Power Operation Research Articles

Potentials and Limitations of Using Sentinel Data for Power System Operation and Control: Case Study of Protection Against Forest Fires and Aerosol Contamination

The power network is directly or indirectly exposed to various weather and climate conditions that can positively and negatively influence it. Forest fires and various aerosol contaminations may negatively affect the power network. Surface observations and measurements (e.g., weather stations) help protect against unfavorable influences, but the network is usually poor. That challenge may be solved by including satellite data in the power network planning, operation, and maintenance. This article presents the possibilities of using Sentinel mission’s data, at various temporal and spatial levels, for those purposes through examples of protection against forest fires and aerosol contamination. In the case of forest fires, satellite data enable a more-precise calculation of forest fire-spreading danger for areas with no installed weather stations, which may be used in the power system contingency analysis calculation. The analysis showed that aerosol contamination has a correlation with outage appearance in the 25 kV 50 Hz electric traction network. Sentinel 5p data may serve as a tool in a surface air-quality measurement data control to confirm/deny the high concentration of PM10 particles. Analysis showed that Sentinel data are favorable for power network operations on intra-day and day-ahead operations, as well as for long-term operations, which include planning and maintenance. The spatial resolution of Sentinel data is more than enough to fulfill the surface weather station network. Satellite data also enable the use of additional data that are not measured at ground stations.

High-power (500 mW) narrow-linewidth (21 kHz) low-RIN (−168 dB/Hz) distributed feedback laser

A high-power, narrow-linewidth, low-relative-intensity-noise (RIN) distributed feedback (DFB) laser is demonstrated. The laser employs four strained AlGaInAs quantum wells and three diluted waveguides to reduce internal loss, and enhance heat dissipation performance to reach a high power and low noise operation. At a test temperature of 15 °C, the DFB laser with a cavity length of 1.5 mm exhibited single mode output power of 517 mW in pulsed mode and 424 mW in continuous-wave (CW) mode. with far-field full width at half maximum (FWHM) divergence angles of 10°×20°. In addition, the DFB laser achieved an intrinsic linewidth of 21 kHz, and an ultra-low RIN of less than −168 dB/Hz.

State and culture in sustainability transformations: structural power, democracy, and the promise of a Marcusian cultural politics of critical meaning-making

The concept of sustainability transformation highlights the need not merely for new environmental policies but for transformational change of societal systems in response to the ecological crisis, including a profound shift in values, ideas, and cultural meanings. Yet, according to historical-institutionalist state theory, the imperatives of the liberal-capitalist democratic welfare state give rise to a “glass ceiling” of transformation that limits prospects for such systemic change beyond quality-of-life improvements that remain consistent with liberal capitalism. Exploring the potential of cultural meaning-making to break this glass ceiling, I draw on various strands of critical theory to identify the operation of ideological power at the level of both the state and the cultural realm as what creates the glass ceiling of transformation. This makes the cultural realm at once a possible emancipatory force and a site of domination of its own. I suggest that the critical thought of Herbert Marcuse offers an account of a critical-democratic culture that promises to arise precisely out of this ambiguity and that must be understood as a crucial foundation for sustainability transformation.

Access and sustainment of ELMy H-mode operation for ITER Pre-Fusion Power Operation plasmas using JINTRAC

Abstract In the initial stages of ITER operation, ELM mitigation systems need to be commissioned. This requires controlled flat-top operation in type-I ELMy H-mode regimes. Hydrogen or helium plasma discharges are used exclusively in these stages to ensure negligible production of neutrons from fusion reactions. With the expected higher L-H power threshold of hydrogen and helium plasmas compared to corresponding D and D/T plasmas, it is uncertain whether available auxiliary power systems are sufficient to operate in stable type-I ELMy H-mode. This has been investigated using integrated core and edge/SOL/divertor modelling with JINTRAC. Assuming that the L-H power threshold is well captured by the Martin08 scaling law, the presented simulations have found that 30 MW of ECRH power is likely required for the investigated hydrogen plasma scenarios, rather than the originally planned 20 MW in the 2016 Staged Approach ITER Baseline. However, past experiments have shown that a small helium fraction (~10 %) can considerably reduce the hydrogen plasma L-H power threshold. Assuming that these results extrapolate to ITER operation regimes, the 7.5MA/2.65T hydrogen plasma scenario is likely to access stable type-I ELMy H-mode operation also at 20 MW of ECRH.

