Power Integration Research Articles

Monogenity and Power Integral Bases: Recent Developments

Monogenity is a classical area of algebraic number theory that continues to be actively researched. This paper collects the results obtained over the past few years in this area. Several of the listed results were presented at a series of online conferences titled “Monogenity and Power Integral Bases”. We also give a collection of the most important methods used in several of these papers. A list of open problems for further research is also given.

Wind energy cost evaluation based on a techno-economic assessment in the Algerian highlands

Wind energy is an important and growing source of renewable energy. It is becoming increasingly integrated into electricity markets around the world. In Algeria in particular, the integration of wind power into the electricity grid is being developed based on a government project to achieve a high penetration rate. In this context, this study is elaborated to analyse the techno-economic assessment of the electrical energy production from four selected wind turbines: Vestas V-80; Suzlon S-82; Enercon E-58 and Gamesa G-114, installed at three sites in the Algerian highlands, namely El Beidh, Sétif and Djelfa. The three highland sites are chosen due to their good wind potential. On the other hand, the Algerian transmission electrical network DZ-114 bus presents some technical problems in the highlands related to low voltage profiles. Therefore, wind farm integration in these three sites can be beneficial as voltage magnitude support. Besides, it can be an important tool to reduce fossil fuel consumption and pollutant gases from conventional plants. Wind potential assessment and economic analysis were carried out for the three selected sites using wind speed data measured 10 m above ground level over 10 years. The results obtained confirm that the Gamesa G114 wind turbine is the most suitable for all the sites and in particular for El Bayadh, as it offers both the highest annual energy production (AEP) and capacity factor (CF) values in all the selected sites and guarantees the lowest unit energy cost (UEC) compared to other types of wind turbines.

Research on the Heat Dissipation in Aviation-Integrated Communication Equipment Based on Graphene Films

Aviation-integrated communication equipment is integral to modern aircraft to ensure its performance and safety. The heat dissipation problems of equipment have become increasingly prominent for the high electronic integration and system power consumption. To solve the above problem, the heat dissipation performance of aviation-integrated communication equipment based on graphene films is deeply studied. This paper establishes a three-dimensional model of aviation-integrated communication equipment to simulate the distribution of temperature fields. The influence between aluminum alloy and graphene films on the surface of magnesium alloy on the heat dissipation performance of aviation-integrated communication equipment is studied. The simulation results show that the heat balance time of the equipment using graphene films on the surface of magnesium alloy is reduced from 3600 s to 800 s, representing an approximately 77.78% improvement; the measured equipment exhibited a reduction in its overall thermal equilibrium temperature, decreasing from 68.1 °C to 66.3 °C, representing an improvement of approximately 2.64%.

Short-Term Forecasting of Photovoltaic Power Using Multilayer Perceptron Neural Network, Convolutional Neural Network, and k-Nearest Neighbors’ Algorithms

Governments and energy providers all over the world are moving towards the use of renewable energy sources. Solar photovoltaic (PV) energy is one of the providers’ favourite options because it is comparatively cheaper, clean, available, abundant, and comparatively maintenance-free. Although the PV energy source has many benefits, its output power is dependent on continuously changing weather and environmental factors, so there is a need to forecast the PV output power. Many techniques have been employed to predict the PV output power. This work focuses on the short-term forecast horizon of PV output power. Multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with success. The performances of these three algorithms have been compared with other algorithms singly or in a hybrid (with other methods) but not with themselves. This study aims to compare the predictive performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions and showcase their robustness in making predictions in this regard. The performance of MLPNN, CNN, and kNN are compared using solar PV (hourly) data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in South Africa that may be prone to extreme weather conditions. Our data does not have lots of missing data and many data spikes. The kNN algorithm was found to have an RMSE value of 4.95%, an MAE value of 2.74% at its worst performance, an RMSE value of 1.49%, and an MAE value of 0.85% at its best performance. It outperformed the others by a good margin, and kNN could serve as a fast, easy, and accurate tool for forecasting solar PV output power. Considering the performance of the kNN algorithm across the different seasons, this study shows that kNN is a reliable and robust algorithm for forecasting solar PV output power.

