Power Scenarios Research Articles

Robust power control for PV and battery systems: integrating sliding mode MPPT with dual buck converters

This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future.

A Time-sensitive Traffic Scheduling Algorithm for Industrial Network of Power Grid

Industrial Internet communications in power scenarios are highly time-sensitive and require low network latency and high stability. Therefore, in order to ensure the smooth operation of the automated production line, we propose a time-sensitive traffic scheduling (TsTS) algorithm combined with SRv6 technology based on the application background of industrial and civil converged networks. The method is: through the previous hop node sensing of network congestion Link congestion, and then adjust the detour path for time-sensitive traffic through Dijkstra’s algorithm. Moreover, in order to effectively reduce the link overhead of SRH and prevent bandwidth waste caused by large IPv6 addresses, we also propose a node list compression algorithm for detour paths. The core method is to put only necessary nodes into SRH, that is, Sensitive traffic can be forwarded with the help of ordinary routing. Experiments show that this method performs well in terms of delay and link utilization.

“Duty, Love, and Hate…”: Russian-Polish Relations in the First Third of the 19th Century

This review examines a book by E. M. Boltunova devoted to the “second coronation” of Nicholas I in Warsaw in 1829 and the memory of the Russian-Polish conflicts between the seventeenth and early nineteenth centuries. The novelty of the book is determined by the conceptualisation of the coronation through the methodology of the history of emotions and the history of memory in the contextual framework of R. Wartman’s “power scenarios”. Over the course of ten chapters, the author of the book reconstructs the spectra of emotions in the context of the formation of imperial symbolic and political mechanisms of governance of the Kingdom of Poland in the first third of the nineteenth century. The reviewer draws attention to the ambiguity of the number of interpretations offered by the author, resulting from the chosen methodological optics. The reviewer concludes that the book makes a serious contribution to the existing historiography of Russian-Polish relations in the first third of the nineteenth century by portraying, through the reconstruction of emotions, the Russian Empire as an extremely dynamic space.

Reinforcement learning for energy management of an islanded microgrid: Analysis of state, state space and reinforcement function

The energy management (EM) of a microgrid is a challenging problem because of the stochasticity of renewable sources and loads, which can be formulated as a multi-stage decision-making problem. Reinforcement learning (RL), is found to be suitable for solving multi-stage decision-making problems under uncertainty. With the recent advancements in deferrable types of loads like that of electric vehicles, there is flexibility to manage peak demand, thereby saving the microgrid's operation cost. In this paper, we systematically explain for the first time how RL can be used to solve the scheduling problem of an isolated MG. A novel Q-learning algorithm employing a state-dependent action set is proposed to solve an isolated microgrid's EM problem in the presence of critical and deferrable loads. Various scenarios of wind turbine output power are generated to test the algorithm under a stochastic environment. In this work, the action set is divided into two: one with all the actions included and the second with a reduced number of actions. The entire state space is also divided into two: one set in which the deferrable load needs to be allotted and other where it need not be allotted. The advantage of such a method is that it substantially reduces the computational burden even if the state space's dimension is higher. Also, the judicious selection of reinforcement function to meet all the objectives and constraints is illustrated with case studies. The proposed method yields optimized scheduling with significant cost reductions compared to existing work.

Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction

Long sequence prediction has broad and significant application value in fields such as finance, wind power, and weather. However, the complex long-term dependencies of long sequence data and the potential domain shift problems limit the effectiveness of traditional models in practical scenarios. To this end, we propose an Adaptive Meta-Learning Probabilistic Inference Framework (AMPIF) based on sequence decomposition, which can effectively enhance the long sequence prediction ability of various basic models. Specifically, first, we decouple complex sequences into seasonal and trend components through a frequency domain decomposition module. Then, we design an adaptive meta-learning task construction strategy, which divides the seasonal and trend components into different tasks through a clustering-matching approach. Finally, we design a dual-stream amortized network (ST-DAN) to capture shared information between seasonal-trend tasks and use the support set to generate task-specific parameters for rapid generalization learning on the query set. We conducted extensive experiments on six datasets, including wind power and finance scenarios, and the results show that our method significantly outperforms baseline methods in prediction accuracy, interpretability, and algorithm stability and can effectively enhance the long sequence prediction capabilities of base models. The source code is publicly available at https://github.com/Zhu-JP/AMPIF.

