Power Scenarios Research Articles

Scaling of HeatLMD-simulated impurity outflux from COMPASS-U liquid metal divertor

Abstract The Liquid Metal Divertor (LMD) concept offers a promising solution to manage extreme heat loads in plasma devices. This study presents predictive simulations using the HeatLMD model for the COMPASS-U tokamak with a full toroidal liquid metal divertor, expected to achieve reactor-relevant divertor heat flux densities. We derive the scaling of the Li|Sn outflux over 7 assumed independent parameters, transferable to other tokamaks. Its transport to LCFS (via ERO2.0) and its radiation (via Aurora and FACIT) predict acceptably low lithium concentration and negligible plasma cooling. However, for tin, the medium power scenario requires backside cooling beyond the capability of ITER-like water-cooled divertor, though a temporary heat absorber can approximate this for a 1-second plasma pulse. For incident divertor power exceeding 2 MW and strike point Te<10 eV, HeatLMD predicts significant tin plasma radiative disruption.

Resisting or negotiating wind energy dispossession: class and caste in changing rural Kutch

ABSTRACT This article analyses the political reactions of Kutch district villagers, in Western India, facing wind turbine projects as a complex political terrain of resistance shaped by the materiality of dispossession and local configurations of agrarian power. I argue that the terrain of resistance got diluted and dispersed towards individual actions aimed at negotiating dispossession through caste capital, while other power and caste scenarios have rendered collective resistance movements possible, up to a certain point. Deploying a class-caste analysis of land conflicts in rural India, this reveals continuity not only with traditional agrarian struggles but also with caste politics and excluding forms of mobilisation.

Addressing the limitation of Operational Flexibility in Power Generation with Pumped Hydro Energy Storage System – Indian Perspective

The global climate change scenario has impacted and taken corrective measures in shifting the sourcing of primary energy and efficient end-use pattern with more focus to utilise renewable energy resources to replace the depleting fossil fuels. Electric power generation capacity in the Indian subcontinent has gradually been improved to substantially contribute to intermittent and variable renewable power generation mix up to 40% of grid demand. However, the injection of grid-integrated renewable power as per the availability of generation potential will necessitate limiting the power generation from generating plants running on fossil fuel. But the operational flexibility of fossil fuel power plants is mostly designed to run efficiently with a 55% minimum technical load on the machines. In the above situation, stable grid availability and operation with a desirable spinning reserve may be taken care of suitably with an on-grid energy storage system, and in the Indian context, a pumped hydro energy storage system may play a critical role to take care of grid operational variability with adequate measures on frequency and voltage correction through periodic power generation and power draw. In this paper, the feasibility of a pumped hydro storage system from an Indian power sector perspective has been reviewed about the present national power scenario.

Analysis of interference effect in VL‐NOMA network considering signal power parameters performance

AbstractThis study analyses the interference effect in a visible light‐non‐orthogonal multiple access (VL‐NOMA) network that considers the signal power parameters performance for near and far users. The light‐emitting diode (LED) as a carrier transmits signals, and we investigate the interference effect. The interference effect challenge is a result of unaligned signal power parameters, thereby producing noise or echo during the signal transmission. The signal power parameters are successfully aligned, and NOMA techniques are deployed, which improves the signal performance in terms of bit‐error rate (BER), achieved data rate, and signal‐to‐interference plus noise ratio (SINR). Furthermore, the deployed NOMA techniques, such as power allocations (PA) to assign the signals appropriately, then superposition coding (SC) encodes the entire signal, and successive interference cancellation (SIC) cancels the interference within the signals. The signal behavior of the aligned and the unaligned signal power parameters performance are used to investigate the interference effect. We observed that unaligned signal power parameters reduce the signal performance of achieved data rate, BER, and SINR. Further, the aligned signal power parameter with NOMA techniques improves the signal performance. Moreover, in the aligned signal power scenario of NOMA, the near user performed better than the far user.

Analyzing error probability in aerial full-duplex relay systems: Exact formulations and optimization techniques

In this study, we investigate the performance of aerial full-duplex relay (AFDR) systems. In particular, we examine two practical scenarios: one where there is no direct link between the transmitter and user, and another where such a link exists. For both scenarios, we develop mathematical models to calculate the closed-form expressions of symbol error probabilities (SEPs) for AFDR systems, using a realistic channel aligned with the fifth generation (5G) and beyond yardsticks. To take care of residual self-interference (RSI) in AFDR, we propose an optimal power allocation strategy. Our numerical findings indicate that the performance in the case with a direct link is considerably higher than that in the case without this link. Additionally, we analyze thoroughly the effects of key parameters such as transmit power, RSI levels, carrier frequencies, and AFDR positions. The effect of RSI is significantly strong, and our proposed optimal power allocation method substantially improves system performance, especially in high transmit power scenarios where error floors may occur. Importantly, the optimal power level is dramatically lower than the conventional value where the optimal power cannot be found. Thus, besides reducing the SEPs, optimal power also helps to prolong the time of operation of AFDR. Monte-Carlo simulations are performed to confirm the accuracy of our derived expressions and demonstrate the efficacy of the proposed approaches in practical scenarios.

