Power Integration Research Articles

Study on TSV insulating medium based on SACVD process

TSV silicon switching board technology has the advantages of short global interconnection, small delay, low power consumption and high integration. The dielectric insulation performance in the TSV hole will directly affect the signal integrity and power integrity of the TSV silicon switching board. The insulating dielectric layer of TSV requires good forming ability and step covering, and lower leakage current and good heat resistance reliability. In this paper, the filling capacity of SACVD process is analyzed, the coverage rate of SACVD process is studied under different process parameters, and the leakage current of silicon dioxide film is measured by planar capacitance structure. Finally, the leakage current test of the TSV pairs is carried out online. Before the substrate thinning, the leakage current of the two blind hole TSV pairs is measured by the probe station. The voltage ranges form 0 to 50V, and the leakage current ranges from a few pA to a few hundred pA.

Intraday two-stage hierarchical optimal scheduling model for multiarea AC/DC systems with wind power integration

To make full use of the flexible adjustment capability of DC tie-lines in multiarea AC/DC systems and to coordinate the generation resources and load demand of multiarea AC/DC systems, this paper presents an intraday two-stage hierarchical optimal scheduling model for multiarea AC/DC systems based on analytical target cascading (ATC). To avoid repeated adjustment and overadjustment of DC tie-lines after wind power integration, a two-stage rolling coordinated scheduling model for the area subsystem based on model predictive control (MPC) is proposed. The two-stage rolling coordinated scheduling method takes into consideration the influence of the predicted value in the future finite time domain and the latest measured DC tie-line power on the current scheduling state. Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed that takes into consideration the DC tie-line adjustment constraints and the area coupling constraints. The optimal scheduling model for the upper-level system formulates the two-stage DC tie-line plan for the multiarea AC/DC system, and the two-stage rolling coordinated scheduling model of the area subsystem solves the subproblems of the generation plan for each area subsystem in a parallel manner considering the area coupling constraints. The proposed method can achieve intraday two-stage decoupling scheduling of multiarea AC/DC systems and promote the cross-area consumption of large-scale wind power through flexible adjustment of the DC tie line. This approach also reduces the communication burden between the area subsystems and ensures the efficiency of the solution algorithm.

Smart Grids Data Aggregation Method on Paillier Homomorphic Encryption

ABSTRACT Smart grid (SGs) is a highly integrated power system, it is gradually replacing the traditional power grid, but at present there are problems in the SGs data aggregation, such as user privacy leakage, grid data query inflexibly and data leakage. In order to solve these problems, this paper studied the SGs data aggregation method based on Paillier homomorphic encryption (HE). In this paper, the Paillier HE algorithm was used to study the power grid data, and the encrypted data was numerically calculated under different encryption states. It used the cloud computing center in the blockchain to directly aggregate the ciphertext data generated by users, and used the hash function to set the secret key to prevent the ciphertext data from being tampered with, and obtained the aggregate result of the original data after decryption by the power grid management center. When the number of security parameters was 30,000, the encryption time required by this method was 21.71 seconds. When the number of smart meters (SMs) was 80, the signature verification time required by this method was 2.31 seconds.

A Contour Integral and Complex Power Series Proof of Stirling Formula

A Contour Integral and Complex Power Series Proof of Stirling Formula

An Analysis on Integration of Solar and Wind Power into A Single-Phase Grid with an Optimal Parameterized Fractional-Order Pid Controller

Because of the complimentary nature of solar and wind electricity, hybrid solar-wind power systems are the most effective renewable energy sources. Weather conditions have a significant impact on the amount of power that can be generated by wind and solar photovoltaic systems. Their intermittent nature causes output fluctuations. The purpose of this research is to offer a technique for a hybrid wind–solar power plant that makes the most efficient contribution of renewable energy resources and is backed by technology that allows batteries to store energy. The fact that solar and wind power display power profiles that are complimentary was the driving force for the construction of the hybrid solar–wind power system.