Multiphysics and multi-scale design and analysis of nuclear thermal propulsion pellet bed reactor based on spherical cermet fuel element

Due to the advantages of high thrust and high specific impulse, nuclear thermal propulsion (NTP) is a preferred near-term rocket engine technology for future Mars missions. This article introduces the reactor design based on spherical cermet fuel element, with the aim of studying the feasibility of applying this new fuel in NTP system. First, design requirements are determined based on the unique features of NTP. In terms of neutronics, the effect of rocket launch accidents and fuel loss caused by high-temperature hydrogen are considered. Thermal hydraulics refer to the requirements of NASA's Mars Design Reference Architecture 5.0 (DRA 5.0) for impulse, thrust, and thrust to weight ratio. Second, based on the requirements above, the optimal reactor configuration parameters are obtained with the goal of maximizing the thrust to weight ratio: reactor radius 51.1 cm, height 106 cm, aspect ratio 1.04, reflector 13 cm, and reactor mass 3940 kg. Finally, the performances of the optimal reactor design are analyzed. The neutron spectrum shows a characteristic of fast neutron spectrum, and fuel loss analysis shows that the core can withstand a fuel mass loss of about 4 wt%. In addition, the core axial and radial power distribution are calculated, with an overall power peak factor of 1.26. The thermal hydraulic calculation provides a range of core operating conditions that satisfy the design requirements of DRA 5.0 and temperature, with power of 620 ∼ 950 MW and flow rate of 16 ∼ 24 kg/s. Compared with the ANL200/2000 reactor using prismatic cermet, it has the advantage of higher specific impulse. The main disadvantage is the large pressure drop, which prevents it from being applied to the scenarios of high power and high thrust. Therefore, for NTP reactor using spherical cermet elements, it should be applied to low to medium power scenarios.

CO2 Emissions from Blade Waste Treatments under Wind Power Scenario in Japan from 2021 to 2100

Wind power generation has been introduced to reduce carbon emissions; however, recycling or recovering the waste of wind blades, which contain fibre-reinforced plastic, is difficult. Converting the recovered materials for secondary use is also difficult owing to the decreased strength and low material value. Many countries, including Japan, have not considered the future energy and CO2 emission scenarios, particularly CO2 emissions from wind blade waste. Based on these scenarios, Japan has planned to introduce large amounts of onshore/offshore wind power generation through 2050. Therefore, we aimed to evaluate quantitatively the total amount of waste and the global warming potential (GWP) from multiple blade waste treatment processes. Based on the average lifetime of blades (20–25 years), we found that the GWP of wind blade waste treatment in Japan may reach a maximum of 197.3–232.4 MtCO2eq by 2060–2065. Based on this lifetime, the wind blade treatment in 2050 accounted for 63.9–80.1% of the total greenhouse gas emissions in 2050. We also showed that the rise in CO2 emissions from the wind blade wastes would make up 82.5–93.6% of the potential reduction in the GWP, which is achievable by shifting from thermal to wind power generation.

Spectral normalization generative adversarial networks for photovoltaic power scenario generation

AbstractThe large‐scale integration of new energy generation into the power transmission network introduces uncertainty and fluctuations, posing a threat to the secure operation of the transmission network. Modelling and analysing the uncertainty in photovoltaic (PV) generation is essential. This paper introduces a method for generating scenarios for centralized PV station based on spectral normalization generative adversarial networks (SNGAN). Data‐driven scenario generation methods, with generative adversarial networks (GAN) as representatives, are commonly used today to analyse the uncertainty of new energy generation. In addressing the uncertainty of centralized PV power generation, this paper introduces SNGAN, makes improvements to the discriminator, enhances training stability, and generates PV power generation scenarios. The incorporation of spectral normalization as a regularization technique contributes to the stability and convergence of the proposed GAN model. The effectiveness of the selected method is validated through comparisons with actual data.