Benefit evaluation of HVAC and HVDC for offshore wind power transmission system under the multidimensional index

With the development of offshore wind power transmission technology and the emergence of new dimensions in deep-sea wind power scenarios, some indexes in the existing evaluation system cannot meet the comprehensive benefit evaluation of multiple offshore wind power transmission schemes in new operating scenarios, making the identification of the optimal economic distance of different schemes vague. Therefore, this paper integrates multiple parameters into the total life cycle cost and levelized cost of energy indexes for traditional and new transmission schemes in deep-sea scenarios, considers power generation income, environmental and carbon index, and proposes a multidimensional evaluation method for offshore wind power transmission systems, and analyzes the sensitivity of the constructed indicators. Then, using actual geographic information data, the established parameter fusion multidimensional evaluation method is used to analyze the technical and economic ranges of four offshore transmission schemes. The results show that the optimal economic critical interval of active commutation type high voltage direct current transmission system (CSC) is 98 km, which is about 23 % higher than that of voltage source converter based high voltage direct current transmission system (VSC). Therefore, CSC is more suitable for deep offshore wind power transmission than VSC and has better comprehensive technical and economic performance.

Negative triangularity scenarios: from TCV and AUG experiments to DTT predictions

Abstract Experiments, gyrokinetic simulations and transport predictions were performed to investigate if a negative triangularity (NT) L-mode option for the Divertor Tokamak Test (DTT) full-power scenario would perform similarly to the positive triangularity (PT) H-mode reference scenario, avoiding the harmful edge localized modes (ELMs). The simulations show that a beneficial effect of NT coming from the edge/scrape-off layer (SOL) region ρ tor > 0.9 is needed to allow the actual NT L-mode option to perform like the PT H-mode. Dedicated experiments at TCV and AUG, with DTT-like shapes, show an optimistic picture. In TCV, experiments indicate that even with the relatively small triangularity of the DTT NT scenario, a large beneficial effect of NT comes from the plasma edge and SOL, allowing NT L-modes to outperform PT L-modes with the same power input, reaching the same central pressures as PT H-modes with twice as much applied heating power. For AUG, NT plasmas go into H-mode more easily than for TCV, but always present much smaller pedestals compared with PT plasmas with the same input power, showing a much weaker or absent ELM activity. However, NT has a smaller beneficial effect for AUG than for TCV, with NT pulses outperforming PT pulses with the same input power only for an ECRH-only case with relatively low input power. For the considered AUG cases, PT pulses perform better than NT ones at higher ECRH power or with mixed NBI and ECRH power. Based on this analysis, the NT option is a viable alternative for the DTT full power scenario, providing high performance plasmas with reduced or absent ELMs.

Optimizing a hybrid solid oxide fuel cell-gas turbine and geothermal energy system for enhanced efficiency and economic performance in power and hydrogen production scenario

Geothermal energy is utilized due to its sustainability and ability to provide a continuous low-emission source of heat, making it an ideal complement to the high-efficiency requirements of modern power plants. This study investigates a novel configuration coupling a solid oxide fuel cell-gas turbine unit with a dual-flash binary geothermal structure, enhanced by low-temperature electricity generation and hydrogen production subsystems. This configuration incorporates a regenerative steam Rankine cycle and a proton exchange membrane electrolyzer to achieve an efficient overall design. The setup is evaluated from thermodynamic and economic perspectives using a non-dominated sorting genetic algorithm-II for optimization. A complete parametric study assesses the sensitivity of critical variables. Multi-objective optimization is conducted across three scenarios considering exergy efficiency, hydrogen production rate, sum unit cost of products, and the payback period as objective functions. Results indicate an optimal exergy efficiency of 56.95% and a hydrogen production rate of 0.22 kg/h, with a product unit cost of $7.06/GJ and a payback period of 1.12 years. The parametric study underscores the significant impact of the number of solid oxide fuel cells and their existing density on key variables of the presented configuration. This study highlights the system's potential for efficient and economically feasible power and hydrogen production.

Study of Power Equivalent Continuous Approximation Based on the Recent Consensus Recommendations for Brain Tumor Imaging with Pulsed Chemical Exchange Saturation Transfer at 3T.