Identification method of all-operating-point admittance model for wind farms considering frequency-coupling characteristics

Admittance identification is an effective tool to solve the issue of black-box admittance modeling in wind farms. However, due to the large operating range of wind farms, it is difficult to accurately identify the admittance model of wind farms under all operating conditions, which limits the research on wideband oscillation caused by the interaction between wind farms and the power grid. This paper analyses the characteristics of the frequency-coupling admittance for wind farms. It is found that the power-sharing ratio of wind turbine generators (WTGs) affects the admittance characteristic of the wind farm, which leads to a huge number of operating points for the wind farm. The all-operating-point (AOP) admittance model of wind farms considering frequency-coupling characteristics is hard to directly identify. Aiming for this problem, this paper proposes to first identify the AOP-admittance model for WTGs. Then, based on the operating points of each WTG and the network structure, the AOP-admittance model of the wind farm is obtained via the admittance aggregation and model reduction method. This proposed method solves the problem of black-box admittance modeling and addresses the problem of dimension disaster in admittance modeling for wind farms. The identified model of the wind farm can be used to accurately assess the stability of the wind power integration system under various operating conditions. Finally, the effectiveness of the proposed method is verified through the admittance measurement and stability analysis of the wind farm.

Non-smooth finite-time adaptive stabilisation of cascade switched systems with asymmetric output constraints

In this article, the problem of non-smooth finite-time (FT) adaptive state feedback stabilisation is investigated for asymmetric output-constrained cascade switched nonlinear systems (AOCCSNSs) with mixed powers, parametric uncertainties and zero dynamics. To better address the uncertain parameters and the asymmetric output constraints, a new type of common Lyapunov function (CLF) including a quadratic squared term of the estimated error and a tangent-type barrier Lyapunov function ( T t -BLF) is constructed. Then, with some mild assumptions, such as input-to-state stability (ISS) property of zero dynamics, upper boundedness of subsystems' nonlinear terms and some well-known small signal conditions, state feedback controllers (SFCs) constructed by adding a power integrator (AAPI) technique and the update law to estimate the unknown parameter are proposed to guarantee resultant closed-loop switched systems FT stable. The proposed method for switched systems is presented in a unified manner, capable of addressing FT stabilisation by effectively handling situations with or without output constraints. This is achieved through the construction of T t -BLF, which degenerates into a quadratic form when the output constraint disappears or approaches infinity. At last, the method is verified by a switched RLC circuit system (RLC-CS) and a numerical simulation.

Abstract 6929: Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics

Abstract Rare clonotypes within pre-cancerous tissues can drive progression to cancer. However, the evolution of rare clonotypes in tumors or normal tissue cannot be defined in the absence of single-cell resolution. At this single-cell level, multiomic interrogation across the Central Dogma of Biology provides enhanced power to reconstruct such evolutionary trajectories, defining the mutational profile, cell identify, and receptor expression within each subpopulation. Leveraging a multiomic approach, we aimed to define how different mutations in the same oncogenic driver observed in the same tumor resection associate with copy number variation (CNV) across the genome. We analyzed individual ductal carcinoma in situ/invasive ductal carcinoma cells using a unified whole-genome and full-transcript RNAseq workflow (ResolveOME™, BioSkryb Genomics) coupled with panel-level extracellular protein information through oligo-conjugated antibodies (BioLegend). We sequenced the exomes and transcriptomes of ResolveOME-amplified single cells from mastectomy samples from twelve patients. At the single nucleotide variant (SNV) level, we identified an allelic series of PIK3CA oncogenic driver mutations in the same tumor resection. A single amino acid, in-frame deletion of E109 dominated the sample, followed by H1047R and K111E in decreasing subclonal abundance. A fourth mutation, E345T, not present in the first sample, was detected as the sole PIK3CA variant in the second tumor sample. Intriguingly, each respective PIK3CA mutation class was associated with a distinct copy number alteration profile revealed by low-coverage whole-genome sequencing: Cells harboring the predominant ΔE109 mutation displayed chromosome 8p,16p, and 17 loss while the less abundant H1047R mutation was in single cells harboring 1q gain, 4q loss, and 22 loss in addition to the 8p and 16q loss present in the ΔE109 cells. PIK3CA K111E had a quiescent, 2n copy number profile. The transcriptomic arm of ResolveOME, containing an oligo-conjugated antibody readout of surface protein expression, jointly confirmed the epithelial identity for the cells harboring the oncogenic PIK3CA mutations. A subpopulation of cells harboring prototypical breast cancer CNV were typed as non-epithelial with increased stemness characteristics, indicative of the ability to resolve phenotypic cellular states. These results suggest a tight interrelationship between CNV and SNV influencing the relative rate of clonal expansion. They also provide the opportunity to explore CNV:SNV signature association with loci exclusive of PIK3CA, and to exploit the power of multiomic integration for lineage reconstruction and for defining common oncogenic signatures of the evolving tumors. Citation Format: Jon Zawistowski, Isai Salas-Gonzalez, Tia Tate, Tatiana Morozova, Katherine Kennedy, Durga Arvapalli, Jamie Remington, Jeffrey Marks, E. Shelley Hwang, Gary Harton, Victor Weigman, Jay A. West. Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6929.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Angular momentum and energy transport in disc–jet systems. Unravelling the contribution of saturated thermal conduction

