Power Scenarios Research Articles

SALW-Net: a lightweight convolutional neural network based on self-adjusting loss function for spine MR image segmentation.

Segmentation of intervertebral discs and vertebrae from spine magnetic resonance (MR) images is essential to aid diagnosis algorithms for lumbar disc herniation. Convolutional neural networks (CNN) are effective methods, but often require high computational costs. Designing a lightweight CNN is more suitable for medical sites lacking high-computing power devices, yet due to the unbalanced pixel distribution in spine MR images, the segmentation is often sub-optimal. To address this issue, a lightweight spine segmentation CNN based on a self-adjusting loss function, which is named SALW-Net, is proposed in this study. For SALW-Net, the self-adjusting loss function could dynamically adjust the loss weights of the two branches according to the differences in segmentation results and labels during the training; thus, the ability for learning unbalanced pixels is enhanced. Two separate datasets are used to evaluate the proposed SALW-Net. Specifically, the proposed SALW-Net has fewer parameter numbers than U-net (only 2%) but achieves higher evaluation scores than that of U-net (the average DSC score of SALW-Net is 0.8781, and that of U-net is 0.8482). In addition, the practicality validation for SALW-Net is also proceeding, including deploying the model on a lightweight device and producing an aid diagnosis algorithm based on segmentation results. This means our SALW-Net has clinical application potential for assisted diagnosis in low computational power scenarios.

Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather

In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning.

Modeling and analysis of the utilization of common gas resources between Iran and Qatar, considering various scenarios of resource distribution and extraction power: а game theory approach

Modeling and analysis of the utilization of common gas resources between Iran and Qatar, considering various scenarios of resource distribution and extraction power: а game theory approach

YOLOv5s-MC: Lightweight road target detection network

For the problem of large number of target detection algorithm parameters, a lightweight real-time detection algorithm YOLOv5s-MC based on improved YOLOv5s road scenes is proposed. firstly, CA attention is added to the model to improve the sensitivity of the network to detect targets; secondly, in the feature fusion network, add adaptive weight parameters using AS-Concat structure are added to better fuse the feature information of different layers and improve the detection accuracy of the algorithm ; adding a small target detection layer to improve the detection accuracy of tiny targets; finally introducing Mobilnetv2, a lightweight network, as the overall backbone layer to realize the lightweight requirement of the network; to verify the advantages of the proposed algorithm, experiments were conducted on the kitti dataset. The experimental results show that the proposed algorithm, compared with the original network, improves the average accuracy by 0.2% with 55.8% less parameters and 33.7% less computation, and the detection speed reaches 35 FPS, which meets the requirements of real-time detection and improves the ability of algorithm deployment in weak hardware computing power scenarios to a certain extent.

Study on the synergistic regulation strategy of load range and electrolysis efficiency of 250 kW alkaline electrolysis system under high-dynamic operation conditions

Alkaline water electrolysis (AWE) has the highest technological maturity among all the water electrolysis technologies for hydrogen production, however, reducing the minimum load boundary and improving the electrolysis efficiency are the technical challenges of the AWE system that still exist and urgently require optimization. The minimum load is primarily limited by the hydrogen to oxygen (HTO) from cross-diaphragm transfer and lye mixing, with HTO above 2.0% being a significant safety risk. Reducing the lye flow rate and pressure are effective while two of the few ways by regulating the operating parameters to improve the HTO thus extend the minimum load boundary, but will worsen electrolysis efficiency. Therefore, this study proposes a synergistic regulation strategy of pressure and lye flow rate: maximizing pressure and lye flow rate during high load period to ensure high electrolysis efficiency; adjusting lye flow rate and pressure during medium load period to ensure HTO≤2.0% and maximize the electrolysis efficiency; and reducing lye flow rate and pressure to a low level during the low load period to broaden the minimum load so as to improve overall efficiency of AWE system when loading with fluctuant green electric. This work elaborates the HTO routes, influencing factors and parameter optimization mechanism by building a system-level steady-state and dynamic gas purity model. The optimal combination curve of pressure and lye flow rate is obtained and its control effect on performance parameters, in terms of minimum load, system energy consumption, energy utilization, electrolysis efficiency and so on, is compared and verified in high dynamic wind and photovoltaic (PV) power scenarios. Finally, the optimal wind & PV power ratios are explored based on the optimal operation curve, which will provide a reference for the future large-scale development of hydrogen production scenarios direct-coupled with wind and PV power. The minimum load is extended from 42.0% in the lye flow rate alone control to 21.2% in the pressure alone control and finally to 15.6% in the lye flow rate and pressure synergistic control method. In the absence of electrical replenishment, wind and PV energy utilization efficiency can reach up to 98.3% and 95.6%, respectively.