The quantitative analysis of pulsed-chemical exchange saturation transfer (CEST) using a full model-based method is computationally challenging, as it involves dealing with varying RF values in pulsed saturation. A power equivalent continuous approximation of B1 power was usually applied to accelerate the analysis. In line with recent consensus recommendations from the CEST community for pulsed-CEST at 3T, particularly recommending a high RF saturation power (B1 = 2.0 µT) for the clinical application in brain tumors, this technical note investigated the feasibility of using average power (AP) as the continuous approximation. The simulated results revealed excellent performance of the AP continuous approximation in low saturation power scenarios, but discrepancies were observed in the z-spectra for the high saturation power cases. Cautions should be taken, or it may lead to inaccurate fitted parameters, and the difference can be more than 10% in the high saturation power cases.

The influence of radiation modeling on flame characteristics and emissions prediction in microcombustors with Ammonia/Hydrogen blends

This study advances numerical methodologies for designing microcombustor-based thermophotovoltaic systems by incorporating comprehensive analyses of radiation models and absorption coefficients. It examines the impact of non-premixed hydrogen-ammonia-air fuel blends on the micro combustion process using three-dimensional CFD simulations on a Y-shaped microcombustor under atmospheric conditions.Key to this research is modeling radiative heat transfer from multiple species, including ammonia (NH3), nitric oxide (NO), and water vapor, ensuring accurate predictions and optimization of thermal behavior and efficiency. Three modeling approaches were compared: a multispecies Planck-mean approximation radiation model (PMAC), a narrowband model for water vapor, and a scenario excluding radiation effects.The PMAC model, incorporating the absorption coefficients of ammonia and its products, consistently produced lower errors in flame length compared to the NB method across all equivalence ratios, with errors ranging from 0 % to 7 % for PMAC and 10 % to 30 % for NB, with the lowest error observed at Φ = 1.0. Additionally, the prediction of laminar burning velocity (LBV) differed by approximately 4 % between the two models.Considering different fuel blends, two scenarios were compared: constant input thermal power and constant fuel flow rate. Efficiency in the constant input thermal power scenario maintained higher radiation efficiency, in the range of 32–38%. Additionally, radiation efficiency initially increased with increasing H2 content but subsequently decreased for high H2 values due to effects on flame area, flame position, and temperature.

Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

Wasserstein generative adversarial networks-based photovoltaic uncertainty in a smart home energy management system including battery storage devices

Rooftop photovoltaic (PV) power generation uncertainty is one of the prominent challenges in smart homes. Home Energy Management (HEM) systems are essential for appliance and Energy Storage System (ESS) scheduling in these homes, enabling efficient usage of the installed PV panels' power. In this context, effective solar power scenario generation is crucial for HEM load and ESS scheduling with the objective of electricity bill cost reduction. This paper proposes a two-step approach, where a machine learning technique, Wasserstein Generative Adversarial Networks (WGANs), is used for PV scenario generation. Then, the generated scenarios are used as input for the HEM system scheduler to achieve the goal of cost minimization. The generated solar energy scenarios are considered in a single household case study to test the presented method's effectiveness. The WGAN scenarios are evaluated using different metrics and are compared with the scenarios generated by Monte Carlo simulation. The results prove that WGANs generate realistic solar scenarios, which are then used as input to a Mixed Integer Linear Programming (MILP) problem aiming for electricity bill minimization. A 41.5% bill reduction is achieved in the presented case study after scheduling both the load and ESS, with PV fluctuations taken into account, compared to the case where no scheduling, PV, or ESS are considered.

Distributed optimal scheduling for virtual power plant with high penetration of renewable energy

The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid.

Efficiency and longevity trade-off analysis and real-time dynamic health state estimation of solid oxide fuel cell system

Solid oxide fuel cell (SOFC) is a promising energy conversion technology due to its advantages of high efficiency, low emissions, and fuel adaptability. Addressing the balance between efficiency and longevity is crucial for their application on a large scale. Accurate estimation and prediction of the health state of SOFC systems is essential for the study of optimization between system efficiency and degradation. In this work, the influence law of degradation, the relationship between efficiency and longevity, and their joint optimization, alongside the state of health (SOH) estimation of the SOFC system have been deeply investigated, based on a rigorously validated model of a degraded SOFC system. First, the influential variables that predominantly influence system degradation are identified and analyzed via principal component analysis, including stack average temperature, stack temperature gradient, and fuel utilization. Then, the exploration of the trade-off relationship between system efficiency and longevity under all operating conditions is conducted, and the influence laws of influential variables on system degradation are revealed. In light of the lack of accuracy during load changes and the high time cost for existing SOH estimation methodologies, a novel improved SOH estimation strategy, considering both stack voltage and current-related states, is proposed for real-time estimation of system SOH under varying fuel compositions and dynamic power scenarios. The performance of the proposed method is demonstrated by comparing with existing methodologies. This comprehensive study lays the groundwork for the effective and sustainable health management of SOFC systems, ensuring their efficiency and longevity are maintained at an optimal balance.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution (LTE)-Advanced and 5G New Radio (NR) mobile networks. However, these techniques introduce Passive Inter-Modulation (PIM) interference in Frequency-Division Duplexing (FDD) systems. In this paper, a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals, thereby achieving effective PIM Cancellation (PIMC) in multi-band scenarios. In the model, transmitted signals are independently processed to simulate Inter-Modulation Distortions (IMDs) and Cross-Modulation Distortions (CMDs). Furthermore, the Finite Impulse Response (FIR) filter, basis function generation, and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios. Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC, achieving up to 19 dB in PIMC performance. Compared to other models, this approach offers superior PIMC performance, exceeding them by more than 5 dB in high transmitting power scenarios. Additionally, its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