Abstract This study builds upon our prior work to further explore and unravel the effects of saturated thermal conduction within a viscous resistive MHD framework on the intricate transport mechanisms of angular momentum and energy in disc-jet systems. We conducted a series of 2.5-dimensional non-relativistic time-dependent numerical simulations using the PLUTO code. Employing a saturation parameter spanning [0.002-0.01], our results are consistent with previous investigations that omitted consideration of thermal conduction, affirming the established understanding that kinetic torque plays a predominant role in governing the total accretion angular momentum, surpassing the magnetic contribution within the disc. At the initial time-steps of our calculations, we find that thermal conduction enhances this kinetic contribution, while concurrently diminishing the effect of magnetic contribution. In contrast to the prevailing influence of kinetic torque within the disc, we also assert the magnetic torque as the primary contributor to the total ejection angular momentum. We further unveil that doubling the saturation parameter leads to bolstering of approximately 23.7% in the integral dominance of magnetic torque compared to kinetic torque within the jet. Our findings reveal that doubling the effect of thermal conduction improves the integral total accretion power by approximately 2%, thereby slightly amplifying the energy content within the system and increasing overall energy output. We underscore that as the local energy dissipation within the disc intensifies, the significance of the enthalpy accretion flux increases at the expense of the jet power. We reveal that increasing the saturation parameter mitigates enthalpy accumulation within the disc, and further restricts the jet’s energy extraction from the disc. This limitation is determined in our analysis through the decrease in the integral ratio between the bipolar jet and liberated power of approximately 13.8%, for twice the strength of the saturation parameter. We identify the Poynting flux as the primary contributor to total jet power, with thermal conduction exerting minimal influence on magnetic contributions. Additionally, we emphasise the integration of jet enthalpy as another significant factor in determining overall jet power, highlighting a distinct correlation between the rise in saturation parameter and heightened enthalpy contribution. Moreover, we observe the promotion of Poynting flux over kinetic flux at advanced time-steps of our simulations, a trend supported by the presence of thermal conduction, which demonstrates an integral increase of approximately 11.2% when considering a doubling of the saturation parameter.

Adaptive fixed-time dynamic surface tracking control for high-order nonstrict-feedback nonlinear switched systems

This paper considers the adaptive fixed-time neural tracking dynamic surface control (DSC) for high-order nonlinear switched systems. The uncertain nonlinear functions are approximated by neural networks (NNs). And the obstacles of high-order terms are eliminated by the adding a power integrator. A novel fixed-time convergence filter is proposed to refrain from the problem of repeated differentiation of virtual controllers. On the basis of the backstepping control algorithm and the common Lyapunov function (CLF) method, a novel adaptive fixed-time neural DSC strategy is developed. It is proven that the states in the closed-loop system are bounded and the tracking error converges to an area of zero in fixed time. The simulation experiments further verify the feasibility of the mentioned method.

Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

The potential of decentral heat pumps as flexibility option for decarbonised energy systems

Decarbonising the energy system requires high shares of variable renewable generation and sector coupling like power to heat. In addition to heat supply, heat pumps can be used in future energy systems to provide flexibility to the electricity system by using the thermal storage potential of the building stock and buffer tanks to shift electricity demand to hours of high renewable electricity production. Bridging the gap between two methodological approaches, we coupled a detailed building technology operation model and the open-source energy system model Balmorel to evaluate the flexibility potential that decentral heat pumps can provide to the electricity system. Austria in the year 2030 serves as an example of a 100% renewable-based electricity system (at an annual national balance). Results show that system benefits from heat pump flexibility are relatively limited in extent and concentrated on short-term flexibility. Flexible heat pumps reduce system cost, CO2 emissions, and photovoltaics and wind curtailment in all scenarios. The amount of electricity shifted in the assessed standard flexibility scenario is 194 GWhel and accounts for about 20% of the available flexible heat pump electricity demand. A comparison of different modelling approaches and a deterministic sensitivity analysis of key input parameters complement the modelling. The most important input parameters impacting heat pump flexibility are the flexible capacity (determined by installed capacity and share of control), shifting time limitations, and cost assumptions for the flexibility provided. Heat pump flexibility contributes more to increasing low residual loads (up to 22% in the assessed scenarios) than decreasing residual load peaks. Wind power integration benefits more from heat pump flexibility than photovoltaics because of the temporal correlation between heat demand and wind generation.

Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future

Solar energy has emerged as a vital renewable alternative to fossil fuels, enhancing environmental sustainability in response to the pressing need to reduce carbon emissions. However, the integration of solar power into the electrical grid faces challenges due to its unpredictable nature, as a result of solar energy production variability. This research presents an advanced Explainable Artificial Intelligence (XAI) framework to explicate machine learning models decision-making processes, thereby improving the predictability and management of solar energy distribution. The influence of critical parameters such as solar irradiance, module temperature, and ambient temperature on energy yield is studied using the Local Interpretable Model-Agnostic Explainer (LIME). Rigorous testing using four advanced regression models identified Random Forest Regressor as the superior model, with an R2 score of 0.9999 and a low Root Mean Square Error (RMSE) of 0.0061. Furthermore, Partial Dependency Plots (PDP) are used to emphasize the intricate dependencies and interactions among features in the dataset. The application of XAI techniques for solar power generation extends beyond explainability, addressing challenges due to various parameters in solar radiation pattern analysis, error estimation in solar performance, degradation of the battery function, and also provides interpretable insights for enhancing the lifespan of solar panels, contributing to advancements in sustainable energy technologies. The results of this study show how XAI has the potential to transform power system management (PSM) and strategic planning, propelling us toward a future of energy that is more resilient, efficient, and environmentally friendly.

Distributed observer‐based containment control for unknown time‐varying P‐normal nonlinear multiagent systems

AbstractThis work investigates the containment control for unknown time‐varying p‐normal (TVP) nonlinear multiagent systems (MASs) under directed topology. First, by innovatively constructing the dynamic update law in the distributed observer design process, the corresponding virtual leader trajectory can be accurately estimated at an exponential rate. Subsequently, the containment problem of MASs is transformed into a tracking problem through appropriate state transformation. Then, based on backstepping and power integrator techniques, a recursive algorithm is proposed to construct the adaptive controller to guarantee that the followers are driven into the convex hull consisting of dynamic leaders and the closed‐loop system is stable. Finally, the effectiveness of the containment control scheme is verified through a numerical simulation and a practical example of single‐link manipulators.

Thermodynamics, economic and carbon emission analysis of power-to-methanol process through alkaline electrolysis and monoethanolamine (MEA) carbon capture

The integration of renewable power for hydrogen production, carbon capture, and methanol synthesis process presents a novel approach to produce green methanol and promote renewable energy utilization. There are few studies on full process modeling, and the impact of CO2 boundaries on carbon emissions and levelized costs has not been thoroughly analyzed. A complete modeling of the renewable power-to-methanol process was conducted in this work, considering energy, exergy, economy, and the environment perspectives. Thermodynamic and economic models were established, and a life cycle assessment model was conducted to evaluate the CO2 emissions. The renewable power-to-methanol process achieved energy and exergy efficiencies of 36% and 30%, respectively. The methanol levelized cost is 840.50 $/ton. The main factor affecting the levelized cost is the price of renewable energy. Different CO2 calculation boundaries lead to opposite carbon emissions results.

