting mobility and network limits. Sensitivity analyses show that the relative benefits depend on charging strategy, control parameters, and renewable output, highlighting design trade-offs between response magnitude, duration, and battery usage. Overall, heavy-duty EV fleets-when coordinated by appropriate charging and V2G controls-offer a viable resource for strengthening primary frequency control on the California grid and mitigating stability challenges associated with increasing renewable penetration.

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.

Luca Orusa, Mattia Di Mauro, Fiorenza Donato

The flux of astrophysical neutrinos is now measured with unprecedented accuracy and over several decades of energy spectrum. Their origin traces back to hadronic collisions between protons and nuclei in the cosmic rays with hydrogen and helium in the target gas. To accurately interpret the data, a precise determination of the underlying cross sections is therefore mandatory. We present a new evaluation of the neutrino production cross section from $p+p$ collisions, building on our previous analysis of the production cross section for $π^\pm$, $K^\pm$, and minor baryonic and mesonic channels. Cross sections for scatterings involving nuclei heavier than protons are also derived. The novelty of our approach is the analytical description of the Lorentz invariant cross section $σ_{\rm inv}$, and the fit of the model to the available accelerator data. We work with neutrino energies from $10$ GeV to $10^7$ GeV, and, correspondingly, to incident proton (nuclei) energies from $10$ GeV to $10^9$ GeV (GeV/n). We obtain the total differential cross section, $dσ(p+p\rightarrow ν+X)/dE_ν$ as a function of neutrino and proton energies, with an estimated uncertainty of 5% for neutrino energies below 100 GeV, increasing to 10% above TeV energies. Predictions are given for $ν_e, ν_μ, \bar{ν_e}$ and $\bar{ν_μ}$. A comparison with state-of-the-art cross sections, all relying on Monte Carlo generators, is also presented. To facilitate the use by the community, we provide numerical tables and a script for accessing our energy-differential cross sections.

Yingjie Li, Fang Liu, Ke Chen et al.

K Rajkumar, C. Nayanathara, K. Ambika et al.

Jiaxin Dong, Teo Chiaki, Anurag Sharma et al.

WANG Huating, CHEN Heng, XU Gang et al.

The energy saving and emission reduction transformation of thermal power enterprises can reduce the coal consumption of thermal power supply, and then effectively reduce the growth of carbon dioxide emissions, which is of great significance to achieve the goal of carbon peak and carbon neutralization. Taking a 630 MW unit as an example, the system units of four waste heat utilization schemes (low-temperature economizer scheme, secondary low-temperature economizer scheme, bypass flue scheme and turbine boiler coupling scheme) were compared, and the key technical parameters and power saving effect were compared and analyzed. Moreover, a reference for the upgrading and technical transformation of energy conservation and emission reduction in China’s power industry was put forward. The results show that the exhaust gas temperature is reduced to 90 ℃, The coal consumption rate of power supply is reduced by 1.88 g/(kW⋅h) in the low-temperature economizer scheme, 2.16 g/(kW⋅h) in the secondary low-temperature economizer scheme, 2.29 g/(kW⋅h) in the bypass flue scheme, and 2.66 g/(kW⋅h) in the turbine boiler coupling scheme.

Guangyuan Liu, Yu Zhang, Tianshi Ming et al.

Electric vehicles face significant energy supply challenges due to long charging times and congestion at charging stations. Battery swapping stations (BSSs) offer a faster alternative for energy replenishment, but their deployment costs are considerably higher than those of charging stations. As a result, selecting optimal locations for BSSs is crucial to improve their accessibility and utilization. Most existing studies model the BSS location problem using deterministic and static approaches, often overlooking the impact of stochastic and dynamic factors on solution quality. This paper addresses the facility location problem for BSSs within a city network, considering stochastic battery swapping demand. The objective is to optimize the placement of a given set of BSSs to minimize demand loss. To achieve this, we first develop a mathematical programming model for the problem. Then, we propose a simulation optimization method based on a large neighborhood search framework to handle large-scale instances. To reduce the computational cost of simulations, Bayesian optimization is employed to solve the single-station allocation subproblem during the repair process. Numerical experiments demonstrate the efficiency of the proposed approach and highlight the importance of incorporating dynamic factors in decision-making.

Wen Shang, Yuan Liao, Vasilis Friderikos et al.

Due to their ability to anchor into tall urban landforms, such as lampposts or street lights, robotic aerial base stations (RABSs) can create a hyper-flexible wireless multi-hop heterogeneous network to meet the forthcoming green, densified, and dynamic network deployment to support, inter alia, high data rates. In this work, we propose a network infrastructure that can concurrently support the wireless backhaul link capacity and access link traffic demand in the millimeter-wave (mmWave) frequency band. The RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. Robotic base stations capitalize on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., 'follow' the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer linear problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. Compared to baseline models, which are heterogeneous networks with randomly deployed fixed small cells and pre-allocated RBs for wireless access and backhaul links, a wide set of numerical investigations reveals that robotic base stations could improve the served traffic demand. Specifically, the proposed mode serves at most 65\% more traffic demand compared to an equal number of deployed fixed small cells.

Yingchun Shi, Shu Cheng, Chunyang Chen et al.

The nature of renewable energy resources (RERs), such as wind energy, makes them highly unstable, unpredictable, and intermittent. As a result, they must be optimized to reduce costs and emissions, increase reliability, and also to find the optimal size and location for RERs and energy storage systems (ESSs). Microgrids (MG) can be modified using ESSs to gradually reduce traditional energy use. In order to integrate RERs in a financially viable scheme, ESSs should be sized and operated optimally. The paper presents an enhanced biogeography-driven optimization algorithm for optimizing the operations and sizes of battery ESSs (BESSs) taking into account MGs that experience wind energy penetration in a way that migration rates are adaptively adjusted based on habitat suitability indexes and differential perturbations added to migration operators. An optimization problem was applied to a BESS to determine its depth of discharge and lifespan. This paper considers three different scenarios in using simulations and compares them to existing optimization methods for the purpose of demonstrating the effectiveness of the offered scheme. Out of all the case studies examined, the optimized BESS-linked case study was the least expensive. We also show that a BESS must be of an optimum size to function both economically and healthily. For economic and efficient functioning of MGs, it has been shown that finding the optimum size of the ESS is important and potentially extends battery lifespan. The IBBOA obtained a more precise size for BESS’s volume, and the final outcomes are compared in this paper with other methods.