Multi-Objective Power Supply Restoration in Distribution Networks Based on Graph Calculation and Information Collected by Multi-Source Sensors

With the increasing complexity of the distribution network structure, enhancing the efficiency and reliability during fault restoration has become a focal point. Based on the multi-source information collected by traditional sensors, such as CT and PT, and intelligent sensors, such as D-PMU, and the graph calculation model, the fault recovery problem of a multi-objective distribution network is studied. Firstly, a power flow calculation model and operation constraint adaptable to topology changes are proposed under the graph calculation framework. The minimum spanning tree theory is utilized to define the blackout range and recovery path set. Secondly, the intelligent sensor D-PMU is configured to collect fault information to ensure that at least one of any two connected load vertices is configured with D-PMU. Thirdly, a topological evolution model is established that considers repeated primary and secondary transfer in outage areas while exploring possible recovery strategies deeply. Finally, a distribution network in Shaanxi Province is taken as an example to verify the model. The experiment shows that the strategy in this paper dynamically adjusts the recovery strategy through four means—one transfer, one repeat transfer in the outage area, two transfers, and cutting off part of the outage load—and the overall recovery rate is increased by more than 20%.

The Interplay of Affect and Power Structures in Mountains May Depart

In recent years, affective theory has gained significant attention in Western film studies and gradually influenced Chinese cinema research. However, its application in Chinese film studies remains superficial, particularly in the lack of in-depth exploration of the political nature of affect and its interaction with power structures. As a representative of China's ‘Sixth Generation’ directors, Jia Zhangke is not primarily known for political narratives, but his works centre on social realities, implicitly revealing the oppression and influence of social power on individuals through the depiction of ordinary lives. This study applies affective theory to analyse the interaction between affect and power structures in Jia Zhangke’s Mountains May Depart. The analysis focuses on three dimensions: affect as the embodiment of power, affect as the physical manifestation of power, and affect as a means of resistance and self-rescue. Through specific analyses of the film texts, the study demonstrates that affective movement is both an important medium for emotional expression and a key to the operation and resistance of power in film narratives. The study finds that Mountains May Depart fuses individual emotions with social power through the multidimensional expression of affective movement. The affective movement not only demonstrates the implicit operation of power, but also expresses the individual's resistance to social oppression and the reconstruction of subjectivity through the transformation of emotions.

Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network

Accurate and comprehensive wind speed forecasting is crucial for improving efficiency in offshore wind power operation systems in coastal regions. However, raw wind speed data often suffer from noise and missing values, which can undermine the prediction performance. This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). Using wind speed data from the Shidao, Xiaomaidao, and Lianyungang stations as case studies, a fitness function based on the Pearson correlation coefficient was developed to optimize the VMD mode count and penalty factor. A comparative analysis of different Intrinsic Mode Function (IMF) selection ratios revealed that selecting a 50% IMF ratio effectively retains the intrinsic information of the raw data while minimizing noise. For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. The results consistently showed that the Seq2Seq-Attention model achieved superior predictive accuracy across all forecast horizons, with the correlation coefficient of its prediction results greater than 0.9 in all cases. The proposed VMD-RUN-Seq2Seq-Attention framework outperformed other methods in the denoising, data cleansing, and reconstruction of the original wind speed dataset, with a maximum improvement of 21% in accuracy, producing highly accurate and reliable results. This approach offers a robust methodology for improving data quality and enhancing wind speed forecasting accuracy in coastal environments.