Integrated System of Mechanical Regulator and Electrical Circuitry on Triboelectric Energy Harvesting with Near‐Field Communication for Low Power Consumption

AbstractThis paper presents an integrated power generation, conversion, and storage system with a temperature monitoring system, including passive near‐field communication (PGCS‐TMS‐pNFC), based on triboelectric nanogenerators (TENGs). The system consists of three parts; first, a mechanical regulation system (MRS) with kinematic design and multiphase control; second, a phase control rectifying circuit (PCRC) with a battery charging circuit (BCC); and third, a data processing and communication system (TMS‐pNFC) for low power consumption. The MRS, consisting of a reel spring and gear train, achieves a high‐frequency output exceeding 1000 Hz, and long‐lasting operation. PCRC provides stable and minimizing fluctuated DC output with a ripple effect as RMS voltage and current of 423 V and 46 µA, for 2 s, with a crest factor of 1.15 and 1.08. The PGCS‐TMS‐pNFC, harvesting building energy, such as opening and closing doors, is used to sense and store temperature data for more than 29 min, and the data is transmitted to the receiver via pNFC through smartphone tagging. Additionally, the PGCS with BCC is implemented on a bicycle frame to charge electronic devices while riding. This research underscores the integrated approach and practical applications of TENG technology that contribute to energy efficiency and sustainability in the lives.

Bi-Layer Planning of Integrated Energy System by Incorporating Power-to-Gas and Ground Source Heat Pump for Curtailed Wind Power and Economic Cost Reduction

The popularization of renewable energy is limited by wasteful problems such as curtailed wind power and high economic costs. To tackle these problems, we propose a bi-layer optimal planning model with the integration of power to gas and a ground source heat pump for the existing integrated energy system. Firstly, the inner layer optimizes the daily dispatch of the system, with the minimum daily operation cost including the penalty cost of curtailed wind power. Then, the enumeration method of outer-layer optimization determines the device capacity of various schemes. After that, optimal planning can be achieved with the minimum daily comprehensive cost. The result of this example shows that the improved system can reduce curtailed wind power and system costs, thus improving the overall economy. Finally, the influences of algorithms and gas prices on planning optimization are studied.

Operational cost and wind power integration capacities of the integrated heat and power dispatch (IHPD) system under different heating regulation modes and time steps

The utilization of the district heating system (DHS) thermal inertia is greatly influenced by heating regulation modes and time steps, however, which has been rarely analysed in almost all integrated heat and power dispatch system (IHPD) models, for the purpose of wind power integration. In this study, an IHPD model considering the thermal inertia of the DHS was established, in which a data interaction problem amongst the power sources, heat sources and district heating networks was solved. Thereafter, four most commonly used heating operation modes and different heating regulation time steps were applied to the IHPD model, respectively, to analyse and compare their effects on the IHPD system under different operation conditions, mainly considering the operation cost and the wind power integration. Results of the case studies showed that the variable flow and variable supply temperature mode produced the lowest operation cost of the IHPD system during a whole heating season. Consequently, this mode is recommended to be applied in the optimization operation of the IHPD system with the wind power integration. Meanwhile, a heating regulation time step within 3 h could make full use of the DHS thermal inertia, thus to improve the operational flexibility of the IHPD system.

Global Practical Output Tracking for a Class of Uncertain Nonlinear Systems

This paper addresses the problem of global practical output control using output feedback for a class of uncertain nonlinear systems with time-varying delay. First, a homogeneous output feedback controller is designed for a nominally uncertain system by adding power integrator technology. Then, with the help of an appropriate Lyapunov-Krasovsky functional and a reduced-order observer, using the homogeneous dominance approach and adding a power integrator method, an output feedback controller is successfully designed to ensure that all states of the closed-loop system remain constrained while simultaneously making the tracking error arbitrarily small.