First-principle based predictions of the effects of negative triangularity on DTT scenarios

Plasmas with negative triangularity (NT) shape have been recently shown to be able to achieve H-mode levels of confinement in L-mode, avoiding detrimental edge localised modes. Therefore, this plasma geometry is now studied as a possible viable option for a future fusion reactor. Within this framework, an NT option is under investigation for the full power scenario of the Divertor Tokamak Test (DTT) facility, under construction in Italy, with δtop=−0.32/δbottom≃0.02 top/bottom triangularity values at the separatrix. The transport properties of this scenario are studied in this work. Gyrokinetic GENE simulations and integrated modelling using ASTRA with the quasi-linear trapped gyro-Landau fluid (TGLF) model have been performed. The emerging picture from the ASTRA-TGLF runs with boundary conditions at ρtor=0.94 is that, in the L-mode NT option, the larger peaking of the kinetic profiles in the edge region is not sufficient to recover the loss of the PT H-mode pedestal, and reach similar central temperature values. Two additional shapes are also considered, obtained by flipping the triangularity of the scenarios, to single out the effect of the triangularity sign. A negligible ‘direct’ effect of the triangularity is found for the L-mode, while a small beneficial effect is observed for the H-mode. The ASTRA-TGLF results are validated by GENE and TGLF stand-alone at two selected radii. GENE shows ITG dominant micro-instability and explains the small beneficial effect of the NT for the H-mode as due to a strong reduction of the heat fluxes, when reversing the triangularity, with a relatively high Ti stiffness. An improvement of the predicted performances of the NT DTT scenario could come from ρtor≳0.9 , as indicated by some recent experiments at the tokamak à configuration variable (TCV) and ASDEX Upgrade.

Small resonant magnetic perturbations result in three-dimensional material transport in the fusion plasma edge

Erosion patterns in a 3D magnetic topology are significantly altered when compared to an axisymmetric scenario due to differences in the impurity transport in a plasma environment. When resonant magnetic perturbations are applied in L-Mode discharges, eroded impurities can buildup in regions where an axisymmetric plasma would otherwise lead to zones of net erosion across the full toroidal length of the divertor. The reduction on the local and integrated erosion observed across a parametric study of the anomalous diffusion and impurity content in a plasma, can lead to the extension of divertor lifetimes in low power scenarios when 3D fields are applied. By contrast, in axisymmetric scenarios, most of the carbon eroded from the divertor are carried away to the far scrape-off layer.

The numerical analysis in heat transfer, fluid flow, and irreversibility of a pin-fin heatsink under the ultrasonic vibration with different transducer power assignment scenarios

The hydrothermal performance and entropy generation rate in a pin–fin heatsink were numerically investigated under different vibration transducer power distribution scenarios between 11 transducers located at 3 walls of the heatsink. Two cases were investigated; Case#A and Case#B which are different in 3 transducer locations on one wall of the heatsink. The highest convection coefficient (h) in Case#A was obtained for a variable power scenario, which escalated pressure drop (ΔP) by 91.94%. However, the highest h in Case#B was obtained for the constant power scenario. In addition, h, average temperature of CPU, and thermal resistance factor in Case#A are 5.84% higher than, 0.41% lower than, and 5.34% lower than those in Case#B. The PEC factor for Case#A is higher than unity (1.31) only under the constant power scenario, while the PEC of Case#B is higher than unity under different studied scenarios. Frictional irreversibility (Ṡfr) for Case#A was obtained as 1.45–74.56% higher than that for Case#B due to the swirl flow generated by the high-power transducers and creating the huge velocity gradients in Case#A. Nevertheless, the high flow mixing in Case#A leads to reducing the temperature gradients against Case#B, thereby thermal irreversibility (Ṡth) in Case#A is almost 7.05–19.69% lower than that of Case#B.

Experimental and numerical analysis of variable cross-section channel liquid cooling plate for server chips thermal management