Perturbation-theory informed integrators for cosmological simulations

Large-scale cosmological simulations are an indispensable tool for modern cosmology. To enable model-space exploration, fast and accurate predictions are critical. In this paper, we show that the performance of such simulations can be further improved with time-stepping schemes that use input from cosmological perturbation theory. Specifically, we introduce a class of time-stepping schemes derived by matching the particle trajectories in a single leapfrog/Verlet drift-kick-drift step to those predicted by Lagrangian perturbation theory (LPT). As a corollary, these schemes exactly yield the analytic Zel'dovich solution in 1D in the pre-shell-crossing regime (i.e. before particle trajectories cross). One representative of this class is the popular ‘FastPM’ scheme by Feng et al. 2016[1], which we take as our baseline. We then construct more powerful LPT-inspired integrators and show that they outperform FastPM and standard integrators in fast simulations in two and three dimensions with O(1−100) timesteps, requiring fewer steps to accurately reproduce the power spectrum and bispectrum of the density field. Furthermore, we demonstrate analytically and numerically that, for any integrator, convergence is limited in the post-shell-crossing regime (to order 32 for planar-wave collapse), owing to the lacking regularity of the acceleration field, which makes the use of high-order integrators in this regime futile. Also, we study the impact of the timestep spacing and of a decaying mode present in the initial conditions. Importantly, we find that symplecticity of the integrator plays a minor role for fast approximate simulations with a small number of timesteps.

Identification of vulnerable lines in power grids with wind power integration based on topological potential

Vulnerable transmission lines are one of the weak links in the operation of the power system, as their damage can trigger widespread blackouts and cascading failures. The integration of large-scale wind power generation into the grid has resulted in significant changes to both the topology and distribution patterns of power flows inside the system, exacerbating line vulnerabilities. Identifying these lines can enhance power system safety through vigilant monitoring or augmenting their carrying capacity. In this paper, a method for identifying vulnerable lines based on the theory of topological potential is proposed. The method is based on the N-1 fault model of optimal DC power flow, and a corresponding network is constructed for the power system. The task of pinpointing weak points in a complicated network is converted into an assessment problem of important nodes. The proposed index comprehensively considers the in-degree and out-degree topological potentials of nodes, and uses the entropy weight method for weight allocation. Meanwhile, the TLS distribution is used to fit the wind power prediction error. Finally, simulation experiments are conducted on the IEEE 39-bus system. Simulation results indicate that the most vulnerable transmission lines tend to be located at the hubs of the system’s topological network and in close proximity to wind farms.

Impact of large-scale renewable energy integration on the grid voltage stability

Nowadays, the production of renewable energy is increasing rapidly due to its enormous potential and environmental advantages. Still, the addition of renewable energy to the grid has resulted in some volatility in the electrical system. In this work, the national grid of Ethiopia is used as an example to examine the impact of significant wind power integration on grid stability. In particular, issues with voltage stability in both steady-state and emergency scenarios are taken into account for the network. The dynamic modeling of the wind farm is taken into consideration for contingency-based analysis, and the whole nation's power network, including the largest wind farm in the nation, Adama-II, is modeled for base-case analysis using MATLAB's built-in PSAT software. The result indicates that the bus voltages are within an acceptable standard voltage range for the base-case system. Nevertheless, the wind farm will be disconnected from the network in compliance with the global manufacturer's guideline in the case of a three-phase fault at the network point of common coupling (PCC), where the voltage at PCC is equivalent to 0.045 per unit voltage. The results also demonstrate that the voltage profile throughout the whole nation power network is lower when there is a problem at PCC. As a result, as Ethiopia is building several huge wind farms, it is advised that fault detection and control methods be thoroughly designed before connecting the major wind farms to the grid.