Overview of Offshore Wind Power Technologies

Optimizing offshore wind power technology and reducing the levelized cost of electricity throughout the lifecycle are key measures for the large-scale development of offshore wind power, contributing significantly to the transition toward sustainable energy systems. However, compared to onshore wind power, the internal flow dynamics of offshore wind farms are more complex, which poses challenges for operation and maintenance. Therefore, there is an urgent need for updated, smarter, more efficient, and economic offshore intelligent operation control technologies to facilitate the large-scale development and utilization of offshore wind power. This paper approaches the topic from two perspectives, offshore wind turbines and offshore wind farms, introducing popular research directions and technical bottlenecks in these two related fields. This includes offshore wind turbine capacity development and fundamental technologies, offshore wind power forecasting technology, and offshore wind power operation and control technology, offshore intelligent operation and maintenance technology, as well as offshore wind power and integrated marine area utilization technology. Firstly, the challenges faced by the intensive development of offshore wind resources and operational environments are analyzed. Secondly, the challenges encountered in the aforementioned technological areas and their potential solutions are summarized. Finally, a systematic reflection and outlook on the large-scale development of offshore wind power are provided, reinforcing its critical role in achieving global sustainability goals.

Graphene/TiO2 Heterostructure Integrated with a Micro-Lightplate for Low-Power NO2 Gas Detection.

Low-power gas sensors that can be used in IoT (Internet of Things) systems, consumer devices, and point-of-care devices will enable new applications in environmental monitoring and health protection. We fabricated a monolithic chemiresistive gas sensor by integrating a micro-lightplate with a 2D sensing material composed of single-layer graphene and monolayer-thick TiO2. Applying ultraviolet (380 nm) light with quantum energy above the TiO2 bandgap effectively enhanced the sensor responses. Low (<1 μW optical) power operation of the device was demonstrated by measuring NO2 gas at low concentrations, which is typical in air quality monitoring, with an estimated limit of detection < 0.1 ppb. The gas response amplitudes remained nearly constant over the studied light intensity range (1-150 mW/cm2) owing to the balance between the photoinduced adsorption and desorption processes of the gas molecules. The rates of both processes followed an approximately square-root dependence on light intensity, plausibly because the electron-hole recombination of photoinduced charge carriers is the primary rate-limiting factor. These results pave the way for integrating 2D materials with micro-LED arrays as a feasible path to advanced electronic noses.

Understanding fatigue and recovery mechanism in Hf0.5Zr0.5O2 capacitors for designing high endurance ferroelectric memory and neuromorphic hardware

Novel non-volatile memories are under intense investigation to revolutionize information processing for ultra energy-efficient implementation of artificial intelligence and machine learning tasks. Ferroelectric memories with ultra-low power and fast operation,...

Upgrade of the CMS Drift Tube electronics for the High Luminosity LHC

The High Luminosity LHC (HL-LHC) upgrade mandates a comprehensive overhaul of the CMS Drift Tubes (DT) electronics due to trigger rates surpassing current capabilities. Accordingly, a new DT electronics has been designed that not only overcomes the readout limitations but also, will allow to improve the performance of the muon barrel trigger system by increasing the resolution and reducing the fake rates. The on-detector part of this new electronics will continue to perform the time digitization of the DT chamber signals with the required 0.78 ns but will achieve factors closer to 10 in terms of integration, reducing the power dissipation by half. The core of this new on-detector electronics is the On-detector Board for DT (OBDT) board that will allow integrating up to 240 time digitization channels and forward them without data losses, with ∼10 W of power consumption and operating under radiation environment. The backend system will be based in Advanced Telecommunications Computing Architecture (ATCA) boards and for the first time we have successfully implemented the DT trigger primitive algorithm capable of using the OBDT streamline data and performing the DT muon track reconstruction in the available latency and with very similar to offline resolution. The deployment of OBDTs in a sector of the CMS has allowed to obtain the first results that validate the new architecture and its performance. Rigorous testing procedures have been conducted to ensure the suitability of these systems for the demanding operating conditions of the HL-LHC, with a primary emphasis on functionality and reliability. This report outlines an innovative approach to DT electronics, promising enhanced performance in a challenging HL-LHC environment.