Utilizing high-performance chips results in increased heat generation, necessitating more effective heat dissipation method. To address challenges in microchannel liquid cooling plates for server chips cooling the study presents the variable cross-section channel liquid cooling plate designs. To investigate the effect of channel structure on the performance of liquid cooling plate, two types of samples, one featuring side wall ribs and bottom cavities (SC) and the other featuring side wall ribs and bottom waves (SW), are evaluated through experimental analyses under various heating power and flow rate conditions. Comparing their performance to a conventional rectangular channel (RC) liquid cooling plate, their heat transfer and flow attributes were assessed. Finally numerical analysis was conducted to investigate the flow behavior inside the channel and extreme performance prediction were made using the numerical study. Results demonstrate that the SC and SW designs outperform the RC in terms of heat transfer capabilities, despite having the weaker flow characteristics. Under 300 W heat source power, the SW variants enable the heat source surface temperature to stay below 77.8 °C with a 194 mL/min flow rate. Notably, the SW variant showcases lower average heat source surface temperature and demonstrates lower pressure drop compared to the SC variant. As the flow rate increases, the heat transfer efficiency of the SW variant is further amplified while simultaneously highlighting the relatively suboptimal flow behavior of the SC variant. Predictive numerical models underscore that the SW variants offers improved thermal performance when subjected to high heat source power scenarios. Remarkably, under the heat source power of 500 W, a mere augmentation in flow rate proves sufficient to reduce the heat source temperature below the prescribed threshold value. The combination design of side wall ribs and bottom waves should enable a low cost, simple and high efficiency electronics cooling.

Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model.

A novel wasserstein generative adversarial network for stochastic wind power output scenario generation

AbstractA novel Wasserstein generative adversarial network (WGAN) is proposed for stochastic wind power output scenario generation. Wasserstein distance with gradient penalty adapts to the gradient vanishing problem that is easy to occur in the new energy generation scenario. This model has better robustness and generalization ability than the traditional generative adversarial network. WGAN is optimized to simulate ideal wind power scenarios. The generated data are measured by cumulative distribution function (CDF) and continuously ranked probability score to evaluate the performance of the proposed model. Compared with the probability models, the proposed model is data‐driven, that is, it can simulate wind power scenarios based on historical samples rather than probability hypothesis, and it can independently learn the space‐time correlation of wind power generation in different locations. Experiments show that the CDF curve of data generated by the proposed WGAN is highly coincident with that of real data.

Heat transfer performance study of a separated pulsating heat pipe

In order to study the heat transfer performance of the pulsating heat pipe (PHP) under the separation arrangement of evaporating and condensing ends, this paper designs a separated PHP for experimental investigation, and describes its heat transfer capability in terms of the wall temperature oscillation performance, the heat transfer temperature difference and the heat transfer thermal resistance. Under different heating powers, the effects of different placement inclinations and their structures on the heat transfer performance of the in-line and bent separated PHP are analyzed. The study shows that in the low heat transfer power scenario, the performance of the in-line type separated PHP is more excellent, and with the increase of the placement of the inclination of the heat transfer situation is further optimized, 90 ° placement of the inclination of the optimal performance of the heat transfer; bent type separated PHP condensing end of a certain level of inclination is conducive to low-power startup and operation, the condensing section of the 15 ° horizontal angle of the optimal performance; and in the high heat transfer power, the two performances are similar, but the arrangement of flexible bent type separated PHP is more suitable for the changing heat transfer scenarios.

Novel material-oriented valorization of biogas can achieve more carbon reduction than traditional utilization by bioelectricity or biomethane

Two novel biogas upgrading strategies that recover high-value chemicals or CO2 liquid fertilizer from biogas besides biomethane were evaluated from the view of global warming potential (GWP) through life cycle assessment in comparison with conventional approaches. Results show that the scenarios producing biomethane with nano calcium carbonate or CO2 liquid fertilizer from biogas present significantly lower GWP (–3.4 kgCO2-eq/Nm3-biogas and –4.4 kgCO2-eq/Nm3-biogas, respectively), compared to combined heat and power scenario (–2.4 kgCO2-eq/Nm3-biogas) and biogas upgrading by high pressure water scrubbing scenario (–1.3 kgCO2-eq/Nm3-biogas). The carbon sequestration and utilization from CO2-rich water significantly enhanced carbon reduction in overall biogas management. Furthermore, considering cleaner electricity in the future, strategies focusing on managing biogas for materials will align more with climate change goals than energy-focused strategies. This study provides insight for decision-makers in developing roadmaps for carbon reduction pathways in biogas-relating sectors.