Impact of temperature dependent coefficient of performance of heat pumps on heating systems in national and regional energy systems modelling

To successfully transition towards fully renewable energy systems, energy sectors that today are primarily autonomous should be closely integrated to increase system flexibility and maximise synergy from effective sector coupling. Heat pumps are one of the sector coupling technologies which enable a mutual integration of power and heat sectors. Heat pumps are viewed as a low-cost source of sustainable heat for both space heating and industrial processes. However, representation of heat pumps in energy system models is typically oversimplified by using a uniform coefficient of performance, which limits perception of the hourly temperature changes on heat pump efficiency, or by using generic heat pumps for all individual residential, district, or industrial heat supplies. The LUT Energy System Transition Model was expanded to examine the impact of utilisation of hourly coefficient of performance profiles and specific heat pumps for different applications. The results indicate that, in cold climate countries, the use of hourly coefficient of performance profiles has little impact on the heat supply structure and overall primary energy demand, but has a noticeable impact on the electricity supply and energy storage system. Though the cost of the optimised system stays on the same level, application of hourly COP profiles leads to a more accurate simulation of electricity consumption and energy storage operation. The separation of district and industrial heat pumps leads to a higher share of heat pumps in industrial heat supply and allows for a reduction in overall energy system primary energy demand by 1.8%.

Safe Autonomous Docking of Spacecraft: A RRT-combined Output-constrained Recursive Control Method

In addressing the challenges of short-range spacecraft docking in the presence of obstacles and disturbances, it is critical to integrate guidance and motion control to ensure autonomous and reliable operation. Traditional methods that separate these two layers often struggle with accurately tracking predefined paths, increasing the risk of collisions. In light of this, a proposed scheme integrating guidance and control in an organic manner has been put forth. This scheme employs the rapidly-exploring random trees (RRT) algorithm within the guidance layer to generate a collision-avoidance trajectory for the control layer, efficiently navigating the spacecraft towards its target. Then the control layer implements a second-order output-constrained controller by adding a power integrator and a novel barrier Lyapunov function (BLF) together, to guarantee that the tracking error of the predefined trajectory remains bounded and the system asymptotically converges to the target. To account for tracking errors, obstacle radii are expanded during path planning through a dilation constant. Based on theoretical derivation and simulation experiments, the effectiveness and advancement of the proposed method are validated.

An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers

The precise, simultaneous, and rapid detection of essential biomarkers in human tears is imperative for monitoring both ocular and systemic health. The utilization of a wearable colorimetric biochemical sensor exhibits potential in achieving swift and concurrent detection of pivotal biomarkers in tears. Nevertheless, challenges arise in the collection, interpretation, and sharing of data from the colorimetric sensor, thereby restricting the practical implementation of this technology. To overcome these challenges, this research introduces an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) for rapid, non-invasive, and simultaneous detection of key biomarkers in human tears, including vitamin C, H+(pH), Ca2+, and proteins. The sensor consists of a flexible microfluidic epidermal patch that collects tears and facilitates the colorimetric reaction, and a deep-learning neural network-based cloud server data analysis system (CSDAS) embedded in a smartphone enabling color data acquisition, interpretation, auto-correction, and display. To enhance accuracy, a well-trained multichannel convolutional recurrent neural network (CNN-GRU) corrects errors in the interpreted concentration data caused by varying pH and color temperature in different measurements. The test set determination coefficients (R2) of 1D-CNN-GRU for predicting pH and 3D-CNN-GRU for predicting the other three biomarkers were as high as 0.998 and 0.994, respectively. This correction significantly improves the accuracy of the predicted concentration, enabling accurate, simultaneous, and quick detection of four critical tear biomarkers using only minute amounts of tears ( ~ 20 μL). This research demonstrates the powerful integration of a flexible microfluidic colorimetric biosensor and deep-learning algorithm, which holds immense potential to revolutionize the fields of health monitoring.