Interval-Valued Intuitionistic Fuzzy Yager Power Operators and Possibility Degree-Based Group Decision-Making Model

As an extended form of intuitionistic fuzzy set, the theory of interval-valued intuitionistic fuzzy set (IVIFS) can describe fuzziness more flexibly. This study aims to develop a group decision-making model based on the distance measure, Yager power aggregation operators and the possibility measure in the context of IVIFSs. For this purpose, new distance measure is proposed to quantify the dissimilarity between two IVIFSs. In addition, comparison with existing distance measures is performed to illustrate the efficiency of introduced measure. Combining the Yager’s triangular norms with the proposed distance-based power operators, a series of interval-valued intuitionistic fuzzy (IVIF) Yager power aggregation operators are introduced with their desirable properties. Moreover, a possibility measure is developed for pairwise comparisons of IVIFSs, which overcomes the shortcomings of existing IVIF-score function, IVIF-accuracy function, and IVIF-possibility measures. The developed possibility measure is further utilized to compute the weights of criteria. To prove the practicality and effectiveness of introduced model, it is applied to a case study of manufacturing plant location selection problem with IVIF information. Finally, sensitivity and comparative analyses are carried out to test the stability and robustness of the proposed method under the setting of IVIFSs.

Minor actinides transmutation in pressurized water reactors. 2. Using uranium and thorium fuel to burn minor actinides in a system with several VVER reactors

There is currently a consensus among the scientific and engineering community regarding the solution to the problem of minor actinides (MAs) formed in the process of nuclear power operation: MAs need to be converted to fission products during burnup in power reactors. Fast neutron reactors (BN, BREST) and molten salt reactors (MSR) are considered largely to this end. Despite the advantages of using fast reactors, there are no currently power unit designs with BN or BREST reactors available for commercial operation. The possibility of using VVER reactors for this purpose is rarely covered in scientific literature, despite the fact that the technology of light water reactors has long been mastered, and preparing a VVER-based MA burner reactor is technically simpler than on the basis of a pilot commercial BN technology or BREST or MSR reactors at different R&amp;D stages. In the previously published first part of the study by Kazansky and Karpovich 2022, the fuel cycle for VVER reactors was investigated: minor actinides obtained during reactor operation were extracted from spent nuclear fuel and added to fresh fuel for the same reactor. It has been found that implementing this cycle and bringing the concentration of MAs up to 4 wt. % makes it possible to reduce the amount of minor actinides produced in the VVER reactor by a factor of 8, and without a loss in the NPP unit power generation. This paper investigates, based on an idea of closing the fuel cycle in terms of minor actinides, the relationship between the MA burnup depth in a VVER-1200 reactor and the enrichment of fresh fuel, the dynamics of unloaded heavy nuclei, and the amount of MAs in additionally loaded fuel. Under investigation are two types of additionally loaded fuel with oxides of minor actinides added to it: based on enriched uranium dioxide or a mixture of (1 – x) 232ThO2+x (96% UO2) (Kazansky et al. 2023). At the same time, the number of heavy nuclei in fuel does not change when there is a change in the number of loaded MA nuclei, which is measured in the number of the VVER-1200 reactors “served” (the mass of accumulated MA nuclei is about 1% and is removed annually from spent fuel). The number of minor actinides entering fuel is kept at a level that allows having a reactivity margin for the reactor operation at a power of over 2% before the annual refueling. Calculations were undertaken using a fuel assembly model with fuel elements broken down into a number of groups with different fuel burnup depths to simulate the actual loading of the reactor core. This model makes it possible to avoid the power peaking effects and give an answer about the neutronic characteristics of the fuel cycle involving MAs. The calculations made it possible to make a number of important conclusions: more refueling cycles lead to a dynamic equilibrium taking place between the minor actinide amounts loaded and burnt; the number of the reactors served is directly proportional to the enrichment of the make-up fuel and a 20% UO2 enrichment makes it possible to serve up to 23 VVER-1200 reactors, while using 80% 232ThO2 + 20% (96% UO2) fuel allows 17 VVER-1200 reactors to be served; using MAs extracted from SNF with a burnup of 20 to 50 MW*day/kg leads to the MA composition effect on the reactivity balance being within 4 to 5 βeff.