Predictive modeling of the JET D–T plasma boundary

AbstractThe SOLPS‐ITER3.0.8 code with new Grad‐Zhdanov module was applied for predictive modeling of tritium plasma and deuterium‐tritium mixture in tokamak JET. The moderate power scenario of shot JPN #85278 modeled earlier for deuterium plasma was taken as a starting point. Effect of puffing increase, which was applied in experiment, on divertor conditions and inter‐ELM tungsten sputtering for both working gases and on isotope separation in their mixture is analyzed. Both for attached and for semi‐detached outer targets, the tritium plasma shows moderate increase in SOL width compared to deuterium one. The tungsten sputtering at the outer target in the inter‐ELM period for tritium is an order of magnitude bigger than that for deuterium. The D–T separation in the D–T mixture is moderate.

Effective hybrid search technique based constraint mixed-integer programming for smart home residential load scheduling

In this paper, the problem of scheduling smart homes (SHs) residential loads is considered aiming to minimize electricity bills and enhance the user comfort. The problem is addressed as a multi-objective constraint mixed-integer optimization problem (CP-MIP) to model the constrained load operation. As the CP-MIP optimization problem is non-convex, a novel hybrid search technique, that combines the Relaxation and Rounding (RnR) approach and metaheuristic algorithms to enhance the accuracy and relevance of decision variables, is proposed. This search technique is implemented through two stages: the relaxation stage in which a metaheuristic technique is applied to get the optimal rational solution of the problem. Whereas, the second stage is the rounding process which is applied via stochastic rounding approach to provide a good-enough feasible solution. The scheduling process has been done under time-of-use (ToU) dynamic electricity pricing scheme and two powering modes (i.e., powering from the main grid only or powering from a grid-tied photovoltaic (PV) residential power system), in addition, four metaheuristics [i.e., Binary Particle Swarm Optimization (BPSO), Self-Organizing Hierarchical PSO (SOH-PSO), JAYA algorithm, and Comprehensive Learning JAYA algorithm (CL-JAYA)] have been utilized. The results reported in this study verify the effectiveness of the proposed technique. In the 1st powering mode, the electricity bill reduction reaches 19.4% and 20.0% when applying the modified metaheuristics, i.e. SOH-PSO and CL-JAYA, respectively, while reaches 56.1%, and 54.7% respectively in the 2nd powering scenario. In addition, CL-JAYA superiority is also observed with regard to the user comfort.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of wind- driven generators, a min-max-min two-stage robust optimization model is presented, considering the unit commitment, source-network load collaboration, and control of the load demand response. After the constraint functions are linearized, the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method. The minimum-maximum of the original problem was continuously maximized using the iterative method, and the optimal solution was finally obtained. The constraint conditions expressed by the matrix may reduce the calculation time, and the upper and lower boundaries of the original problem may rapidly converge. The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately; otherwise, it is easy to cause excessive accommodation of wind power at some nodes, leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power. Thus, the most economical optimization scheme for the worst scenario of the output power of the generators is obtained, which proves the economy and reliability of the two-stage robust optimization method.

Scenario Reduction of Power Systems with Renewable Generations Using Improved Time-GAN

To investigate the uncertainties and spatiotemporal complexities of renewable energy represented by wind and photovoltaic, a scenario reduction of power systems with renewable generations uses improved time series generative adversarial networks (Time GAN). The long short-term memory neural network is used to construct the generative adversarial networks, and the time-series supervision loss function and generative adversarial loss function are introduced to jointly optimize the generator network for better generating the daily renewable energy power scenarios. Based on the results of scenario generation, the silhouette coefficient method is used to improve K-means for constructing a scenario reduction model. Finally, the case analysis shows that the proposed method can obtain typical renewable energy power scenarios with spatiotemporal correlation and provide a reference for the analysis of power system operation scenarios.

Two-stage Robust Optimization Scheduling Considering Wind Uncertainty and Multi-area Frequency Dynamic Safety

The large-scale wind power grid connection exacerbates the problem of uneven distribution of inertia between different regions, posing challenges to the frequency security defense of multi-area systems. This article proposes a two-stage robust optimization scheduling model that considers both the uncertainty of wind power output and the dynamic safety of multi-area frequencies. Firstly, according to the conditional value at risk theory, the operating cost and operating risk of the multi-region system are optimized, and the acceptable safety boundary of wind power is obtained. Secondly, multi-zone frequency dynamic safety verification is carried out in harsh wind power scenarios. Decouple the reserve capacity into non-accident reserve and accident reserve, and nest them separately in a two-stage model to achieve clear solution ideas. Finally, the two-stage optimization problem is solved through the column and constraint generation algorithm. The simulation results of numerical examples show that the model and method can effectively improve the frequency response capability of the multi-region system, so as to ensure the safe and economical operation of the system.