A MILP-based power system parallel restoration model with the support of mobile energy storage systems

During bulk power system restoration, black-start (BS) units are used to crank non-BS units in such a way that the over system generation capability is maximized. However, traditional power system restoration methods rely on stationary BS units, whose number is limited due to high investment and geographic conditions, impeding a fast system restoration. To enhance restoration efficiency, this paper proposes an integrated power system parallel restoration method considering the support of mobile energy storage systems (MESSs), which can provide cranking power to non-BS units and transform them into MESS-assisted BS units. MESSs are routed and scheduled via transportation network, along with restoration planning in power network by integrating them into a mixed integrated linear programming model. Specifically, to describe the interactive behavior between MESSs and the power system, a mobility model for MESSs is proposed, which indicates the travel path, travel time and connection nodes. Then, an integrated optimization model involving BS unit location, zone partitioning, network reconfiguration and generator start-up is formulated. The proposed method was simulated in the modified IEEE 30-bus transmission system coupled with 11-node transportation system. The results show that, in contrast with a decoupled restoration method, the proposed method increases system generation capability by 6.3%.

Energy saving and emission reduction technologies integrating energy power and mechanical engineering

This paper discusses the importance and application prospect of energy-saving and emission-reduction technology combining energy, power and mechanical engineering in the current energy and environment fields. Firstly, the concept of integration of energy power and mechanical engineering and its significance in energy saving and emission reduction are introduced, and the important role of this technology in realizing sustainable development and meeting the challenge of climate change is emphatically expounded. Subsequently, the application practice of this technology in industrial production, construction, transportation and other fields is analyzed, and its remarkable effects in energy saving and emission reduction are summarized. This paper further discusses the important role of technological innovation and policy support in promoting the development of this technology, and emphasizes the importance of interdisciplinary cooperation and exchange in the process of technology popularization and application. Finally, the future research direction and development trend are prospected, and all parties are called upon to make joint efforts to promote the energy-saving and emission-reduction technology integrating energy, power and mechanical engineering to be more widely applied and popularized around the world, so as to contribute to building a clean, low-carbon and sustainable energy future.

Review on Power Cycling Reliability of SiC Power Device

The rising demand for increased integration and higher power outputs poses a hidden risk to the long-term reliable operation of third-generation semiconductors. Thus, the power cycling test (PCT) is widely regarded as the utmost critical test for assessing the packaging reliability of power devices. In this work, low-thermal-resistance packaging design structures of SiC devices are introduced, encompassing planar packaging with dual heat dissipation, press-pack packaging, three-dimensional (3D) packaging, and hybrid packaging. PCT methods and their control strategies are summarized and discussed. Direct-current PCT is the focus of this review. The failure mechanisms of SiC devices under PCT are pointed out. The electrical and temperature-sensitive parameters adopted to monitor the aging of SiC devices are organized. The existing international standards for PCT are evaluated. Due to the lack of authoritative statements for SiC devices, it is difficult to achieve comparison research results without consistent preconditions. Furthermore, the lifetimes of the various packaging designs of the tested SiC devices under PCTs are statistically analyzed. Additionally, problems related to parameter monitoring and test equipment are also summarized. This review explores the broader landscape by delving into the current challenges and main trends in PCTs for SiC devices.

Finite-time stability of a class of nonlinear cascaded systems with time-varying output constraints

The finite-time stabilization problem of a class of cascaded systems with time-varying output constraints is investigated in this paper. The system consists of input-to-state stable inverse dynamics and a nonholonomic system. The technology of changing the supply rate eliminates dynamic uncertainty. A new tan-type barrier Lyapunov function is utilized to handle time-varying output constraints. Under the unified framework of constrained and unconstrained systems, combined with the addition of power integrator technology and switch control strategy, a state feedback control strategy is proposed to ensure that the state of the closed-loop system converges to zero in a finite-time and always satisfies time-varying output constraints. Finally, simulation results verify the proposed control scheme’s effectiveness.