Realizing linear synaptic plasticity in electric double layer-gated transistors for improved predictive accuracy and efficiency in neuromorphic computing

Abstract Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses with their ionic response and offer low power operations, making them suitable for neuromorphic applications. A critical consideration for artificial neural networks (ANNs) is achieving linear and symmetric plasticity (i.e. weight updates) during training, as this directly affects accuracy and efficiency. This study uses finite element modeling to explore EDLTs as artificial synapses in ANNs and investigates the underlying mechanisms behind the nonlinear weight updates observed experimentally in previous studies. By solving modified Poisson–Nernst–Planck equations, we examined ion dynamics within an EDL capacitor and their effects on plasticity, revealing that the rates of EDL formation and dissipation are concentration-dependent. Fixed-magnitude pulse inputs result in decreased formation and increased dissipation rates, leading to nonlinear weight updates. For a pulse magnitude of 1 V, both 1 ms 500 Hz and 5 ms 100 Hz pulse inputs saturated at less than half of the steady state EDL concentration, limiting the number of accessible states and operating range of devices. To address this, we developed a predictive linear ionic weight update solver (LIWUS) in Python to predict voltage pulse inputs that achieve linear plasticity. We then evaluated an ANN with linear and nonlinear weight updates on the Modified National Institute of Standards and Technology classification task. The ANN with LIWUS-provided linear weight updates required 19% fewer (i.e. 5) epochs in both training and validation than the network with nonlinear weight updates to reach optimal performance. It achieved a 97.6% recognition accuracy, 1.5–4.2% higher than with nonlinear updates, and a low standard deviation of 0.02%. The network model is amenable to future spiking neural network applications, and the performance with linear weight updates is expected to improve for complex networks with multiple hidden layers.

Optimal Scheduling Strategy for Mobile Charging Stations Considering Long and Short-term Uncertainties

Trunk Mobile charging stations (TMCS) have garnered significant attention domestically and internationally due to their excellent mobility and flexible scheduling capabilities. However, their potential application value has not been fully explored. This paper proposes a two-stage collaborative optimization scheduling strategy for TMCS to coordinate their operation in providing charging services for electric vehicles (EVs) and engaging in energy arbitrage with the power grid. In the day-ahead optimization phase, the growth curve function simulates the future development trend of EVs, and a stochastic optimization method determines the collaborative operation plan of TMCS. In the real-time scheduling phase, the chance-constrained method adjusts and updates the TMCS schedule based on real-time traffic and power operation data, resulting in an optimized TMCS scheduling outcome considering short-term uncertainty. Case analysis shows that the proposed method effectively addresses the impact of long- and short-term uncertainties on the TMCS scheduling plan, ensuring the quality of charging services while guaranteeing the profitability of charging facility operators.

544-nm Tb3+-doped LiYF4 laser pumped by 488-nm InGaN laser diodes and high peak power operation with cavity-dumped Q-switching

We report a 544-nm Tb3+-doped LiYF4 (Tb:YLF) laser pumped by two ∼488-nm, 2-W InGaN laser diodes in continuous-wave (CW) and cavity-dumped Q-switch operations. We obtained a CW output power of 482 mW with a slope efficiency of 43%. Cavity-dumped Q-switching generated 6-ns pulses with a pulse energy of 40 µJ at a repetition frequency of 100 Hz, resulting in the highest peak power of 6.7 kW ever reported for Tb3+-based lasers, to our knowledge. We analytically examined the influence of excited-state absorption and energy transfer upconversion on the small-signal gain of a Tb:YLF laser and concluded that their influence was negligible under our operating conditions.