A SSSC optimal configuration method to enhance available transfer capability considering multi‐wind farm access

AbstractIn the context of electricity market, the heavy load of some inter‐provincial and intra‐provincial lines limits available transfer capability (ATC). As a series compensation device, static synchronous series compensator (SSSC) can reallocate the power flow of adjacent lines. In order to maximize the inter‐regional ATC, a two‐stage optimal SSSC allocation method considering the uncertainty and correlation of multi‐wind farm is proposed. Firstly, the initial wind power scenarios are generated based on latin hypercube sampling (LHS) and reduced by the improved iterative self‐organizing data analysis techniques algorithm (I‐ISODATA). Then, a multi‐objective optimization model is constructed with the maximum expected power flow transfer entropy (PFTE) and minimum expected SSSC investment cost in all typical wind power scenarios, and non‐dominated sorting genetic algorithm‐II (NSGA‐II) method is used to determine the location of SSSC. In addition, based on the reduced wind power scenarios, the ATC calculation model containing SSSC is constructed, and the modern interior point method is used to determine the control parameters and configuration capacity of SSSC. Finally, based on the improved IEEE39 and IEEE118 node systems, case study proves that the proposed model can determine the optimal SSSC configuration scheme and significantly improves the ATC.

Top-Down Thickness Reduction Synthesis of Biomass-Derived Carbon Nanosheets with Hierarchical Pore Structure for High-Performance Supercapacitors.

Carbon electrode materials with superior rate performance are highly demanded in application scenarios of high power output/input, especially when paired with organic electrolyte for extended voltage window and high energy storage. By extracting thin sheets of entangled cellulose fibers from cell wall structures, porous carbon nanosheets as templated from the cellulose sheets are synthesized. Evident thickness reduction effect has been demonstrated with thickness reduced from several micrometers to several nanometers of the obtained thickness-reduced activated carbon nanosheets (TRAC), which endow the material with a large surface area and high pore volume. The obtained TRAC exhibits significantly enhanced ion diffusion kinetics and superior rate capability thanks to the shortened diffusion pathway and suitable pore size distribution. Effects of sonication time have also been investigated, showing a trade-off between ion diffusion kinetics and pseudocapacitive contribution. This thickness-reduction method is extendable to many other biomass sources as cellulose sheets are widely existed in nature, offering a practical and easy-to-implement strategy of fabricating porous carbon nanostructures for efficient energy storage and utilization.

Optimal Scheduling of Virtual Power Plant Considering Revenue Risk with High-Proportion Renewable Energy Penetration

Distributed power supplies have gradually become a new trend in power supply development, but access to a large number of distributed energy sources has a certain impact on the stable operation of the power grid. A virtual power plant (VPP) can integrate a variety of distributed power sources for coordination and optimization; thus, it can effectively solve the difficulties faced by a distributed energy grid connection and promote the complementarity of energy sources. However, renewable energy often has a degree of volatility and randomness when distributed, which can bring certain risks to the operation of the VPP. In order to consider the risks brought by renewable energy, an optimal scheduling model of the VPP, based on an improved generative adversarial network (GAN) and the conditional value at risk (CVaR), was proposed to measure the relationship between the benefits and risks. Firstly, the uncertainty of new energy is analyzed, and wind power and photovoltaic scenarios are generated by the improved GAN; then, typical scenarios are generated by the k-medoids method. Finally, based on the CVaR, the optimal scheduling model of the VPP is established to study the effect of risk weight on VPP revenue. The results show that the model can effectively measure the relationship between the benefits and risks and can provide some references for the VPP to make reasonable operational decisions with different risk preferences.

Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining

The perception of anomalies in power scenarios plays a crucial role in the safe operation and fault prediction of power systems. However, traditional anomaly detection methods face challenges in identifying difficult samples due to the complexity and uneven distribution of power scenarios. This paper proposes a power scene anomaly perception method based on high-resolution networks and difficult sample mining. Firstly, a high-resolution network is introduced as the backbone for feature extraction, enhancing the ability to express fine details in power scenarios and capturing information on small target anomaly regions. Secondly, a strategy for mining difficult samples is employed to focus on learning and handling challenging and hard-to-recognize anomaly samples, thereby improving the overall anomaly detection performance. Lastly, the method incorporates GIOU loss and a flexible non-maximum suppression strategy to better adapt to the varying sizes and dense characteristics of power anomaly targets. This improvement enables higher adaptability in detecting anomalies in power scenarios. Experimental results demonstrate significant improvements in power scene anomaly perception and superior performance in handling challenging samples. This study holds practical value for fault diagnosis and safe operation in power systems.