Carbon-wise utilization of lignin-related compounds by synergistically employing anaerobic and aerobic bacteria

BackgroundLignin is a highly abundant but strongly underutilized natural resource that could serve as a sustainable feedstock for producing chemicals by microbial cell factories. Because of the heterogeneous nature of the lignin feedstocks, the biological upgrading of lignin relying on the metabolic routes of aerobic bacteria is currently considered as the most promising approach. However, the limited substrate range and the inefficient catabolism of the production hosts hinder the upgrading of lignin-related aromatics. Particularly, the aerobic O-demethylation of the methoxyl groups in aromatic substrates is energy-limited, inhibits growth, and results in carbon loss in the form of CO2.ResultsIn this study, we present a novel approach for carbon-wise utilization of lignin-related aromatics by the integration of anaerobic and aerobic metabolisms. In practice, we employed an acetogenic bacterium Acetobacterium woodii for anaerobic O-demethylation of aromatic compounds, which distinctively differs from the aerobic O-demethylation; in the process, the carbon from the methoxyl groups is fixed together with CO2 to form acetate, while the aromatic ring remains unchanged. These accessible end-metabolites were then utilized by an aerobic bacterium Acinetobacter baylyi ADP1. By utilizing this cocultivation approach, we demonstrated an upgrading of guaiacol, an abundant but inaccessible substrate to most microbes, into a plastic precursor muconate, with a nearly equimolar yields (0.9 mol/mol in a small-scale cultivation and 1.0 mol/mol in a one-pot bioreactor cultivation). The process required only a minor genetic engineering, namely a single gene knock-out. Noticeably, by employing a metabolic integration of the two bacteria, it was possible to produce biomass and muconate by utilizing only CO2 and guaiacol as carbon sources.ConclusionsBy the novel approach, we were able to overcome the issues related to aerobic O-demethylation of methoxylated aromatic substrates and demonstrated carbon-wise conversion of lignin-related aromatics to products with yields unattainable by aerobic processes. This study highlights the power of synergistic integration of distinctive metabolic features of bacteria, thus unlocking new opportunities for harnessing microbial cocultures in upgrading challenging feedstocks.

Artificial Intelligence with Metaheuristic Algorithm Based Optimization for a Hybrid Energy Storage System

This paper presents an optimization framework for a hybrid energy storage system (HESS) integrating photovoltaic (PV) and wind energy conversion systems (WECS). The PV system voltage is stepped up using a Cuk converter, controlled by a Particle Swarm Optimization (PSO) tuned Artificial Neural Network (ANN) controller to enhance dynamic performance and ensure maximum power point tracking (MPPT). The Doubly Fed Induction Generator (DFIG) based WECS is interfaced through a Pulse Width Modulation (PWM) rectifier regulated by a Proportional Integral (PI) controller, maintaining stable and efficient power conversion. Both converter outputs converge at a common DC link, whose voltage is subsequently applied to a single phase Voltage Source Inverter (VSI). This VSI is connected to the grid through an LC filter, ensuring smooth power delivery with minimal harmonics. The proposed system combines AI and metaheuristic algorithms for optimized control, enhancing overall efficiency, reliability and stability of the renewable energy integration into the grid. Simulation results demonstrate the effectiveness of the PSO ANN and PI control strategies in managing the dynamic interactions and power quality of the hybrid system. The proposed hybrid controller shows 92.47% tracking efficiency and the proposed converter shows 89.6% efficiency.

Interstate power interconnection along with carbon dioxide emission constraint collaboration: Effective tool for low-carbon electric power expansion in Northeast Asia

Interstate electric power integration with the formation of interstate electric ties and power interconnection in Northeast Asia has been studied for several years. Research has examined the directions, indicators, costs, and systemic (integration) benefits (technical, economic, environmental, etc.) of forming such ties and grids. Studies conducted in different Northeast Asia countries have shown that despite the significant investments required to create an interstate electric ties infrastructure, which serves as the basis for interstate power grids and electricity markets development, participants of these interstate power grids and markets gain substantial benefits. These include reduced needs for installed and reserve generation capacity, lower electricity prices, increased overall flexibility of power resources, and accordingly, greater capability to integrate renewable energy sources. This results in reduced carbon dioxide (CO2) emissions from fossil fuel power plants. The latter undoubtedly contributes to achieving the sustainable development goals set by the United Nations and reducing anthropogenic climate impact, which many countries globally (including Northeast Asia) have targeted via announced carbon neutrality by 2050–2060. The main target of the article is to conduct and present new multi-scenario study on forming a potential Northeast Asia’s interstate power grid under national and grid-wide (for the entire power system interconnection) carbon emission constraints. The mathematical model of electric power system expansion and dispatching was modified to account for carbon dioxide emission constraints and used for the study. The results of the study showed that the creation of interstate power grid in Northeast Asia accompanied by climate change collaboration among countries to control carbon dioxide emission would lead to effectively constraining of carbon dioxide emission in the subregion, which is the novelty of the study. The results of the study are useful and applicable for choosing particular ways, options, stages of formation and expansion of low-carbon power system interconnection in Northeast Asia.

Power Network Vulnerability Assessment Based on Electric Power System Cyber Security Simulation Platform

Abstract This paper introduces a modeling approach for the integration of electric power and information networks, along with the creation of an electric power system network simulation platform. The system, achieved through the deep integration of computation, communication, and control technologies, offers new insights into power grid intelligence. The paper discusses the concept of power system vulnerability and presents a modeling scheme that effectively simulates and analyzes power systems. Using the Analytic Hierarchy Process (AHP), the impact of cyber attacks under different load conditions is evaluated, revealing that attacks have more severe consequences under heavy loads. In conclusion, this research contributes to enhancing the security and stability of power systems.

Research on optimization and improvement method of new energy access grid stability based on transient stability margin index

Abstract This paper focuses on the potential local voltage stability risks and transient stability margin decline in the power grid after the large-scale integration of new energy. Based on the transient stability margin index of the power grid, a method for evaluating and optimizing the transient stability margin after the integration of wind power into the power grid is proposed. This paper provides guidance for the grid connection points and voltage level determination of the wind power access system during the planning phase, as well as the configuration of reactive power compensation devices such as SVGs.

Reducing Off-State and Leakage Currents by Dielectric Permittivity-Graded Stacked Gate Oxides on Trigate FinFETs: A TCAD Study.

Since its invention in the 1960s, one of the most significant evolutions of metal-oxide semiconductor field effect transistors (MOSFETs) would be the 3D version that makes the semiconducting channel vertically wrapped by conformal gate electrodes, also recognized as FinFET. During recent decades, the width of fin (Wfin) and the neighboring gate oxide width (tox) in FinFETs has shrunk from about 150 nm to a few nanometers. However, both widths seem to have been leveling off in recent years, owing to the limitation of lithography precision. Here, we show that by adapting the Penn model and Maxwell-Garnett mixing formula for a dielectric constant (κ) calculation for nanolaminate structures, FinFETs with two- and three-stage κ-graded stacked combinations of gate dielectrics with SiO2, Si3N4, Al2O3, HfO2, La2O3, and TiO2 perform better against the same structures with their single-layer dielectrics counterparts. Based on this, FinFETs simulated with κ-graded gate oxides achieved an off-state drain current (IOFF) reduced down to 6.45 × 10-15 A for the Al2O3: TiO2 combination and a gate leakage current (IG) reaching down to 2.04 × 10-11 A for the Al2O3: HfO2: La2O3 combination. While our findings push the individual dielectric laminates to the sub 1 nm limit, the effects of dielectric permittivity matching and κ-grading for gate oxides remain to have the potential to shed light on the next generation of nanoelectronics for higher integration and lower power consumption opportunities.