Research on peak load shifting for hybrid energy system with wind power and energy storage based on situation awareness

The uncertainty of wind power and load fluctuations can elevate the peaking pressure on the power grid and influence the optimization strategy for peak load shifting. Additionally, there is a need to explore the trade-off and dynamic adjustment between economic considerations and the effectiveness of peak load shifting strategies. In this paper, based on the situation awareness theory, an optimization model on peak load shifting is proposed for a hybrid energy system with wind power and energy storage unit. First, in the situation perception stage, simulations that incorporate stochastic volatility are executed for renewable energy outputs and electric loads, leading to the establishment of uncertainty models for both wind power and electric load. During the situation comprehension stage, an optimization model with multi-objective is proposed to simulate the hybrid system situation, in which the mutual constraints and games are considered between peak load shifting effects and system economy. Then, in the stage of situation presentation and situation orientation, by setting quantitative indicators to analyze and judge the system situation, the problem of changing uncertainty from the weights of various attributes in multi-objective decision-making is transformed into the prediction problem of the adjustable factor, and a hybrid network model is proposed to realize the interval prediction of the adjustable factor, further balancing the goals of load leveling and system economy. Example simulations demonstrate that the proposed optimization model for peak load shifting can effectively reduce the peak-valley difference ratio of the net load by over 39.08 %, thereby smoothing the overall net load fluctuation. In the face of uncertainty, the model shows minimal impact on the peak-valley difference ratio of the net load, experiencing only 3.91 % and 7.13 % changes in response to wind power and load fluctuations, respectively. These findings underscore the model's sensitivity to different levels of uncertainty in both wind power and load scenarios. The MAML-LSTM-FC model can clarify the selection range of the adjustable factor according to the requirements of system quantitative indexes, thereby achieving dynamic coordination between the effects of peak load shifting and economics.

A short-term photovoltaic power interval forecasting method based on fuzzy granular computing and CNN-BiGRU

Abstract This paper presents a short-term PV power interval prediction method combining fuzzy information granulation and CNN-BiGRU model. First, historical data of PV power generation is processed using fuzzy information granulation to determine the interval range. Subsequently, a CNN-BiGRU model is constructed, where the CNN module extracts local features of the interval range and the BiGRU module captures temporal patterns. The interval range is then fed into the CNN-BiGRU model for training, which enables accurate prediction of short-term PV power generation intervals. Finally, the predicted power generation interval ranges are given. The experimental results show that this hybrid interval prediction method is first applied to the prediction of short-term PV power scenarios, which fully utilizes the advantages of information granularity to express the data intervals and CNN-BiGRU to learn the complex time series patterns, and realizes the accurate and reliable prediction of short-term PV power intervals. Compared with direct numerical prediction, the method provides an expression of prediction uncertainty, which provides an important reference for the operation decision of PV power plants.

Optimal scheduling of integrated energy systems considering wind power uncertainty

Abstract To reduce the impact of high wind power penetration rate on integrated energy systems, a wind power scenario generation method based on polynomial regression is proposed and an optimization scheduling model is constructed accordingly. First, a probability distribution model for wind power is constructed. Then, scenario generation is performed through Latin hypercube sampling and typical scenarios are obtained using the synchronous back propagation elimination method. Finally, the optimal output of each piece of equipment in the system is obtained with the lowest overall cost as the optimization objective. The simulation results validate the effectiveness and low-carbon nature of this method, while reducing costs.

Comparative analysis of bidding strategies for auction-based local energy markets

In view of the ongoing integration of distributed energy resources (DERs) and energy storage into the energy system, conventional consumers are transitioning into prosumers and flexumers. Local energy markets (LEMs) enables these end users to trade electricity directly with each other in order to obtain lower energy prices and to increase the local self-consumption. Since bidding strategies have a decisive impact on efficient trading in auction-based LEMs, the comparison and consistent evaluation of bidding strategies is an important research area. As novelty we propose an uniform evaluation methodology keeping all market features equal except the used bidding strategy. For the first time we evaluate a device-oriented (DO) strategy including an incentive price signal with a zero-intelligence (ZI) strategy, a learning-intelligence (LI) strategy employing the modified Roth–Erev learning algorithm. As evaluation we analyze the resulting market trading with various key performance indicators (KPIs), which consider both cost savings and local energy supply. Our findings reveal that the chosen bidding strategy has a decisive impact on cost savings and the distribution of gains between the buyer and seller side. In a photovoltaic (PV) and combined heat and power (CHP) scenario with different technology penetrations, the end users’ gains attain the highest values for the DO bidding strategy with 7909 € and 16454 €. Also the average market clearing prices are for the DO bidding strategy the highest with 0.269 €/kWh for the and 0.3032 €/kWh, which implies that the seller side predominantly obtains the gains.