Aircraft radiation exposure assessment in a radioactive environment: Ambient dose equivalents and effective doses

The release of radioactive plumes can occur due to nuclear power plant accidents, terrorist attacks, or scenarios in the chemical, biological, radiological, and nuclear defense (CBRN) field. Rescue aircraft may be exposed to ionizing radiation in such contaminated zones. Previous experimental assessments of these scenarios to estimate the potential dangers crew and passengers face are usually intangible. Using predictive software, it is possible to extract information from an external radiation field to which an aircraft would be subject. However, such codes cannot internally estimate the impact of this field on the aircraft. Using simulations based on the Monte Carlo method, it was possible to reproduce the scenario of plumes in the external environment (atmosphere) and, from this scenario, generate relevant information about their impact on the aircraft's internal environment. These assessments comprise the interaction of radiation from a radioactive plume with the aircraft structures and fuel tanks to estimate the radiation doses received by the crew and onboard electronics. This work evaluates the fluence rate, ambient dose equivalent rate (Ḣ*(10)), and effective dose rate (Ė) inside an aircraft flying through a radioactive plume resulting from a nuclear power plant accident. The simulation results suggest that radiation dose rates vary widely depending on the position within the aircraft. The ambient dose equivalent rate for photons varies by approximately 80% depending on the position within the aircraft. These differences reach around 98% for the ambient dose equivalent and effective dose rates for electrons. The data obtained may also be incorporated into risk assessments and support the development of protective measures to counter CBRN events.

Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

Design and analysis of 1.0 KVA grid-connected micro-grid PV-Systems for a residential setting in Delta State, Nigeria

The design and analysis of the 1.0 KVA solar systems was based on a thorough load assessment, precise calculation of required solar panel quantity, proper battery selection and appropriate inverter sizing. The purchase included a high-quality complete solar panel with an impressive rating of 180 W, along with two dependable 200 Ah solar batteries, a reliable 800 W inverter and a durable charge controller. These elements were meticulously put together with essential protective mechanisms such as cut out switches to ensure optimal performance and safety standards for the anticipated power output of 1.0 KVA. To optimize sun exposure for efficient energy collection, the strategic installation of the solar power consist of 6 x 180 W solar panel,2 x 200Ah battery connected in parallel and 1.0 KVA power inverter. Based on the design analysis, this well-planned setup is expected to effectively fulfill all electrical requirements even in situations without access to central power supply, presenting itself as a dependable and sustainable solution for off-grid or unreliable power scenarios.

Calculation and prediction of divertor detachment via impurity seeding by using one-dimensional model

Abstract Achieving the divertor detachment helps to alleviate excessive heat load and sputtering problems on the target plates, thereby prolonging the lifetime of divertor components for fusion devices. In order to provide a rapid but relatively reliable prediction of plasma parameters along the flux tube for future devices design, a one-dimensional (1D) modeling code for the impurity seeded detached divertor operational point has been developed based on the Python language, which is a fluid model based on the previous work.[1] The experimental observation of the onset of divertor detachment by neon (Ne) and argon (Ar) seeding in EAST is well reproduced by using the 1D modeling code. The comparison of the 1D modeling and 2D simulation by SOLPS-ITER code for CFETR detachment operation with Ne and Ar seeding also shows good consistency. The predictions of the radiative power loss and requirement of the corresponding impurity concentration for achieving divertor detachment via different impurity seeding for the high heating power scenarios on EAST and CFETR Phase II by the 1D model are also presented. Based on the predictions, the optimized parameter space for divertor detachment operation on EAST and CFETR has also been determined. Such a simple but reliable 1D model can provide a reasonable parameter inputs for a detailed and accurate analysis by 2D or 3D modeling tools through rapid parameter scanning.