X-point radiator control and its dynamics in ASDEX Upgrade and JET deuterium–tritium discharges

Abstract Control of heat exhaust is essential for the operation of power producing fusion reactors. Here, we present results of heat exhaust feedback control experiments in JET and AUG. In JET, we demonstrate the first X-point radiator (XPR) control in DD and DT discharges using argon seeding. In AUG, we improve the XPR control with nitrogen seeding, resulting in achieving the first detached L-H and H-L transition (in a single discharge). The controllers are designed using a model-based design procedure. The required models are obtained experimentally using perturbative (system identification) experiments. We study the dynamic response of the XPR to various seeding species and varying operating conditions. We find that the sensitivity (relative gain) of the XPR varies as function the height of the radiator inside the confined region but that the relative phase is consistent for all operating points. In AUG, the XPR is also less sensitive to impurity seeding changes for higher heating powers. In JET, we show that the XPR dynamics are the same for DD and DT plasmas. However, we observe that XPR control is only possible with argon and not with neon. The results show that a controller might well be designed in earlier stages of operation of a future device, but remains applicable and can be further tuned for full power operation.

Compressor Development for CO2-Based Pumped Thermal Energy Storage (PTES) Systems

Abstract Pumped thermal energy storage (PTES) offers a cost-effective means to store electrical energy for long duration by utilizing a heat pump cycle to transfer thermal energy from a low temperature reservoir (LTR) to a high temperature reservoir (HTR). The heat pump compressor, which represents a significant driver to the cost, performance and operating characteristics of the PTES system. During charging (&amp;gt;100 MW), traditional compressor scaling charts indicate that the operating conditions needed would be best served by a multistage axial compressor. The conceptual design of a large-scale CO2 axial compressor was completed, including mean-line estimates of the compressor performance at full power conditions. At steady state, full power operation, the isentropic efficiency and mechanical efficiency of the compressor have significant impact on the cycle design and round trip efficiency of the PTES system. The results of the conceptual design were used to refine the PTES cycle design and updated operating conditions provided for further aero design optimization. An important characteristic of the PTES system is its ability to charge at variable rate, which provides significant challenges on compressor operability, especially for a compressor that will be coupled to a fixed-speed synchronous motor. Cycle studies of variable charging rate processes have been conducted, and the impact of compressor operating map characteristics explored. Based on initial modeling studies, single compressor operation can be achieved down to at least 50% of rated power, with further reductions possible depending on the characteristics of the compressor map speedlines.

Modeling Daily Load Curve Parameters for a University under Prevailing Power Supply Conditions in Nigeria’s Local Grids Using Probabilistic Statistics

This study addressed an identified issue of representative daily load curves and parameters of Nigerian higher academic institutions and communities to eliminate prevailing application of assumed models that are representative of other climes with reliable power supply. Normal probabilistic distribution statistic, relative frequency statistic and per unit maximum load concept algorithms were composed and applied to meter-logged experiential daily load data sets with apparent inconsistent patterns and chaotic cluster. This made it impossible for local power operators to determine a representative daily load pattern for local generation planning and load management. The relative statistic-based model was used for comparison purpose. With a root mean square error of 5.6%, the probabilistic statistical algorithm was able to produce desired standard representative curve and parameters for a typical and relevant academic institution of technology in the Nigerian space, indicating a peak load at about 13:00 hour and a load factor of 0.54. The composed and applied methodology can be used for characterizing other load points of interest and categories in the region.

Time-dependent low-rank input–output operator for forced linearized dynamics with unsteady base flows

Understanding the linear growth of disturbances due to external forcing is crucial for flow stability analysis, flow control, and uncertainty quantification. These applications typically require a large number of forward simulations of the forced linearized dynamics, often in a brute-force fashion. When dealing with simple steady-state or periodic base flows, there exist powerful and cost-effective solution operator techniques. Once these solution operators are constructed, they can be used to determine the response to various forcings with negligible computational cost. However, these methods do not apply to problems with arbitrarily time-dependent base flows. This paper develops and investigates reduced-order modelling with time-dependent bases to build low-rank solution operators for forced linearized dynamics with arbitrarily time-dependent base flows. In particular, we use forced optimally time-dependent decomposition (f-OTD), which extracts the time-dependent correlated structures of the flow response to various excitations. Several demonstrations are included to illustrate the utility of the f-OTD low-rank approximation for performing global transient stability analysis. Additionally, we demonstrate the application of f-OTD in computing the post-transient response of linearized Navier–Stokes equations to a large number of impulses, which has applications in flow control.