Multi-criteria thermo-economic analysis of solar-driven tri-generation systems equipped with organic Rankine cycle and bottoming absorption refrigeration and Kalina cycles

Optimal thermo-economic integration of renewable energy sources with multi-generation energy systems is a prime research topic today. The present study proposes a multi-criteria evaluation method of such integration, based on combined heating and power (CHP), and combined cooling and power (CCP) scenarios, for three different solar intensities. Three novel solar-driven tri-generation systems are selected. They include different organic Rankine cycle (ORC) architectures and a Kalina cycle system (KCS) and a double-effect absorption refrigeration cycle as bottoming cycles. Evaluation of the tri-generation systems, both with and without the KCS system, indicates a performance improvement of up to 23% in various thermoeconomic characteristics when the KCS system is present. Selection of the suitable tri-generation system for each condition and optimization of the working fluid are carried out based on a multi-attribute decision-making method. P-xylene is found as the optimal organic working fluid for ORC and ORC (ORC integrated with internal heat exchanger) based systems, and benzene for the regenerative ORC-based system in both CHP and CCP scenarios. Multi-criteria analysis shows that ORC-based system outperforms other systems with net outranking flow of 0.44 (0.39) for CHP (CCP) application. The optimal configuration gives 95.6 M$ and 1.99 years for net present value and dynamic payback period, and 83.03% and 34.55% for energy and exergy efficiencies, respectively.

Hybrid Reinforcement Learning for Power Transmission Network Self-Healing Considering Wind Power.

Transmission network self-healing considering uncertain wind power becomes crucial with increasing penetration of wind power. A hybrid reinforcement learning (HRL) method combining offline self-learning with online Monte Carlo tree search (MCTS) is designed to deal with the strong uncertainty induced by wind power restoration. The HRL method trains a policy network with offline self-learning based on historical wind and transmission system data. It then applies the policy network to guide MCTS to realize step-by-step transmission network self-healing based on real-time and forecast data in different wind power scenarios. Besides, a model predictive control method for active power dispatch is proposed to improve wind power generation credibility during self-healing. Simulation results of both test and real-life power systems demonstrate that the proposed method can realize online transmission system self-healing reliably. Comparisons among different reinforcement learning methods indicate that the number of scenarios dominated by HRL is more than twice that dominated by MCTS and a dozen times that dominated by deep Q-network. Meanwhile, the online method is more flexible in uncertain wind power scenarios than optimization methods.

Rethinking colonialism through early modern global diplomacy: A tale of Pampangan mobility

AbstractThis study is an intervention in early modern global diplomacy. Integrating an indigenous community of the Philippines into foreign relations and maritime connections, the article reevaluates the complex story of the Pampangans of Luzon, allegedly long-term allies of the Spanish conquerors, and the narrative of indigenous collaboration. Foregrounding the Pampangans’ involvement in military campaigns, as well as territorial and maritime expansion in the early decades of the 1600s, the article introduces three scenarios of Pampangan power bargaining with global consequences. The focus on Pampangan foreign relations opens new analytical perspectives on the role of language and knowledge for internal coloniality on the one hand, foreign and diplomatic negotiations on the other. Methodologically, it proposes a deep (re-)reading of the polyvocal archive of the colonial-indigenous encounter and integrates insights with the largely separated scholarship of diplomatic and indigenous history as a new avenue in global history.

First Prototype of the Long Orbit Nested Corrector for HL-LHC

2.5-m-long nested orbit correctors (so-called MCBXFA) are necessary for the upgrade of LHC, in the framework of HL-LHC project. These magnets provide 2.1 T in vertical and horizontal direction and they share the same cross-section of their shorter version MCBXFB, for which two prototypes and the first two series magnets have been manufactured and tested. In this paper we first present the specific aspects of MCBXFA magnet design: (i) field quality in the different powering scenarios, and (ii) mechanical analysis, with special attention to torque under combined operation. We then overview the manufacturing techniques, which are very similar to those used for MCBXFB, pointing out some difficulties that have arisen during the coil vacuum impregnation. The components of the prototype magnet are being produced by CIEMAT and the magnet will be assembled at CERN facilities.

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems.

IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global-Local Fusion.

Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON.